Tags are labels that you can use to group your projects, equipment items, contacts, crew members, vehicles, invoices and tasks. First, you add a tag to your item(s) in a module. Afterwards, you can filter the shown items in a module by selecting your tag in the top right at label Tags expand_more.
Style (no italics) + remove image
Currently, the mutant of the flavinogenic yeast Candida famata dep8 isolated by classic mutagenesis
No __notes were added.
A Firefox/Hypothesis extension has been in the works for quite a while,
I published an unofficial Firefox extension here. Just download and install the XPI file. Go here for a discussion about it. So far only thing I found that doesn't work is annotating local PDFs, because Firefox is more restrictive about this than Chrome.
properties to 'unfold'
How search engines understand websites Imagine being a search engine crawler scanning down a 10,000-word article about how to bake a cake. How do you identify the author, recipe, ingredients, or steps required to bake a cake? This is where schema markup comes in. It allows you to spoon-feed search engines more specific classifications for what type of information is on your page.Schema is a way to label or organize your content so that search engines have a better understanding of what certain elements on your web pages are. This code provides structure to your data, which is why schema is often referred to as “structured data.” The process of structuring your data is often referred to as “markup” because you are marking up your content with organizational code.JSON-LD is Google’s preferred schema markup (announced in May ‘16), which Bing also supports. To view a full list of the thousands of available schema markups, visit Schema.org or view the Google Developers Introduction to Structured Data for additional information on how to implement structured data. After you implement the structured data that best suits your web pages, you can test your markup with Google’s Structured Data Testing Tool.In addition to helping bots like Google understand what a particular piece of content is about, schema markup can also enable special features to accompany your pages in the SERPs. These special features are referred to as "rich snippets," and you’ve probably seen them in action. They’re things like:Top Stories carouselsReview starsSitelinks search boxesRecipesRemember, using structured data can help enable a rich snippet to be present, but does not guarantee it. Other types of rich snippets will likely be added in the future as the use of schema markup increases.Some last words of advice for schema success:You can use multiple types of schema markup on a page. However, if you mark up one element, like a product for example, and there are other products listed on the page, you must also mark up those products.Don’t mark up content that is not visible to visitors and follow Google’s Quality Guidelines. For example, if you add review structured markup to a page, make sure those reviews are actually visible on that page.If you have duplicate pages, Google asks that you mark up each duplicate page with your structured markup, not just the canonical version.Provide original and updated (if applicable) content on your structured data pages.Structured markup should be an accurate reflection of your page.Try to use the most specific type of schema markup for your content.Marked-up reviews should not be written by the business. They should be genuine unpaid business reviews from actual customers.Tell search engines about your preferred pages with canonicalizationWhen Google crawls the same content on different web pages, it sometimes doesn’t know which page to index in search results. This is why the rel="canonical" tag was invented: to help search engines better index the preferred version of content and not all its duplicates.The rel="canonical" tag allows you to tell search engines where the original, master version of a piece of content is located. You’re essentially saying, "Hey search engine! Don’t index this; index this source page instead." So, if you want to republish a piece of content, whether exactly or slightly modified, but don’t want to risk creating duplicate content, the canonical tag is here to save the day.
How do websites communicate with search engines is there a special code that
You can add a table of contents to a Markdown file, wiki page, or issue/merge request description, by adding the tag [[_TOC_]] on its own line. It will appear as an unordered list that links to the various headers.
<div class="templates:surround?with=templates/page.html&at=content"> <h1>Table of Contents</h1> <div data-template="app:toc"/> </div>
This seems to combine two different ways of HTML templating:
Should it be done in this way?
-ta most likely to be verbs
-ta are most likely ablatives. That's why we use those particles to tag morphology. Checkout the morphology charts.
For Ur III sumerian, what is most telling about the nature of the word is its placement in the text. Suffixes are rarely present as compared to other genres.
Using the Git SHA in your image tag makes this less necessary since each job will be unique and you shouldn't ever have a stale image. However, it's still possible to have a stale image if you re-build a given commit after a dependency has changed.
GitHub
why don't just just show the script tag to embed Hypothesis in any website. No need to "head to Github" to spend more time, hassles etc.
„Ein Bild: der höchste Alpengipfel, ausgehauen zu einem Gesicht unter wuchtendem Stahlhelm, das still und ernst über die Lande schaut, den deutschen Rhein hiunter aufs freie Meer. –Einst wird kommen der Tag....“
Ich finde es sehr schön, wenn die Passagen aus dem Buch in Absätzen und somit hervorgehoben zitiert werden! Aber hier zitierst Du jetzt anders als in Deiner vorherigen Buchstelle (S.3). Hier benutzt Du Anführungszeichen und beendest mit einfachen Doppelpunkten ohne die eckigen Klammern. Eventuell nochmal darauf achten, dass dies alles einheitlich gemacht wird.
am Tag nach dem Webinar hier.
Falls Du schon vorab nähere Informationen suchst, empfehle ich Dir diesen Blogbeitrag im eBildungslabor
Herzog, S. (2020, May 21). Boosting COVID-19 related behavioral science by feeding and consulting an eclectic knowledge base. Psychonomic Society Featured Content. https://featuredcontent.psychonomic.org/boosting-covid-19-related-behavioral-science-by-feeding-and-consulting-an-eclectic-knowledge-base/
git describe [--tags] describes the current branch in terms of the commits since the most recent [possibly lightweight] tag in this branch's history. Thus, the tag referenced by git describe may NOT reflect the most recently created tag overall.
You can get an RSS or Atom feed for annotations made at a specific url, a specific Hypothesis tag or group, or for a specific Hypothesis user.
Domain-level query is also available with RSS and Atom feeds. This tool here makes the process of getting these feed urls a bit easier.
Tag` в экземпляре `MicroScan`
Выделить или хотя бы убрать `
Filter by tag
Change image, add tags button and change some text.
Afterwards, you can filter the shown items in a module by clicking on the label tags expand_more button and selecting your tag(s).
Change sentence and add tags button
The Analytics JavaScript Tag When a JavaScript-enabled web browser loads a page with the Analytics tag (ga.js or analytics.js), it does two things asynchronously: load and process the Analytics function queue and request the Analytics JavaScript. The function queue is a JavaScript array where the different Analytics configuration and collection functions are pushed. These functions, which are set by the site owner when implementing Analytics can include functions like specifying the Analytics account number and actually sending page view data to the Analytics Collection Network for processing. When the Analytics JavaScript runs a function from the function queue that triggers data to be sent to the Analytics Collection Network (this function is typically ga('send', 'pageview') in the analytics.js JavaScript library and _trackPageview in the ga.js library), it sends the data as URL parameters attached to an HTTP request for http://www.google-analytics.com/_utm.gif (for ga.js) and http://www.google-analytics.com/collect (for analytics.js). If the anonymization function has been called prior to the page tracking function, an additional parameter is added to the pixel request. The IP anonymization parameter looks like this: &aip=1 The Analytics Collection Network The Analytics Collection Network is the set of servers that provide two main services: the serving of ga.js and analytics.js (the Analytics JavaScript) and the collection of data sent via requests for _utm.gif and /collect. When a request for ga.js, analytics.js, _utm.gif, or /collect arrives, it includes additional information in the HTTP request header (i.e. the type of browser being used) and the TCP/IP header (i.e. the IP address of the requester). As soon as a request for _utm.gif arrives, it is held in memory for anonymization. If the &aip=1 parameter is found in the request URL (as it would have been placed by the Analytics JavaScript after processing the anonymization function in ga.js or analytics.js ), then the last octet of the user IP address is set to zero while still in memory. For example, an IP address of 12.214.31.144 would be changed to 12.214.31.0. (If the IP address is an IPv6 address, the last 80 of the 128 bits are set to zero.) Only after this anonymization process is the request written to disk for processing. If the IP anonymization method is used, then at no time is the full IP address written to disk as all anonymization happens in memory nearly instantaneously after the request has been received.
To be fully compliant with GDPR, you would also need to enable Show Reject All Button setting.
contain
Isn't there a way for an exact tag match? I mean, a way in which "web" would only match a "web" tag, and not a "web annotation" tag too, for example.
Und Tag für Tag nahm die Pest zu, die Sommersonne brannte auf die Stadt herab, es fiel kein Regentropfen, es rührte sich kein Wind,
In den ersten Wochen während des Fernunterrichts war das Wetter ähnlich: Kein Regen für eine sehr lange Zeit, die Sonne schien; es war sehr warm (insbesondere für März/April).
(Optional) Click save 'Save to item' to connect this tag to its equipment
new line, CSS
A "tag" is a snippet of code that allows digital marketing teams to collect data, set cookies or integrate third-party content like social media widgets into a site.
This is a bad re-purposing of the word "tag", which already has specific meanings in computing.
Why do we need a new word for this? Why not just call it a "script" or "code snippet"?
Filter by tag
Deleted screenshots, texbox, and explanation (one and all)
Add a tag
Deleted screenshots and text box about what you can do in widgets
Don't forget to press enter before you click Save. Otherwise, your tag will not be added.
"press enter", CSS Save
Choose an existing tag to apply, search for an existing tag to apply, or type in a new tag to apply and press enter.
new
My current research interests include human-computer interaction
test
Within minutes of testing out the app with me, my friends discovered we could play tag for fun, or have little footraces around the map. Behaviors I didn't design into the app at all.
Cool example of emergent behavior in software
For example, if two groups collaborate on a project, it can make sense that there is a primary contact for each group.
How would this be modelled in the underlying JATS XML data?
Although many journals specify only one corresponding author, in other journals there is no limit to the number of contributors who may be designated to receive correspondence for the article. Accordingly, more than one <contrib> element may have its @corresp attribute set to “yes”.
From: https://jats.nlm.nih.gov/articleauthoring/tag-library/1.1d2/attribute/corresp.html
Don’t share your children’s photos with their real names Adults are able to un-tag themselves from images that they don’t want to be identified in, but children don’t have that option. A lot of parents these days are referring to their children as a hashtag or a nickname which protects their identity without taking the fun out of sharing family photos. This also has an added bonus of giving the child a clean slate when they are older and building their own web presence, instead of being able to Google their name and seeing hundreds of baby photos that their relatives posted in the past.
Google Tag Manager allows you to avoid tagging scripts as described below, although this is limited to a certain category of scripts – scripts that are not positional/do not define a position. It, therefore, does not handle embed scripts such as those related to advertising banners, youtube video widgets, facebook like buttons etc.
won the CWA World Tag Team Championship
More men than women in pro wrestling.
Click on each price tag to learn about the costs of fashion.
I like the illustrations, but as an interactive graphic, they feel a bit arbitrary and repetitive. What distinguishes one illustration from another, in terms of how they introduce their sections? Make unique illustrations for each section, e.g., a tree for Deforestation.
direct usage from web applications (e.g. for tag extraction/suggestion; or text completion in search fields), 'smart' content workflows or email routing based on extracted entities, topics, etc.
Thus the most problematic behavior is implicitly encouraged and enabled. Grrrrrrr. It could be a misspelling. It could be a slip of the finger. It could be a different capitalization, punctuation, or tense, whatever. No warning or indication is given, and a divergent tag is created, for you to hopefully notice and fix later, hopefully before you rely on it.
This is a problem in the note taking app Roam. All it takes is a hashtag or two [[brackets]] around a word to create a new page/note.
This leads to many dead pages from typos or just copypasted content with the hashtag symbol in them.
The community meals include menu options like prime rib, salmon, vegan stew, loaves of bread, Arcana’s homemade ice cream and various side dishes, costs $20 per person, with a sliding scale option for those who cannot afford the price tag.
This menu appears to include traditionally high-cost items like prime rib and treats like ice cream. Interpretations: this restaurant is committed to providing all members of its community with healthy, hearty, and economical food options. Interpretations: $20/meal is still a hefty price tag when many fast food restaurants are able to provide a family of four a whole meal for $20.
ing was that the teacher-education students in our study did not see their set online tasks as being valuable to their
FOllow up later
Unfortunately, in their quest for peer acceptance, many middle schoolers believe that drinking or using drugs will make them more popular.
While fitting in is very important to kids in middle school many kids are looking to develope the tag of "being popular". Many of these students believe that in order to make themselves more popular and socially accepted by their peers then they need to develope the habits of using drugs. This is a negative stance because while some may see themselves as being more popular what they don't see is the effect these substances have on their developement and their bodies.
Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Reviewer #1:
**Summary**
Jang et al., address the important question of spatially localized or compartmentalized metabolic enzymes with a focus on the glycolytic enzyme PFK1. Using a good strategy of inserting a fluorescent tag at the endogenous PFK1 locus with tissue-specific inducible expression in C. elegans, combined with strong quantitative longitudinal imaging and innovative bioengineered microfluidic-hydrogels to control oxygen availability as well as optogenetic approaches, they show PFK1 condensates, which are not stress granules and not seen in normoxia, assemble with hypoxia. PFK1 condensates are dynamic, reversible, localized at the synapse in neurons, and recruit aldolase, another glycolytic enzyme. Although glycolytic proteins were previously shown to compartmentalize near the plasma membrane, and PFK1 was previously shown to assemble into filaments in vitro and be punctate at the plasma membrane in mammalian cells, evidence for cellular localized PFK1 condensates in animals is highly significant. The work includes strong biophysical characterization of PFK1 phase-separated condensates, but no clear indication of the composition of condensates. More significantly, the findings lack functional significance related to PFK1 activity or glycolytic flux with hypoxia vs normoxia. Despite previous work by this group showing that disrupting subcellular localization of glycolytic enzymes impairs neuronal activity in response with hypoxia, the reader is left with questions on the importance of localized and PFK1 condensates and their make-up .
**Major comments:**
Key conclusions are convincing, and most experimental approaches, biophysical characterization including thermodynamic principles, and data analysis are exemplary and well described. However, as indicated above, the work is limited to a descriptive analysis of cellular localization of PFK1 condensates and their biophysical properties without insights on functional significance relative to enzyme activity - or at least glycolytic flux or metabolic reprogramming with hypoxia. At best, only correlations can be drawn from hypoxia-induced localized PFK1 condensates and the authors' previous report (Jang et al., 2016) on hypoxia-regulated neuronal activity. Some insight or at least prediction in the discussion on the differences in spatially localized PFK1 in muscle vs neurons with regard to metabolic or energy distinctions should be included.
We have added additional discussions on the differences of the spatially localized PFK-1.1 in muscles versus neurons, explaining that in both tissues the cellular enrichment appears to be at sites predicted to have high ATP consumption (lines 128-133; 482-484).
Despite the strong biophysical analysis of condensates, several important features are not determined. First is at best a rudimentary analysis of the composition of condensates and also how PFK1 is assembled into these structures. For the former, is the core of the condensate predominantly PFK1 with perhaps aldolase only recruited to the periphery or is aldolase an integral component of the structure. Hence, is it a PFK1 condensate or a glycolytic condensate? For the latter question, is there a particular orientation for PFK1 in condensates, i.e a collection of filaments as previously reported, which might provide insight on assembly? Finally, and less critical but also important is the criterion for spherical, which is not well defined, and at least some idea or speculation on determinants for a spherical morphology - compared with filaments that have been reported for other non-glycolytic metabolic enzymes.
We have now co-expressed PFK-1.1 and ALDO-1 and examined their dynamic formation during hypoxic conditions. We observe PFK-1.1 and ALDO-1 form condensates simultaneously, with gradual enrichment of both molecules. We now include this new data in Figure 7E and Video 8; lines 422-441, 964-989). We also include genetic data demonstrating the ALDO-1 requires pfk-1.1 to form condensates, and that PFK-1.1 requires aldo-1 as well. Therefore, the enzymes are interdependent on each other to form condensates (Figures 7G, 7H, S7B, and S7C).
The spheroid geometry reflects liquid-like properties, which arises from surface tension of molecules loosely held together via multi-valent interactions. Filamentous arrangements reflect crystalline-like structures resulting from more stable interactions between molecules into solid-like states. While we did not perform high resolution studies, like Cryo-EM, to resolve this question, the spheroid geometry of PFK-1.1 condensates, along with its fluid-like properties, suggest the condensates are liquid-like compartment distinct to filamentous structures. We now add this discussion in lines 467-470.
The work is an important advance in our understanding on the self-assembly of metabolic enzymes by showing hypoxia-induced PFK1 condensates in vivo, their spatially-restricted subcellular localization in muscle cells and neurons, and their biophysical properties, the latter being distinct from those of stress granules. Taken together, these findings are more extensive than many previous reports on the assembly of metabolic enzymes into filaments or condensates, but fall short for new insights on functional significance.
We focus this study on the biophysical characterization of the condensates, and how that results in compartmentalized enrichment of glycolytic proteins. Examination of the functional significance of the phase separation to the enzymatic reactions in vivo is not currently possible because we lack probes we can use in vivo to measure the metabolites resulting from the reaction. We have now added discussion acknowledging this and framing its significance in the context of what has been published in the field (lines 484-492). For example, a recent manuscript in ChemRxiv demonstrated, in vitro, that the enzymatic activity of glycolytic proteins, hexokinase and glucose-6 phosphate dehydrogenase, promote these enzymes condensing into liquid droplets. The authors further found that the condensation accelerated the glycolytic reactions (Ura et al., 2020). This raises the question whether glycolytic proteins compartmentalize, and form condensates, in vivo, which we address in this manuscript. We capture this point in (lines 444-464) where we explain that, while it has long been hypothesized that glycolytic proteins like PFK-1 could be compartmentalized, this remained controversial due to lack of dynamic in vivo imaging. In our study, and through a systematic examination of endogenous PFK-1.1 via the use of a hybrid microfluidic-hydrogel device, we conclusively determine that PFK-1.1 indeed displays distinct patterns of subcellular localization in specific tissues in vivo.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
This paper reports on the condensation of the glycolytic enzyme PFK-1 in response to hypoxic conditions in neurons of C. elegans. The authors employ a microfluidic-hydrogel device to dynamically monitor the relocalisation of PFK-1 from a mostly diffuse state to clusters in response to hypoxia and show that PFK-1 can undergo multiple rounds of PFK-1 clustering and dissolution. The authors work through the key features of a liquid-like compartment (sphericity, fusion, fast internal rearrangements) and give evidence that PFK-1 may have all three. Finally, the authors tag PFK-1 with the light-inducible multimerization domain Cry2 and find that even without light PFK-1 will constitutively form clusters that sequestrate endogenous PFK-1 as well as other glycolytic proteins. The strength of this work is that it is characterizing what appears to likely be phase separation in the context of a whole animal experiencing a stress that it could encounter in the natural world. A limitation of the work is that it is unclear what the functional implications are of condensates of PFK-1 at the molecular or cell scale.
**Major comments:**
-All experiments were performed using fluorescently tagged PFK-1 expressed from endogenous promoter or from the native genetic locus which is important for excluding overexpression artifacts. However, there is still risk that the GFP tag is driving the assembly process. In order to exclude tag-specific effects that may cause aggregation of the tetrameric PFK-1, ideally a control would be done in which PFK-1 is visualized through immunofluorescence experiments of WT cells. Alternatively, a short tag (e.g HA, His) as epitope for is an alternative .
We used fluorescent tags to observe the dynamic relocalization in vivo. While in the study we have not performed immunofluorescence, we established the validity of the labeling method by: 1) using monomeric versions of GFP; 2) using different fluorophores to show the same condensation phenomenon; 3) performing CRISPR for single copy insertions; 4) Demonstrating that different glycolytic proteins form condensates; 5) demonstrating the GFP-tagged versions of the protein are capable of rescuing the loss-of-function alleles and 6) Now adding new data demonstrating the observed localization specifically depend on the presence of other glycolytic proteins. This last result supports that GFP tag is not driving the assembly process of glycolytic condensate and that the glycolytic condensate formation requires the presence of specific molecules in the pathway. I add that we routinely use fluorophore markers to over a dozen distinct proteins that label subcellular compartments, and we have never observed the dynamic relocalization reported here, with the exception of other glycolytic proteins that interact with PFK, suggesting this is a property specific to glycolytic proteins, and, based on the genetic studies, dependent on the glycolytic reaction. We add and discuss these findings in Figures 7G, 7H, S7B, and S7C; lines 422-441, 964-989.
-For the Cry2-section, the complementation of the pfk-1 mutant supports functionality of the synaptic clustering phenotype. Are there other features of function that can be evaluated or could you look at how Cry-2 vs wt worms recover from different durations of stress or frequencies. Could you see if the Cry-2-fusion will rescue function to a partial-loss-of-function allele or a tetramerization deficient allele? A detailed analysis of the effects of constitutive presence of PFK-1-Cry2 clusters would be necessary to bolster claims that this is fully functional construct. Can enzyme activity be somehow monitored?
We did not observe any difference between wild-type worms and CRY2-expressing worms with regards to their development, survival, locomotive behavior or synaptic phenotype. While we can not discard the possibility that this is not a full rescue, with available tools, we can not distinguish the recue with PFK-1-Cry2 from that of just PFK-1.
-The analysis of the sphericity of clusters (4A) is limited due to the diffraction limit of light which limits an analysis of a compartment of this size. While this is a limitation of the live organism, this should be more clearly acknowledged.
We have included in the Methods section our criteria for quantifying condensates and avoiding diffraction limit artifacts. Briefly, “Considering the resolution limit of a spinning disc confocal (approximately 300nm), any structure with a diameter less than 500nm and an area smaller than 0.2 µm2 was excluded from the analyses”. To better clarify this point, we also now add a description of the criteria used in the main text (lines 242-243).
In addition, we observed that PFK-1.1 condensates are not perfect spheres, but constrained spheroids (which can not be explained by diffraction-limited point spread functions). We can explain the observed spheroid shapes based on liquid-like properties of the condensates, and the constrains of the diameter of the neurite. To better highlight this finding, we have now moved Figure S4E into the main figure (Figure 4B’).
-Fusion experiments (4C) do not fully exclude that clusters overlap instead of merging. It would be beneficial to show the foci for several subsequent frames. One would expect that upon fusion, the condensate size would increase, but video 3 suggests the opposite. It would be useful to quantify condensate size before and after fusion for several separate fusion events. -an alternative possible experiment would be the tagging of PFK-1 with a photoconvertible fluorophore (e.g. Dendra2) and subsequent analysis of fusion events
To better show the fusion events in Figure 4C, we now include all xy, yz, and zx plane views of before and after fusion events of Figure 4C (Figure S5B). We also added a quantification of four independent fusion events in which we compare the sum of the areas of the two puncta before fusion and the size of the area of the single punctum after fusion (Figure S5C). These data support that we are observing fusions events.
-4D). It is unclear if foci are indeed undergoing fission or if two clusters next to each other are moving apart.
For Figure 4D, in all the frames we had recorded, a single structure maintains a continuous signal until fission occurs and splits into two structures. To better present this event, we now include an unabridged version of figure of 4D in the supplement that shows all the frames captured (Figure S5D).
-The analysis of side-by-side growth and dissolution kinetics are interesting and a novel view into the non-equilibrium aspects of phase separation in cells.
-Purification of PFK-1 and in vitro reconstitution of condensates would be supportive of liquid-like characteristics although I don't think it is necessary however it would add a lot to the relevance to show enzyme activity is different +/- condensate state but I am not sure if an easy enzymatic assay exists in vitro.
We agree. But the significance of this particular paper, specifically in the context of the in vitro enzymatic work on glycolytic proteins, is to examine the dynamic in vivo localization and the biophysical characteristics of the condensates. To better underscore this in the context of the field, we add discussion of a recent in vitro manuscript demonstrating that liquid droplet formation of glycolytic proteins affect their enzymatic activity (Ura et al., 2020) (lines 444-464; 484-492). While we see the value of future studies reconstituting the glycolytic particles, we believe that is beyond the scope of this particular in vivo study.
