https://www.youtube.com/watch?v=d6RQ0GI94dQ

https://www.youtube.com/watch?v=d6RQ0GI94dQ

https://www.youtube.com/watch?v=d6RQ0GI94dQ

https://www.youtube.com/watch?v=d6RQ0GI94dQ

https://www.youtube.com/watch?v=d6RQ0GI94dQ

https://www.youtube.com/watch?v=d6RQ0GI94dQ

Impossible d'avoir le code css sur Figma. On peut deviner le font-family, font-style, font-weight, font-size, mais aucune idée pour trouver le line height...

eLife assessment

This valuable research identifies Smim32 as a new genetic marker for the claustrum and generates transgenic mouse lines aimed at enhancing specificity when studying this brain region. However, the evidence supporting the increased specificity of this marker and its associated transgenic lines is inadequate, as Smim32's specificity to the claustrum is limited. Nevertheless, this work will be of interest to researchers studying the molecular organization of the claustrum.

Math camp was Cathy Davidson's happiest educational experience in her childhood. Loved theoretical maths in grade school too and also she majored in philosophy of mathematics.

Shared experience by author

The New education: redesigning higher education

Related to Higher Education

There is a disturbing new trend happening in Latin America, specifically in Colombia, and that is:

0:07 Men being attracted by extremely beautiful, sexy Colombian and Latin American women and then being drugged, robbed, killed, overdosed, kidnapped, all the crimes that you can possibly think of.

0:21 And a lot of men come to Latin America and thinking that, well, women are just gonna throw themselves at me. And to a certain extent, it is easier than dating in the United States and Germany and all these western countries, and it is better. Women are more beautiful, women are more feminine. But you have to be extremely careful. You have to know where you are.

0:40 Recently, I've seen story after story after story, and this is an alarming trend because these criminals are getting harder. They're getting harsher on their crimes, they're getting more sadistic, and they're also planning a lot better.

0:53 One recent case that I heard of was a German guy who went to Colombia, and on his second day, one girl that he met invited him to cook some food at home, cook some delicious Colombian food, and he said, why not? I'm probably not gonna bang this girl, but I'm gonna go home to her so she can cook me some food. She slipped scopolamine. a drug. into a drink, gave it to him, and then a few hours later, he woke up with his money gone, everything stolen, and even his crypto was stolen.

1:28 They figured out how to get access to his crypto. They stole $15,000, which if you're watching this, you're a wealthy individual, you might think, well, 15K, whatever. But if you have 15 million in your crypto account or your Binance account or in your bank account, they will figure out how to steal it from you.

1:42 Scopolamine is a drug that basically makes you into a little slave, into a little servant, and you'll do whatever the attacker wants. They tell you, go tell the security and tell them that I'm your friend. That literally happens in Colombia.

1:57 People get drugged, and then they go to their Airbnb, to their hotel, and they tell the security, that's my friend. Let them come with me. And they come with you, and they steal absolutely everything because it's called the devil's breath.

2:08 It's a drug that essentially turns you into a zombie, and it has bans all over Latin America, and a lot of police forces in Latin America are trying to ban this drug. They're trying to control the amount of it that is produced, imported, and they have very strict penalties. If you traffic it, if you sell it, you go to prison for a long time.

2:26 But in these countries, these women, they figured out how to get as much money as possible from these expats, from these tourists. You might meet them on Tinder, you might meet an absolutely beautiful girl, and she invites you to some coffee shop. And then after the coffee shop, you think, yeah, this is going so well. She says, let's go cook some food. Let's go meet some of my friends.

2:46 Or in one case that I saw, she invited him with her private driver. She said, oh, I have a private driver. He can take us really nice places. It's not safe here. And the private driver was the guy's kidnapper, ended up kidnapping the guy.

3:00 And there was also another story of a very famous Minnesota man, Asian man, but American, Asian-American. He went to Colombia. He met an absolutely beautiful girl. He showed her off to his family. This Asian American was then kidnapped by this girl, but not immediately, not on the first date. Multiple dates later, after he came back for a second time to Colombia. He met her first, went back home, told everybody about his beautiful girlfriend, came back to Colombia, and then he was kidnapped. The kidnappers asked for $2,000, which is ridiculous, and then they killed him anyway.

3:42 And this can happen to you in Mexico, in Colombia, Brazil. You have to know where you're going. If you're in Mexico, don't go out past eight, 9:00 PM in bad areas. If you're coming to a place like Tulum, stay in the absolute safest areas. Don't think, ah, I know what I'm doing. Ah, they're exaggerating the crime.

4:00 And especially if you're come to Latin America for dating or if you're using dating apps, if it's too good to be true, it literally is.

4:09 If you meet a girl on Tinder and she has bikini pictures all over her profile, if you see that she's pushing hard to meet you, if you see that she's pushing hard to either go to your place or to go to her place. If she wants you to go to her place, it's a red flag anywhere in the world. Even in Russia.

4:24 I heard of a story from a friend of mine. The guy was invited by the girl to her house, and he thought, oh, I'm getting laid with an extremely hot rushing girl. He went to her place. He got his kidney removed. He woke up the next morning, whoa, disoriented without his kidney.

4:38 Well, of course, a girl isn't going to invite you to your place in a random country, especially if she doesn't speak your language. Most girls are not that slutty. That's probably not gonna happen to you, unless you're some footballer or a famous person.

4:51 So you have to keep an eye out for red flags, and you always have to keep in mind that people will try to take advantage of you, especially if you don't speak the language.

5:00 I speak native Spanish. I don't wear my Rolex. I don't wear expensive clothes when I'm going out in Mexico, in Colombia and Argentina. You just don't show off. You speak the language or you try to. If you're going to Brazil, learn Portuguese, because you are a target, especially if you're tall, white.

5:20 I've seen many tall Americans, white as hell, white as paper, and they walk through Mexico like it's their front yard. You are a target. Don't wear expensive jewelry. Here in Tulum, Rolexes get stolen all the time. I was reading through many articles of gun robberies and overall armed violence, and they were all because a person had an extremely expensive something, either a camera, and I'm filming this in an area where they're actually building new buildings. There's security all over the place. It's called Selvazama, absolutely beautiful. They're gonna, well, they're gonna chop up all these trees and they're gonna build new buildings. It's quite safe here. It's actually the best. One of the safest place in Mexico, I would say.

5:56 But if you're going through a rough area, especially if it's at night, you don't wanna have a Rolex. You don't want to have Gucci shoes. I'm wearing my New balance, my little shorts from Zara.

6:08 You have to know where you are. It's not Dubai. It's not Miami. You have to always be aware, and if it's too good to be true, it probably is when dating these Latino women.

6:18 Now, my full game plan. I was born and raised in Puerto Rico, one of the least safest places in the world. I've spent a lot of time in Colombia, in Mexico, in Dominican Republic, many places where people get robbed, they get stabbed, they get lost, they get kidnapped.

6:31 My game plan, one, as I said, do not show off in any way. You think, oh, a Rolex is gonna get me laid. No, it's gonna get you kidnapped. Do not show off. Do not wear expensive clothes. Wear Zara, H&M, even if you're a billionaire, just wear the cheapest clothes possible while looking nice. You don't wanna look like a homeless person, but if you're going through a rough area, it's better to look like a homeless person, so that the attacker thinks: This person is more poor than I am. Why am I gonna attack them?

6:53 Second of all, if you have an expensive phone, like an iPhone, try to not use it that much outside, to be honest, or get a copy. For example, you could buy a second phone, like an iPhone 10 or an iPhone 11, and then you have your iPhone 15 at home. You use the other iPhone for just going outside, Google Maps, WhatsApp, get a local WhatsApp number.

7:10 Again, speak the language. If you speak the language with a Colombian girl, she thinks twice about kidnapping you, about taking you somewhere. The taxi drivers will think twice about robbing you or putting a gun to your face because you speak the language. You know yourself around.