**Minor comments:**
-Stress granules in other organisms (yeast paper) have different composition depending on stress type. To make the claim that the PFK-1 compartments are independent of SGs one would ideally test multiple different SG markers.
We selected the stress granule protein TIAR-1 because it is one of the most studied stress granule markers in C. elegans and it is reportedly one of the core proteins and universal components of stress granules irrespective of a stress type (Buchan et al., 2011; Gilks et al., 2004; Huelgas-Morales et al., 2016; Kedersha et al., 1999). Although we did not include images in the manuscript, we had tested a total of three stress granule markers: TIAR-1, TDP-43, and G3BP1 with similar results. We now added that as data not shown (lines 193-194).
-it should be stated in the main text that the microfluidic-hydrogel device was fabricated following previously published protocols
We have added the reference in the main text (line 170) to supplement what we had written in the Methods section: “A reusable microfluidic PDMS device was fabricated to deliver gases through a channel adjacent to immobilized animals, following protocols as previously described (Lagoy and Albrecht, 2015)”.
-Figure 4b: Y-axis should be changed from probability to fraction of occurrence
We have corrected this in both the figure and the figure legends (Figure 4B).
-The discussion should be less speculative concerning any effects seen in PFK1-Cry2 expressing C. elegans
We have modified the discussion as suggested.
-it is perplexing that a protein known to tetramerize with no disordered or RNA-binding domains forms condensates like this. Is there anything known from other systems of additional interacting proteins that may have features that promote liquidity and serve to fluidize these assemblies?
Condensates can form via multivalent interactions, which include, but is not exclusive, to disordered or RNA-binding domains. Because glycolytic proteins have dihedral symmetries that can facilitate multivalent interactions, we believe these structural properties, in combination with regulated conformational changes, promote multivalent interactions leading to their condensation. We had a statement in the discussion (lines 494-519) now add this more clearly in the results (lines 395-398).
Reviewer #2 (Significance (Required)):
Stimulus-induced phase separation has been observed for dozens of metabolic enzymes from various different pathways (reviewed in Prouteau, 2018). Several studies have published the formation of condensates through PFK-1 in diverse organisms (C. elegans, Yeast, human cancer cells) in response to hypoxia or in some cancer lines also without hypoxia (Jin, 2017, Jang, 2016, Kohnhorst 2017, etc.). A yeast study showed that PFK-1 condensates contain various other glycolytic enzymes and that condensate formation enhances glycolytic rates (Jin, 2017).
This study gives the advance of analyzing the dynamics of PFK-1 condensate formation in vivo in the context of a live animal using a microfluidic-hydrogel device and showing that PFK-1 relocalizes to reversible condensates within minutes of hypoxia. If further appropriate experiments (as mentioned above) are performed, this study would strongly suggest that the underlying process of PFK-1 condensate formation is liquid-liquid phase separation. Ideally, if at all feasible, it would be strengthened if there was some insight into the functional consequences of the condensed assemblies formed in hypoxia. These findings may be interesting to researchers working on glycolysis and metabolism in different cells but particularly in neurons.
Field of expertise
-Phase separation, microscopy, in vitro reconstitution
-no experience with C. elegans biology and do not have a practical handle on ease or difficulties of genetic manipulation of C. elegans or metabolic assays for PFK-1
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
**Summary:**
In this manuscript, the authors focus on the subcellular localization of the key glycolytic enzyme PFK-1.1 in C. elegans, initially in whole animals through GFP tagging of the endogenous locus and subsequently in single cells/tissues using a clever genome editing strategy that permitted tissue-specific expression of GFP-tagged PFK-1.1 from its endogenous locus. They observe that PFK-1.1 localization differs from cell-type to cell-type and can be dynamically reorganized in response to exogenous cues. Focusing on hypoxia, they observe that PFK-1.1 forms foci near synapses in neurons under this stress condition. These foci are not stress granules and they are dissolved upon re-oxygenation. These condensates have properties of liquid droplets and can mature (harden) over time. PFK-1.1 fused to the CRY domain can trigger condensate formation under normoxic conditions, which can co-recruit WT PFK-1.1 as well as aldolase.
**Major comments:**
The conclusions are convincing but the impact could be increased if the authors were able to demonstrate the physiological role that the observed phase separation plays in this stress response. Would it be possible to assess glycolytic flux under hypoxia vs normoxia?
It is currently not possible to assess glycolytic flux in vivo in our system, as we lack metabolic sensors (an active area of work we are trying to address, but will take several years to perform correctly). We have added discussion of new in vitro studies examining the consequences of metabolic flux due to glycolytic compartmentation into liquid droplets (Ura et al., 2020), and the significance of those findings in the context of our in vivo studies (lines 444-464; 484-492).
The authors should comment on viability during the hypoxia time course.
C. elegans can survive anoxic condition for a day (Powell-Coffman, 2010). Our hypoxic conditions last minutes, and we can rescue live C. elegans upon completion of the assays. We now include a description of this in the Methods (lines 1216-1218).
The co-clustering of ALDO-1 and PFK-1.1::mCh::CRY2 in Figure 7 should be properly quantified/statistically analyzed
We quantified the fraction of animals that displays ALDO-1 clustering in PFK-1.1::mCh::CRY2 co-expressing animals, as suggested (Figure S7C).
A control of mCh::CRY2 + ALDO-1::EGFP is missing from the experiments shown in Figure 7. Is the presence of mCh::CRY2 sufficient to drive ALDO-1::EGFP clustering?
As a control for the CRY2 tag promoting the formation of glycolytic condensates, we had co-expressed mCh::CRY2 with PFK-1.1::EGFP, which is insufficient to cause the formation of the condensate (Figure 7C). We have now added a new data where we show that in pfk-1.1 deletion mutants, ALDO-1 condensate formation is suppressed, which further demonstrates the dependency between PFK-1.1 and ALDO-1 (Figures 7H and S7C).
Does hypoxia trigger co-clustering of ALDO-1 and PFK-1.1?
To answer this question, we examined the dynamic formation of ALDO-1 and PFK-1.1 condensates by co-expressing the two proteins together and observed that hypoxia triggers their co-clustering. We now include this in Figure 7E and Video 8.
The authors speculate that hypoxia acts via diminished energy (altered ATP AMP ratios). Can this be measured? To support this hypothesis, the authors may wish to test if similar phase separation is triggered by mitochondrial poisons.
We currently lack sensors that can reliably measure, in vivo, the subcellular changes in energy or metabolic flux in C. elegans neurons. However, we previously did test mitochondrial mutants and observed that in those mutants we observe glycolytic condensates (Jang et al., 2016), supporting the idea that defects in energy production promotes the formation of glycolytic condensates.
**Minor comments:** Is 21% O2 not hyperoxic for worms?
While C. elegans are known to prefer lower percentage of oxygen than those in air, in the lab animals are reared in normal air. We therefore used 21% oxygen present in air as our normoxic conditions.
Can the authors speculate more on how do these condensates exhibit "memory" (how they're able to cluster in the same place repeatedly)? Is there any role for the cytoskeleton in mediating nucleation and/or condensation of PFK and glycolytic enzymes?
When we were testing the reversibility of PFK-1.1 condensates, we were not expecting the reappearance of PFK-1.1 condensates in the same place repeatedly. Our current speculation is that, because many glycolytic enzymes, such as PFK-1.1, are allosterically regulated by nucleotides, AMP/ATP ratio may play a role on where glycolytic condensates appear. In other words, the specific synaptic areas, where PFK-1.1 condensate repeatedly reappeared, may have different AMP/ATP ratio that may trigger the condensation of the glycolytic proteins in those locationsupon conformational changes in PFK-1. We can’t exclude, currently, the presence of nucleating factors at synapses that facilitate PFK-1 clustering, but we have not yet identified them. We now include a discussion of this (lines 494-519).
Do the authors think that these clusters are effectively G-bodies from yeast?
G-bodies from yeast also shows glycolytic proteins changing from its diffuse localization to punctate localization in response to hypoxia (Jin et al., 2017). G-bodies, like C. elegans glycolytic condensates, are forms of subcellular glycolytic organization within eukaryotic cells. Yet, G-bodies take 24 hours to form, while we observe the glycolytic clusters in C. elegans within minutes of hypoxic conditions. We will need to understand the composition and function of both to determine if these forms of glycolytic subcellular organization represent the same structure. We note that glycolytic clusters have also been observed in some human cancer cell lines (Kohnhorst et al., 2017). Observation of glycolytic compartments in multiple different species and cell types suggest that, although the regulation, composition and formation kinetics of the glycolytic condensates may differ, compartmentalization of glycolytic enzymes may be a conserved feature. We now add a sentence discussing this (line 535-537).
Reviewer #3 (Significance (Required)):
It is much appreciated that this study tackles the cell biology of signaling and metabolism, which is a fascinating but difficult to study aspect of molecular biology. This work conclusively documents the dynamic reorganization of metabolic enzymes in vivo in response to physiological stimuli. Such reorganization had been proposed previously but was controversial and difficult to study in a controlled way. This work not only confirms previous observations but further demonstrates that the dynamic reorganization is mediated by a liquid-liquid phase separation. What is lacking is a demonstration that this phase separation is physiologically important. Such observations would generate interest from a much broader audience; the present audience presently targeting people specifically interested in non-membrane organelles per se. The reviewer has expertise in cell signalling and its regulation by phase separation.
As we explain for Reviewer 1, we focus this study on the biophysical characterization of the condensates, and how that results in compartmentalized enrichment of glycolytic proteins. Examination of the functional significance of the phase separation to the enzymatic reactions in vivo is not currently possible because we lack probes we can use in vivo to measure the metabolites resulting from the reaction. We have now added discussion acknowledging this and framing its significance in the context of what has been published in the field (lines 484-492). For example, a recent manuscript in ChemRxiv demonstrated, in vitro, that the enzymatic activity of glycolytic proteins, hexokinase and glucose-6 phosphate dehydrogenase, promote these enzymes condensing into liquid droplets. The authors further found that the condensation accelerated the glycolytic reactions (Ura et al., 2020). This raises the question whether glycolytic proteins compartmentalize, and form condensates, in vivo, which we address in this manuscript. We capture this point in (lines 444-464) where we explain that, while it has long been hypothesized that glycolytic proteins like PFK-1 could be compartmentalized, this remained controversial due to lack of dynamic in vivo imaging. In our study, and through a systematic examination of endogenous PFK-1.1 via the use of a hybrid microfluidic-hydrogel device, we conclusively determine that PFK-1.1 indeed displays distinct patterns of subcellular localization in specific tissues in vivo.
**REFEREES CROSS-COMMENTING** Globally it seems that all reviewers feel that impact would be increased if the physiological consequence of PFK-1.1 condensates was examined. Other, specific comments seem fair.
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This paper reports on the condensation of the glycolytic enzyme PFK-1 in response to hypoxic conditions in neurons of C. elegans. The authors employ a microfluidic-hydrogel device to dynamically monitor the relocalisation of PFK-1 from a mostly diffuse state to clusters in response to hypoxia and show that PFK-1 can undergo multiple rounds of PFK-1 clustering and dissolution. The authors work through the key features of a liquid-like compartment (sphericity, fusion, fast internal rearrangements) and give evidence that PFK-1 may have all three. Finally, the authors tag PFK-1 with the light-inducible multimerization domain Cry2 and find that even without light PFK-1 will constitutively form clusters that sequestrate endogenous PFK-1 as well as other glycolytic proteins. The strength of this work is that it is characterizing what appears to likely be phase separation in the context of a whole animal experiencing a stress that it could encounter in the natural world. A limitation of the work is that it is unclear what the functional implications are of condensates of PFK-1 at the molecular or cell scale.
Major comments:
-All experiments were performed using fluorescently tagged PFK-1 expressed from endogenous promoter or from the native genetic locus which is important for excluding overexpression artifacts. However, there is still risk that the GFP tag is driving the assembly process. In order to exclude tag-specific effects that may cause aggregation of the tetrameric PFK-1, ideally a control would be done in which PFK-1 is visualized through immunofluorescence experiments of WT cells. Alternatively, a short tag (e.g HA, His) as epitope for is an alternative .
-For the Cry2-section, the complementation of the pfk-1 mutant supports functionality of the synaptic clustering phenotype. Are there other features of function that can be evaluated or could you look at how Cry-2 vs wt worms recover from different durations of stress or frequencies. Could you see if the Cry-2-fusion will rescue function to a partial-loss-of-function allele or a tetramerization deficient allele? A detailed analysis of the effects of constitutive presence of PFK-1-Cry2 clusters would be necessary to bolster claims that this is fully functional construct. Can enzyme activity be somehow monitored?
-The analysis of the sphericity of clusters (4A) is limited due to the diffraction limit of light which limits an analysis of a compartment of this size. While this is a limitation of the live organism, this should be more clearly acknowledged.
-Fusion experiments (4C) do not fully exclude that clusters overlap instead of merging. It would be beneficial to show the foci for several subsequent frames. One would expect that upon fusion, the condensate size would increase, but video 3 suggests the opposite. It would be useful to quantify condensate size before and after fusion for several separate fusion events.
-an alternative possible experiment would be the tagging of PFK-1 with a photoconvertible fluorophore (e.g. Dendra2) and subsequent analysis of fusion events
-4D). It is unclear if foci are indeed undergoing fission or if two clusters next to each other are moving apart.
-The analysis of side-by-side growth and dissolution kinetics are interesting and a novel view into the non-equilibrium aspects of phase separation in cells.
-Purification of PFK-1 and in vitro reconstitution of condensates would be supportive of liquid-like characteristics although I don't think it is necessary however it would add a lot to the relevance to show enzyme activity is different +/- condensate state but I am not sure if an easy enzymatic assay exists in vitro.
Minor comments:
-Stress granules in other organisms (yeast paper) have different composition depending on stress type. To make the claim that the FPK-1 compartments are independent of SGs one would ideally test multiple different SG markers.
-it should be stated in the main text that the microfluidic-hydrogel device was fabricated following previously published protocols
-Figure 4b: Y-axis should be changed from probability to fraction of occurrence
-The discussion should be less speculative concerning any effects seen in PFK1-Cry2 expressing C. elegans
-it is perplexing that a protein known to tetramerize with no disordered or RNA-binding domains foms condensates like this. Is there anything known from other systems of additional interacting proteins that may have features that promote liquidity and serve to fluidize these assemblies?
Stimulus-induced phase separation has been observed for dozens of metabolic enzymes from various different pathways (reviewed in Prouteau, 2018). Several studies have published the formation of condensates through PFK-1 in diverse organisms (C. elegans, Yeast, human cancer cells) in response to hypoxia or in some cancer lines also without hypoxia (Jin, 2017, Jang, 2016, Kohnhorst 2017, etc.). A yeast study showed that PFK-1 condensates contain various other glycolytic enzymes and that condensate formation enhances glycolytic rates (Jin, 2017).
This study gives the advance of analyzing the dynamics of PFK-1 condensate formation in vivo in the context of a live animal using a microfluidic-hydrogel device and showing that PFK-1 relocalizes to reversible condensates within minutes of hypoxia. If further appropriate experiments (as mentioned above) are performed, this study would strongly suggest that the underlying process of PFK-1 condensate formation is liquid-liquid phase separation. Ideally, if at all feasible, it would be strengthened if there was some insight into the functional consequences of the condensed assemblies formed in hypoxia. These findings may be interesting to researchers working on glycolysis and metabolism in different cells but particularly in neurons.
Field of expertise
-Phase separation, microscopy, in vitro reconstitution
-no experience with C. elegans biology and do not have a practical handle on ease or difficulties of genetic manipulation of C. elegans or metabolic assays for PFK-1
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Summary
Jang et al., address the important question of spatially localized or compartmentalized metabolic enzymes with a focus on the glycolytic enzyme PFK1. Using a good strategy of inserting a fluorescent tag at the endogenous PFK1 locus with tissue-specific inducible expression in C. elegans, combined with strong quantitative longitudinal imaging and innovative bioengineered microfluidic-hydrogels to control oxygen availability as well as optogenetic approaches, they show PFK1 condensates, which are not stress granules and not seen in normoxia, assemble with hypoxia. PFK1 condensates are dynamic, reversible, localized at the synapse in neurons, and recruit aldolase, another glycolytic enzyme. Although glycolytic proteins were previously shown to compartmentalize near the plasma membrane, and PFK1 was previously shown to assemble into filaments in vitro and be punctate at the plasma membrane in mammalian cells, evidence for cellular localized PFK1 condensates in animals is highly significant. The work includes strong biophysical characterization of PFK1 phase-separated condensates, but no clear indication of the composition of condensates. More significantly, the findings lack functional significance related to PFK1 activity or glycolytic flux with hypoxia vs normoxia. Despite previous work by this group showing that disrupting subcellular localization of glycolytic enzymes impairs neuronal activity in response with hypoxia, the reader is left with questions on the importance of localized and PFK1 condensates and their make-up .
Major comments:
Key conclusions are convincing, and most experimental approaches, biophysical characterization including thermodynamic principles, and data analysis are exemplary and well described. However, as indicated above, the work is limited to a descriptive analysis of cellular localization of PFK1 condensates and their biophysical properties without insights on functional significance relative to enzyme activity - or at least glycolytic flux or metabolic reprogramming with hypoxia. At best, only correlations can be drawn from hypoxia-induced localized PFK1 condensates and the authors' previous report (Jang et al., 2016) on hypoxia-regulated neuronal activity. Some insight or at least prediction in the discussion on the differences in spatially localized PFK1 in muscle vs neurons with regard to metabolic or energy distinctions should be included.
Despite the strong biophysical analysis of condensates, several important features are not determined. First is at best a rudimentary analysis of the composition of condensates and also how PFK1 is assembled into these structures. For the former, is the core of the condensate predominantly PFK1 with perhaps aldolase only recruited to the periphery or is aldolase an integral component of the structure. Hence, is it a PFK1 condensate or a glycolytic condensate? For the latter question, is there a particular orientation for PFK1 in condensates, i.e a collection of filaments as previously reported, which might provide insight on assembly? Finally, and less critical but also important is the criterion for spherical, which is not well defined, and at least some idea or speculation on determinants for a spherical morphology - compared with filaments that have been reported for other non-glycolytic metabolic enzymes.
The work is an important advance in our understanding on the self-assembly of metabolic enzymes by showing hypoxia-induced PFK1 condensates in vivo, their spatially-restricted subcellular localization in muscle cells and neurons, and their biophysical properties, the latter being distinct from those of stress granules. Taken together, these findings are more extensive than many previous reports on the assembly of metabolic enzymes into filaments or condensates, but fall short for new insights on functional significance.
Expertise is published on topic
You should regularly re-build the image using the--no-cacheoption
And perhaps make sure to tag your good / working container before you do!
Perhaps it’s just the ability to see everything at once and then filter it down to what I need ot focus on, but also, it’s forcing me to plan and move things into realistic periods when I can get them done.
that's a good point, relates to the bigger idea of project-based (in one file) or tag-based (in different files) todo management
we
Instead of just saying we, say my team. This gives you the opportunity to tag your partner(s) and give an over view of the project.
I recommend beginning case studies with a description of the project, your roles, and the timeline.
Project: E-commerce (Insert DEVICE here) Website Concept Roles: UX/UI Designer Timeline: 2 Week Sprint
Whether you’ve just purchased a new PC or reinstalled Windows, the first task you’ll likely do is installing apps.
True that
Plusieurs théories tentent d’expliquer cette sensation d’accélération du temps avec l’âge. L’une d’elle évoque une dégradation progressive de notre horloge biologique, due au ralentissement naturel de notre métabolisme au fil des ans : quand nous vieillissons, notre respiration et nos battements de cœur ralentissent. Chez les enfants, au contraire, le cœur bat plus vite et les poumons s’activent davantage. C’est cette plus grande intensité de l’activité biologique qui leur donne l’illusion d’un temps dilaté.
argument epistémique abductif. Tag: IED_QA 3 L'auteur utilise l'argument de la vitesse de métabolisme (fait connu) comme une possible hypothese pour expliquer la perception différente du temps entre l'âge adulte et l'enfance.
If ocean plastic pollution was one of the major environmental challenges we finally woke up to in 2018, the ebb and flow of public opinion could and should turn to electronic waste in 2019.
I think it is important that this issue gets more publicity. However, we cannot shift the focus to ewaste and loss sight of plastic pollution. I believe we need to effectively tackle one issue at a time. Ocean plastic pollution has gotten more publicity since 2018, but nowhere near enough. Furthermore, drastic enough measures still have not been taken to address ocean plastic pollution. https://www.e-cycle.com/tag/e-waste-effects-to-the-human-body/
adding tags
When you add tags, you can see all the content for a certain tag on its own web page, like this one: https://hypothes.is/search?q=tag:%22e-Learning%20Guides%22
Configuring Your Raspberry
You also have to enable the gui in boot settings
but either way there is no price tag on the value of education.
shouldn't this be moved later when you discuss value for the money? Here you are just talking about the rise in cost, right?
Offering your product in a free and paid version is nothing new and it’s entirely legitimate for OSS products,
I feel as if this has been popular in the whole entire software industry!
By far the most common method of income is to provide a service alongside the OSS product. Pick any OSS project from random and there’s a good chance that they utilize this method in one way or another.
This makes sense
self-taught lone wolves
I wonder why this is
open source and profit are mutually exclusiv
wow I did not know this
This method has the advantage of being quite fast but with the limit of working only for scripts that don’t require a specific position. Google Tag Manager is therefore not effective for all scripts that display a specific element in a specific position of the page (such as the Facebook Like button).
If a note is broad (i.e. contains more than one concept), it makes finding any concept contained in that note harder (since you have to sort through concepts). (This is “solvable” by storing an additional copy of just that one concept somewhere else, but if you do that, why not store just the concept to begin with and refer to it from within the broad note to avoid duplication and extra note maintenance?)
I haven’t thought about reducing [[note maintenance]] yet. I largely just use the [[develop]] tag for that. Interesting.
Adams administration!
I decided to tag the "Adams administration" as an orgName because it describes all of the politicians involved in Adams's presidency.
Vice President
I would tag Vice President as a "concept"
British henchmen
I did not tag soldiers or henchman as people because I did not find it necessary to tag individuals who are not important or directly mentioned
tonight
I decided not to tag words talking about a time as an event. This is because there would be too many things considered events then. I believe events are something that is important
the story of tonight
I chose to tag "the story of tonight" as an event because the characters are talking about how what they do tonight will be leave a mark in their legacy and will be so influencial that their children will tell "the story of tonight."
father
I did not tag relationship names such as father, mother, brother, or sister as person names
President
I did not tag president as a person because i only decided to tag names for people
South
I decide to tag the South as a place because during this time the North and the South were very specific and seperate
The bullets out your gun!
I decided to tag all objects that you can physically grasp and touch. Things that are very large i did not consider objects
Immigrants:
I did not tag immigrants as a person because i decided to only tag names for people
Immigrants:
I would definitely consider immigrants to be a group of people Orr organization as denoted by a pink tag. Obviously immigrants are a group of people, which would aid me in deciding that this was a group and an organization.
my shot
I think we could potentially tag 'my shot' as a concept as it's less of a physical/tangible thing but a very important concept that is partially behind Hamilton's work ethic and drive.
diamond in the rough, a shiny piece of coal
I thought that this was an interesting thing to tag, not only because it is an object but also is a reflection on Hamilton being unique.