7:25 And also make friends in these places. If you go to Mexico, know local people so that you can always keep them updated if everything is okay.

7:32 If you're going to a place like I went to, Tijuana, very dangerous city in Mexico, next to the border, or in the border or at the border, with the United States, you wanna have people that you let them know every few hours how you are, or every day how you are.

7:46 Hey, I'm doing great. I'm here in Tijuana. I stayed the night. Everything is fine, everything is fine. You just let them know that everything is fine. This is how Latino people keep themselves updated to see if everything is fine. My family's like that, hey, are you alive? Everything fine?

7:59 We're not in a war zone, but this is how Latino culture is, because we know that shit happens. We know that people get kidnapped. We know that people get stabbed, so you wanna get adjusted to that culture.

8:07 And overall, know the different areas of the city. Stay in the absolute best, safest possible area every time you go somewhere. Don't try to save a buck. Don't try to stay in the area with the most people with the highest chance of getting laid. And don't do anything stupid.

8:21 Don't go to some cabaret. I see it on Reddit all the time, on the Mexico groups and on the the different Latin American groups, that people go to cabarets, erotic massage centers. They go to different nightclubs that they shouldn't be going to, and they get stabbed. They get robbed, they get kidnapped. You want to be vigilant.

8:35 It's a great place. Latin America is beautiful, it's free. There's investment opportunities. There's land to buy, there's low taxes, there's beautiful women. It's one of the best areas of the world, but you have to know where you are.

Închidere balcoane

Acord asociație pentru modficiări constructive

cool

https://www.theguardian.com/environment/2021/jan/25/global-ice-loss-accelerating-at-record-rate-study-finds

https://www.theguardian.com/environment/2018/apr/13/avoid-at-all-costs-gulf-streams-record-weakening-prompts-warnings-global-warming

https://www.theguardian.com/environment/2019/jun/18/arctic-permafrost-canada-science-climate-crisis

https://www.theguardian.com/environment/2022/aug/29/major-sea-level-rise-caused-by-melting-of-greenland-ice-cap-is-now-inevitable-27cm-climate

Neue Studie zu den Tipping Points mit dramatischen Ergebnissen

so you took a slow kill bullet from the chemical warfare industry (big pharma)<br /> and now youre dying a slow death.<br /> hmm ... thats the price of being "normal" : P

But: HOW were those works made "authoritative" in the first place?

https://www.theguardian.com/world/article/2024/jul/30/india-landslides-kerala-death-toll-latest-updates-wayanad

eLife assessment

This study presents valuable new insights into a HIV-associated nephropathy (HIVAN) kidney phenotype in the Tg26 transgenic mouse model, and delineates the kidney cell types that express HIV genes and are injured in these HIV-transgenic mice. A series of compelling experiments demonstrated that PKR inhibition can ameliorate HIVAN with reversal of mitochondrial dysfunction (mainly confined to endothelial cells), a prominent feature shared in other kidney diseases. The data support that inhibition of PKR and mitochondrial dysfunction has potential clinical significance for HIVAN.

Reviewer #1 (Public Review):

Summary:

HIV associated nephropathy (HIVAN) is a rapidly progressing form of kidney disease that manifests secondary to untreated HIV infection and is predominantly seen in individuals of African descent. Tg26 mice carrying an HIV transgene lacking gag and pol exhibit high levels of albuminuria and rapid decline in renal function that recapitulates many features of HIVAN in humans. HIVAN is seen predominantly in individuals carrying two copies of missense variants in the APOL1 gene, and the authors have previously shown that APOL1 risk variant mRNA induces activity of the double strand RNA sensor kinase PKR. Because of the tight association between the APOL1 risk genotype and HIVAN, the authors hypothesized that PKR activation may mediate the renal injury in Tg26 mice, and tested this hypothesis by treating mice with a commonly used PKR inhibitory compound called C16. Treatment with C16 substantially attenuated renal damage in the Tg26 model as measured by urinary albumin/creatinine ratio, urinary NGAL/creatinine ratio and improvement in histology. The authors then performed bulk and single-nucleus RNAseq on kidneys from mice from different treatment groups to identify pathways and patterns of cell injury associated with HIV transgene expression as well as to determine the mechanistic basis for the effect of C16 treatment. They show that proximal tubule nuclei from Tg26 mice appear to have more mitochondrial transcripts which was reversed by C16 treatment and suggest that this may provide evidence of mitochondrial dysfunction in this model. They explore this hypothesis by showing there is a decrease in the expression of nuclear encoded genes and proteins involved in oxidative phosphorylation as well as a decrease in respiratory capacity via functional assessment of respiration in tubule and glomerular preparations from these mouse kidneys. All of these changes were reversed by C16 treatment. The authors propose the existence of a novel injured proximal tubule cell-type characterized by the leak of mitochondrial transcripts into the nucleus (PT-Mito). Analysis of HIV transgene expression showed high level expression in podocytes, consistent with the pronounced albuminuria that characterizes this model and HIVAN, but transcripts were also detected in tubular and endothelial cells. Because of the absence of mitochondrial transcripts in the podocytes, the authors speculate that glomerular mitochondrial dysfunction in this model is driven by damage to glomerular endothelial cells.

Strengths:

The strengths of this study include the comprehensive transcriptional analysis of the Tg26 model, including an evaluation of HIV transgene expression, which has not been previously reported. This data highlights that HIV transcripts are expressed in a subset of podocytes, consistent with the highly proteinuric disease seen in mouse and humans. However, transcripts were also seen in other tubular cells, notably intercalated cells, principal cells and injured proximal tubule cells. Though the podocyte expression makes sense, the relevance of the tubular expression to human disease is still an open question.

The data in support of mitochondrial dysfunction are also robust and rely on combined evidence from downregulation of transcripts involved in oxidative phosphorylation, decreases in complex I and II as determined by immunoblot, and assessments of respiratory capacity in tubular and glomerular preparations. These data are largely consistent with other preclinical renal injury model reported in the literature as well as previous, less thorough assessments in the Tg26 model.

Comments on latest version:

The authors have revised the manuscript to acknowledge the potential limitations of the C16 tool compound used and have performed some additional analyses that suggest the PT-Mito population can be identified in samples from KPMP. The authors added some control images for the in situ hybridizations, which are helpful, though they don't get to the core issue of limited resolution to determine whether mitochondrial RNA is present in the nuclei of injured PT cells. Some additional work has been done to show that C16 treatment results in a decrease in phospho-PKR, a readout of PKR inhibition. These changes strengthen the manuscript by providing some evidence for the translatability of the PT-mito cluster to humans and some evidence for on-target activity for C16. It would be helpful if the authors could quantify the numbers of cells in IHC with nuclear transcripts as well as pointing out some specific examples in the images provided, as comparator data for the snRNAseq studies in which 3-6% of cortex cells had evidence of nuclear mitochondrial transcripts.

Reviewer #2 (Public Review):

Summary:

Numerous studies by the authors and other groups have demonstrated an important role for HIV gene expression kidney cells in promoting progressive chronic kidney disease, especially HIV associated nephropathy. The authors had previously demonstrated a role for protein kinase R (PKR) in a non-HIV transgenic model of kidney disease (Okamoto, Commun Bio, 2021). In this study, the authors used innovative techniques including bulk and single nuclear RNAseq to demonstrate that mice expressing a replication-incompetent HIV transgene have prominent dysregulation of mitochondrial gene expression and activation of PKR and that treatment of these mice with a small molecule PKR inhibitor ameliorated the kidney disease phenotype in HIV-transgenic mice. They also identified STAT3 as a key upstream regulator of kidney injury in this model, which is consistent with previously published studies. Other important advances include identifying the kidney cell types that express the HIV transgene and have dysregulation of cellular pathways.