Washington
I think we should tag Washington as a persName.
church
I would tag church as a "placeName"
Obedient Servant
I really like this tag as a person. I feel like the class would be a 50/50 split on whether or not to tag this as a person or even tag it at all, but it is obvious that the Obedient Servant that is being mentioned is Burr himself, as he is promising to abide by the duel that he is proposing to Hamilton.
mainland
I chose to tag this because the whole point of this song is to give background on Hamiltons life and the reference to mainland is Burr talking about the colonies as Hamilton was from a British island
to form a harem
https://www.merriam-webster.com/dictionary/harem Very interesting tag. This is what it means
Die Nennung von Fällen ohne Bezugsgrößen ist irreführend. So werden beispielsweise für die einzelnen Länder, Bundesländer oder Regionen lediglich Rohdaten berichtet, ohne Bezug zur Bevölkerungsgröße. Die Angaben könnten sich jeweils auf 100.000 Einwohner beziehen. Auch werden keine zeitlichen Bezugsgrößen genannt. So heißt es etwa "bisher gibt es 10.000 Fälle". Die Nennung von Rohdaten ohne Bezug zu anderen Todesursachen führt zur Überschätzung des Risikos. In Deutschland versterben etwa 2.500 Personen pro Tag. Die Angaben zu den Todesfällen durch Covid-19 sollten daher entweder die täglich oder wöchentlich verstorbenen Personen mit Angabe der Gesamttodesfälle in Deutschland berichten. Auch ein Bezug zu Todesfällen durch andere akute respiratorische Infektionen wäre angemessen.
hä?! Macht RKI doch ... was können die dafür dass die meisten Reporter zu dumm sind das so weiterzugeben?
"Extensive experience and research show that hydroxychloroquine builds up in the body and continues to work for an average of 40 days even after the last dose is taken. By then, we expect the drug manufacturers to have ramped up production to meet the increased demand. Until then, we are no longer refilling routine prescriptions to ensure we have adequate supply to care for our sickest patients," Gin said.{"adPos":"promo-inline3","adType":"ex","isInfinite":true,"platform":"autodetect","position":3,"renderLookahead":"x0.25","size":[[5,5],[728,90],[300,250],"fluid",[320,50]],"targeting":{"badges":["viral","coronavirus"],"bid":"5376488","brain_tags":["adult-0","crime-30","safe-70","non_profane"],"nbs":0,"nsfw":0,"pos":["promo-inline3"],"sensitive":0,"tag":[],"trending":0,"user":"tanyachen","wid":"212-1","infinite_index":1},"viewability":"high","wid":"212-1","zone1":"bfnews"} Advertisement [tl-ut] { content: ""; } div#BuzzfeedNews_Desktop_inarticle_MPU_Flex_container { position: relative; box-sizing: border-box; margin: 0px auto; display: flex; flex-flow: row nowrap; align-items: initial; max-width: 100%; max-height: none; background-color: rgb(255, 255, 255); border: 1px solid rgba(0, 0, 0, 0.1); width: 100%; height: auto; padding: 0px; font-size: 16px; 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Learn more! Sponsored by Loyola University Chicago See More "Kaiser Permanente physicians and pharmacists are also working together on an evidence-based approach to identify alternative therapies for patients with lupus," Gin added in a follow-up statement.Dale said she immediately called her doctor and has been scheduled for a phone call next week. { "id": "1241213117872234496", "params": { "conversation": "none" } } { "id": 124409135 } Despite thin evidence for the drug’s effectiveness against coronavirus infections, shortages of chloroquine have erupted since Trump called it a “game changer” at a White House news conference late last week. The drug, a derivative of an antimalarial drug, has been added to the regimen for treating COVID-19 infections in China and South Korea and is being tested in clinical trials in the US.However, experts on drug testing have been skeptical of the evidence for its benefits. A frequently cited French study of 20 patients saw several drop out of the trial to instead go into intensive care.{"adPos":"promo-inline4","adType":"ex","isInfinite":true,"platform":"autodetect","position":4,"renderLookahead":"x0.25","size":[[5,5],[728,90],[300,250],"fluid",[320,50]],"targeting":{"badges":["viral","coronavirus"],"bid":"5376488","brain_tags":["adult-0","crime-30","safe-70","non_profane"],"nbs":0,"nsfw":0,"pos":["promo-inline4"],"sensitive":0,"tag":[],"trending":0,"user":"tanyachen","wid":"213-1","infinite_index":1},"viewability":"high","wid":"213-1","zone1":"bfnews"} Advertisement An Arizona man died on Monday after self-medicating with a related drug, chloroquine phosphate, where chloroquine was also touted at White House news conferences.“For many people with lupus there are no alternatives to these medications,” the Lupus Foundation of America said in a statement on Monday, warning of shortages. “Hydroxychloroquine or chloroquine are the only methods of preventing inflammation and disease activity that can lead to pain, disability, organ damage, and other serious illness.”Dale, who's been calling multiple pharmacies in her local area, said, "I have learned that all area pharmacies are completely out of hydroxychloroquine." { "id": 124409399 } <img src="https://img.buzzfeed.com/buzzfeed-static/static/2020-03/25/15/asset/705a16f1d755/sub-buzz-3668-1585151028-3.jpg" alt="" class="xs-block"/> healthy.kaiserpermanente.org { "id": 124409135 } "In their mission statement, Kaiser says that they aim 'to provide high-quality, affordable health care services and to improve the health of our members and the communities we serve,'" Dale said. "How is denying medication for a chronically ill, immunocompromised patient during a pandemic improving my health?""I want Kaiser to follow their own mission statement and reverse the decision to withhold my medication." UPDATE March 25, 2020, at 12:48 p.m. This story has been updated to include a follow-up statement from Kaiser Permanente. More on this Chloroquine Is Being Touted As A Miracle Drug For Coronavirus, But There Are Reasons To Be Wary Dan Vergano · March 23, 2020 A Man Died After Self-Medicating With A Form Of A Drug That Trump Promoted As A Potential Treatment For The Coronavirus Brianna Sacks · March 23, 2020 Trump Said He Wants To Give Coronavirus Patients An Experimental Drug Called Chloroquine Dan Vergano · March 19, 2020 Tanya Chen is a social news reporter for BuzzFeed and is based in Chicago. Contact Tanya Chen at tanya.chen@buzzfeed.com. Got a confidential tip? Submit it here. Dan Vergano is a science reporter for BuzzFeed News and is based in Washington, DC. 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This may be true but I am still confused about how people know this helps fight COVID-19
Change title tag:
Affordable Bathroom Renovations, Auckland - Add Value Renovation
hypothes.is (use the hashtag/tag #chromosomenomenclature)
Please add comments directly on the key parts of the commentary you would like to raise any issues with.
But everything’s changed. Notes are stored locally so there is no lag, search is blazing fast, and the search bar provides recommendations and suggestions that are far more accurate and helpful than tags
I would like less lag time in adjusting and reformulating my searches as I go. The lag time at this point has more to do with how slow Roam is to render things than how slow the process could be, since the sidebar should allow you to pull up many things, explore new threads of thought, and change your question as you go. Thing is, text search with filtration is still powerful as a tag based search if you’re looking at purely functionally, but tagging also gears you towards asking, “when do I want this to resurface?” So you end up coming to more relevant stuff than simply what is written in the base text. This means you're more likely to resurface notes when you take a tagging approach because it gears you to behave differently towards your notes.
That being said, I don't see this as one or the other. Sometimes I think it's better to just search for a note with text. Only problem is that primarily works for searches where you can clearly formulate your question/goal, you know what you're looking for, and you know where to find it. That essentially turns search into something that requires memory, whereas tag based searches allow for exploratory browsing.
So which behaviors are desired and undesired when it comes to organization? In any organizational system, the constant temptation is to overorganize, i.e. to create too many categories, too many subdivisions that are too specific. As the number of tags grows arithmetically, their complexity grows geometrically, for multiple reasons, both technological and cognitive (see memory fatigue above). This phenomenon is all the more problematic with unlimited digital information that never runs into physical constraints.
This could be true, it’s a good point. I come back to Conor’s framework of reduce, filter, and map though. If the user is doing behaviors along the way that reduce the amount of information they’ll see when they look in the future, filter to specify what you want, and then map it out to new contexts, then this bug becomes a feature. Progressive summarization helps in that process, and filter specific tags also helps, as well as straight up deleting things or applying an archive tag that you can always filter out.
This problem also gets reduced when you have aliases and hierarchy abilities along with the tags, because then you’re able to better express what you mean in fewer words/tags. Problem is that Roam doesn't have aliases yet to let the user reduce clutter in their pages and with the different ways that users express the same thing. Tags with bidirectional and unidirectional aliases allow us to express meaning, rather than remember specific words.
Thus the most problematic behavior is implicitly encouraged and enabled. Grrrrrrr. It could be a misspelling. It could be a slip of the finger. It could be a different capitalization, punctuation, or tense, whatever. No warning or indication is given, and a divergent tag is created, for you to hopefully notice and fix later, hopefully before you rely on it.
Aliases and hierarchy! Would also be helpful if the app suggested aliases to you based on frequent usages together or similar letter combinations.
As good as your brain is at recognizing patterns, it is terrible at storing and recalling multiple patterns precisely, since the patterns of neuronal activation interfere with each other. Yet this is exactly what you’re doing with tags. What a terrifically unnecessary expenditure of mental resources
See my comment about pattern recognition above. The functionality of tagging can be set up so you have frequent/spaced repetition. Also sort of a feature, not a bug. If something doesn’t get consistently reinforced, then it should sink away. You just need to balance search strategies. Don’t always go in with one predefined query with filters. Let yourself follow train of thoughts, and if it resurfaced, then tag it up more then or build on it. If not, it probably wasn’t too valuable anyway.
You can also program your attention so some tags never go away by making index pages. I’d say this all just depends on your behavior with notes more than anything, I don’t think about it so much as tags vs. folders.
However, I would totally say that you need to behave a certain way with your notes or it does become unnecessary expenditure. If you aren’t searching your notes and conversing with them, you won’t see the benefits.
Using + to trace Spam Some site collect your information to sell to other people. To detect this, you can use a custom tag in your email address for every site you sign up for. For instance, if you were joining the Washington Post email list, you could sign up with your_username+washingtonpost@protonmail.com. This will deliver the email into your account, while allowing you to identify where you inputted the email address. If you receive email to this address from anyone other than the Washington Post you will know they either sold your data or experienced a data breach. To learn more about using + in your email address, please see: Addresses and Aliases.
DEM.Encrypt(K, L, M)
Here, in contrast to the DEM used in the Tag-KEM paper, we have a label (additional data).
Theorem 3.1[Tag-KEM/DEM Composition Theorem] If the Tag-KEM is CCA secure and theDEM is one-time secure then the Hybrid PKE scheme in Section3is CCA secure. In particular,²pke<2²tkem+²dem.
Could we reuse this theorem? Or at least replicate this proof in CryptoVerif?
Notethat, in the above syntactic definition,τis not included inψand explicitly given toTKEM.Dec
If we change DHKEM to use a context when deriving zz, does this make DHKEM a Tag-KEM?
You can tag public or shared resources, but the tags you assign are available only to your AWS account and not to the other accounts sharing the resource.
tags on AMI cannot be used for search across accounts
Lydia Sigourney
You could, if you wanted, tag all of the Connecticut authors or locations in a document.
sometimes called
The HTML5 audio broken for several reasons: you can't use the same name for a class and ID ('audio'), and you can't use an already reserved tag name (<audio> and 'audio'). Also, it looks like the path to the audio file is wrong, or the file is missing.
Cookies may not be detected by scanner if the related tag is triggered by actions such as form submission, scroll depth, timing delay, etc. These tags will need to be controlled by manual methods.
With all these caveats listed, it makes me wonder for which tags auto-blocking does work. Only script tags inside of head?
They are a bit vague in their "how it works" description...
Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
__Reviewer #1 (Evidence, reproducibility and clarity (Required)):
In this article, the authors characterize a complex formed by sec22b-stx1-Esyt2. They investigate how such interactions are involved in the modulation of dynamics of the plasma membrane in the context of neuritogenesis. They conclude that the contact sites between the ER and the plasma membrane, mediated by the afore-mentioned complex, contribute the expansion of the plasma membrane.
**Major comments:**
Overall, the article clearly shows that in mammalian cells there is an interaction between sec22b-stx1-Esyt2 which appears to be important for filopodia formation and possibly neuritogenesis in neurons. However, performing additional experiments to better clarify some links and assumptions made by authors could strengthen the article.
The manuscript relies on work performed either on cell lines (HeLA, PC12) or primary neuronal cultures. Although it is clear the value of the findings obtained using cell lines, they should be seen as a complementary rather than an exclusive approach. This is particularly important as the authors often make claim about neuron-related cellular biology.
For instance, the biochemistry-based findings on the interaction and characterization of the protein complex (Figure 1) are all derived from experiments perfomed in Hela or PC12. As the authors have the capacity to culture and manipulate primary neuronal cultures, such findings should be validates in neuronal cells. The authors could also consider performing biochemical experiments (i.e. co-ip) of the endogenous proteins in neuronal cultures or brain tissue.*
->Endogenous Co-IP has been tried in E18 brain tissue. One experiment using brain tissue demonstrated co-immunoprecipitation of endogenous Sec22b and E-Syt2. Unfortunately, repetitions of this experiment failed due to high background in negative control (naïve Rabbit IgG). We agree with the reviewer that this data is worth trying again. We will carry out this co-immunoprecipitation experiment from cultured neurons to answer the reviewer’s request.
The authors do show some evidence regarding the complex in neuronal cells using PLA (proximity ligation assay, figure 2) or super resolution microscopy, however, these findings should be corroborated by stronger findings targeting interaction and not based on simple proximity.
->We agree with this reviewer that PLA is limited in demonstrating the occurrence of a protein complex. We would like to stress that we have used PLA complementarily to immunoprecipitation and that we already have shown STED super-resolution data (Figure 3). In order to strengthen the STED data, we will include more details in the figure, as a supplementary movie and a supplementary spreadsheet with the quantification of the distance between the E-Syt2/Sec22b clusters to the plasma membrane stained using WGA. The STED data demonstrate that 50% of the clusters are closer than 33.6nm to the plasma membrane, a distance in the range of ER-PM contact sites.
A similar critique regards the experiments using RNA-interference of Figure 4. Performing loss-of-function experiments in neuronal cultures would strengthen and complement the results obtained via over-expression approaches shown in Figure 5.
->The loss-of-function experiments in neuronal cultures using siRNA were attempted unsuccessfully. The three E-Syts have largely different cDNA sequences thus three distinct siRNAs must be transfected in order to silence all three simultaneously. This is quite challenging in neuronal cultures and we were never able to get strong silencing of the three E-Syts. In the following points, we plan to carry out further experiments using expression of a fragment of Sec22b (Longin domain). We are confident that this is a better approach to demonstrate the importance of Sec22b/E-Syt interaction.
*Given that the authors have already in place all the necessary technology for the suggested biochemical and morphological-related experiments, these could be carried out swiftly within 3-4 months.
**Minor comments:**
The manuscript is really technical and at times tough to follow; it could benefit from key sentences to better guide the reader, particularly if not coming from the specialist field, in the appreciation of the experiments and results.
Authors should submit the manuscript to a severe round of proofreading. There are several inconsistencies and sometimes what looks like internal comments: i.e. in the methods "STED Missing" or the fact that "LTP" is used everywhere but not defined and considering that the targeted audience is most likely neuroscience-based could easily lead to confusion.
*
->We fully agree with this reviewer and apologize for leaving behind such errors. We will carefully proofread the revised ms.
*The experiments appear to have been repeated a sufficient number of times and the statistics seem adequate. It would be advisable to show in dot-plots the findings rather than in bar graphs all findings and not just the morphometrics-relative ones.
*
->We will modify the figures according to this reviewer’s suggestion.
Reviewer #1 (Significance (Required)):
*This work closely follows the excellent previous work from the Galli laboratory. As such, it is mostly incremental from a technical perspective and does not present particularly novel findings. An interesting aspect would be in addressing directly the influence of the described interactions in the lipid transfer between ER and the plasma membrane but in that sense the manuscript falls short. Although it is to be appreciated the functional readouts in terms of neuritogenesis, in the present state the manuscript features findings suitable for a very specific audience.
I believe that the appropriate audience for the present manuscript lies within the neuroscientific community interested in development, specifically neuritogenesis, and/or membrane dynamics. Additionally, it might be interesting also for researchers outside of the neuroscience community and interested in the dynamics between ER and plasma membrane.
*
->We are happy to read the comments of this reviewer. Nevertheless, we would like to stress the importance of deciphering precise molecular mechanisms in any biological process. Here, we are the first to demonstrate an interaction between lipid-transfer proteins E-Syts and ER v-SNARE Sec22b. As an example, the molecular mechanism connecting synaptic SNAREs and synaptotagmin has been the topic of more than 500 publications since seminal articles in the early 1990’s. We think that the first article linking E-Syts to SNAREs cannot be considered as a mere increment from our previous work.
The activity of E-Syts to transfer lipids in vitro has been well established __(1–3) In addition, recent work by the De Camilli lab using Origami showed that reducing the distance between liposomes enhanced the lipid transfer mediated by E-Syt2 (3). Therefore, we did not carry out experiments such as combining SNAREs and E-Syt2 in artificial membranes in vitro because we considered that there would not be much more to demonstrate than what has already been done. Furthermore, we considered the experiments in cells, particularly neurons, much more critical at this point. Demonstrating transfer of glycerophospholipid between ER and PM in cells cannot be performed like other lipids’ transfer at other membrane interfaces for the following reasons: phospholipids are very abundant (4) and they are not modified upon transfer (1)__, there are no specific dyes to detect glycerophospholipids (unlike phosphoinositides), and ER and PM are too close to distinguish if a glycerophospholipid is in one or the other membrane. Such a challenging experiment would require the ability to setup a specific biochemical assay circumventing these constraints. We think that this is out of the scope of the present study focused on the role of E-Syt/Sec22b complex.
Nevertheless, in order to get further insights on this question, we will express WT and mutant E-Syt2, purify the PM using the protocol of Figure 4 in Saheki et al __(1)__, followed by lipidomics analysis. We hope that this approach further supports our idea that E-Syts mediate an important lipid transfer mechanism towards the PM.
* Keywords regarding my expertise: Molecular and Cellular Neuroscience, Morphometrics, Dendrite, Neurons, Dendritogenesis, Biochemistry, Imaging, Microscopy.
__
Reviewer #2 (Evidence, reproducibility and clarity (Required)): *This manuscript identifies and characterises a novel interaction between E-Syts and Sec22b and demonstrates that lipid transfer between the ER and PM contributes to the development of filopodia and neuronal expansion. This interaction with E-Syt2 occurs through the Longin domain of Sec22b Sec22b association. The authors suggest a continuum with further interactions with syntaxin1, that mediates neurite outgrowth. Overall I find this study very interesting and convincing. The experimental analysis is well carried out and the claims are well aligned with their results.
I only have minor issues:
Figure 1. Some of the western blots have several bands and it is difficult to know which band is the relevant one. They should be indicated in the fig panel. Further panel E and F are barely readable and should be redrawn with the appropriate line and font size.*
->We will make the changes requested by this reviewer in Figure 1.
Figure 2: is there a difference between the number of dots in axons and dendrites? Can the author elaborate on this aspect as it is not clear from the image presented.
->We could not combine PLA with further staining of MAP2 and TAU. Indeed, to perform PLA, neurons are already double labelled to detect the proteins of interest. At the stage of the neurons used in this study, both axons and dendrites are growing. Therefore, we did not invest in distinguishing between axons and dendrites. Because growth cones are known to be the major sites of membrane growth, we instead distinguished dots within neurites and in growth cones. We will make the other changes requested by this reviewer in Figure 2.
Figure 7: statistical analysis should be indicated from the BoNT/A and BoNT/C as BoNT/A represent an appropriate control cleaving SNAP25 but not Syntaxin.
->We agree with this request and we will add statistical analysis as suggested, using BoNT/A as an additional control.
On top of controlling fusion and neuronal outgrowth, syntaxin has a role in survival and its cleavage leads to neuronal death. Is this pathway mediated by E-Syts interactions?
->We have stated in the ms: “Since exposure to BoNT/C1 at high concentrations and for long incubation periods causes degeneration of neurons in culture __(5,6)__, various concentrations and incubation times were tested, and a 4-hour treatment of neurons with 1nM BoNTs was chosen to avoid such deleterious effects.” Accordingly, we did not see any degeneration in our experimental conditions.__ __
Reviewer #2 (Significance (Required)): This papers identifies the molecular mechanism of neuronal outgrowth. It is highly significant. ->We are very grateful to this reviewer for pointing out the high significance of our article.
__Reviewer #3 (Evidence, reproducibility and clarity (Required)):
__*1. The evidence for the claim that the Sec22b/Stx1 complex and E-Syts colocalize in native cells (neurons) and bind in heterologous cells is strong (3 independent lines of evidence: co-immunoprecipitation, Proximity Ligation (PLA) and STED super-resolution microscopy) However, the current title of the paper makes a claim beyond this interaction/proximity, based on evidence that is obtained with E-Syt over-expression in wildtype cells. The physiological relevance of the effects remain elusive with over-expression in wildtype cells only.
Furthermore, it is plausible that overexpression of membrane binding/bending C2-domains promotes neurite outgrowth and ramifications by a non-specific effect (as shown for copine C2 domains, PMID:25450385 and indirect evidence for synaptotagmins1,2,7).*
* This issue is especially relevant in the light of the fact that loss of all 3 Extended Synaptotagmins does not affect normal mouse development and viability (PMID: 27399837)
It would be more appropriate to choose a more descriptive title*
->We agree with this reviewer that the original title may be too strong and are now proposing the following, more descriptive title:
Role of the Sec22b/E-Syt complex in neurite growth and ramification
We are fully aware that proteins harbouring C2 domains could potentially promote non-specific effects when overexpressed. However, we do not think this is the case here because none of the morphological effects of E-Syt2 expression in neurons and HeLa cells were reproduced by mutants lacking the SMP or the membrane-anchoring domains. Based on work on Copine __(7)__, a cytosolic protein, E-Syt2 still containing 3xC2 domains but lacking the membrane-anchoring domain should have shown a morphological effect if non-specific binding to phosphoinositides was the mechanism of action. We will discuss this point in the ms.
The evidence for the working model that the morphological effects of E-Syt2 depends on the Sec22b/Stx1 complex is not strong. Although plausible, the positive effect on neurite outgrowth (E-Syt2 overexpression) and the negative effects (inhibition by Stx1 cleaveage, Sec22b-Longin or Sec22b extended linker expression) may in fact be independent
The evidence could be strengthened by PLA measurements in neurons over-expressing Myc-E-Syt2 and Sec22b to assess MSC density. It is predicted that in both conditions, MCS density increases. MCS density by PLA measurements could also be performed in Sec22b-P33 and DLongin overexpressed and BoNT/C1 treated neurons. According to the model, the number of MCS should go down. This is of special interest for BoNT/C1 treatment, as it is important to show that the altered morphology is not purely caused by a pre-state of degeneration that is known to be induced by BoNT/C1. In addition, EM measurements of ER-PM distances might provide an independent line of evidence.*
->We agree with this reviewer that additional experiments could strengthen the description of the molecular mechanism. To this end, we will carry out the following experiments:
1/Co-immunoprecipitation experiments of endogenous Syntaxin, Sec22b and E-Syt2 in cells expressing GFP as control or Longin-GFP to demonstrate that expression of the Longin domain perturbs the association of Sec22b with E-Syt2 and Syntaxin.
2/PLA measuring the association between E-Syt2 and Syntaxin in cells expressing GFP as control or Longin-GFP to demonstrate that expression of the Longin domain perturbs the association between E-Syt2 and Syntaxin using a complementary approach.
Unfortunately, membrane-associated, BoNTC1-cleaved syntaxin corresponds to a short fragment undetectable by available antibodies whereas the fragment detected by the antibody after BoNTC1 cleavage lacks the transmembrane domain (Figure 7a). Therefore, we cannot perform PLA in BoNTC1-treated neurons.
We are confident that further exploring the mechanism of action of the Longin domain, together with the data already in the ms, will make it very clear that the morphological effects of E-Syt2 depends on the Sec22b/Stx1 complex.
->The mutants and siRNA have been already used and validated in Giordano et al. 2013 __(8)__, therefore we did not carry out experiments aiming at basic characterization of these reagents. To answer this request, we will show images of the subcellular localization by ICC of WT and mutant E-Syt2 in the revised Figure 6 or in a Supplementary Figure.
In addition, the authors might make the link between neurite outgrowth and lipid transfer stronger by examining PM lipid levels and distribution in control, Myc-E-Syt2 and E-Syt2 mutant neurons.
->We agree with this reviewer that this question is of high relevance. In order to answer this request, we will express WT and mutant E-Syt2, purify the PM using the protocol of Figure 4 in Saheki et al __(1)__, followed by lipidomics analysis. We hope that this approach further supports our idea that E-Syts mediate an important lipid transfer mechanism towards the PM.
* ->We will make it clear that our model in Figure 9 is a hypothetical model.
An apparent discrepancy exists between the TKD E-syts effects (i.e. reduced MSC density, Fig 4) and the lack of neurite outgrowth defects in TKO E-syts. According to the proposed model, the levels of E-syt correlate with the number of MSCs and thereby neurite outgrowth. Furthermore, to knock down E-Syts, single siRNAs against the three E-syts were used in Fig4. Off-target effects are not controlled in this approach. Using multiple siRNAs and/or siRNA resistant rescues would be required for robust conclusions.
*
->The mutants and siRNA have already been used and validated in Giordano et al. 2013 __(8)__, therefore we did not carry out experiments aiming at basic characterization of these reagents. In addition, we would like to stress the complexity of carrying out a rescue experiment of a triple KD of proteins.
Statistical analysis is incomplete. It is not clear whether statistical assumptions (e.g. normal distribution) were checked before performing the tests, and whether non-parametric alternatives where used if assumptions were not met.
->We thank this reviewer for making this important alert. We would like to stress that we have always checked whether samples followed the normal distribution and made non-parametric tests__. We will include this comment in the methods.__
In Fig4, a T-test is used between multiple groups. This test can only be used when comparing two groups. Number of (independent) measurements is not clear in Fig1, 2, 4
->In all the figure legends the number of repetitions is specified
All figures: display all individual data points in all bar graphs (as shown in 5c)
*
*
\*Minor comments:**
Fig1A: Please clarify the multiple bands? for Stx3 (anti-eGFP).
->These additional bands are recognised by the anti-GFP antibody, the tag being N-terminal, thus they represent proteolytic fragments. We consistently observe these in our experiments.
Fig2: There is no size marker for panels C1-C6
->We will make the appropriate correction.
Fig3: Both proteins seem to show a diffuse pattern. Please specify the validity of measuring average distance. A higher magnification zoom of staining pattern in the growth cone and visualization of the calculation could benefit interpretation.
->We agree with this reviewer that Figure 3 was not optimal to show all the extent of our STED data. In order to strengthen this part, we will include more details in both the figure and as a supplementary movie and supplementary spreadsheet with the quantification of the distance between the E-Syt2/Sec22b clusters to the plasma membrane stained using WGA. The STED data demonstrate that 50% of the clusters are closer than 33.6nm to the plasma membrane, a distance in the range of ER-PM contact sites.
__->We added the red line to highlight the shift of SNAP25 band in BoNTA samples. If misleading, it can be removed
Reviewer #3 (Significance (Required)):__
A growing body of literature recognizes the importance of close proximities between membranes, facilitating direct interaction between organelles (Scorrano et al., 2019). Membrane Contact Sites (MCSs) are shown to be important for a wide range of cellular functions, such as lipid and calcium transfer. E-Syts have been recognized as one of the key players in neuronal MCSs, mediating lipid transport (Fernández-Busnadiego et al., 2015). A study published in 2014 by the authors of the current study revealed another two proteins important for MSCs in neurons (Petkovic et al., 2014). ER protein Sec22b and PM SNARE Syntaxin1 were shown to form a non-fusogenic trans-SNARE complex, important for lipid-transfer mediated neurite outgrowth. Gallo and colleagues have now provided important new evidence that these two components (E-Syts and Stx1/Sec22b) are together and may work together at MSCs.
->We thank this reviewer for stressing the importance of our article and agree with the conclusion of __Fernández-Busnadiego et al. (9) on E-Syts being one of the key players in neuronal MCSs, mediating lipid transport. We think that our work is a further key piece of evidence in the demonstration of the importance of E-Syts in neuronal development.__
Bibliography
Saheki Y, Bian X, Schauder CM, Sawaki Y, Surma MA, Klose C, et al. Control of plasma membrane lipid homeostasis by the extended synaptotagmins. Nat Cell Biol. 2016 Apr 11;18(5):504–515. Yu H, Liu Y, Gulbranson DR, Paine A, Rathore SS, Shen J. Extended synaptotagmins are Ca2+-dependent lipid transfer proteins at membrane contact sites. Proc Natl Acad Sci USA. 2016 Apr 19;113(16):4362–4367. Bian X, Zhang Z, Xiong Q, De Camilli P, Lin C. A programmable DNA-origami platform for studying lipid transfer between bilayers. Nat Chem Biol. 2019 Jul 18;15(8):830–837. Alberts B, Johnson A, Lewis J, Raff M. The lipid bilayer. Molecular Biology of …. 2002; Osen-Sand A, Staple JK, Naldi E, Schiavo G, Rossetto O, Petitpierre S, et al. Common and distinct fusion proteins in axonal growth and transmitter release. J Comp Neurol. 1996 Apr 1;367(2):222–234. Igarashi M, Kozaki S, Terakawa S, Kawano S, Ide C, Komiya Y. Growth cone collapse and inhibition of neurite growth by Botulinum neurotoxin C1: a t-SNARE is involved in axonal growth. J Cell Biol. 1996 Jul;134(1):205–215. Park N, Yoo JC, Lee Y-S, Choi HY, Hong S-G, Hwang EM, et al. Copine1 C2 domains have a critical calcium-independent role in the neuronal differentiation of hippocampal progenitor HiB5 cells. Biochem Biophys Res Commun. 2014 Nov 7;454(1):228–233. Giordano F, Saheki Y, Idevall-Hagren O, Colombo SF. PI (4, 5) P2-dependent and Ca2+-regulated ER-PM interactions mediated by the extended synaptotagmins. Cell. 2013; Fernández-Busnadiego R, Saheki Y, De Camilli P. Three-dimensional architecture of extended synaptotagmin-mediated endoplasmic reticulum-plasma membrane contact sites. Proc Natl Acad Sci USA. 2015 Apr 21;112(16):E2004–13.
vec des articles de presse, des textes scientifiques, des textes d’histoire ou des commentaires d’œuvres
Dommage, je ne peux pas choisir la couleur du surlignage, mais je peux mettre un tag
unidad_COVID2019
unidad_COVID2019,favorito
To be fully compliant with GDPR, you would also need to enable Show Reject All Button setting.
Consent Model. In the case of GDPR, you must choose the Opt-in. This means that you cannot start tracking people before the consent was given.
This cookie consent notification is just a tool for getting consent, it’s not capable of managing your tracking tags because every website and every GTM container is unique, therefore there is no universal solution. As a result, you will have to manually update all your tracking tags with additional firing rules.
Configuring OneTrust’s cookie consent solution is just half of the task. Your tracking scripts (like Google Analytics, Google Adwords, etc.) will still continue working as they always did unless you import my GTM recipe and then reconfigure all of your tracking tags. Yup, there’s a lot of manual work waiting ahead.
if you are using some tools/scripts on your website that are used to identify individuals and their data is processed by you or 3rd parties), that can be done only when a person gives consent
ron and Headq
hello - edit
hello
started
Where will we tag this?remind me.Where will we tag this?And with what?remind me
Use JSDOC https://jsdoc.app/about-getting-started.html format.
Standardise your JavaScript comments:
/** This is a description of the foo function. */
function foo() {
}
As explained by Flanders, “An element has three core constituent parts: the start tag, the end tag, and the content, which is whatever lies between the start tag and the end tag” (Flanders 106).
This is a great quote fro Julia Flanders, I came upon this text as well and really helped me understand the reason behind encoding.
Tags in the digital marketing and analytics context are similar to, but distinct from, the standard HTML tags that developers will use to code web pages. The analytics version of the word "tag" is derived from the fact that the tags provided by vendors are often encapsulated by HTML <script> or <img> tags. When we speak of tags in an HTML context, we refer to tags such as <body>, <p>, <li>, <blockquote>, and so on. When we refer to tags used in the analytics and marketing industry, we refer to code that an organization provides to install the desired product or functionality on your website or mobile app.
The advantages of XML over JSON for trees becomes more pronounced when we introduce different node types. Assume we wanted to introduce departments into the org chart above. In XML, we can just use an element with a new tag name
Create a Git repository for every new project. Learn more about what a Git repo is in this beginner Learning Git with GitKraken tutorial. Always create a new branch for every new feature and bug. Regularly commit and push changes to the remote branch to avoid loss of work. Include a gitignore file in your project to avoid unwanted files being committed. Always commit changes with a concise and useful commit message. Utilize git-submodule for large projects. Keep your branch up to date with development branches. Follow a workflow like Gitflow. There are many workflows available, so choose the one that best suits your needs. Always create a pull request for merging changes from one branch to another. Learn more about what a pull request is and how to create them in this intermediate Learning Git with GitKraken tutorial. Always create one pull request addressing one issue. Always review your code once by yourself before creating a pull request. Have more than one person review a pull request. It’s not necessary, but is a best practice. Enforce standards by using pull request templates and adding continuous integrations. Learn more about enhancing the pull request process with templates. Merge changes from the release branch to master after each release. Tag the master sources after every release. Delete branches if a feature or bug fix is merged to its intended branches and the branch is no longer required. Automate general workflow checks using Git hooks. Learn more about how to trigger Git hooks in this intermediate Learning Git with GitKraken tutorial. Include read/write permission access control to repositories to prevent unauthorized access. Add protection for special branches like master and development to safeguard against accidental deletion.
Git Dos
SetEnvIfNoCase Host "^www" crawlable Header set X-Robots-Tag "noindex, nofollow" env=!crawlable
Throughout the process, there were situations where I was torn between different ways to tag certain words or phrases.
From class discussion and personal experience, this problem was common for the entirety of the assignment. In regards to your encounter with the dog, by identifying if the dog contains human traits would allow you to label him as a person versus an object. In this case, the dog is compared to a person, ensuring why the dog should be tagged as a person.
I found this quote to be very true because although it was harder to do the semantic encoding because I had to make certain decisions about what to tag, I found it more interesting. The structural encoding was very tedious because I had to go line by line giving the same tags over and over again.
I agree. The structural encoding was repetitive, tedious, and boring. Whereas the semantic encoding differed in regards to tagging objects, people, places, and concepts. However, both are required to acquire a PDF document comprised with color coded words in relationship to their specific tagging.
The <meta name="robots" content="noindex" /> tag or directive applies to search engine crawlers. To block non-search crawlers, such as AdsBot-Google, you might need to add directives targeted to the specific crawler
Learning will not happen quickly and it will be a good journey to further educate myself in the space.
I think you did a great job, but maybe for the module assignment talk about your experience collaborating with the class about how to tag specific words.
. I felt that finishing this before the rest of the encoding made things simpler because once we started encoding different parts of the songs,
I followed the same process to my encoding as you because I felt that it also made it easier to tag specific words after encoding the lines, speakers, speech, and title.
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Rapid Application Development – für starke Anwendungen <img loading="lazy" class='avia_image' src='https://bp387lpw.myraidbox.de/wp-content/uploads/2020/02/illu-startup-01.svg' alt='Die Abbildung zeigt einen Mann, der vor einem Desktop steht' title='illu-startup-01' height="500" width="489" itemprop="thumbnailUrl" /> Systemintegratoren, die Tools in einem Bruchteil der Zeit und mit geringeren Kosten integrieren <img loading="lazy" class='avia_image' src='https://bp387lpw.myraidbox.de/wp-content/uploads/2020/02/illu-data_analysis-01.svg' alt='Die Abbildung zeigt drei Personen, die um einen Tisch herum stehen.' title='illustration-drei-personen-arbeiten-erfolgreich-an-tisch' height="500" width="458" itemprop="thumbnailUrl" /> Anwendungsdesigner, die leistungsstarke, inhaltsbasierte Applikationen erstellen <img loading="lazy" class='avia_image' src='https://bp387lpw.myraidbox.de/wp-content/uploads/2020/02/illu-web-hosting-01.svg' alt='Die Abbildung zeigt einen Mann, der vor 4 Servern steht mit grauem Schatten' title='illu-web-hosting-01' height="428" width="500" itemprop="thumbnailUrl" /> IT-Abteilungen, die kundenspezifische Anwendungen auf webbasierter IT-Ebene betreiben Das yuuvis® RAD Framework ist die ideale Lösung für kleinere bis mittelständische Unternehmen, die on-premise hoch performante Plattformen betreiben wollen. Außerdem ist es optimal, um bestehende Anwendungen um ein Archiv zu erweitern. Die mit yuuvis® RAD implementierten Applikationen skalieren dank des modernen Microservices-Stack und des responsiven Web-Clients (Angular/HTML5) auch in mittelgroßen, weltweit verteilten Firmennetzen. Finden Sie heraus, wer von yuuvis® RAD profitieren kann Warum yuuvis® RAD? Schnelle Anwendungsentwicklung – die Vorteile auf einen Blick State of the ArtHigh-End-ECM auf dem neuesten Stand der Technik. Perfekt für content-zentrische Anwendungen. Offene ArchitekturREST-API gewährleistet einfache Integration in bestehende Systemlandschaften. Low CodeWeniger Code bedeutet weniger potenzielle Fehle, einfachere Wartung und geringere Kosten. ZukunftssicherHoher Investitionsschutz durch ein nachhaltiges Konzept gegen zukünftige disruptive Technologien. Einfaches HandlingGemeinsame Benutzerverwaltung mit sehr gut integriertem Workflow und Datenmanagement. Custom MicroservicesKomplexe Anwendungsszenarien können mit eigenen passenden Erweiterungen im Client ergänzt werden. Erfahren Sie mehr über die Vorzüge von yuuvis® RAD Sie wollen mit yuuvis® RAD arbeiten? Kontaktieren Sie uns! Wie funktioniert yuuvis® RAD? Die Technik hinter unserer Rapid Application Development-Anwendung Offene Technologie yuuvis® RAD basiert auf offenen Standardtechnologien. Alle serverseitigen Komponenten sind java-basiert und implementieren eine saubere, skalierbare und leichtgewichtige Microservices-Architektur. Diese wird von Spring Cloud Netflix orchestriert. Kommunikation und Protokoll basieren auf HTTP(S). Der Webclient ist eine hundertprozentige HTML5 Angular (Typeskript) Anwendung. Die serverseitige API ist rein RESTbasiert. Neueste Standards Die strikte Anwendung neuester Standards ermöglicht den sofortigen Zugriff auf das größte Netzwerk von Software-Experten der Welt. Proprietär ist ein Fremdwort in yuuvis® RAD. Es bietet damit die schnellste und sicherste Architektur, die heute verfügbar ist. Rechenzentrumsfreundlich Dank des Webclients ist der Rollout auf Endbenutzerarbeitsplätzen einfach und unkompliziert, ohne lokalen Installationsaufwand. Das HTTP(S)/REST-Protokoll zwischen dem Webclient und dem Server entspricht vollständig den Standard-Firewall-Technologien. Microservices-basiert Der Microservices-Stack bietet eine umfassende Integration von Wartung, Lastausgleich, Failover und Health-Check in die Management- und Überwachungswerkzeuge von Rechenzentren. yuuvis® RAD bedient sowohl die S3-Schnittstelle, den De-facto-Standard der Datenspeicherung als auch OAuth 2.0 zur standardisierten, sicheren API-Autorisierung für Desktop-, Web- und Mobile-Anwendungen. <img loading="lazy" class='avia_image ' src='https://bp387lpw.myraidbox.de/wp-content/uploads/2020/02/yuuvis-rad-framework-infografik.jpg' alt='Infografik zeigt das yuuvis® RAD Framework' title='yuuvis-rad-framework-Konzept' itemprop="thumbnailUrl" /> Das Rahmenkonzept der yuuvis® RAD Plattform Lesen Sie mehr über die Technologie von yuuvis® RAD Wer nutzt yuuvis® RAD? Diese Unternehmen vertrauen auf schnelle Anwendungsentwicklung Sie wollen mehr erfahren? 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<div class="nf-mp-header"></div> <div class="nf-mp-body"></div> <div class="nf-mp-footer"></div> {{{ data.renderProgressBar() }}} {{{ data.renderBreadcrumbs() }}} {{{ data.renderPartTitle() }}} <h3> {{{ data.title }}} </h3> {{{ data.renderNextPrevious() }}} <ul class="nf-next-previous"> <# if ( data.showPrevious ) { #> <li class="nf-previous-item"> <input type="button" class="nf-previous" value="{{{ data.prevLabel }}}" /> </li> <# } #> <# if ( data.showNext ) { #> <li class="nf-next-item"> <input type="button" class="nf-next" value="{{{ data.nextLabel }}}" /> </li> <# } #> </ul> <ul class="nf-breadcrumbs"> <# _.each( data.parts, function( part, index ) { #> <li class="{{{ ( data.currentIndex == index ) ? 'active' : '' }}} {{{ ( part.errors ) ? 'errors' : '' }}}"> <a href="#" class="nf-breadcrumb" data-index="{{{ index }}}">{{{ ( part.errors ) ? '' : '' }}} {{{ part.title }}}</a> </li> <# } ); #> </ul> <div class="nf-progress-container"> <div class="nf-progress" style="width: {{{ data.percent }}}%;"></div> </div> <nf-fields></nf-fields> <nf-cells></nf-cells> Q&A
Hier wird die Q&A Seite direkt geladen und nicht in einem neuen TAB
Tag-teaming vocalists Eaddy and theOGM don’t emerge in wedding dresses, smashing all equipment in sight like they did on 2015’s Warped Tour, causing their removal.
This is a prepostion its the how it tell how the arrived in 2015
The biggest drawback of algorithmic feeds is that you might be looking at irrelevant content. When you see something on your timeline and want to comment, you will have to check the timestamp to see if your comment is still relevant or not.
Use google image search or the website site used last class to find illustrations to pair with a THREE quotes from the book. Briefly explain your choices. Categorize: Reading Response; tag your post: class10
I totally forgot how to use this
V16, V13 and V9 types
The numbers refer to the diameter of the tag, with different sizes being better suited to a range of animal sizes and data types.
When a consumer (subscription) is registered, messages will be delivered (pushed) by RabbitMQ using the basic.deliver method. The method carries a delivery tag, which uniquely identifies the delivery on a channel.
Messages are delivered with Unique Identifiers as delivery tag
From Rain RFID Tag IC point of view it is therefore of highest importance to ensure correctness of these data.
**Delete
Entrepreneurship Defined
TIP for OERu LEARNERS: Remember to add the course code tag in the tag field when annotating with Hypothes.is. Eg BMAN111 or IENT101. (Hit the enter key after typing the tag.)
four-component instructional design (4C/ID)
4C/ID tag might be one of the models I want to look into as I might use it for the Art Modules Class
Top 10 Important Online Marketing Tools for Entrepreneurs 4.0 Thank you for your rating! Why not leave a message? Any additional feedback or questions are welcome below. Your Name Your Email Address Phone Number Message the Author Cancel By Shiv | Contact Author less than a minute ago Marketing tools are like a helping hand for many entrepreneurs. As time is money using online marketing tools make your job easy and fast and also saves money. Marketing tools are like a helping hand that makes your job so easy. These tools are designed in such a way that it saves you time, effort and money. Apart from finding customers, one of the most difficult problems faced by entrepreneurs is to save time and manage cash flow. Using tools for marketing your business ensures you meet your goal on time. Here are the top 10 most important online marketing tools for entrepreneurs- Hubspot- Inbound Marketing Tool If you want to buy one tool to grow your business exponentially, this is the tool you should buy. Hubspot offers an inbound marketing tool that promises you with attracting leads, converting them and closing the deal. This tool acts as a funnel for your business that narrows downs your audience into your customers and finally generates revenue out of them. You can get started using the Hubspot tool with just $113 per month. You can also get online tutorials on how to use Hubspot tools online or you can always call or email their support team to help you out. Freelancing Graphic Design Platform by EveryDesigns Marketing is impossible without good designs. In marketing, we have a lot of promotional content that uses heavy design work to attract customers. We, humans, are visual animals and it is easier to influence us visually rather than any other means. You can use the EveryDesigns platform to get designs for your business. It is a one-stop destination for your business to get any graphic design services. The innovation that they offer in their services is by organizing a contest for your designs where multiple designers work from around the world. You choose the best design submitted by designers and pay for only that design. You get multiple design options to choose from, along with a 100% money-back guarantee. MailChimp- Email Marketing MailChimp is the best tool when it comes to email marketing. As an entrepreneur, you know how important email marketing is. If you strategize your email marketing outreach, you can attract very valuable customers to your business website. The best part is that it provides some of its features for free, which is enough for a startup or small business to get started and if you want to buy the paid version, then it is as low as $10 per month. Zoom- Conference Meeting Tool Zoom is a web as well as an app-based conference meeting organization tool that helps you in connecting with different team members throughout the world. You can also use this tool to organize a webinar and post it on youtube by recording it. You can pretty much do anything, it is a better version of skype where you can whiteboard as well. The basic version can be used for free and the paid version with pro features starets at $20 for each member. Klusster- Publication Community Kluster is a tool used by digital marketers to spread content and attract customers to their website. Kluster offers a platform to publish content written by you and promote business. As an entrepreneur, you should understand the importance of the backlink for your website. Klusster is one such platform providing quality publications for businesses. The best part it's free. Grammarly-Content Optimization Tool As a marketer or entrepreneur, you know the power of content marketing. It is the most powerful way of branding your business. Creating content can be tough but, the most important thing is creating grammatically correct content. That’s where Grammarly comes and saves the day. It automatically suggests your corrections for every mistake you made. The basic version is free and the paid version with advanced features is $30. You can really see the difference in your writing after using Grammarly. Zoho Social-Social Media Marketing Tool Social media is a platform that has numerous potential and if you are not using social media then you are missing out on a huge opportunity. There are around 4.3 billion social media users and the primary rule of business is where there are people, there is business. Zoho Social is a social media management tool that helps you in promoting your business on multiple social media channels. You can use different channels like Facebook, Twitter Instagram, etc. and post your content, images using one app. UberSuggest- Digital Marketing Tool UberSuggest is a free digital marketing tool provided by Neil Patel Digital. This tool is best for a digital marketer who does not want to spend a big amount of money on buying digital marketing tools. This tool is very easy to use and offers many features that are enough for you to get started with your website’s SEO. Yoast SEO- WordPress Plugin for SEO Yoast SEO is a plugin that you can install on your WordPress site. This tool is a well-researched tool that recommends you SEO corrections that you should make in order to rank your website for specific keywords. It is the best on-page SEO management tool and comes with a one-time price tag of $89 for one website. You can also subscribe to their service starting from $19 per month where you get all the premium features with multiple plugins in one. If you do not want to buy Yoast SEO Plugin then you can also use the free version of the plugin but it is recommended that you should use the premium version if you want to stay ahead off of your competitors. Google- Search Engine & Other Tools You heard it right. Google is a tool that everyone uses and earlier in this post, I’ve said where there are people there is business, therefore, you should also use it. Google's market share when it comes to search engines is 80-90%. As an entrepreneur, you should aim for ranking your website higher in the high volume search keywords. To do that, you would need your website to be optimized according to the Google guidelines and recommendations. Google Offers multiple tools like search console, analytics, tag manager, etc to manage and optimize your website. You should use these tools to make your website better. Conclusion You can use these tools to have leverage over your competitors and quickly increase sales for your business. You are an entrepreneur and time is money for you, therefore, you should use these tools to save time and money. Comment below, what tool are you using to manage your business along with pricing and if you are using any of the above-mentioned tools, tell us your experience. Tags marketing tools / List of best marketing tools
Marketing tools are like a helping hand for many entrepreneurs. As time is money using online marketing tools make your job easy and fast and also saves money. Top 10 Important Online Marketing Tools for Entrepreneurs
Add Tags” button
Important in order to add the tag.
dieses Mal führte uns Google Maps über einen anderen Weg. Zu Beginn bemerkten wir es gar nicht, aber hier und dort fuhren wir plötzlich Landstraße, wo wir bisher immer auf die Autobahn gelenkt worden waren. Da es keine größeren Umwege waren, dachte ich mir nichts dabei und folgte stur den Anweisungen des Gerätes. Kurz vor dem Ziel wollten wir noch mal ein Stück Autobahn fahren, wichen von der vorgeschlagenen Route ab und steckten plötzlich fest – eine offenbar eben erst errichtete Straßenblockade einiger Bauern versperrte uns den Weg. Google Maps hielt kurz inne und schlug schnell eine Umfahrung vor. Dann hörte ich im Radio, dass französische Bauern schon den ganzen Tag über landesweit für höhere Lebensmittelpreise streikten, viele Auffahrten und Kreuzungen seien von den wütenden Bauern blockiert worden. Da erst wurde mir klar, dass Google uns den ganzen Tag um die Blockaden herumgelenkt hatte
Staus waren Demos, Google ersparte den Kontakt mit einer schwierigen und unbequemen Wirklichkeit. Was zeigt das Beispiel wirklich? Wo ist hier das Problem? Was ist anders als sonst in der urban-technischen "realen Welt"?