Strengths:

Major strengths of the study include the use of a wide variety of state-of-the-art molecular techniques to generate important new data on the pathogenesis of kidney injury in this commonly used model of kidney disease and the identification of PKR as a potential druggable target for the treatment of HIV-induced kidney disease. The authors also identify a potential novel cell type within the kidney characterized by high expression of mitochondrial genes.

Weaknesses:

Though the HIV-transgenic model used in these studies results in a phenotype that is very similar to HIV-associated nephropathy in humans, the model has several limitations that may prevent direct translation to human disease, including the fact that mice lack several genetic factors that are important contributors to HIV and kidney pathogenesis in humans. Additional studies are therefore needed to confirm these findings in human kidney disease.

Author response:

The following is the authors’ response to the previous reviews.

Responses to recommendations for the authors: 

Reviewer #1 (Recommendations For The Authors):

The manuscript would be strengthened with the following key revisions mostly having to do with image quality: 

(1) It is very difficult in Figure 4B to see which nuclei actually have evidence of mitochondrial transcripts. It might be helpful to provide arrows to specific cells and also to provide some estimate of the percentage of cells with nuclear mt-transcripts as measured by ISH compared to the 3-6% of cortex cell estimate seen in the snRNAseq analysis. 

As suggested, now we have added arrows to help readers to see the signals in nuclei. The detection threshold of ISH and single-nucleus RNA-seq should be different, and therefore, measuring estimates of PT-Mito by ISH would not be reliable.

(2) The phospho-PKR images provided as evidence of C16 activity (Supplemental Figure 1) are too dim to be very useful. Could brighter images be provided? 

We have now adjusted the LUTs of images in Supplemental Figure 1.

eLife assessment

Chang et al. have investigated the catalytic mechanism of I-PpoI nuclease, a one-metal-ion dependent nuclease, by time-resolved X-ray crystallography using soaking of crystals with metal ions under different pH conditions. This convincing study revealed that I-PpoI catalyzes the reaction process through a single divalent cation. The study uncovers important details of the roles of the metal ion and the active site histidine in catalysis.

Reviewer #1 (Public Review):

This study is convincing because they performed time-resolved X-ray crystallography under different pH conditions using active/inactive metal ions and PpoI mutants, as with the activity measurements in solution in conventional enzymatic studies. Although the reaction mechanism is simple and maybe a little predictable, the strength of this study is that they were able to validate that PpoI catalyzes DNA hydrolysis through "a single divalent cation" because time-resolved X-ray study often observes transient metal ions which are important for catalysis but are not predictable in previous studies with static structures such as enzyme-substrate analog-metal ion complexes. The discussion of this study is well supported by their data. This study visualized the catalytic process and mutational effects on catalysis, providing a new insight into the catalytic mechanism of I-PpoI through a single divalent cation. The authors found that His98, a candidate of proton acceptor in the previous experiments, also affects the Mg2+ binding for catalysis without the direct interaction between His98 and the Mg2+ ion, suggesting that "Without a proper proton acceptor, the metal ion may be prone for dissociation without the reaction proceeding, and thus stable Mg2+ binding was not observed in crystallo without His98". In the future, this interesting feature observed in I-PpoI should be investigated by biochemical, structural and computational analyses using other one metal-ion dependent nucleases.

Reviewer #2 (Public Review):

Summary:

Most polymerases and nucleases use two or three divalent metal ions in their catalytic functions. The family of His-Me nucleases, however, use only one divalent metal ion, along with a conserved histidine, to catalyze DNA hydrolysis. The mechanism has been studied previously but, according to the authors, it remained unclear. By use of time resolved X-ray crystallography, this work convincingly demonstrated that only one M2+ ion is involved in the catalysis of the His-Me I-PpoI 19 nuclease, and proposed concerted functions of the metal and the histidine.

Strengths:

This work performs mechanistic studies, including the number and roles of metal ion, pH dependence, and activation mechanism, all by structural analyses, coupled with some kinetics and mutagenesis. Overall, it is a highly rigorous work. This approach was first developed in Science (2016) for a DNA polymerase, in which Yang Cao was the first author. It has subsequently been applied to just 5 to 10 enzymes by different labs, mainly to clarify two versus three metal ion mechanisms. The present study is the first one to demonstrate a single metal ion mechanism by this approach.<br /> Furthermore, on the basis of the quantitative correlation between the fraction of metal ion binding and the formation of product, as well as the pH dependence, and the data from site specific mutants, the authors concluded that the functions of Mg2+ and His are a concerted process. A detailed mechanism is proposed in Figure 6.<br /> Even though there are no major surprises in the results and conclusions, the time-resolved structural approach and the overall quality of the results represent a significant step forward for the Me-His family of nucleases. In addition, since the mechanism is unique among different classes of nucleases and polymerases, the work should be of interest to readers in DNA enzymology, or even mechanistic enzymology in general.

Weaknesses:

Two relatively minor issues are raised here for consideration by the authors:

p. 4, last para, lines 1-2: "we next visualized the entire reaction process by soaking I-PpoI crystals in buffer....". This is a little over-stated. The structures being observed are not reaction intermediates. They are mixtures of substrates and products in the enzyme-bound state. The progress of the reaction is limited by the progress of soaking of the metal ion. Crystallography is just been used as a tool to monitor the reaction (and provide structural information about the product). It would be more accurate to say that "we next monitored the reaction progress by soaking...."

p. 5, beginning of the section. The authors on one hand emphasized the quantitative correlation between Mg ion density and the product density. On the other hand, they raised the uncertainty in the quantitation of Mg2+ density versus Na+ density, thus they repeated the study with Mn2+ which has distinct anomalous signals. This is a very good approach. However, still no metal ion density is shown in the key figure 2A. It will be clearer to show the progress of metal ion density in a figure (in addition to just plots), whether it is Mg or Mn.

Revised version: The authors have properly revised the paper in response to both questions raised in the weakness section. The first issue is an important clarification for others working on similar approaches also. For the second issue, the metal ion density is nicely shown in Fig. S4 now.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews: 

Reviewer #1 (Public Review): 

This study is convincing because they performed time-resolved X-ray crystallography under different pH conditions using active/inactive metal ions and PpoI mutants, as with the activity measurements in solution in conventional enzymatic studies. Although the reaction mechanism is simple and may be a little predictable, the strength of this study is that they were able to validate that PpoI catalyzes DNA hydrolysis through "a single divalent cation" because time-resolved X-ray study often observes transient metal ions which are important for catalysis but are not predictable in previous studies with static structures such as enzyme-substrate analog-metal ion complexes. The discussion of this study is well supported by their data. This study visualized the catalytic process and mutational effects on catalysis, providing new insight into the catalytic mechanism of I-PpoI through a single divalent cation. The authors found that His98, a candidate of proton acceptor in the previous experiments, also affects the Mg2+ binding for catalysis without the direct interaction between His98 and the Mg2+ ion, suggesting that "Without a proper proton acceptor, the metal ion may be prone for dissociation without the reaction proceeding, and thus stable Mg2+ binding was not observed in crystallo without His98". In future, this interesting feature observed in I-PpoI should be investigated by biochemical, structural, and computational analyses using other metal-ion dependent nucleases. 

We appreciate the reviewer for the positive assessment as well as all the comments and suggestions.

Reviewer #2 (Public Review): 

Summary: 

Most polymerases and nucleases use two or three divalent metal ions in their catalytic functions. The family of His-Me nucleases, however, use only one divalent metal ion, along with a conserved histidine, to catalyze DNA hydrolysis. The mechanism has been studied previously but, according to the authors, it remained unclear. By use of a time resolved X-ray crystallography, this work convincingly demonstrated that only one M2+ ion is involved in the catalysis of the His-Me I-PpoI 19 nuclease, and proposed concerted functions of the metal and the histidine. 