In this volume in the MIT Press Essential Knowledge series, Richard Cytowic, the expert who returned synesthesia to mainstream science after decades of oblivion, offers a concise, accessible primer on this fascinating human experience.
This would be a great gift!
I add a tag for the event and a tag for the person the gift might be great for. Sometimes, when a site is dynamic (so I might not be able to get all the way back) I include an image and enough info to identify the gift.! Back to the 100K blog for another adventure?
uld argue the text is most guilty of exoticism as it reflects a sense of wonder about different groups of people without any reference to respective racial superiority or inferi
guilty of exoticism - wonder, "otherness"
“the Anglo-Saxon readers and viewers of these texts probably considered them true”
early english readers probably considered the wonders real
CRM means greater efficiency for multiple teams. Automatically stored communication allows you to view emails, calendar, and phone call details in one easily accessible place. Add that to the ability for multiple teams to access the same information, and the amount of achievable progress simply skyrockets. Sales, marketing, and customer service teams can share valuable information about clients to continue to funnel them down the pipeline to get the desired result of closing a sale, knowledge of new products, or excellent customer service. Every department can now tag team to get the right information to the right individual. With this new found ease, teams can seamlessly work together to improve the bottom line.
Good CRM can help people to focus on high value activities. And it is very important to avoid duplicating tasks among different groups.
m Bereich der Theorie.
Das stimmt nicht ganz, alljährlich herrscht in Moskau bei der Parade über Nazi-Deutschland Sonnenschein, nur möglich mit sogenannter "Wolkenimpfung":
Einen Tag vor der großen Militärparade regnet es kräftig in Moskau. Auch für Dienstag ist Schneeregen angekündigt. Doch voraussichtlich wird die Sonne scheinen. Wie geht das?
https://www.dw.com/de/wolkenimpfung-sch%C3%B6nes-wetter-auf-bestellung/a-38751024
For whatever be the knowledge which we are able to obtain of God, either by perception or reflection, we must of necessity believe that He is by many degrees far better than what we perceive Him to be.
tag: interesting
It’s worth noting that first line of the script starts with #!. It is a special directive which Unix treats differently.
Term hash tag at top of bash scripts are NOT comments... they are important
spielerischer Überwachung keineswegs nur in Fernost, sondern auch im Westen angewandt werden, treffen Berichte über das chinesische SCS hierzulande meist auf großes Befremden.
In der spielerischen Überwachung liegt eine der großen Gefahren für die westlich sozialisierte Welt. Da China durch seine autoritäre Regierungsform und das zensierte Internet eine Sonderrolle einnimmt, die für Europäer weit entfernt von ihrem Alltag erscheint, werden SCS für ebenfalls befremdlich und genauso weit entfernt gehalten.
Dabei schleichen sich diese immer weiter ein. Neben den seit langem geläufigen Bonitätsprüfungen für Kreditnehmer versuchen viele Teile der digitalen Welt Nutzerverhalten durch Belohnungen zu steuern.
Dies beginnt bei in der Freizeit verwendeten Diensten und Videospielen, die für tägliches Einloggen und Nutzen bzw. das Erledigen kleiner banaler Aufgaben einen Bonus vergeben - z.T. sogar für kumulierte Zeiträume, die dann die Notwendigkeit erzeugen, keinen Tag verpassen zu dürfen.
Andererseits eröffnen sich ähnliche Systeme auch schon außerhalb der rein hobbymäßigen Tätigkeiten. Die meisten der Krankenkassen in Deutschland arbeiten bereits mit Scoring-Systemen (Bonus-Programmen), die einem bei ausreichender Teilnahme an Sportveranstaltungen und gesundheitlicher Vorsorge Prämien auszahlen.
Die genannten Beispiele finden natürlich alle auf freiwilliger Basis statt und sind bisher ohne Negativ-Folgen bei Missachtung , produzieren trotzdem gleichzeit wieder Daten über das Nutzerverhalten. Sollten solche Systeme in Zukunft spontan in Pflichtprogramme verwandelt werden, kann sich am Beispiel der Krankenversicherung eine freiwillige Zusatzprämie schnell in eine nicht genehmigte Kassenleistung verwandeln, da die erforderlichen Punkte nicht erbracht wurden.
Dass bei einigen Krankenversicherungen beispielsweise eine Zahnreinigung im Jahr kostenlos ist, bei anderen wiederum erst Punkte dafür gesammelt werden müssen, ist bereits jetzt schon Realität.
Equivalency Theory
another tag ?
Theory of Interaction and Communication
tag
Theory of Industrialization of Teaching
another tag.
tag of annotation type
[mp4 @ 0x7fe98400dc00] track 1: could not find tag, codec not currently supported in container This means that pcm_mulaw is not allowed in the MP4 output container format. Your choices are: Use a different output container format ffmpeg -i input -codec copy output.mkv Or re-encode the audio ffmpeg -i input -c:v copy -c:a aac output.mp4
Encode the audio to a supported format, such as AAC by replacing -acodec copy with -c:a aac, or use a more flexible output container format that does support pcm_alaw, such as MOV or MKV.
it's an embarrassment to Wikipedia to have the AfD tag on top
Article for Deletion
p-value=PH0[|¯¯¯¯Y−μY,0|>|¯¯¯¯Yact−μY,0|](3.2)(3.2)p-value=PH0[|Y¯−μY,0|>|Y¯act−μY,0|]\begin{equation} p \text{-value} = P_{H_0}\left[ \lvert \overline{Y} - \mu_{Y,0} \rvert > \lvert \overline{Y}^{act} - \mu_{Y,0} \rvert \right] \tag{3.2} \end{equation} In (3.2), ¯¯¯¯YactY¯act\overline{Y}^{act} is the mean of the sample actually computed. Consequently, in order to compute the ppp-value as in (3.2), knowledge about the sampling distribution of ¯¯¯¯YY¯\overline{Y} when the null hypothesis is true is required. However in most cases the sampling distribution of ¯¯¯¯YY¯\overline{Y} is unknown. Fortunately, as stated by the CLT (see Key Concept 2.7), the large-sample approximation ¯¯¯¯Y≈N(μY,0,σ2¯¯¯¯Y) , σ2¯¯¯¯Y=σ2YnY¯≈N(μY,0,σY¯2) , σY¯2=σY2n \overline{Y} \approx \mathcal{N}(\mu_{Y,0}, \, \sigma^2_{\overline{Y}}) \ \ , \ \ \sigma^2_{\overline{Y}} = \frac{\sigma_Y^2}{n} can be made under the null. Thus, ¯¯¯¯Y−μY,0σY/√n∼N(0,1).Y¯−μY,0σY/n∼N(0,1). \frac{\overline{Y} - \mu_{Y,0}}{\sigma_Y/\sqrt{n}} \sim \mathcal{N}(0,1). So in large samples, the ppp-value can be computed without knowledge of the exact sampling distribution of ¯¯¯¯YY¯\overline{Y}. Calculating the p-Value when the Standard Deviation is Known For now, let us assume that σ¯¯¯¯YσY¯\sigma_{\overline{Y}} is known. Then, we can rewrite (3.2) as p-value=PH0⎡⎢⎣∣∣ ∣∣¯¯¯¯Y−μY,0σ¯¯¯¯Y∣∣ ∣∣>∣∣ ∣∣¯¯¯¯Yact−μY,0σ¯¯¯¯Y∣∣ ∣∣⎤⎥⎦=2⋅Φ⎡⎢⎣−∣∣ ∣∣¯¯¯¯Yact−μY,0σ¯¯¯¯Y∣∣ ∣∣⎤⎥⎦.(3.3)p-value=PH0[|Y¯−μY,0σY¯|>|Y¯act−μY,0σY¯|](3.3)=2⋅Φ[−|Y¯act−μY,0σY¯|].\begin{align} p \text{-value} =& \, P_{H_0}\left[ \left\lvert \frac{\overline{Y} - \mu_{Y,0}}{\sigma_{\overline{Y}}} \right\rvert > \left\lvert \frac{\overline{Y}^{act} - \mu_{Y,0}}{\sigma_{\overline{Y}}} \right\rvert \right] \\ =& \, 2 \cdot \Phi \left[ - \left\lvert \frac{\overline{Y}^{act} - \mu_{Y,0}}{\sigma_{\overline{Y}}} \right\rvert\right]. \tag{3.3} \end{align} The ppp-value can be seen as the area in the tails of the N(0,1)N(0,1)\mathcal{N}(0,1) distribution that lies beyond ±∣∣ ∣∣¯¯¯¯Yact−μY,0σ¯¯¯¯Y∣∣ ∣∣
This part is very confusing
Alternatively, you can use the Explore function to ask Google Sheets any number of questions about your to do list. It'll give you suggestions like "Most frequent Task" or "What percentage of Tag is [tag name]" and so on. This can give you insight into what you're working on the most and how you're spending your time, which can help you plan your workdays more productively.
As neither microautophagy, which involves direct uptake ofbulk cargo by lysosomes [10], nor CMA, which degrades a small subset of proteins via recognitionof a specific protein tag [11], have been implicated in host-pathogen defense against noroviruses,macroautophagy will primarily be discussed here
Microautophagy: direct bulk uptake by lysosomes Chaperone-mediated autophagy: requires protein tag recognition to degrade small subset of proteins Macroautophagy: involved in host-pathogen interactions
I may need a new (data-driven) Hypothesis tag.
What is telling is the list of blogs that have refereed traffic to my blog are all written by men. I am not sure what – or if – there is anything to make out of that fact, but anytime I see a list of people that is exclusively male it does make me pause and go hmmmm.
As another white male sending traffic back to your blog, I am left thinking.
And I am planning on cutting back on my personal use of social media (easier said than done) and want to try to return to using my blog more than Twitter for sharing.
certainly a laudable goal!
It helped me a lot to simply delete most of the social media apps off of my phone. I scribbled a bit about the beginning of the process back in November and there's a link there to a post by Ben doing the same thing on his own website.
More people are leaving social feeds for RSS feeds lately. I've recently started following Jeremy Felt who is taking this same sort of journey himself. See: https://jeremyfelt.com/tag/people-still-blog/
Kudos as well to making the jump here:
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>Taking a bit of a Twitter break. I'm going to try to stay off until the new year, but likely lack the willpower to stay off for more than a few hours. Wish me luck!<br><br>....but silently. Not via reply to to this tweet. Cause that'll just suck me back into the vortext.
— Clint Lalonde (he/him) (@edtechfactotum) December 19, 2019
In part, it's what prompted me to visit your site to write a comment. (Sorry for upping your cis-gendered white male count, but 2019 was a bad year, and hopefully we can all make 2020 better as you've indicated.)
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Classes encapsulate code into instantiable units. Namespaces group functions, classes, and constants into spaces where their name is unique.The namespace declaration must occur straight after the opening <?php tag and no other statements may precede it.Namespaces affect constants, but you must declare them with the const keyword and not with define()
Rule of using Namespaces
Types of questions and where to ask: How do I? -- ask on Server Fault (tell them what tags to use -- your product tag at minimum) I got this error, why? -- ask on Server Fault I got this error and I'm sure it's a bug -- report it on your own site I have an idea/request -- report it on your own site Why do you? -- ask in your own community (support forum, etc) When will you? -- ask in your own community
Since the time of Henry I, it has been used by the reigning monarch and is the longest-occupied palace in Europe
Make an note.
Archiving and downloading annotations Annotation viewing and export, from Hypothesis Labs Link: https://jonudell.info/h/facet Screencast: https://jonudell.net/h/facet.mp4 Description: View annotations by user, group, URL, or tag. Export results to HTML, CSV, text, or Markdown.
Did anyone tried to use this to feed mind/concept-mapping tools like Tinderbox or Cmap?
Leo: Stephen in Glasgow, Scotland shares his recent NFC experience: I think I know of a problem with NFC. When I first got my Galaxy S3 it would quite often beep for no apparent reason. Every time I put it in my jacket pocket or on my table, it would beep. Then I noticed it was when I put it on my table resting on my wallet that it was beeping. I felt like an idiot for not figuring it out. Some of my newer credit cards have RFID chips inside for the new contactless payment systems. We don't have these in the U.S. yet; or, if we do, they're in very limited areas.Steve: Yes.Leo: One of the problems I had, when you go to Europe it's hard to buy gas because our credit cards are dumb, and you need a smart credit card to buy gas. And the Galaxy S3's reader was shouting out, "Hey, I found a tag." And sure enough, when I downloaded an NFC app from the Android store, the beep would then be accompanied by the card info displayed on the screen when I put my S3 near my wallet. If these phones are going to go crazy when we put them near a wallet with RFID cards, no wonder Apple is holding back. As far as I can see, there is no way to tell Android to ignore a tag. And even if you could, would that use battery as the RFID tag in your wallet was constantly shouting out, "Hey, hey, I'm here," and your phone listened to the details before ignoring it again? Love the show. That's a great question.
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The url also contains an optional refspec (#v0.0.1) that tells npm which branch, commit, or in this case tag you want to have checked out.
We passed Milestone A!
Annotation (or tag) required to make this visible to Public/group. But you can still "Post to Only Me".
Alexander Samuel reflects on tagging and its origins as a backbone to the social web. Along with RSS, tags allowed users to connect and collate content using such tools as feed readers. This all changed with the advent of social media and the algorithmically curated news feed.
Tags were used for discovery of specific types of content. Who needs that now that our new overlords of artificial intelligence and algorithmic feeds can tell us what we want to see?!
Of course we still need tags!!! How are you going to know serendipitously that you need more poetry in your life until you run into the tag on a service like IndieWeb.xyz? An algorithmic feed is unlikely to notice--or at least in my decade of living with them I've yet to run into poetry in one.
function flow(bytes32 ilk) external note { require(debt != 0, "End/debt-zero"); require(fix[ilk] == 0, "End/fix-ilk-already-defined"); (, uint rate,,,) = vat.ilks(ilk); uint256 wad = rmul(rmul(Art[ilk], rate), tag[ilk]); fix[ilk] = rdiv(mul(sub(wad, gap[ilk]), RAY), debt); }
fix[ilk] is the exchange ratio between collateral and Dai. This is where the haircut could be applied to dai holders. The gap[ilk] is the aggregation of insolvency across all under water CDPsfunction skim(bytes32 ilk, address urn) external note { require(tag[ilk] != 0, "End/tag-ilk-not-defined"); (, uint rate,,,) = vat.ilks(ilk); (uint ink, uint art) = vat.urns(ilk, urn); uint owe = rmul(rmul(art, rate), tag[ilk]); uint wad = min(ink, owe); gap[ilk] = add(gap[ilk], sub(owe, wad)); require(wad <= 2**255 && art <= 2**255, "End/overflow"); vat.grab(ilk, urn, address(this), address(vow), -int(wad), -int(art)); }
sin is accrued in the vow than ink accrued in the Endsin is accrued in the vow as ink value in Endfunction skip(bytes32 ilk, uint256 id) external note { require(tag[ilk] != 0, "End/tag-ilk-not-defined"); (address flipV,,) = cat.ilks(ilk); Flippy flip = Flippy(flipV); (, uint rate,,,) = vat.ilks(ilk); (uint bid, uint lot,,,, address usr,, uint tab) = flip.bids(id); vat.suck(address(vow), address(vow), tab); vat.suck(address(vow), address(this), bid); vat.hope(address(flip)); flip.yank(id); uint art = tab / rate; Art[ilk] = add(Art[ilk], art); require(int(lot) >= 0 && int(art) >= 0, "End/overflow"); vat.grab(ilk, usr, address(this), address(vow), int(lot), int(art)); }
vow.suck used to accrue (TODO - still not entirely sure, but I think it has something to do with refunding the last bidder with their Dai bid)vow.suck accrues the amount of dai in End from the last bid to be returned to the bid.usr(which is the bitten urn) in the form of artvat.grab returns dai and collateral from End to bid.usr. This same process decreases the amount of sin that was accrued in the second vow. Now the CDP is an over/undercollateralized and can be skim'edEnd and finally back to the bid.usr. In other words, it pulls a CDP back into a state that can be skim'edA UMD build will let people use your module via a global variable by dropping it into a <script> tag - this makes it easier to try without any build tooling in an HTML file, and in tools like JS Bin and CodePen.
”,
needs a tag (p#).<br> The quotation needs more integration to show how "industry norms" fit in with the complex layers you're pointing to in "Dawn"
c.
bitte tag erweitern: "de simpt. c." c. 3
The narrative is a hypertext story that changes every time the reader interacts with it, to create the sense of an unreliable narrator and stand as an allegory for the process of memory.
you are closely summarizing the editor's and author's introductions to the work on the ELC3. You even use many of the same words.
Without a citation, this is plagiarism. Please revise to include citations either with the lead in format or all in the tag (cite)
For this option to work we will need to: * work through a way to tag the landing page in a consistent way
Questions:
Let's ditch the Civil Engineers, Surveyors and Planners part for this logo. It makes our tag line repetitive.
Jacqueline knew better than to interrupt her sister when she was this heated; you don’t get to be the richest woman in the tech industry without a ruthless reputation.
Maybe add another dialogue tag for Lizzie; she should probably be mentioned since these are her words.
Jacqueline fired back at her sister.
MY BAD, there is a tag here lol; possibly shift it up earlier in the dialogue though.
Lizzie said, still shouting.
Take out this dialogue tag, but add one for Jacqueline in the next paragraph.
Zuhause
Hier ploppt einem doch zwingend der Hype-Begriff Smart-Home auf, oder? Gerade, wenn einer der größten Global Player des Daten(Überwachung)Kapitalismus – Google – an vorderster Front mitspielt. Mit der Google Home Reihe gab's schon drei Lautsprechergrößen mit Mikrofonen, die zu erst zum abrufen allgemeiner Daten benutzt wurden und sich dann Stück für Stück über User-Profile individualisieren lassen. Sprich: Benutze ich das Google Home mit meinem privaten Account, kann ich mir meine Mails vorlesen lassen, meine Musik hören (über mein verbundenes Streamingkonto), kann auf Nachrichten antworten und und und. Und das halt immer zurückverfolgbar auf genau mein Konto. Man stelle sich den Funnel jetzt so vor: Mein Tag lief doof. Ich komme nach Hause, stelle mein Licht, dass ich ins Google Home integriert habe auf die hälfte der Heiligkeit ein und lasse Google traurige Musik spielen. Google weiß also, mir gehts nicht so gut. Und kann das a) in das Werbeprofil, das über mich angelegt wurde eintragen. Langfristig heißt es dann womöglich: Student, männlich, vor 3 Wochen getrennt. Mit der mittelbaren Folge: Dem kann man jetzt zugeschnitten darauf Werbung ausspielen. Bei Amazon läuft's mit Alexa genauso und Apple hat wohl bis jetzt einen ganz guten Schnitt, wenn es um Datenschutz geht. Dabei kann man über dieses Datensammeln nicht nur ein Profil von Menschen erstellen, um ihnen effizient Produkte zu verkaufen, sondern dieses eben auch nutzen, um Ideen zu verkaufen.
Anything inside the <FancyBorder> JSX tag gets passed into the FancyBorder component as a children prop. Since FancyBorder renders {props.children} inside a <div>, the passed elements appear in the final output.
key point - the 'magic' of 'children'
America the safe haven and America the self-absorbed, even the self-righteous America that assumes the right to impose enlightenment on foreign nations
America was the place you ran to. We were base in a life or death game of tag. We have also been the country with the biggest "stick" for so long that it almost seems laughable that someone would try to take over our country. Americans have this hero mentality and a good guy complex.
a man is rich in proportion to the number of things which he can afford to let alone
Good example of one of his quotable quotes. If you can picture it on a mug, calendar, or tea tag, he wins (but so does capitalism).
comment trouver les principes ?
Ah! La belle question épistémologique des principes de l’architecture.
(Une piste : Principes universels du Design, ouvrage publié chez Eyerolles, réunit de nombreux principes applicables notamment en architecture, reposant sur des textes fondateurs et des études en psychologie : organisation de l'espace urbain, environnement et sécurité, mais aussi des effets a posteriori, après la construction, comme les boucles de rétroaction).
decoding and deterritorialization
As noted before, capitalism "deterritorializes" by kicking things out of their meaningful place and putting a price on them, allowing them to be traded utterly out of their context.
"Decoding" is the same idea. Perhaps a better word is "de-symbolizing". Consider the crucifix. It is a sacred symbol for Christians. Now slap a price tag on it. The symbolism is lost. 1 Crucifix = 1 Subway Sandwich.
This
Domain
Annotation two
Example
Annotation one
result.
Corollary tag missing.
uint owe = rmul(rmul(u.art, i.rate), tag[ilk]);
owe is the amount of debt in CDP (urn) in units of collateral (ink)
tag[ilk] = wdiv(spot.par(), uint(spot.ilks(ilk).pip.read()));
Determine the cage price, which is tag[ilk] = ilk/usd in math notation ($200 for ETH/USD would lead to a tag[eth] of 1/200).
mapping (bytes32 => uint256) public tag; // cage price [ray]
Cage price / price per collateral type at time of settlement.
type FindByTag<Union, Tag> = Union extends { tag: Tag } ? Union : never; function cast<A extends Foo["tag"]>(foo: Foo, expectedTag: A): FindByTag<Foo, A> { if (foo.tag !== expectedTag) throw Error(`expected tag ${expectedTag} but was ${foo.tag}`) return foo as FindByTag<Foo, A>; }
Another is entitled “The Great Fight.” It was between Tom Hyer and Yankee Sullivan, both bare-knuckled, at Still Pond Heights, Maryland, in 1849. It was won by Hyer in sixteen rounds, and the prize was $10,000. The judges wore top hats. The brass title tag on another engraving reads, “Rescue of Colonel Thomas J. Kelly and Captain Timothy Deacy by Members of the Irish Revolutionary Brotherhood from the English Government at Manchester, England, September 18, 1867.” A copy of the Emancipation Proclamation is on this wall; so, almost inevitably, is a facsimile of Lincoln’s saloon licence. An engraving of Washington and his generals hangs next to an engraving of a session of the Great Parliament of Ireland.
This list of INCREDIBLY specific details basically just shows that there's a ton of stuff on the walls and many many details to notice on the walls. It seems to reflect something about Old John's interests: famous episodes in history and current events, sports, etc. but overall the effect is just to overwhelm the reader with details to create the feeling of a teeming multitude of memorabilia.
A conexão e colaboração entre os associados é um valor levado a sério pelos coworkings. E nada melhor do que criar e manter uma rede de relacionamento bem estruturada, de acordo com os seus objetivos. E como esses espaços prezam pelo compartilhamento e trabalho co-criativo,
Explicar como os espaços de coworking exploram a conexão entre integrantes do local
o
Annotating a PDF.
an anxious teacher isn’t the most convincing, so I try to swallow my nerves and to smile confidently at them as they enter the room.
I took Ashlee's idea about playing freeze tag after students gave one another feedback on their initial creative nonfiction drafts yesterday. I shy away from doing stuff like that usually because I give in if some of them start to groan about having to get up/not wanting to. Yesterday I basically just said we're doing it, and they had SO much fun. I wonder if sometimes I approach these practices that help students to get out their own heads with tentativeness that allows them to resist.
Letters of transit signed by General de Gaulle. Cannot be rescinded, not even questioned.Rick appears ready to take them form Ugarte.
Heavy price tag. Visa's are the currency in Casablanca.
As a prototype it hits a sweet spot: it's challenging - it's no small feat to recognize handwritten digits - but it's not so difficult as to require an extremely complicated solution, or tremendous computational power. Furthermore, it's a great way to develop more advanced techniques, such as deep learning. And so throughout the book we'll return repeatedly to the problem of handwriting recognition. Later in the book, we'll discuss how these ideas may be applied to other problems in computer vision, and also in speech, natural language processing, and other domains.Of course, if the point of the chapter was only to write a computer program to recognize handwritten digits, then the chapter would be much shorter! But along the way we'll develop many key ideas about neural networks, including two important types of artificial neuron (the perceptron and the sigmoid neuron), and the standard learning algorithm for neural networks, known as stochastic gradient descent. Throughout, I focus on explaining why things are done the way they are, and on building your neural networks intuition. That requires a lengthier discussion than if I just presented the basic mechanics of what's going on, but it's worth it for the deeper understanding you'll attain. Amongst the payoffs, by the end of the chapter we'll be in position to understand what deep learning is, and why it matters.PerceptronsWhat is a neural network? To get started, I'll explain a type of artificial neuron called a perceptron. Perceptrons were developed in the 1950s and 1960s by the scientist Frank Rosenblatt, inspired by earlier work by Warren McCulloch and Walter Pitts. Today, it's more common to use other models of artificial neurons - in this book, and in much modern work on neural networks, the main neuron model used is one called the sigmoid neuron. We'll get to sigmoid neurons shortly. But to understand why sigmoid neurons are defined the way they are, it's worth taking the time to first understand perceptrons.So how do perceptrons work? A perceptron takes several binary inputs, x1,x2,…x1,x2,…x_1, x_2, \ldots, and produces a single binary output: In the example shown the perceptron has three inputs, x1,x2,x3x1,x2,x3x_1, x_2, x_3. In general it could have more or fewer inputs. Rosenblatt proposed a simple rule to compute the output. He introduced weights, w1,w2,…w1,w2,…w_1,w_2,\ldots, real numbers expressing the importance of the respective inputs to the output. The neuron's output, 000 or 111, is determined by whether the weighted sum ∑jwjxj∑jwjxj\sum_j w_j x_j is less than or greater than some threshold value. Just like the weights, the threshold is a real number which is a parameter of the neuron. To put it in more precise algebraic terms: output={01if ∑jwjxj≤ thresholdif ∑jwjxj> threshold(1)(1)output={0if ∑jwjxj≤ threshold1if ∑jwjxj> threshold\begin{eqnarray} \mbox{output} & = & \left\{ \begin{array}{ll} 0 & \mbox{if } \sum_j w_j x_j \leq \mbox{ threshold} \\ 1 & \mbox{if } \sum_j w_j x_j > \mbox{ threshold} \end{array} \right. \tag{1}\end{eqnarray} That's all there is to how a perceptron works!That's the basic mathematical model. A way you can think about the perceptron is that it's a device that makes decisions by weighing up evidence. Let me give an example. It's not a very realistic example, but it's easy to understand, and we'll soon get to more realistic examples. Suppose the weekend is coming up, and you've heard that there's going to be a cheese festival in your city. You like cheese, and are trying to decide whether or not to go to the festival. You might make your decision by weighing up three factors: Is the weather good? Does your boyfriend or girlfriend want to accompany you? Is the festival near public transit? (You don't own a car). We can represent these three factors by corresponding binary variables x1,x2x1,x2x_1, x_2, and x3x3x_3. For instance, we'd have x1=1x1=1x_1 = 1 if the weather is good, and x1=0x1=0x_1 = 0 if the weather is bad. Similarly, x2=1x2=1x_2 = 1 if your boyfriend or girlfriend wants to go, and x2=0x2=0x_2 = 0 if not. And similarly again for x3x3x_3 and public transit.Now, suppose you absolutely adore cheese, so much so that you're happy to go to the festival even if your boyfriend or girlfriend is uninterested and the festival is hard to get to. But perhaps you really loathe bad weather, and there's no way you'd go to the festival if the weather is bad. You can use perceptrons to model this kind of decision-making. One way to do this is to choose a weight w1=6w1=6w_1 = 6 for the weather, and w2=2w2=2w_2 = 2 and w3=2w3=2w_3 = 2 for the other conditions. The larger value of w1w1w_1 indicates that the weather matters a lot to you, much more than whether your boyfriend or girlfriend joins you, or the nearness of public transit. Finally, suppose you choose a threshold of 555 for the perceptron. With these choices, the perceptron implements the desired decision-making model, outputting 111 whenever the weather is good, and 000 whenever the weather is bad. It makes no difference to the output whether your boyfriend or girlfriend wants to go, or whether public transit is nearby.By varying the weights and the threshold, we can get different models of decision-making. For example, suppose we instead chose a threshold of 333. Then the perceptron would decide that you should go to the festival whenever the weather was good or when both the festival was near public transit and your boyfriend or girlfriend was willing to join you. In other words, it'd be a different model of decision-making. Dropping the threshold means you're more willing to go to the festival.Obviously, the perceptron isn't a complete model of human decision-making! But what the example illustrates is how a perceptron can weigh up different kinds of evidence in order to make decisions. And it should seem plausible that a complex network of perceptrons could make quite subtle decisions: In this network, the first column of perceptrons - what we'll call the first layer of perceptrons - is making three very simple decisions, by weighing the input evidence. What about the perceptrons in the second layer? Each of those perceptrons is making a decision by weighing up the results from the first layer of decision-making. In this way a perceptron in the second layer can make a decision at a more complex and more abstract level than perceptrons in the first layer. And even more complex decisions can be made by the perceptron in the third layer. In this way, a many-layer network of perceptrons can engage in sophisticated decision making.Incidentally, when I defined perceptrons I said that a perceptron has just a single output. In the network above the perceptrons look like they have multiple outputs. In fact, they're still single output. The multiple output arrows are merely a useful way of indicating that the output from a perceptron is being used as the input to several other perceptrons. It's less unwieldy than drawing a single output line which then splits.Let's simplify the way we describe perceptrons. The condition ∑jwjxj>threshold∑jwjxj>threshold\sum_j w_j x_j > \mbox{threshold} is cumbersome, and we can make two notational changes to simplify it. The first change is to write ∑jwjxj∑jwjxj\sum_j w_j x_j as a dot product, w⋅x≡∑jwjxjw⋅x≡∑jwjxjw \cdot x \equiv \sum_j w_j x_j, where www and xxx are vectors whose components are the weights and inputs, respectively. The second change is to move the threshold to the other side of the inequality, and to replace it by what's known as the perceptron's bias, b≡−thresholdb≡−thresholdb \equiv -\mbox{threshold}. Using the bias instead of the threshold, the perceptron rule can be rewritten: output={01if w⋅x+b≤0if w⋅x+b>0(2)(2)output={0if w⋅x+b≤01if w⋅x+b>0\begin{eqnarray} \mbox{output} = \left\{ \begin{array}{ll} 0 & \mbox{if } w\cdot x + b \leq 0 \\ 1 & \mbox{if } w\cdot x + b > 0 \end{array} \right. \tag{2}\end{eqnarray} You can think of the bias as a measure of how easy it is to get the perceptron to output a 111. Or to put it in more biological terms, the bias is a measure of how easy it is to get the perceptron to fire. For a perceptron with a really big bias, it's extremely easy for the perceptron to output a 111. But if the bias is very negative, then it's difficult for the perceptron to output a 111. Obviously, introducing the bias is only a small change in how we describe perceptrons, but we'll see later that it leads to further notational simplifications. Because of this, in the remainder of the book we won't use the threshold, we'll always use the bias.I've described perceptrons as a method for weighing evidence to make decisions. Another way perceptrons can be used is to compute the elementary logical functions we usually think of as underlying computation, functions such as AND, OR, and NAND. For example, suppose we have a perceptron with two inputs, each with weight −2−2-2, and an overall bias of 333. Here's our perceptron: Then we see that input 000000 produces output 111, since (−2)∗0+(−2)∗0+3=3(−2)∗0+(−2)∗0+3=3(-2)*0+(-2)*0+3 = 3 is positive. Here, I've introduced the ∗∗* symbol to make the multiplications explicit. Similar calculations show that the inputs 010101 and 101010 produce output 111. But the input 111111 produces output 000, since (−2)∗1+(−2)∗1+3=−1(−2)∗1+(−2)∗1+3=−1(-2)*1+(-2)*1+3 = -1 is negative. And so our perceptron implements a NAND gate!The NAND example shows that we can use perceptrons to compute simple logical functions. In fact, we can use networks of perceptrons to compute any logical function at all. The reason is that the NAND gate is universal for computation, that is, we can build any computation up out of NAND gates. For example, we can use NAND gates to build a circuit which adds two bits, x1x1x_1 and x2x2x_2. This requires computing the bitwise sum, x1⊕x2x1⊕x2x_1 \oplus x_2, as well as a carry bit which is set to 111 when both x1x1x_1 and x2x2x_2 are 111, i.e., the carry bit is just the bitwise product x1x2x1x2x_1 x_2: To get an equivalent network of perceptrons we replace all the NAND gates by perceptrons with two inputs, each with weight −2−2-2, and an overall bias of 333. Here's the resulting network. Note that I've moved the perceptron corresponding to the bottom right NAND gate a little, just to make it easier to draw the arrows on the diagram: One notable aspect of this network of perceptrons is that the output from the leftmost perceptron is used twice as input to the bottommost perceptron. When I defined the perceptron model I didn't say whether this kind of double-output-to-the-same-place was allowed. Actually, it doesn't much matter. If we don't want to allow this kind of thing, then it's possible to simply merge the two lines, into a single connection with a weight of -4 instead of two connections with -2 weights. (If you don't find this obvious, you should stop and prove to yourself that this is equivalent.) With that change, the network looks as follows, with all unmarked weights equal to -2, all biases equal to 3, and a single weight of -4, as marked: Up to now I've been drawing inputs like x1x1x_1 and x2x2x_2 as variables floating to the left of the network of perceptrons. In fact, it's conventional to draw an extra layer of perceptrons - the input layer - to encode the inputs: This notation for input perceptrons, in which we have an output, but no inputs, is a shorthand. It doesn't actually mean a perceptron with no inputs. To see this, suppose we did have a perceptron with no inputs. Then the weighted sum ∑jwjxj∑jwjxj\sum_j w_j x_j would always be zero, and so the perceptron would output 111 if b>0b>0b > 0, and 000 if b≤0b≤0b \leq 0. That is, the perceptron would simply output a fixed value, not the desired value (x1x1x_1, in the example above). It's better to think of the input perceptrons as not really being perceptrons at all, but rather special units which are simply defined to output the desired values, x1,x2,…x1,x2,…x_1, x_2,\ldots.The adder example demonstrates how a network of perceptrons can be used to simulate a circuit containing many NAND gates. And because NAND gates are universal for computation, it follows that perceptrons are also universal for computation.The computational universality of perceptrons is simultaneously reassuring and disappointing. It's reassuring because it tells us that networks of perceptrons can be as powerful as any other computing device. But it's also disappointing, because it makes it seem as though perceptrons are merely a new type of NAND gate. That's hardly big news!However, the situation is better than this view suggests. It turns out that we can devise learning algorithms which can automatically tune the weights and biases of a network of artificial neurons. This tuning happens in response to external stimuli, without direct intervention by a programmer. These learning algorithms enable us to use artificial neurons in a way which is radically different to conventional logic gates. Instead of explicitly laying out a circuit of NAND and other gates, our neural networks can simply learn to solve problems, sometimes problems where it would be extremely difficult to directly design a conventional circuit.Sigmoid neuronsLearning algorithms sound terrific. But how can we devise such algorithms for a neural network? Suppose we have a network of perceptrons that we'd like to use to learn to solve some problem. For example, the inputs to the network might be the raw pixel data from a scanned, handwritten image of a digit. And we'd like the network to learn weights and biases so that the output from the network correctly classifies the digit. To see how learning might work, suppose we make a small change in some weight (or bias) in the network. What we'd like is for this small change in weight to cause only a small corresponding change in the output from the network. As we'll see in a moment, this property will make learning possible. Schematically, here's what we want (obviously this network is too simple to do handwriting recognition!): If it were true that a small change in a weight (or bias) causes only a small change in output, then we could use this fact to modify the weights and biases to get our network to behave more in the manner we want. For example, suppose the network was mistakenly classifying an image as an "8" when it should be a "9". We could figure out how to make a small change in the weights and biases so the network gets a little closer to classifying the image as a "9". And then we'd repeat this, changing the weights and biases over and over to produce better and better output. The network would be learning.The problem is that this isn't what happens when our network contains perceptrons. In fact, a small change in the weights or bias of any single perceptron in the network can sometimes cause the output of that perceptron to completely flip, say from 000 to 111. That flip may then cause the behaviour of the rest of the network to completely change in some very complicated way. So while your "9" might now be classified correctly, the behaviour of the network on all the other images is likely to have completely changed in some hard-to-control way. That makes it difficult to see how to gradually modify the weights and biases so that the network gets closer to the desired behaviour. Perhaps there's some clever way of getting around this problem. But it's not immediately obvious how we can get a network of perceptrons to learn.We can overcome this problem by introducing a new type of artificial neuron called a sigmoid neuron. Sigmoid neurons are similar to perceptrons, but modified so that small changes in their weights and bias cause only a small change in their output. That's the crucial fact which will allow a network of sigmoid neurons to learn.Okay, let me describe the sigmoid neuron. We'll depict sigmoid neurons in the same way we depicted perceptrons: Just like a perceptron, the sigmoid neuron has inputs, x1,x2,…x1,x2,…x_1, x_2, \ldots. But instead of being just 000 or 111, these inputs can also take on any values between 000 and 111. So, for instance, 0.638…0.638…0.638\ldots is a valid input for a sigmoid neuron. Also just like a perceptron, the sigmoid neuron has weights for each input, w1,w2,…w1,w2,…w_1, w_2, \ldots, and an overall bias, bbb. But the output is not 000 or 111. Instead, it's σ(w⋅x+b)σ(w⋅x+b)\sigma(w \cdot x+b), where σσ\sigma is called the sigmoid function* *Incidentally, σσ\sigma is sometimes called the logistic function, and this new class of neurons called logistic neurons. It's useful to remember this terminology, since these terms are used by many people working with neural nets. However, we'll stick with the sigmoid terminology., and is defined by: σ(z)≡11+e−z.(3)(3)σ(z)≡11+e−z.\begin{eqnarray} \sigma(z) \equiv \frac{1}{1+e^{-z}}. \tag{3}\end{eqnarray} To put it all a little more explicitly, the output of a sigmoid neuron with inputs x1,x2,…x1,x2,…x_1,x_2,\ldots, weights w1,w2,…w1,w2,…w_1,w_2,\ldots, and bias bbb is 11+exp(−∑jwjxj−b).(4)(4)11+exp(−∑jwjxj−b).\begin{eqnarray} \frac{1}{1+\exp(-\sum_j w_j x_j-b)}. \tag{4}\end{eqnarray}At first sight, sigmoid neurons appear very different to perceptrons. The algebraic form of the sigmoid function may seem opaque and forbidding if you're not already familiar with it. In fact, there are many similarities between perceptrons and sigmoid neurons, and the algebraic form of the sigmoid function turns out to be more of a technical detail than a true barrier to understanding.To understand the similarity to the perceptron model, suppose z≡w⋅x+bz≡w⋅x+bz \equiv w \cdot x + b is a large positive number. Then e−z≈0e−z≈0e^{-z} \approx 0 and so σ(z)≈1σ(z)≈1\sigma(z) \approx 1. In other words, when z=w⋅x+bz=w⋅x+bz = w \cdot x+b is large and positive, the output from the sigmoid neuron is approximately 111, just as it would have been for a perceptron. Suppose on the other hand that z=w⋅x+bz=w⋅x+bz = w \cdot x+b is very negative. Then e−z→∞e−z→∞e^{-z} \rightarrow \infty, and σ(z)≈0σ(z)≈0\sigma(z) \approx 0. So when z=w⋅x+bz=w⋅x+bz = w \cdot x +b is very negative, the behaviour of a sigmoid neuron also closely approximates a perceptron. It's only when w⋅x+bw⋅x+bw \cdot x+b is of modest size that there's much deviation from the perceptron model.What about the algebraic form of σσ\sigma? How can we understand that? In fact, the exact form of σσ\sigma isn't so important - what really matters is the shape of the function when plotted. Here's the shape: -4-3-2-1012340.00.20.40.60.81.0zsigmoid function function s(x) {return 1/(1+Math.exp(-x));} var m = [40, 120, 50, 120]; var height = 290 - m[0] - m[2]; var width = 600 - m[1] - m[3]; var xmin = -5; var xmax = 5; var sample = 400; var x1 = d3.scale.linear().domain([0, sample]).range([xmin, xmax]); var data = d3.range(sample).map(function(d){ return { x: x1(d), y: s(x1(d))}; }); var x = d3.scale.linear().domain([xmin, xmax]).range([0, width]); var y = d3.scale.linear() .domain([0, 1]) .range([height, 0]); var line = d3.svg.line() .x(function(d) { return x(d.x); }) .y(function(d) { return y(d.y); }) var graph = d3.select("#sigmoid_graph") .append("svg") .attr("width", width + m[1] + m[3]) .attr("height", height + m[0] + m[2]) .append("g") .attr("transform", "translate(" + m[3] + "," + m[0] + ")"); var xAxis = d3.svg.axis() .scale(x) .tickValues(d3.range(-4, 5, 1)) .orient("bottom") graph.append("g") .attr("class", "x axis") .attr("transform", "translate(0, " + height + ")") .call(xAxis); var yAxis = d3.svg.axis() .scale(y) .tickValues(d3.range(0, 1.01, 0.2)) .orient("left") .ticks(5) graph.append("g") .attr("class", "y axis") .call(yAxis); graph.append("path").attr("d", line(data)); graph.append("text") .attr("class", "x label") .attr("text-anchor", "end") .attr("x", width/2) .attr("y", height+35) .text("z"); graph.append("text") .attr("x", (width / 2)) .attr("y", -10) .attr("text-anchor", "middle") .style("font-size", "16px") .text("sigmoid function"); This shape is a smoothed out version of a step function: -4-3-2-1012340.00.20.40.60.81.0zstep function function s(x) {return x < 0 ? 