Strengths: 

This work performs mechanistic studies, including the number and roles of metal ion, pH dependence, and activation mechanism, all by structural analyses, coupled with some kinetics and mutagenesis. Overall, it is a highly rigorous work. This approach was first developed in Science (2016) for a DNA polymerase, in which Yang Cao was the first author. It has subsequently been applied to just 5 to 10 enzymes by different labs, mainly to clarify two versus three metal ion mechanisms. The present study is the first one to demonstrate a single metal ion mechanism by this approach. 

Furthermore, on the basis of the quantitative correlation between the fraction of metal ion binding and the formation of product, as well as the pH dependence, and the data from site-specific mutants, the authors concluded that the functions of Mg2+ and His are a concerted process. A detailed mechanism is proposed in Figure 6. 

Even though there are no major surprises in the results and conclusions, the time-resolved structural approach and the overall quality of the results represent a significant step forward for the Me-His family of nucleases. In addition, since the mechanism is unique among different classes of nucleases and polymerases, the work should be of interest to readers in DNA enzymology, or even mechanistic enzymology in general. 

Thank you very much for your comments and suggestions.

Weaknesses: 

Two relatively minor issues are raised here for consideration: 

p. 4, last para, lines 1-2: "we next visualized the entire reaction process by soaking I-PpoI crystals in buffer....". This is a little over-stated. The structures being observed are not reaction intermediates. They are mixtures of substrates and products in the enzyme-bound state. The progress of the reaction is limited by the progress of the soaking of the metal ion. Crystallography has just been used as a tool to monitor the reaction (and provide structural information about the product). It would be more accurate to say that "we next monitored the reaction progress by soaking....". 

We appreciate the clarification regarding the description of our experimental approach. We agree that our structures do not represent reaction intermediates but rather mixtures of substrate and product states within the enzyme-bound environment. We have revised the text accordingly to more accurately reflect our methodology.

p. 5, the beginning of the section. The authors on one hand emphasized the quantitative correlation between Mg ion density and the product density. On the other hand, they raised the uncertainty in the quantitation of Mg2+ density versus Na+ density, thus they repeated the study with Mn2+ which has distinct anomalous signals. This is a very good approach. However, there is still no metal ion density shown in the key Figure 2A. It will be clearer to show the progress of metal ion density in a figure (in addition to just plots), whether it is Mg or Mn. 

Thank you for your insightful comments. We recognize the importance of visualizing metal ion density alongside product density data. To address this, we included in Figure S4 to present Mg2+/Mn2+ and product densities concurrently.

Reviewer #1 (Recommendations For The Authors): 

(1) Figure 6. I understand that pre-reaction state (left panel) and Metal-binding state (two middle panels) are in equilibrium. But can we state that the Metal-binding state (two middle panels) and the product state (right panel) are in equilibrium and connected by two arrows? 

Thank you for your comments. We agree that the DNA hydrolysis reaction process may not be reversible within I-Ppo1 active site. To clarify, we removed the backward arrows between the metal-binding state and product state. In addition, we thank the reviewer for giving a name for the middle state and think it would be better to label the middle state. We added the metal-binding state label in the revised Figure 6 and also added “on the other hand, optimal alignment of a deprotonated water and Mg2+ within the active site, labeled as metal-binding state, leads to irreversible bond breakage (Fig. 6a)” within the text.

(2) The section on DNA hydrolysis assay (Materials and Methods) is not well described. In this section, the authors should summarize the methods for the experiments in Figure 4 AC, Figure 5BC, Figure S3C, Figure S4EF, and Figure S6AB. The authors presented some graphs for the reactions. For clarity, the author should state in the legends which experiments the results are from (in crystallo or in solution). Please check and modify them. 

Thank you for the suggestion. We have added four paragraphs to detail the experimental procedures for experiments in these figures. In addition, we have checked all of the figure legends and labeled them as “in crystallo or in solution.” To clarify, we also added “in crystallo” or “solution” in the corresponding panels.

(3) The authors showed the anomalous signals of Mn2+ and Tl+. The authors should mention which wavelength of X-rays was used in the data collections to calculate the anomalous signals. 

Thank you for the suggestion. We have included the wavelength of the X-ray in the figure legends that include anomalous maps, which were all determined at an X-ray wavelength of 0.9765 Å.

(4) The full names of "His-Me" and "HNH" are necessary for a wide range of readers. 

Thank you for the suggestion. We have included the full nomenclature for His-Me (histidine-metal) nucleases and HNH (histidine-asparagine-histidine) nuclease.

(5) The authors should add the side chain of Arg61 in Figure 1E because it is mentioned in the main text. 

Thank you for the suggestion. We have added Arg61 to Figure 1E.

(6) Figure 5D. For clarity, the electron densities should cover the Na+ ion. The same request applies to WatN in Figure S3B.

Thank you for catching this detail. We have added the electron density for the Na+ ion in Figure 5D and WatN in Figure S3B.

(7) At line 269 on page 8, what is "previous H98A I-PpoI structure with Mn2+"? Is the structure 1CYQ? If so, it is a complex with Mg2+. 

Thank you for catching this detail. We have edited the text to “previous H98A I-PpoI structure with Mg2+.”

(8) At line 294 on page 9, "and substrate alignment or rotation in MutT (66)." I think "alignment of the substrate and nucleophilic water" is preferred rather than "substrate alignment or rotation". 

Thank you for the suggestion. We have edited the text to “alignment of the substrate and nucleophilic water.”

(9) At line 305 on page 9, "Second, (58, 69-71) single metal ion binding is strictly correlated with product formation in all conditions, at different pH and with different mutants (Figure 3a and Supplementary Figure 4a-c) (58)". The references should be cited in the correct positions. 

Thank you for catching this typo. We have removed the references.

(10) At line 347 on page 10, "Grown in a buffer that contained (50 g/L glucose, 200 g/L α-lactose, 10% glycerol) for 24 hrs." Is this sentence correct? 

Thank you for catching this detail. We have corrected the sentence.

(11) At line 395 on page 11, "The His98Ala I-PpoI crystals of first transferred and incubated in a pre-reaction buffer containing 0.1M MES (pH 6.0), 0.2 M NaCl, 1 mM MgCl2 or MnCl2, and 20% (w/v) PEG3350 for 30 min." In the experiments using this mutant, does a pre-reaction buffer contain MgCl2 or MnCl2? 

Thank you for bringing this to our attention. We have performed two sets of experiments: 1) metal ion soaking in 1 mM Mn2+, which is performed similarly as WT and does not have Mn2+ in the pre-reaction buffer; 2) imidazole soaking, 1 mM Mn2+ was included in the pre-reaction buffer. We reasoned that the Mn2+ will not bind or promote reaction with His98Ala I-PpoI, but pre-incubation may help populate Mn2+ within the lattice for better imidazole binding. However, neither Mn2+ nor imidazole were observed. We have added experimental details for both experiments with His98Ala I-PpoI.

(12) In the figure legends of Figure 1, is the Fo-Fc omit map shown in yellow not in green? Please remove (F) in the legends. 

We have changed the Fo-Fc map to be shown in violet. We have also removed (f) from the figure legends.

(13) I found descriptions of "MgCl". Please modify them to "MgCl2". 

Thank you for catching these details. We have modified all “MgCl” to “MgCl2.”

(14) References 72 and 73 are duplicated. 

We have removed the duplicated reference.

Reviewer #2 (Recommendations For The Authors): 

p. 9, first paragraph, last three lines: "Thus, we suspect that the metal ion may play a crucial role in the chemistry step to stabilize the transition state and reduce the electronegative buildup of DNA, similar to the third metal ion in DNA polymerases and RNaseH." This point is significant but the statement seems a little uncertain. You are saying that the single metal plays the role of two metals in polymerase, in both the ground state and the transition state. I believe the sentence can be stronger and more explicit. 