0 : 1;} var m = [40, 120, 50, 120]; var height = 290 - m[0] - m[2]; var width = 600 - m[1] - m[3]; var xmin = -5; var xmax = 5; var sample = 400; var x1 = d3.scale.linear().domain([0, sample]).range([xmin, xmax]); var data = d3.range(sample).map(function(d){ return { x: x1(d), y: s(x1(d))}; }); var x = d3.scale.linear().domain([xmin, xmax]).range([0, width]); var y = d3.scale.linear() .domain([0,1]) .range([height, 0]); var line = d3.svg.line() .x(function(d) { return x(d.x); }) .y(function(d) { return y(d.y); }) var graph = d3.select("#step_graph") .append("svg") .attr("width", width + m[1] + m[3]) .attr("height", height + m[0] + m[2]) .append("g") .attr("transform", "translate(" + m[3] + "," + m[0] + ")"); var xAxis = d3.svg.axis() .scale(x) .tickValues(d3.range(-4, 5, 1)) .orient("bottom") graph.append("g") .attr("class", "x axis") .attr("transform", "translate(0, " + height + ")") .call(xAxis); var yAxis = d3.svg.axis() .scale(y) .tickValues(d3.range(0, 1.01, 0.2)) .orient("left") .ticks(5) graph.append("g") .attr("class", "y axis") .call(yAxis); graph.append("path").attr("d", line(data)); graph.append("text") .attr("class", "x label") .attr("text-anchor", "end") .attr("x", width/2) .attr("y", height+35) .text("z"); graph.append("text") .attr("x", (width / 2)) .attr("y", -10) .attr("text-anchor", "middle") .style("font-size", "16px") .text("step function"); If σσ\sigma had in fact been a step function, then the sigmoid neuron would be a perceptron, since the output would be 111 or 000 depending on whether w⋅x+bw⋅x+bw\cdot x+b was positive or negative* *Actually, when w⋅x+b=0w⋅x+b=0w \cdot x +b = 0 the perceptron outputs 000, while the step function outputs 111. So, strictly speaking, we'd need to modify the step function at that one point. But you get the idea.. By using the actual σσ\sigma function we get, as already implied above, a smoothed out perceptron. Indeed, it's the smoothness of the σσ\sigma function that is the crucial fact, not its detailed form. The smoothness of σσ\sigma means that small changes ΔwjΔwj\Delta w_j in the weights and ΔbΔb\Delta b in the bias will produce a small change ΔoutputΔoutput\Delta \mbox{output} in the output from the neuron. In fact, calculus tells us that ΔoutputΔoutput\Delta \mbox{output} is well approximated by Δoutput≈∑j∂output∂wjΔwj+∂output∂bΔb,(5)(5)Δoutput≈∑j∂output∂wjΔwj+∂output∂bΔb,\begin{eqnarray} \Delta \mbox{output} \approx \sum_j \frac{\partial \, \mbox{output}}{\partial w_j} \Delta w_j + \frac{\partial \, \mbox{output}}{\partial b} \Delta b, \tag{5}\end{eqnarray} where the sum is over all the weights, wjwjw_j, and ∂output/∂wj∂output/∂wj\partial \, \mbox{output} / \partial w_j and ∂output/∂b∂output/∂b\partial \, \mbox{output} /\partial b denote partial derivatives of the outputoutput\mbox{output} with respect to wjwjw_j and bbb, respectively. Don't panic if you're not comfortable with partial derivatives! While the expression above looks complicated, with all the partial derivatives, it's actually saying something very simple (and which is very good news): ΔoutputΔoutput\Delta \mbox{output} is a linear function of the changes ΔwjΔwj\Delta w_j and ΔbΔb\Delta b in the weights and bias. This linearity makes it easy to choose small changes in the weights and biases to achieve any desired small change in the output. So while sigmoid neurons have much of the same qualitative behaviour as perceptrons, they make it much easier to figure out how changing the weights and biases will change the output.If it's the shape of σσ\sigma which really matters, and not its exact form, then why use the particular form used for σσ\sigma in Equation (3)σ(z)≡11+e−zσ(z)≡11+e−z\begin{eqnarray} \sigma(z) \equiv \frac{1}{1+e^{-z}} \nonumber\end{eqnarray}$('#margin_387419264610_reveal').click(function() {$('#margin_387419264610').toggle('slow', function() {});});? In fact, later in the book we will occasionally consider neurons where the output is f(w⋅x+b)f(w⋅x+b)f(w \cdot x + b) for some other activation function f(⋅)f(⋅)f(\cdot). The main thing that changes when we use a different activation function is that the particular values for the partial derivatives in Equation (5)Δoutput≈∑j∂output∂wjΔwj+∂output∂bΔbΔoutput≈∑j∂output∂wjΔwj+∂output∂bΔb\begin{eqnarray} \Delta \mbox{output} \approx \sum_j \frac{\partial \, \mbox{output}}{\partial w_j} \Delta w_j + \frac{\partial \, \mbox{output}}{\partial b} \Delta b \nonumber\end{eqnarray}$('#margin_727997094331_reveal').click(function() {$('#margin_727997094331').toggle('slow', function() {});}); change. It turns out that when we compute those partial derivatives later, using σσ\sigma will simplify the algebra, simply because exponentials have lovely properties when differentiated. In any case, σσ\sigma is commonly-used in work on neural nets, and is the activation function we'll use most often in this book.How should we interpret the output from a sigmoid neuron? Obviously, one big difference between perceptrons and sigmoid neurons is that sigmoid neurons don't just output 000 or 111. They can have as output any real number between 000 and 111, so values such as 0.173…0.173…0.173\ldots and 0.689…0.689…0.689\ldots are legitimate outputs. This can be useful, for example, if we want to use the output value to represent the average intensity of the pixels in an image input to a neural network. But sometimes it can be a nuisance. Suppose we want the output from the network to indicate either "the input image is a 9" or "the input image is not a 9". Obviously, it'd be easiest to do this if the output was a 000 or a 111, as in a perceptron. But in practice we can set up a convention to deal with this, for example, by deciding to interpret any output of at least 0.50.50.5 as indicating a "9", and any output less than 0.50.50.5 as indicating "not a 9". I'll always explicitly state when we're using such a convention, so it shouldn't cause any confusion. Exercises Sigmoid neurons simulating perceptrons, part I \mbox{} Suppose we take all the weights and biases in a network of perceptrons, and multiply them by a positive constant, c>0c>0c > 0. Show that the behaviour of the network doesn't change.Sigmoid neurons simulating perceptrons, part II \mbox{} Suppose we have the same setup as the last problem - a network of perceptrons. Suppose also that the overall input to the network of perceptrons has been chosen. We won't need the actual input value, we just need the input to have been fixed. Suppose the weights and biases are such that w⋅x+b≠0w⋅x+b≠0w \cdot x + b \neq 0 for the input xxx to any particular perceptron in the network. Now replace all the perceptrons in the network by sigmoid neurons, and multiply the weights and biases by a positive constant c>0c>0c > 0. Show that in the limit as c→∞c→∞c \rightarrow \infty the behaviour of this network of sigmoid neurons is exactly the same as the network of perceptrons. How can this fail when w⋅x+b=0w⋅x+b=0w \cdot x + b = 0 for one of the perceptrons? The architecture of neural networksIn the next section I'll introduce a neural network that can do a pretty good job classifying handwritten digits. In preparation for that, it helps to explain some terminology that lets us name different parts of a network. Suppose we have the network: As mentioned earlier, the leftmost layer in this network is called the input layer, and the neurons within the layer are called input neurons. The rightmost or output layer contains the output neurons, or, as in this case, a single output neuron. The middle layer is called a hidden layer, since the neurons in this layer are neither inputs nor outputs. The term "hidden" perhaps sounds a little mysterious - the first time I heard the term I thought it must have some deep philosophical or mathematical significance - but it really means nothing more than "not an input or an output". The network above has just a single hidden layer, but some networks have multiple hidden layers. For example, the following four-layer network has two hidden layers: Somewhat confusingly, and for historical reasons, such multiple layer networks are sometimes called multilayer perceptrons or MLPs, despite being made up of sigmoid neurons, not perceptrons. I'm not going to use the MLP terminology in this book, since I think it's confusing, but wanted to warn you of its existence.The design of the input and output layers in a network is often straightforward. For example, suppose we're trying to determine whether a handwritten image depicts a "9" or not. A natural way to design the network is to encode the intensities of the image pixels into the input neurons. If the image is a 646464 by 646464 greyscale image, then we'd have 4,096=64×644,096=64×644,096 = 64 \times 64 input neurons, with the intensities scaled appropriately between 000 and 111. The output layer will contain just a single neuron, with output values of less than 0.50.50.5 indicating "input image is not a 9", and values greater than 0.50.50.5 indicating "input image is a 9 ". While the design of the input and output layers of a neural network is often straightforward, there can be quite an art to the design of the hidden layers. In particular, it's not possible to sum up the design process for the hidden layers with a few simple rules of thumb. Instead, neural networks researchers have developed many design heuristics for the hidden layers, which help people get the behaviour they want out of their nets. For example, such heuristics can be used to help determine how to trade off the number of hidden layers against the time required to train the network. We'll meet several such design heuristics later in this book. Up to now, we've been discussing neural networks where the output from one layer is used as input to the next layer. Such networks are called feedforward neural networks. This means there are no loops in the network - information is always fed forward, never fed back. If we did have loops, we'd end up with situations where the input to the σσ\sigma function depended on the output. That'd be hard to make sense of, and so we don't allow such loops.However, there are other models of artificial neural networks in which feedback loops are possible. These models are called recurrent neural networks. The idea in these models is to have neurons which fire for some limited duration of time, before becoming quiescent. That firing can stimulate other neurons, which may fire a little while later, also for a limited duration. That causes still more neurons to fire, and so over time we get a cascade of neurons firing. Loops don't cause problems in such a model, since a neuron's output only affects its input at some later time, not instantaneously.Recurrent neural nets have been less influential than feedforward networks, in part because the learning algorithms for recurrent nets are (at least to date) less powerful. But recurrent networks are still extremely interesting. They're much closer in spirit to how our brains work than feedforward networks. And it's possible that recurrent networks can solve important problems which can only be solved with great difficulty by feedforward networks. However, to limit our scope, in this book we're going to concentrate on the more widely-used feedforward networks.A simple network to classify handwritten digitsHaving defined neural networks, let's return to handwriting recognition. We can split the problem of recognizing handwritten digits into two sub-problems. First, we'd like a way of breaking an image containing many digits into a sequence of separate images, each containing a single digit. For example, we'd like to break the imageinto six separate images, We humans solve this segmentation problem with ease, but it's challenging for a computer program to correctly break up the image. Once the image has been segmented, the program then needs to classify each individual digit. So, for instance, we'd like our program to recognize that the first digit above,is a 5.We'll focus on writing a program to solve the second problem, that is, classifying individual digits. We do this because it turns out that the segmentation problem is not so difficult to solve, once you have a good way of classifying individual digits. There are many approaches to solving the segmentation problem. One approach is to trial many different ways of segmenting the image, using the individual digit classifier to score each trial segmentation. A trial segmentation gets a high score if the individual digit classifier is confident of its classification in all segments, and a low score if the classifier is having a lot of trouble in one or more segments. The idea is that if the classifier is having trouble somewhere, then it's probably having trouble because the segmentation has been chosen incorrectly. This idea and other variations can be used to solve the segmentation problem quite well. So instead of worrying about segmentation we'll concentrate on developing a neural network which can solve the more interesting and difficult problem, namely, recognizing individual handwritten digits.To recognize individual digits we will use a three-layer neural network: The input layer of the network contains neurons encoding the values of the input pixels. As discussed in the next section, our training data for the network will consist of many 282828 by 282828 pixel images of scanned handwritten digits, and so the input layer contains 784=28×28784=28×28784 = 28 \times 28 neurons. For simplicity I've omitted most of the 784784784 input neurons in the diagram above. The input pixels are greyscale, with a value of 0.00.00.0 representing white, a value of 1.01.01.0 representing black, and in between values representing gradually darkening shades of grey.The second layer of the network is a hidden layer. We denote the number of neurons in this hidden layer by nnn, and we'll experiment with different values for nnn. The example shown illustrates a small hidden layer, containing just n=15n=15n = 15 neurons.The output layer of the network contains 10 neurons. If the first neuron fires, i.e., has an output ≈1≈1\approx 1, then that will indicate that the network thinks the digit is a 000. If the second neuron fires then that will indicate that the network thinks the digit is a 111. And so on. A little more precisely, we number the output neurons from 000 through 999, and figure out which neuron has the highest activation value. If that neuron is, say, neuron number 666, then our network will guess that the input digit was a 666. And so on for the other output neurons.You might wonder why we use 101010 output neurons. After all, the goal of the network is to tell us which digit (0,1,2,…,90,1,2,…,90, 1, 2, \ldots, 9) corresponds to the input image. A seemingly natural way of doing that is to use just 444 output neurons, treating each neuron as taking on a binary value, depending on whether the neuron's output is closer to 000 or to 111. Four neurons are enough to encode the answer, since 24=1624=162^4 = 16 is more than the 10 possible values for the input digit. Why should our network use 101010 neurons instead? Isn't that inefficient? The ultimate justification is empirical: we can try out both network designs, and it turns out that, for this particular problem, the network with 101010 output neurons learns to recognize digits better than the network with 444 output neurons. But that leaves us wondering why using 101010 output neurons works better. Is there some heuristic that would tell us in advance that we should use the 101010-output encoding instead of the 444-output encoding?To understand why we do this, it helps to think about what the neural network is doing from first principles. Consider first the case where we use 101010 output neurons. Let's concentrate on the first output neuron, the one that's trying to decide whether or not the digit is a 000. It does this by weighing up evidence from the hidden layer of neurons. What are those hidden neurons doing? Well, just suppose for the sake of argument that the first neuron in the hidden layer detects whether or not an image like the following is present:It can do this by heavily weighting input pixels which overlap with the image, and only lightly weighting the other inputs. In a similar way, let's suppose for the sake of argument that the second, third, and fourth neurons in the hidden layer detect whether or not the following images are present:As you may have guessed, these four images together make up the 000 image that we saw in the line of digits shown earlier:So if all four of these hidden neurons are firing then we can conclude that the digit is a 000. Of course, that's not the only sort of evidence we can use to conclude that the image was a 000 - we could legitimately get a 000 in many other ways (say, through translations of the above images, or slight distortions). But it seems safe to say that at least in this case we'd conclude that the input was a 000.Supposing the neural network functions in this way, we can give a plausible explanation for why it's better to have 101010 outputs from the network, rather than 444. If we had 444 outputs, then the first output neuron would be trying to decide what the most significant bit of the digit was. And there's no easy way to relate that most significant bit to simple shapes like those shown above. It's hard to imagine that there's any good historical reason the component shapes of the digit will be closely related to (say) the most significant bit in the output.Now, with all that said, this is all just a heuristic. Nothing says that the three-layer neural network has to operate in the way I described, with the hidden neurons detecting simple component shapes. Maybe a clever learning algorithm will find some assignment of weights that lets us use only 444 output neurons. But as a heuristic the way of thinking I've described works pretty well, and can save you a lot of time in designing good neural network architectures.Exercise There is a way of determining the bitwise representation of a digit by adding an extra layer to the three-layer network above. The extra layer converts the output from the previous layer into a binary representation, as illustrated in the figure below. Find a set of weights and biases for the new output layer. Assume that the first 333 layers of neurons are such that the correct output in the third layer (i.e., the old output layer) has activation at least 0.990.990.99, and incorrect outputs have activation less than 0.010.010.01. Learning with gradient descentNow that we have a design for our neural network, how can it learn to recognize digits? The first thing we'll need is a data set to learn from - a so-called training data set. We'll use the MNIST data set, which contains tens of thousands of scanned images of handwritten digits, together with their correct classifications. MNIST's name comes from the fact that it is a modified subset of two data sets collected by NIST, the United States' National Institute of Standards and Technology. Here's a few images from MNIST: As you can see, these digits are, in fact, the same as those shown at the beginning of this chapter as a challenge to recognize. Of course, when testing our network we'll ask it to recognize images which aren't in the training set!The MNIST data comes in two parts. The first part contains 60,000 images to be used as training data. These images are scanned handwriting samples from 250 people, half of whom were US Census Bureau employees, and half of whom were high school students. The images are greyscale and 28 by 28 pixels in size. The second part of the MNIST data set is 10,000 images to be used as test data. Again, these are 28 by 28 greyscale images. We'll use the test data to evaluate how well our neural network has learned to recognize digits. To make this a good test of performance, the test data was taken from a different set of 250 people than the original training data (albeit still a group split between Census Bureau employees and high school students). This helps give us confidence that our system can recognize digits from people whose writing it didn't see during training.We'll use the notation xxx to denote a training input. It'll be convenient to regard each training input xxx as a 28×28=78428×28=78428 \times 28 = 784-dimensional vector. Each entry in the vector represents the grey value for a single pixel in the image. We'll denote the corresponding desired output by y=y(x)y=y(x)y = y(x), where yyy is a 101010-dimensional vector. For example, if a particular training image, xxx, depicts a 666, then y(x)=(0,0,0,0,0,0,1,0,0,0)Ty(x)=(0,0,0,0,0,0,1,0,0,0)Ty(x) = (0, 0, 0, 0, 0, 0, 1, 0, 0, 0)^T is the desired output from the network. Note that TTT here is the transpose operation, turning a row vector into an ordinary (column) vector.What we'd like is an algorithm which lets us find weights and biases so that the output from the network approximates y(x)y(x)y(x) for all training inputs xxx. To quantify how well we're achieving this goal we define a cost function* *Sometimes referred to as a loss or objective function. We use the term cost function throughout this book, but you should note the other terminology, since it's often used in research papers and other discussions of neural networks. : C(w,b)≡12n∑x∥y(x)−a∥2.(6)(6)C(w,b)≡12n∑x‖y(x)−a‖2.\begin{eqnarray} C(w,b) \equiv \frac{1}{2n} \sum_x \| y(x) - a\|^2. \tag{6}\end{eqnarray} Here, www denotes the collection of all weights in the network, bbb all the biases, nnn is the total number of training inputs, aaa is the vector of outputs from the network when xxx is input, and the sum is over all training inputs, xxx. Of course, the output aaa depends on xxx, www and bbb, but to keep the notation simple I haven't explicitly indicated this dependence. The notation ∥v∥‖v‖\| v \| just denotes the usual length function for a vector vvv. We'll call CCC the quadratic cost function; it's also sometimes known as the mean squared error or just MSE. Inspecting the form of the quadratic cost function, we see that C(w,b)C(w,b)C(w,b) is non-negative, since every term in the sum is non-negative. Furthermore, the cost C(w,b)C(w,b)C(w,b) becomes small, i.e., C(w,b)≈0C(w,b)≈0C(w,b) \approx 0, precisely when y(x)y(x)y(x) is approximately equal to the output, aaa, for all training inputs, xxx. So our training algorithm has done a good job if it can find weights and biases so that C(w,b)≈0C(w,b)≈0C(w,b) \approx 0. By contrast, it's not doing so well when C(w,b)C(w,b)C(w,b) is large - that would mean that y(x)y(x)y(x) is not close to the output aaa for a large number of inputs. So the aim of our training algorithm will be to minimize the cost C(w,b)C(w,b)C(w,b) as a function of the weights and biases. In other words, we want to find a set of weights and biases which make the cost as small as possible. We'll do that using an algorithm known as gradient descent. Why introduce the quadratic cost? After all, aren't we primarily interested in the number of images correctly classified by the network? Why not try to maximize that number directly, rather than minimizing a proxy measure like the quadratic cost? The problem with that is that the number of images correctly classified is not a smooth function of the weights and biases in the network. For the most part, making small changes to the weights and biases won't cause any change at all in the number of training images classified correctly. That makes it difficult to figure out how to change the weights and biases to get improved performance. If we instead use a smooth cost function like the quadratic cost it turns out to be easy to figure out how to make small changes in the weights and biases so as to get an improvement in the cost. That's why we focus first on minimizing the quadratic cost, and only after that will we examine the classification accuracy.Even given that we want to use a smooth cost function, you may still wonder why we choose the quadratic function used in Equation (6)C(w,b)≡12n∑x∥y(x)−a∥2C(w,b)≡12n∑x‖y(x)−a‖2\begin{eqnarray} C(w,b) \equiv \frac{1}{2n} \sum_x \| y(x) - a\|^2 \nonumber\end{eqnarray}$('#margin_501822820305_reveal').click(function() {$('#margin_501822820305').toggle('slow', function() {});});. Isn't this a rather ad hoc choice? Perhaps if we chose a different cost function we'd get a totally different set of minimizing weights and biases? This is a valid concern, and later we'll revisit the cost function, and make some modifications. However, the quadratic cost function of Equation (6)C(w,b)≡12n∑x∥y(x)−a∥2C(w,b)≡12n∑x‖y(x)−a‖2\begin{eqnarray} C(w,b) \equiv \frac{1}{2n} \sum_x \| y(x) - a\|^2 \nonumber\end{eqnarray}$('#margin_555483302348_reveal').click(function() {$('#margin_555483302348').toggle('slow', function() {});}); works perfectly well for understanding the basics of learning in neural networks, so we'll stick with it for now.Recapping, our goal in training a neural network is to find weights and biases which minimize the quadratic cost function C(w,b)C(w,b)C(w, b). This is a well-posed problem, but it's got a lot of distracting structure as currently posed - the interpretation of www and bbb as weights and biases, the σσ\sigma function lurking in the background, the choice of network architecture, MNIST, and so on. It turns out that we can understand a tremendous amount by ignoring most of that structure, and just concentrating on the minimization aspect. So for now we're going to forget all about the specific form of the cost function, the connection to neural networks, and so on. Instead, we're going to imagine that we've simply been given a function of many variables and we want to minimize that function. We're going to develop a technique called gradient descent which can be used to solve such minimization problems. Then we'll come back to the specific function we want to minimize for neural networks.Okay, let's suppose we're trying to minimize some function, C(v)C(v)C(v). This could be any real-valued function of many variables, v=v1,v2,…v=v1,v2,…v = v_1, v_2, \ldots. Note that I've replaced the www and bbb notation by vvv to emphasize that this could be any function - we're not specifically thinking in the neural networks context any more. To minimize C(v)C(v)C(v) it helps to imagine CCC as a function of just two variables, which we'll call v1v1v_1 and v2v2v_2:What we'd like is to find where CCC achieves its global minimum. Now, of course, for the function plotted above, we can eyeball the graph and find the minimum. In that sense, I've perhaps shown slightly too simple a function! A general function, CCC, may be a complicated function of many variables, and it won't usually be possible to just eyeball the graph to find the minimum.One way of attacking the problem is to use calculus to try to find the minimum analytically. We could compute derivatives and then try using them to find places where CCC is an extremum. With some luck that might work when CCC is a function of just one or a few variables. But it'll turn into a nightmare when we have many more variables. And for neural networks we'll often want far more variables - the biggest neural networks have cost functions which depend on billions of weights and biases in an extremely complicated way. Using calculus to minimize that just won't work!(After asserting that we'll gain insight by imagining CCC as a function of just two variables, I've turned around twice in two paragraphs and said, "hey, but what if it's a function of many more than two variables?" Sorry about that. Please believe me when I say that it really does help to imagine CCC as a function of two variables. It just happens that sometimes that picture breaks down, and the last two paragraphs were dealing with such breakdowns. Good thinking about mathematics often involves juggling multiple intuitive pictures, learning when it's appropriate to use each picture, and when it's not.)Okay, so calculus doesn't work. Fortunately, there is a beautiful analogy which suggests an algorithm which works pretty well. We start by thinking of our function as a kind of a valley. If you squint just a little at the plot above, that shouldn't be too hard. And we imagine a ball rolling down the slope of the valley. Our everyday experience tells us that the ball will eventually roll to the bottom of the valley. Perhaps we can use this idea as a way to find a minimum for the function? We'd randomly choose a starting point for an (imaginary) ball, and then simulate the motion of the ball as it rolled down to the bottom of the valley. We could do this simulation simply by computing derivatives (and perhaps some second derivatives) of CCC - those derivatives would tell us everything we need to know about the local "shape" of the valley, and therefore how our ball should roll.Based on what I've just written, you might suppose that we'll be trying to write down Newton's equations of motion for the ball, considering the effects of friction and gravity, and so on. Actually, we're not going to take the ball-rolling analogy quite that seriously - we're devising an algorithm to minimize CCC, not developing an accurate simulation of the laws of physics! The ball's-eye view is meant to stimulate our imagination, not constrain our thinking. So rather than get into all the messy details of physics, let's simply ask ourselves: if we were declared God for a day, and could make up our own laws of physics, dictating to the ball how it should roll, what law or laws of motion could we pick that would make it so the ball always rolled to the bottom of the valley?To make this question more precise, let's think about what happens when we move the ball a small amount Δv1Δv1\Delta v_1 in the v1v1v_1 direction, and a small amount Δv2Δv2\Delta v_2 in the v2v2v_2 direction. Calculus tells us that CCC changes as follows: ΔC≈∂C∂v1Δv1+∂C∂v2Δv2.