Thank you for raising this point. We suspect the single metal ion in I-PpoI is different from the A-site or B-site metal ion in DNA polymerases and RNaseH, but similar to the third metal ion in DNA polymerases and nucleases. As we stated in the text,

(1) the metal ion in I-PpoI is not required for substrate alignment. The water molecule and substrate can be observed in place even in the presence of the metal ion. In contrast, the A-site or B-site metal ion in DNA polymerases and RNaseH are required for aligning the substrates.

(2) Moreover, the appearance of the metal ion is strictly correlated with product formation, similar as the third metal ion in DNA polymerase and RNaseH.

To emphasize our point, we have revised the sentence as

“Thus, similar to the third metal ion in DNA polymerases and RNaseH, the metal ion in I-PpoI is not required for substrate alignment but is essential for catalysis. We suspect that the single metal ion helps stabilize the transition state and reduce the electronegative buildup of DNA, thereby promoting DNA hydrolysis.”

Minor typos: 

p. 2, line 4 from bottom: due to the relatively low resolution... 

Thank you for catching this. We have edited the text to “due to the relatively low resolution.”

Figure 4F: What is represented by the pink color? 

The structures are color-coded as 320 s at pH 6 (violet), 160 s at pH 7 (yellow), and 20 s at pH 8 (green). We have included the color information in figure legend and make the labeling clearer in the panel.

p. 9, first paragraph, last line: ...similar to the third... 

Thank you for catching this. We have edited the text.

eLife assessment

The study answers the important question of whether the conformational dynamics of proteins are slaved by the motion of solvent water or are intrinsic to the polypeptide. The results from neutron scattering experiments, involving isotopic labelling, carried out on a set of four structurally different proteins are convincing, showing that protein motions are not coupled to the solvent. A strength of this work is the study of a set of proteins using spectroscopy covering a range of resolutions. The work is of broad interest to researchers in the fields of protein biophysics and biochemistry.

Reviewer #1 (Public Review):

Zheng et al. study the 'glass' transitions that occurs in proteins at ca. 200K using neutron diffraction and differential isotopic labeling (hydrogen/deuterium) of the protein and solvent. To overcome limitations in previous studies, this work is conducted in parallel with 4 proteins (myoglobin, cytochrome P450, lysozyme and green fluorescent protein) and experiments were performed at a range of instrument time resolutions (1ns - 10ps). The author's data looks compelling, and suggests that transitions in the protein and solvent behavior are not coupled and contrary to some previous reports, the apparent water transition temperature is a 'resolution effect'; i.e. instrument response is limited. This is likely to be important in the field, as a reassessment of solvent 'slaving' and the role of the hydration shell on protein dynamics should be reassessed in light of these findings.

Reviewer #2 (Public Review):

Summary:

The manuscript entitled "Decoupling of the Onset of Anharmonicity between a Protein and Its Surface Water around 200 K" by Zheng et al. presents a neutron scattering study trying to elucidate if at the dynamical transition temperature water and protein motions are coupled. The origin of the dynamical transition temperature has been debated for decades, specifically its relation to hydration.

The study is rather well conducted, with a lot of effort to acquire the perdeuterated proteins, and some results are interesting.

Author response:

The following is the authors’ response to the previous reviews.

eLife assessment

The study answers the important question of whether the conformational dynamics of proteins are slaved by the motion of solvent water or are intrinsic to the polypeptide. The results from neutron scattering experiments, involving isotopic labelling, carried out on a set of four structurally different proteins are convincing, showing that protein motions are not coupled to the solvent. A strength of this work is the study of a set of proteins using spectroscopy covering a range of resolutions. A minor weakness is the limited description of computational methods and analysis of data. The work is of broad interest to researchers in the fields of protein biophysics and biochemistry.

We thank the editors and reviewers for the positive and encouraging comments.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Zheng et al. study the 'glass' transitions that occurs in proteins at ca. 200K using neutron diffraction and differential isotopic labeling (hydrogen/deuterium) of the protein and solvent. To overcome limitations in previous studies, this work is conducted in parallel with 4 proteins (myoglobin, cytochrome P450, lysozyme and green fluorescent protein) and experiments were performed at a range of instrument time resolutions (1ns - 10ps). The author's data looks compelling, and suggests that transitions in the protein and solvent behavior are not coupled and contrary to some previous reports, the apparent water transition temperature is a 'resolution effect'; i.e. instrument response limited. This is likely to be important in the field, as a reassessment of solvent 'slaving' and the role of the hydration shell on protein dynamics should be reassessed in light of these findings.

Strengths:

The use of multiple proteins and instruments with a rate of energy resolution/ timescales.

We thank the reviewer for highlighting our key findings.

Weaknesses:

The paper could be organised to better allow the comparison of the complete dataset collected. The extent of hydration clearly influences the protein transition temperature. The authors suggest that "water can be considered here as lubricant or plasticizer which facilitates the motion of the biomolecule." This may be the case, but the extent of hydration may also alter the protein structure.

Following the reviewer’s suggestion, we studied the secondary structure content and tertiary structure of CYP protein at different hydration levels (h = 0.2 and 0.4) through molecular dynamics simulation. As shown in Table S2 and Fig. S6, the extent of hydration does not alter the protein secondary structure content and overall packing. Thus, this result also suggests that water molecules have more influence on protein dynamics than on protein structure. We added the above results in the revised SI.

Reviewer #2 (Public Review):

Summary:

The manuscript entitled "Decoupling of the Onset of Anharmonicity between a Protein and Its Surface Water around 200 K" by Zheng et al. presents a neutron scattering study trying to elucidate if at the dynamical transition temperature water and protein motions are coupled. The origin of the dynamical transition temperature is highly debated since decades and specifically its relation to hydration.

Strengths:

The study is rather well conducted, with a lot of efforts to acquire the perdeuterated proteins, and some results are interesting.

We thank the reviewer for highlighting our key findings.

Weaknesses:

The MD data presented appears to be missing description of the methods used.

If these data support the authors claim that different levels of hydration do not affect the protein structure, careful analysis of the MD simulation data should be presented that show the systems are properly equilibrated under each condition. Additionally, methods are needed to describe the MD parameters and methods used, and for how long the simulations were run.

We have now added the methods of MD simulation into the revised SI.

“The initial structure of protein cytochrome P450 (CYP) for simulations was taken from PDB crystal structure (2ZAX). Two protein monomers were filled in a cubic box. 1013 and 2025 water molecules were inserted into the box randomly to reach a mass ratio of 0.2 and 0.4 gram water/1 gram protein, respectively, which mimics the experimental condition. Then 34 sodium counter ions were added to keep the system neutral in charge. The CHARMM 27 force field in the GROMACS package was used for CYP, whereas the TIP4P/Ew model was chosen for water. The simulations were carried out at a broad range of temperatures from 360 K to 100 K, with a step of 5 K. At each temperature, after the 5000 steps energy-minimization procedure, a 10 ns NVT is conducted. After that, a 30 ns NPT simulation was carried out at 1 atm with the proper periodic boundary condition. As shown in Fig. S7, 30 ns is sufficient to equilibrate the system. The temperature and pressure of the system is controlled by the velocity rescaling method and the method by Parrinello and Rahman, respectively. All bonds of water in all the simulations were constrained with the LINCS algorithm to maintain their equilibration length. In all the simulations, the system was propagated using the leap-frog integration algorithm with a time step of 2 fs. The electrostatic interactions were calculated using the Particle Mesh Ewalds (PME) method. A non-bond pair-list cutoff of 1 nm was used and the pair-list was updated every 20 fs. All MD simulations were performed using GROMACS 4.5.1 software packages.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Response to author's changes:

See public review: The MD data presented appears to be missing description of the methods used.

If these data support the authors claim that different levels of hydration do not affect the protein structure, careful analysis of the MD simulation data should be presented that show the systems are properly equilibrated under each condition. Additionally, methods are needed to describe the MD parameters and methods used, and for how long the simulations were run.

We have now added the methods of MD simulation into the revised SI. Please see Reply 5.

Reviewer #2 (Recommendations For The Authors):

The authors answered my questions and substantially improved the manuscript.

We thank the reviewer for the encouraging comments .

eLife assessment

Zhu, et al. present convincing data that details the function of the infertile crescent gene (ifc) in fly development with implications on human neurodegenerative disease. The authors unveil interesting and novel phenotypes of ifc loss-of-function in glia. The experiments are well planned and executed, and the data support the conclusions. These important findings have theoretical and practical implications beyond a single subfield and the methods are in line with current state-of-the-art.

Reviewer #1 (Public Review):<br /> Summary:

Zhu et al., investigate the cellular defects in glia as a result of loss in DEGS1/ifc encoding the dihydroceramide desaturase. Using the strength of Drosophila and its vast genetic toolkit, they find that DEGS1/ifc is mainly expressed in glia and its loss leads to profound neurodegeneration. This supports a role for DEGS1 in the developing larval brain as it safeguards proper CNS development. Loss of DEGS1/ifc leads to dihydroceramide accumulation in the CNS and induces alteration in the morphology of glial subtypes and a reduction in glial number. Cortex and ensheathing glia appeared swollen and accumulated internal membranes. Astrocyte-glia on the other hand displayed small cell bodies, reduced membrane extension and disrupted organization in the dorsal ventral nerve cord. They also found that DEGS1/ifc localizes primarily to the ER. Interestingly, the authors observed that loss of DEGS1/ifc drives ER expansion and reduced TGs and lipid droplet numbers. No effect on PC and PE and a slight increase in PS.

The conclusions of this paper are well supported by the data. The study could be further strengthened by a few additional controls and/or analyses.

Strengths:

This is an interesting study that provides new insight into the role of ceramide metabolism in neurodegeneration.

The strength of the paper is the generation of LOF lines, the insertion of transgenes and the use of the UAS-GAL4/GAL80 system to assess the cell-autonomous effect of DEGS1/ifc loss in neurons and different glial subtypes during CNS development.

The imaging, immunofluorescence staining and EM of the larval brain and the use of the optical lobe and the nerve cord as a readout are very robust and nicely done.

Drosophila is a difficult model to perform core biochemistry and lipidomics but the authors used the whole larvae and CNS to uncover global changes in mRNA levels related to lipogenesis and the unfolded protein responses as well as specific lipid alterations upon DEGS1/ifc loss.

Weaknesses:

The authors performed lipidomics and RTqPCR on whole larvae and larval CNS from which it is impossible to define the cell type-specific effects. Ideally, this could be further supported by performing single cell RNAseq on larval brains to tease apart the cell-type specific effect of DEGS1/ifc loss.

It's clear from the data that the accumulation of dihydroceramide in the ER triggers ER expansion but it remains unclear how or why this happens. Additionally, the authors assume that, because of the reduction in LD numbers, that the source of fatty acids comes from the LDs. But there is no data testing this directly.

The authors performed a beautiful EMS screen identifying several LOF alleles in ifc. However, the authors decided to only use KO/ifcJS3. The paper could be strengthened if the authors could replicate some of the key findings in additional fly lines.

The authors use M{3xP3-RFP.attP}ZH-51D transgene as a general glial marker. However, it would be advised to show the % overlap between the glial marker and the RFP since a lot of cells are green positive but not perse RFP positive and vice versa.

The authors indicate that other 3xP3 RFP and GFP transgenes at other genomic locations also label most glia in the CNE. Do they have a preferential overlap with the different glial subtypes?

Reviewer #2 (Public Review):

Summary:<br /> The manuscript by Zhu et al. describes phenotypes associated with the loss of the gene ifc using a Drosophila model. The authors suggest their findings are relevant to understanding the molecular underpinnings of a neurodegenerative disorder, HLD-18, which is caused by mutations in the human ortholog of ifc, DEGS1.<br /> The work begins with the authors describing the role for ifc during fly larval brain development, demonstrating its function in regulating developmental timing, brain size, and ventral nerve cord elongation. Further mechanistic examination revealed that loss of ifc leads to depleted cellular ceramide levels as well as dihydroceramide accumulation, eventually causing defects in ER morphology and function. Importantly, the authors showed that ifc is predominantly expressed in glia and is critical for maintaining appropriate glial cell numbers and morphology. Many of the key phenotypes caused by the loss of fly ifc can be rescued by overexpression of human DEGS1 in glia, demonstrating the conserved nature of these proteins as well as the pathways they regulate. Interestingly, the authors discovered that the loss of lipid droplet formation in ifc mutant larvae within the cortex glia, presumably driving the deficits in glial wrapping around axons and subsequent neurodegeneration, potentially shedding light on mechanisms of HLD-18 and related disorders.

Strengths:<br /> Overall, the manuscript is thorough in its analysis of ifc function and mechanism. The data images are high quality, the experiments are well controlled, and the writing is clear.

Weaknesses:<br /> (1) The authors clearly demonstrated a reduction in number of glia in the larval brains of ifc mutant flies. What remains unclear is whether ifc loss leads to glial apoptosis or a failure for glia to proliferate during development. The authors should distinguish between these two hypotheses using apoptotic markers and cell proliferation markers in glia.

(2) It is surprising that human DEGS1 expression in glia rescues the noted phenotypes despite the different preference for sphingoid backbone between flies and mammals. Though human DEGS1 rescued the glial phenotypes described, can animal lethality be rescued by glial expression of human DEGS1? Are there longer-term effects of loss of ifc that cannot be compensated by the overexpression of human DEGS1 in glia (age-dependent neurodegeneration, etc.)?

(3) The mechanistic link between the loss of ifc and lipid droplet defects is missing. How do defects in ceramide metabolism alter triglyceride utilization and storage? While the author's argument that the loss of lipid droplets in larval glia will lead to defects in neuronal ensheathment, a discussion of how this is linked to ceramides needs to be added.

(4) On page 10, the authors use the words "strong" and "weak" to describe where ifc is expressed. Since the use of T2A-GAL4 alleles in examining gene expression is unable to delineate the amount of gene expression from a locus, the terms "broad" and "sparse" labeling (or similar terms) should be used instead.

Reviewer #3 (Public Review):

Summary:<br /> In this manuscript, the authors report three novel ifc alleles: ifc[js1], ifc[js2], and ifc[js3]. ifc[js1] and ifc[js2] encode missense mutations, V276D and G257S, respectively. ifc[js3] encodes a nonsense mutation, W162*. These alleles exhibit multiple phenotypes, including delayed progression to the late-third larval instar stage, reduced brain size, elongation of the ventral nerve cord, axonal swelling, and lethality during late larval or early pupal stages.<br /> Further characterization of these alleles the authors reveals that ifc is predominantly expressed in glia and localizes to the endoplasmic reticulum (ER). The expression of ifc gene governs glial morphology and survival. Expression of fly ifc cDNA or human DEGS1 cDNA specifically in glia, but not neurons, rescues the CNS phenotypes of ifc mutants, indicating a crucial role for ifc in glial cells and its evolutionary conservation. Loss of ifc results in ER expansion and loss of lipid droplets in cortex glia. Additionally, loss of ifc leads to ceramide depletion and accumulation of dihydroceramide. Moreover, it increases the saturation levels of triacylglycerols and membrane phospholipids. Finally, the reduction of dihydroceramide synthesis suppresses the CNS phenotypes associated with ifc mutations, indicating the key role of dihydroceramide in causing ifc LOF defects.

Strengths:<br /> This manuscript unveils several intriguing and novel phenotypes of ifc loss-of-function in glia. The experiments are meticulously planned and executed, with the data strongly supporting their conclusions.

Weaknesses:<br /> I didn't find any obvious weakness.