(7)(7)ΔC≈∂C∂v1Δv1+∂C∂v2Δv2.\begin{eqnarray} \Delta C \approx \frac{\partial C}{\partial v_1} \Delta v_1 + \frac{\partial C}{\partial v_2} \Delta v_2. \tag{7}\end{eqnarray} We're going to find a way of choosing Δv1Δv1\Delta v_1 and Δv2Δv2\Delta v_2 so as to make ΔCΔC\Delta C negative; i.e., we'll choose them so the ball is rolling down into the valley. To figure out how to make such a choice it helps to define ΔvΔv\Delta v to be the vector of changes in vvv, Δv≡(Δv1,Δv2)TΔv≡(Δv1,Δv2)T\Delta v \equiv (\Delta v_1, \Delta v_2)^T, where TTT is again the transpose operation, turning row vectors into column vectors. We'll also define the gradient of CCC to be the vector of partial derivatives, (∂C∂v1,∂C∂v2)T(∂C∂v1,∂C∂v2)T\left(\frac{\partial C}{\partial v_1}, \frac{\partial C}{\partial v_2}\right)^T. We denote the gradient vector by ∇C∇C\nabla C, i.e.: ∇C≡(∂C∂v1,∂C∂v2)T.(8)(8)∇C≡(∂C∂v1,∂C∂v2)T.\begin{eqnarray} \nabla C \equiv \left( \frac{\partial C}{\partial v_1}, \frac{\partial C}{\partial v_2} \right)^T. \tag{8}\end{eqnarray} In a moment we'll rewrite the change ΔCΔC\Delta C in terms of ΔvΔv\Delta v and the gradient, ∇C∇C\nabla C. Before getting to that, though, I want to clarify something that sometimes gets people hung up on the gradient. When meeting the ∇C∇C\nabla C notation for the first time, people sometimes wonder how they should think about the ∇∇\nabla symbol. What, exactly, does ∇∇\nabla mean? In fact, it's perfectly fine to think of ∇C∇C\nabla C as a single mathematical object - the vector defined above - which happens to be written using two symbols. In this point of view, ∇∇\nabla is just a piece of notational flag-waving, telling you "hey, ∇C∇C\nabla C is a gradient vector". There are more advanced points of view where ∇∇\nabla can be viewed as an independent mathematical entity in its own right (for example, as a differential operator), but we won't need such points of view.With these definitions, the expression (7)ΔC≈∂C∂v1Δv1+∂C∂v2Δv2ΔC≈∂C∂v1Δv1+∂C∂v2Δv2\begin{eqnarray} \Delta C \approx \frac{\partial C}{\partial v_1} \Delta v_1 + \frac{\partial C}{\partial v_2} \Delta v_2 \nonumber\end{eqnarray}$('#margin_512380394946_reveal').click(function() {$('#margin_512380394946').toggle('slow', function() {});}); for ΔCΔC\Delta C can be rewritten as ΔC≈∇C⋅Δv.(9)(9)ΔC≈∇C⋅Δv.\begin{eqnarray} \Delta C \approx \nabla C \cdot \Delta v. \tag{9}\end{eqnarray} This equation helps explain why ∇C∇C\nabla C is called the gradient vector: ∇C∇C\nabla C relates changes in vvv to changes in CCC, just as we'd expect something called a gradient to do. But what's really exciting about the equation is that it lets us see how to choose ΔvΔv\Delta v so as to make ΔCΔC\Delta C negative. In particular, suppose we choose Δv=−η∇C,(10)(10)Δv=−η∇C,\begin{eqnarray} \Delta v = -\eta \nabla C, \tag{10}\end{eqnarray} where ηη\eta is a small, positive parameter (known as the learning rate). Then Equation (9)ΔC≈∇C⋅ΔvΔC≈∇C⋅Δv\begin{eqnarray} \Delta C \approx \nabla C \cdot \Delta v \nonumber\end{eqnarray}$('#margin_31741254841_reveal').click(function() {$('#margin_31741254841').toggle('slow', function() {});}); tells us that ΔC≈−η∇C⋅∇C=−η∥∇C∥2ΔC≈−η∇C⋅∇C=−η‖∇C‖2\Delta C \approx -\eta \nabla C \cdot \nabla C = -\eta \|\nabla C\|^2. Because ∥∇C∥2≥0‖∇C‖2≥0\| \nabla C \|^2 \geq 0, this guarantees that ΔC≤0ΔC≤0\Delta C \leq 0, i.e., CCC will always decrease, never increase, if we change vvv according to the prescription in (10)Δv=−η∇CΔv=−η∇C\begin{eqnarray} \Delta v = -\eta \nabla C \nonumber\end{eqnarray}$('#margin_48762573303_reveal').click(function() {$('#margin_48762573303').toggle('slow', function() {});});. (Within, of course, the limits of the approximation in Equation (9)ΔC≈∇C⋅ΔvΔC≈∇C⋅Δv\begin{eqnarray} \Delta C \approx \nabla C \cdot \Delta v \nonumber\end{eqnarray}$('#margin_919658643545_reveal').click(function() {$('#margin_919658643545').toggle('slow', function() {});});). This is exactly the property we wanted! And so we'll take Equation (10)Δv=−η∇CΔv=−η∇C\begin{eqnarray} \Delta v = -\eta \nabla C \nonumber\end{eqnarray}$('#margin_287729255111_reveal').click(function() {$('#margin_287729255111').toggle('slow', function() {});}); to define the "law of motion" for the ball in our gradient descent algorithm. That is, we'll use Equation (10)Δv=−η∇CΔv=−η∇C\begin{eqnarray} \Delta v = -\eta \nabla C \nonumber\end{eqnarray}$('#margin_718723868298_reveal').click(function() {$('#margin_718723868298').toggle('slow', function() {});}); to compute a value for ΔvΔv\Delta v, then move the ball's position vvv by that amount: v→v′=v−η∇C.(11)(11)v→v′=v−η∇C.\begin{eqnarray} v \rightarrow v' = v -\eta \nabla C. \tag{11}\end{eqnarray} Then we'll use this update rule again, to make another move. If we keep doing this, over and over, we'll keep decreasing CCC until - we hope - we reach a global minimum.Summing up, the way the gradient descent algorithm works is to repeatedly compute the gradient ∇C∇C\nabla C, and then to move in the opposite direction, "falling down" the slope of the valley. We can visualize it like this:Notice that with this rule gradient descent doesn't reproduce real physical motion. In real life a ball has momentum, and that momentum may allow it to roll across the slope, or even (momentarily) roll uphill. It's only after the effects of friction set in that the ball is guaranteed to roll down into the valley. By contrast, our rule for choosing ΔvΔv\Delta v just says "go down, right now". That's still a pretty good rule for finding the minimum!To make gradient descent work correctly, we need to choose the learning rate ηη\eta to be small enough that Equation (9)ΔC≈∇C⋅ΔvΔC≈∇C⋅Δv\begin{eqnarray} \Delta C \approx \nabla C \cdot \Delta v \nonumber\end{eqnarray}$('#margin_560455937071_reveal').click(function() {$('#margin_560455937071').toggle('slow', function() {});}); is a good approximation. If we don't, we might end up with ΔC>0ΔC>0\Delta C > 0, which obviously would not be good! At the same time, we don't want ηη\eta to be too small, since that will make the changes ΔvΔv\Delta v tiny, and thus the gradient descent algorithm will work very slowly. In practical implementations, ηη\eta is often varied so that Equation (9)ΔC≈∇C⋅ΔvΔC≈∇C⋅Δv\begin{eqnarray} \Delta C \approx \nabla C \cdot \Delta v \nonumber\end{eqnarray}$('#margin_157848846275_reveal').click(function() {$('#margin_157848846275').toggle('slow', function() {});}); remains a good approximation, but the algorithm isn't too slow. We'll see later how this works. I've explained gradient descent when CCC is a function of just two variables. But, in fact, everything works just as well even when CCC is a function of many more variables. Suppose in particular that CCC is a function of mmm variables, v1,…,vmv1,…,vmv_1,\ldots,v_m. Then the change ΔCΔC\Delta C in CCC produced by a small change Δv=(Δv1,…,Δvm)TΔv=(Δv1,…,Δvm)T\Delta v = (\Delta v_1, \ldots, \Delta v_m)^T is ΔC≈∇C⋅Δv,(12)(12)ΔC≈∇C⋅Δv,\begin{eqnarray} \Delta C \approx \nabla C \cdot \Delta v, \tag{12}\end{eqnarray} where the gradient ∇C∇C\nabla C is the vector ∇C≡(∂C∂v1,…,∂C∂vm)T.(13)(13)∇C≡(∂C∂v1,…,∂C∂vm)T.\begin{eqnarray} \nabla C \equiv \left(\frac{\partial C}{\partial v_1}, \ldots, \frac{\partial C}{\partial v_m}\right)^T. \tag{13}\end{eqnarray} Just as for the two variable case, we can choose Δv=−η∇C,(14)(14)Δv=−η∇C,\begin{eqnarray} \Delta v = -\eta \nabla C, \tag{14}\end{eqnarray} and we're guaranteed that our (approximate) expression (12)ΔC≈∇C⋅ΔvΔC≈∇C⋅Δv\begin{eqnarray} \Delta C \approx \nabla C \cdot \Delta v \nonumber\end{eqnarray}$('#margin_869505431896_reveal').click(function() {$('#margin_869505431896').toggle('slow', function() {});}); for ΔCΔC\Delta C will be negative. This gives us a way of following the gradient to a minimum, even when CCC is a function of many variables, by repeatedly applying the update rule v→v′=v−η∇C.(15)(15)v→v′=v−η∇C.\begin{eqnarray} v \rightarrow v' = v-\eta \nabla C. \tag{15}\end{eqnarray} You can think of this update rule as defining the gradient descent algorithm. It gives us a way of repeatedly changing the position vvv in order to find a minimum of the function CCC. The rule doesn't always work - several things can go wrong and prevent gradient descent from finding the global minimum of CCC, a point we'll return to explore in later chapters. But, in practice gradient descent often works extremely well, and in neural networks we'll find that it's a powerful way of minimizing the cost function, and so helping the net learn.Indeed, there's even a sense in which gradient descent is the optimal strategy for searching for a minimum. Let's suppose that we're trying to make a move ΔvΔv\Delta v in position so as to decrease CCC as much as possible. This is equivalent to minimizing ΔC≈∇C⋅ΔvΔC≈∇C⋅Δv\Delta C \approx \nabla C \cdot \Delta v. We'll constrain the size of the move so that ∥Δv∥=ϵ‖Δv‖=ϵ\| \Delta v \| = \epsilon for some small fixed ϵ>0ϵ>0\epsilon > 0. In other words, we want a move that is a small step of a fixed size, and we're trying to find the movement direction which decreases CCC as much as possible. It can be proved that the choice of ΔvΔv\Delta v which minimizes ∇C⋅Δv∇C⋅Δv\nabla C \cdot \Delta v is Δv=−η∇CΔv=−η∇C\Delta v = - \eta \nabla C, where η=ϵ/∥∇C∥η=ϵ/‖∇C‖\eta = \epsilon / \|\nabla C\| is determined by the size constraint ∥Δv∥=ϵ‖Δv‖=ϵ\|\Delta v\| = \epsilon. So gradient descent can be viewed as a way of taking small steps in the direction which does the most to immediately decrease CCC.Exercises Prove the assertion of the last paragraph. Hint: If you're not already familiar with the Cauchy-Schwarz inequality, you may find it helpful to familiarize yourself with it. I explained gradient descent when CCC is a function of two variables, and when it's a function of more than two variables. What happens when CCC is a function of just one variable? Can you provide a geometric interpretation of what gradient descent is doing in the one-dimensional case? People have investigated many variations of gradient descent, including variations that more closely mimic a real physical ball. These ball-mimicking variations have some advantages, but also have a major disadvantage: it turns out to be necessary to compute second partial derivatives of CCC, and this can be quite costly. To see why it's costly, suppose we want to compute all the second partial derivatives ∂2C/∂vj∂vk∂2C/∂vj∂vk\partial^2 C/ \partial v_j \partial v_k. If there are a million such vjvjv_j variables then we'd need to compute something like a trillion (i.e., a million squared) second partial derivatives* *Actually, more like half a trillion, since ∂2C/∂vj∂vk=∂2C/∂vk∂vj∂2C/∂vj∂vk=∂2C/∂vk∂vj\partial^2 C/ \partial v_j \partial v_k = \partial^2 C/ \partial v_k \partial v_j. Still, you get the point.! That's going to be computationally costly. With that said, there are tricks for avoiding this kind of problem, and finding alternatives to gradient descent is an active area of investigation. But in this book we'll use gradient descent (and variations) as our main approach to learning in neural networks.How can we apply gradient descent to learn in a neural network? The idea is to use gradient descent to find the weights wkwkw_k and biases blblb_l which minimize the cost in Equation (6)C(w,b)≡12n∑x∥y(x)−a∥2C(w,b)≡12n∑x‖y(x)−a‖2\begin{eqnarray} C(w,b) \equiv \frac{1}{2n} \sum_x \| y(x) - a\|^2 \nonumber\end{eqnarray}$('#margin_1246306310_reveal').click(function() {$('#margin_1246306310').toggle('slow', function() {});});. To see how this works, let's restate the gradient descent update rule, with the weights and biases replacing the variables vjvjv_j. In other words, our "position" now has components wkwkw_k and blblb_l, and the gradient vector ∇C∇C\nabla C has corresponding components ∂C/∂wk∂C/∂wk\partial C / \partial w_k and ∂C/∂bl∂C/∂bl\partial C / \partial b_l. Writing out the gradient descent update rule in terms of components, we have wkbl→→w′k=wk−η∂C∂wkb′l=bl−η∂C∂bl.(16)(17)(16)wk→wk′=wk−η∂C∂wk(17)bl→bl′=bl−η∂C∂bl.\begin{eqnarray} w_k & \rightarrow & w_k' = w_k-\eta \frac{\partial C}{\partial w_k} \tag{16}\\ b_l & \rightarrow & b_l' = b_l-\eta \frac{\partial C}{\partial b_l}. \tag{17}\end{eqnarray} By repeatedly applying this update rule we can "roll down the hill", and hopefully find a minimum of the cost function. In other words, this is a rule which can be used to learn in a neural network.There are a number of challenges in applying the gradient descent rule. We'll look into those in depth in later chapters. But for now I just want to mention one problem. To understand what the problem is, let's look back at the quadratic cost in Equation (6)C(w,b)≡12n∑x∥y(x)−a∥2C(w,b)≡12n∑x‖y(x)−a‖2\begin{eqnarray} C(w,b) \equiv \frac{1}{2n} \sum_x \| y(x) - a\|^2 \nonumber\end{eqnarray}$('#margin_214093216664_reveal').click(function() {$('#margin_214093216664').toggle('slow', function() {});});. Notice that this cost function has the form C=1n∑xCxC=1n∑xCxC = \frac{1}{n} \sum_x C_x, that is, it's an average over costs Cx≡∥y(x)−a∥22Cx≡‖y(x)−a‖22C_x \equiv \frac{\|y(x)-a\|^2}{2} for individual training examples. In practice, to compute the gradient ∇C∇C\nabla C we need to compute the gradients ∇Cx∇Cx\nabla C_x separately for each training input, xxx, and then average them, ∇C=1n∑x∇Cx∇C=1n∑x∇Cx\nabla C = \frac{1}{n} \sum_x \nabla C_x. Unfortunately, when the number of training inputs is very large this can take a long time, and learning thus occurs slowly.An idea called stochastic gradient descent can be used to speed up learning. The idea is to estimate the gradient ∇C∇C\nabla C by computing ∇Cx∇Cx\nabla C_x for a small sample of randomly chosen training inputs. By averaging over this small sample it turns out that we can quickly get a good estimate of the true gradient ∇C∇C\nabla C, and this helps speed up gradient descent, and thus learning.To make these ideas more precise, stochastic gradient descent works by randomly picking out a small number mmm of randomly chosen training inputs. We'll label those random training inputs X1,X2,…,XmX1,X2,…,XmX_1, X_2, \ldots, X_m, and refer to them as a mini-batch. Provided the sample size mmm is large enough we expect that the average value of the ∇CXj∇CXj\nabla C_{X_j} will be roughly equal to the average over all ∇Cx∇Cx\nabla C_x, that is, ∑mj=1∇CXjm≈∑x∇Cxn=∇C,(18)(18)∑j=1m∇CXjm≈∑x∇Cxn=∇C,\begin{eqnarray} \frac{\sum_{j=1}^m \nabla C_{X_{j}}}{m} \approx \frac{\sum_x \nabla C_x}{n} = \nabla C, \tag{18}\end{eqnarray} where the second sum is over the entire set of training data. Swapping sides we get ∇C≈1m∑j=1m∇CXj,(19)(19)∇C≈1m∑j=1m∇CXj,\begin{eqnarray} \nabla C \approx \frac{1}{m} \sum_{j=1}^m \nabla C_{X_{j}}, \tag{19}\end{eqnarray} confirming that we can estimate the overall gradient by computing gradients just for the randomly chosen mini-batch. To connect this explicitly to learning in neural networks, suppose wkwkw_k and blblb_l denote the weights and biases in our neural network. Then stochastic gradient descent works by picking out a randomly chosen mini-batch of training inputs, and training with those, wkbl→→w′k=wk−ηm∑j∂CXj∂wkb′l=bl−ηm∑j∂CXj∂bl,(20)(21)(20)wk→wk′=wk−ηm∑j∂CXj∂wk(21)bl→bl′=bl−ηm∑j∂CXj∂bl,\begin{eqnarray} w_k & \rightarrow & w_k' = w_k-\frac{\eta}{m} \sum_j \frac{\partial C_{X_j}}{\partial w_k} \tag{20}\\ b_l & \rightarrow & b_l' = b_l-\frac{\eta}{m} \sum_j \frac{\partial C_{X_j}}{\partial b_l}, \tag{21}\end{eqnarray} where the sums are over all the training examples XjXjX_j in the current mini-batch. Then we pick out another randomly chosen mini-batch and train with those. And so on, until we've exhausted the training inputs, which is said to complete an epoch of training. At that point we start over with a new training epoch.Incidentally, it's worth noting that conventions vary about scaling of the cost function and of mini-batch updates to the weights and biases. In Equation (6)C(w,b)≡12n∑x∥y(x)−a∥2C(w,b)≡12n∑x‖y(x)−a‖2\begin{eqnarray} C(w,b) \equiv \frac{1}{2n} \sum_x \| y(x) - a\|^2 \nonumber\end{eqnarray}$('#margin_85851492824_reveal').click(function() {$('#margin_85851492824').toggle('slow', function() {});}); we scaled the overall cost function by a factor 1n1n\frac{1}{n}. People sometimes omit the 1n1n\frac{1}{n}, summing over the costs of individual training examples instead of averaging. This is particularly useful when the total number of training examples isn't known in advance. This can occur if more training data is being generated in real time, for instance. And, in a similar way, the mini-batch update rules (20)wk→w′k=wk−ηm∑j∂CXj∂wkwk→wk′=wk−ηm∑j∂CXj∂wk\begin{eqnarray} w_k & \rightarrow & w_k' = w_k-\frac{\eta}{m} \sum_j \frac{\partial C_{X_j}}{\partial w_k} \nonumber\end{eqnarray}$('#margin_801900730537_reveal').click(function() {$('#margin_801900730537').toggle('slow', function() {});}); and (21)bl→b′l=bl−ηm∑j∂CXj∂blbl→bl′=bl−ηm∑j∂CXj∂bl\begin{eqnarray} b_l & \rightarrow & b_l' = b_l-\frac{\eta}{m} \sum_j \frac{\partial C_{X_j}}{\partial b_l} \nonumber\end{eqnarray}$('#margin_985072620111_reveal').click(function() {$('#margin_985072620111').toggle('slow', function() {});}); sometimes omit the 1m1m\frac{1}{m} term out the front of the sums. Conceptually this makes little difference, since it's equivalent to rescaling the learning rate ηη\eta. But when doing detailed comparisons of different work it's worth watching out for.We can think of stochastic gradient descent as being like political polling: it's much easier to sample a small mini-batch than it is to apply gradient descent to the full batch, just as carrying out a poll is easier than running a full election. For example, if we have a training set of size n=60,000n=60,000n = 60,000, as in MNIST, and choose a mini-batch size of (say) m=10m=10m = 10, this means we'll get a factor of 6,0006,0006,000 speedup in estimating the gradient! Of course, the estimate won't be perfect - there will be statistical fluctuations - but it doesn't need to be perfect: all we really care about is moving in a general direction that will help decrease CCC, and that means we don't need an exact computation of the gradient. In practice, stochastic gradient descent is a commonly used and powerful technique for learning in neural networks, and it's the basis for most of the learning techniques we'll develop in this book.Exercise An extreme version of gradient descent is to use a mini-batch size of just 1. That is, given a training input, xxx, we update our weights and biases according to the rules wk→w′k=wk−η∂Cx/∂wkwk→wk′=wk−η∂Cx/∂wkw_k \rightarrow w_k' = w_k - \eta \partial C_x / \partial w_k and bl→b′l=bl−η∂Cx/∂blbl→bl′=bl−η∂Cx/∂blb_l \rightarrow b_l' = b_l - \eta \partial C_x / \partial b_l. Then we choose another training input, and update the weights and biases again. And so on, repeatedly. This procedure is known as online, on-line, or incremental learning. In online learning, a neural network learns from just one training input at a time (just as human beings do). Name one advantage and one disadvantage of online learning, compared to stochastic gradient descent with a mini-batch size of, say, 202020. Let me conclude this section by discussing a point that sometimes bugs people new to gradient descent. In neural networks the cost CCC is, of course, a function of many variables - all the weights and biases - and so in some sense defines a surface in a very high-dimensional space. Some people get hung up thinking: "Hey, I have to be able to visualize all these extra dimensions". And they may start to worry: "I can't think in four dimensions, let alone five (or five million)". Is there some special ability they're missing, some ability that "real" supermathematicians have? Of course, the answer is no. Even most professional mathematicians can't visualize four dimensions especially well, if at all. The trick they use, instead, is to develop other ways of representing what's going on. That's exactly what we did above: we used an algebraic (rather than visual) representation of ΔCΔC\Delta C to figure out how to move so as to decrease CCC. People who are good at thinking in high dimensions have a mental library containing many different techniques along these lines; our algebraic trick is just one example. Those techniques may not have the simplicity we're accustomed to when visualizing three dimensions, but once you build up a library of such techniques, you can get pretty good at thinking in high dimensions. I won't go into more detail here, but if you're interested then you may enjoy reading this discussion of some of the techniques professional mathematicians use to think in high dimensions. While some of the techniques discussed are quite complex, much of the best content is intuitive and accessible, and could be mastered by anyone. Implementing our network to classify digitsAlright, let's write a program that learns how to recognize handwritten digits, using stochastic gradient descent and the MNIST training data. We'll do this with a short Python (2.7) program, just 74 lines of code! The first thing we need is to get the MNIST data. If you're a git user then you can obtain the data by cloning the code repository for this book,git clone https://github.com/mnielsen/neural-networks-and-deep-learning.git If you don't use git then you can download the data and code here.Incidentally, when I described the MNIST data earlier, I said it was
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A wireless keyboard available in a classroom can work just as well [as a smartboard], as can simply having people talk to each other and write down the upshot of their conversation.
This is reminding me of the trichotomy theory (I think that's what they called it?) of something being neither greater than nor less than nor equal to--which strikes me as an appropriate description of the three different technologies mentioned in this sentence (the last being a pencil and paper summary).
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the 902 stamps are removed. maybe they were stored in the backfile before the tag was added? we will need to adapt the import profile to keep this.
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More Copy link to Tweet Embed Tweet The FBI said it has stopped using the "Black Identity Extremist" tag and acknowledged that white supremacist violence is the biggest terrorist threat this country faces.
AMEN!! About time somebody mentions this!
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Wow! Just wow! Is all I can say. If you do not understand this poem, or nothing about it resonates with you, allow me to translate. Imagine: it’s 3am, and you hear the loud crash of someone who has broken into your home. There is more than one of them, they are coming through doors, windows, sky-lights. They are bearing guns, batons, stones, chains. They snatch you and your family out of bed and force you outside. You look up and down your street and see all your neighbors outside, screaming, crying. Children in one section, mothers in another, and fathers in another. They separate you and your family in the same fashion. They force the fathers to watch as they violate their wives and daughters. They say to sons, “Your father cannot protect you, he can’t protect your women, he can’t even protect himself!” Then, when they are done violating, killing, and battering the many people in your neighborhood, when they feel they have “made their point”—whatever point that is—they ship you all off in separate directions. They don’t care where you go, or what happens. They don’t care if you die, but you are worth more alive—so they won’t kill ALL of you. That’s bad for business. They take you to a new place, where you do not know anyone. You are a million miles from home, from family. You do not understand this new tongue. You are stripped naked and inspected (from head to toe) by other human beings, just like you, but a different shade. And you are given a price tag. You are bought and sold and bought again. You are forced to work tirelessly, until you physically can’t—you are at their service, whatever service they require. Where is your voice? Imagine, after all this, after a few decades, they release you in the middle of nowhere. They release you into a society that is not your own, and tell you to go back to where you came from, as if that were even an option. That place is not the same home you left. The people are different, your home is not there, you wouldn’t even know where to go if you could. You have been uprooted, assaulted, shipped off, sold, insulted, degraded, and nearly-broken. Are you still with me? All this, and those who did this to you, play the victim—as if it hurt them to starve, rape, whip, work, shoot...I mean lynch your families. They play stupid, like they don’t know what you’re talking about, like what you went through, was simply a bad dream, or a horror film played for Halloween. Now, imagine those few decades were 400 years. How would you feel?