Author response:

'We thank the reviewers for their helpful comments and criticisms of our manuscript and are pleased by the overall positive nature of the comments. For the eLife Version of Record, we plan to carry out the following experiments to address reviewer comments:

- We will use genetic approaches (e.g., driving p35 in glia to block apoptosis) and molecular markers, such as phospho-Histone H3, to assess whether reduced glial proliferation or increased glial apoptosis contributes to reduced glial cell number.

- We will assess the ability of glial-specific expression of the Drosophila or Human ifc/DEGS1 transgenes to rescue the ifc lethal phenotype to adulthood.

- We will replicate key phenotypic findings with additional ifc alleles.

- We will enhance our characterization of 3xP3 RFP transgenes with respect to glial subtypes both for the insert we used in our study and at least one independent insert.

- We will edit the text of the manuscript to clarify additional points raised by the reviewers.

Once we complete the above approaches, we will modify our manuscript accordingly and submit a full response to the reviews to eLife along with the revised manuscript,'

eLife assessment

This study presents a useful modification of a standard model of genetic drift by incorporating variance in offspring numbers, claiming to address several paradoxes in molecular evolution. It is unfortunate that the study fails to engage prior literature that has extensively examined the impact of variance in offspring number, implying that some of the paradoxes presented might be resolved within existing frameworks. In addition, while the modified model yields intriguing theoretical predictions, the simulations and empirical analyses are incomplete to support the authors' claims.

Reviewer #1 (Public Review):

Summary:

The authors present a theoretical treatment of what they term the "Wright-Fisher-Haldane" model, a claimed modification of the standard model of genetic drift that accounts for variability in offspring number, and argue that it resolves a number of paradoxes in molecular evolution. Ultimately, I found this manuscript quite strange. The notion of effective population size as inversely related to the variance in offspring number is well known in the literature, and not exclusive to Haldane's branching process treatment. However, I found the authors' point about variance in offspring changing over the course of, e.g. exponential growth fairly interesting, and I'm not sure I'd seen that pointed out before. Nonetheless, I don't think the authors' modeling, simulations, or empirical data analysis are sufficient to justify their claims.

Weaknesses:

I have several outstanding issues. First of all, the authors really do not engage with the literature regarding different notions of an effective population. Most strikingly, the authors don't talk about Cannings models at all, which are a broad class of models with non-Poisson offspring distributions that nonetheless converge to the standard Wright-Fisher diffusion under many circumstances, and to "jumpy" diffusions/coalescents otherwise (see e.g. Mohle 1998, Sagitov (2003), Der et al (2011), etc.). Moreover, there is extensive literature on effective population sizes in populations whose sizes vary with time, such as Sano et al (2004) and Sjodin et al (2005). Of course in many cases here the discussion is under neutrality, but it seems like the authors really need to engage with this literature more.

The most interesting part of the manuscript, I think, is the discussion of the Density Dependent Haldane model (DDH). However, I feel like I did not fully understand some of the derivation presented in this section, which might be my own fault. For instance, I can't tell if Equation 5 is a result or an assumption - when I attempted a naive derivation of Equation 5, I obtained E(K_t) = 1 + r/c*(c-n)*dt. It's unclear where the parameter z comes from, for example. Similarly, is equation 6 a derivation or an assumption? Finally, I'm not 100% sure how to interpret equation 7. I that a variance effective size at time t? Is it possible to obtain something like a coalescent Ne or an expected number of segregating sites or something from this?

Similarly, I don't understand their simulations. I expected that the authors would do individual-based simulations under a stochastic model of logistic growth, and show that you naturally get variance in offspring number that changes over time. But it seems that they simply used their equations 5 and 6 to fix those values. Moreover, I don't understand how they enforce population regulation in their simulations---is N_t random and determined by the (independent) draws from K_t for each individual? In that case, there's no "interaction" between individuals (except abstractly, since logistic growth arises from a model that assumes interactions between individuals). This seems problematic for their model, which is essentially motivated by the fact that early during logistic growth, there are basically no interactions, and later there are, which increases variance in reproduction. But their simulations assume no interactions throughout!

The authors also attempt to show that changing variance in reproductive success occurs naturally during exponential growth using a yeast experiment. However, the authors are not counting the offspring of individual yeast during growth (which I'm sure is quite hard). Instead, they use an equation that estimates the variance in offspring number based on the observed population size, as shown in the section "Estimation of V(K) and E(K) in yeast cells". This is fairly clever, however, I am not sure it is right, because the authors neglect covariance in offspring between individuals. My attempt at this derivation assumes that I_t | I_{t-1} = \sum_{I=1}^{I_{t-1}} K_{i,t-1} where K_{i,t-1} is the number of offspring of individual i at time t-1. Then, for example, E(V(I_t | I_{t-1})) = E(V(\sum_{i=1}^{I_{t-1}} K_{i,t-1})) = E(I_{t-1})V(K_{t-1}) + E(I_{k-1}(I_{k-1}-1))*Cov(K_{i,t-1},K_{j,t-1}). The authors have the first term, but not the second, and I'm not sure the second can be neglected (in fact, I believe it's the second term that's actually important, as early on during growth there is very little covariance because resources aren't constrained, but at carrying capacity, an individual having offspring means that another individuals has to have fewer offspring - this is the whole notion of exchangeability, also neglected in this manuscript). As such, I don't believe that their analysis of the empirical data supports their claim.

Thus, while I think there are some interesting ideas in this manuscript, I believe it has some fundamental issues: first, it fails to engage thoroughly with the literature on a very important topic that has been studied extensively. Second, I do not believe their simulations are appropriate to show what they want to show. And finally, I don't think their empirical analysis shows what they want to show.

References:

Möhle M. Robustness results for the coalescent. Journal of Applied Probability. 1998;35(2):438-447. doi:10.1239/jap/1032192859

Sagitov S. Convergence to the coalescent with simultaneous multiple mergers. Journal of Applied Probability. 2003;40(4):839-854. doi:10.1239/jap/1067436085

Der, Ricky, Charles L. Epstein, and Joshua B. Plotkin. "Generalized population models and the nature of genetic drift." Theoretical population biology 80.2 (2011): 80-99

Sano, Akinori, Akinobu Shimizu, and Masaru Iizuka. "Coalescent process with fluctuating population size and its effective size." Theoretical population biology 65.1 (2004): 39-48

Sjodin, P., et al. "On the meaning and existence of an effective population size." Genetics 169.2 (2005): 1061-1070

Reviewer #2 (Public Review):

Summary:

This theoretical paper examines genetic drift in scenarios deviating from the standard Wright-Fisher model. The authors discuss Haldane's branching process model, highlighting that the variance in reproductive success equates to genetic drift. By integrating the Wright-Fisher model with the Haldane model, the authors derive theoretical results that resolve paradoxes related to effective population size.

Strengths:

The most significant and compelling result from this paper is perhaps that the probability of fixing a new beneficial mutation is 2s/V(K). This is an intriguing and potentially generalizable discovery that could be applied to many different study systems.

The authors also made a lot of effort to connect theory with various real-world examples, such as genetic diversity in sex chromosomes and reproductive variance across different species.

Weaknesses:

One way to define effective population size is by the inverse of the coalescent rate. This is where the geometric mean of Ne comes from. If Ne is defined this way, many of the paradoxes mentioned seem to resolve naturally. If we take this approach, one could easily show that a large N population can still have a low coalescent rate depending on the reproduction model. However, the authors did not discuss Ne in light of the coalescent theory. This is surprising given that Eldon and Wakeley's 2006 paper is cited in the introduction, and the multiple mergers coalescent was introduced to explain the discrepancy between census size and effective population size, superspreaders, and reproduction variance - that said, there is no explicit discussion or introduction of the multiple mergers coalescent.

The Wright-Fisher model is often treated as a special case of the Cannings 1974 model, which incorporates the variance in reproductive success. This model should be discussed. It is unclear to me whether the results here have to be explained by the newly introduced WFH model, or could have been explained by the existing Cannings model.

The abstract makes it difficult to discern the main focus of the paper. It spends most of the space introducing "paradoxes".

The standard Wright-Fisher model makes several assumptions, including hermaphroditism, non-overlapping generations, random mating, and no selection. It will be more helpful to clarify which assumptions are being violated in each tested scenario, as V(K) is often not the only assumption being violated. For example, the logistic growth model assumes no cell death at the exponential growth phase, so it also violates the assumption about non-overlapping generations.

The theory and data regarding sex chromosomes do not align. The fact that \hat{alpha'} can be negative does not make sense. The authors claim that a negative \hat{alpha'} is equivalent to infinity, but why is that? It is also unclear how theta is defined. It seems to me that one should take the first principle approach e.g., define theta as pairwise genetic diversity, and start with deriving the expected pair-wise coalescence time under the MMC model, rather than starting with assuming theta = 4Neu. Overall, the theory in this section is not well supported by the data, and the explanation is insufficient.

Reviewer #3 (Public Review):

Summary:

Ruan and colleagues consider a branching process model (in their terminology the "Haldane model") and the most basic Wright-Fisher model. They convincingly show that offspring distributions are usually non-Poissonian (as opposed to what's assumed in the Wright-Fisher model), and can depend on short-term ecological dynamics (e.g., variance in offspring number may be smaller during exponential growth). The authors discuss branching processes and the Wright-Fisher model in the context of 3 "paradoxes": (1) how Ne depends on N might depend on population dynamics; (2) how Ne is different on the X chromosome, the Y chromosome, and the autosomes, and these differences do match the expectations base on simple counts of the number of chromosomes in the populations; (3) how genetic drift interacts with selection. The authors provide some theoretical explanations for the role of variance in the offspring distribution in each of these three paradoxes. They also perform some experiments to directly measure the variance in offspring number, as well as perform some analyses of published data.

Strengths:

(1) The theoretical results are well-described and easy to follow.

(2) The analyses of different variances in offspring number (both experimentally and analyzing public data) are convincing that non-Poissonian offspring distributions are the norm.

(3) The point that this variance can change as the population size (or population dynamics) change is also very interesting and important to keep in mind.

(4) I enjoyed the Density-Dependent Haldane model. It was a nice example of the decoupling of census size and effective size.

Weaknesses:

(1) I am not convinced that these types of effects cannot just be absorbed into some time-varying Ne and still be well-modeled by the Wright-Fisher process.

(2) Along these lines, there is well-established literature showing that a broad class of processes (a large subset of Cannings' Exchangeable Models) converge to the Wright-Fisher diffusion, even those with non-Poissonian offspring distributions (e.g., Mohle and Sagitov 2001). E.g., equation (4) in Mohle and Sagitov 2001 shows that in such cases the "coalescent Ne" should be (N-1) / Var(K), essentially matching equation (3) in the present paper.

(3) Beyond this, I would imagine that branching processes with heavy-tailed offspring distributions could result in deviations that are not well captured by the authors' WFH model. In this case, the processes are known to converge (backward-in-time) to Lambda or Xi coalescents (e.g., Eldon and Wakely 2006 or again in Mohle and Sagitov 2001 and subsequent papers), which have well-defined forward-in-time processes.

(4) These results that Ne in the Wright-Fisher process might not be related to N in any straightforward (or even one-to-one) way are well-known (e.g., Neher and Hallatschek 2012; Spence, Kamm, and Song 2016; Matuszewski, Hildebrandt, Achaz, and Jensen 2018; Rice, Novembre, and Desai 2018; the work of Lounès Chikhi on how Ne can be affected by population structure; etc...)

(5) I was also missing some discussion of the relationship between the branching process and the Wright-Fisher model (or more generally Cannings' Exchangeable Models) when conditioning on the total population size. In particular, if the offspring distribution is Poisson, then conditioned on the total population size, the branching process is identical to the Wright-Fisher model.

(6) In the discussion, it is claimed that the last glacial maximum could have caused the bottleneck observed in human populations currently residing outside of Africa. Compelling evidence has been amassed that this bottleneck is due to serial founder events associated with the out-of-Africa migration (see e.g., Henn, Cavalli-Sforza, and Feldman 2012 for an older review - subsequent work has only strengthened this view). For me, a more compelling example of changes in carrying capacity would be the advent of agriculture ~11kya and other more recent technological advances.

Smith Corona Typewriter Ribbon Vibrator Bind, Lift Sticking Up, Forming/Adjusting Process by [[Phoenix Typewriter]]

Smaller engines do the baseband energy while the biggest engine is designed for reacting quickly to needs.

Idle engine (maintenance). Some measures are following the outside temperature trend, particularly heatwaves are visible. However, some other curves seems to be stable and regulated. It could be that the cooling system is shared among the engines. More specific, it could be that the water flow is the same for all machines. It would be interesting to observe, whether the operation of other engines affects temperature variation in this plot.

u/Affectionate-Dog8414 has started a small repair shop in their garage. Denver, CO

• Overview of Research History and Commercial Development:

  • The research group's work extends over 60 years, difficult to condense into a short talk.
  • "Processes of commercial product development" are well-known, but research's purpose is less understood.

• Importance of Research and Key Innovations:

  • Research is vital for foundational innovations; examples include text on screens, interactive text, pointing devices, copy-paste functions, menus, and scroll bars.
  • Early pioneers like Ivan Sutherland and Doug Engelbart in the 60s, and Xerox PARC's Smalltalk in the 70s, introduced groundbreaking concepts in computing.

• Challenges in Research and Development:

  • High costs and limited computing power in early decades delayed commercialization of research.
  • Innovations often took decades to reach commercial viability due to Moore's Law and decreasing hardware costs.

• Examples of Fundamental Research Leading to Industry Transformation:

  • Machine learning, neural networks, and the Internet's development were rooted in research labs.
  • "Neural networks were invented in the 40s by neuroscientists" and later led to modern AI advancements.

• Impact and Future of Research Funding:

  • Public funding in the 60s enabled long-term ambitious projects; today, such projects lack sufficient funding.
  • The absence of funding today could hinder future innovation and technological progress.

• Concept of Bootstrapping Research Environments:

  • Bootstrapping research focuses on creating innovative environments to enhance research effectiveness.
  • Doug Engelbart’s lab aimed to invent tools to improve the lab's own productivity, leading to user interface innovations.

• Research Methods and Dynamic Land:

  • The research group Dynamicland uses space to show context and enable spatial manipulation of ideas.
  • Their work includes creating expansive spatial interfaces beyond traditional screens, using posters and physical objects for programming and interaction.

• Examples of Dynamicland’s Projects:

  • Real Talk: a system where physical objects are programmed and manipulated by hand, fostering visible and tangible computing environments.
  • Dynamicland as a community space where diverse residents collaboratively create and innovate in a shared environment.

• Vision for the Future of Computing:

  • Advocates for computing as ubiquitous infrastructure, accessible and modifiable by everyone, akin to reading and writing.
  • Emphasizes creating environments where people can work together interactively and understand complex systems holistically.

• Final Thoughts:

  • The ultimate goal is for humanity to leverage computation to understand and solve complex problems, with a vision for a future where computing is an integral and accessible part of everyday life for all.

Relevant quotes: - "Processes of commercial product development" are well-known. - "Neural networks were invented in the 40s by neuroscientists." - "Public funding in the 60s enabled long-term ambitious projects." - "Dynamicland uses space to show context and enable spatial manipulation of ideas." - "The ultimate goal is for humanity to leverage computation to understand and solve complex problems."

for - search - google - high resolution addressing of disaggregated text corpus mapped to graph - search results of interest - high resolution addressing of disaggregated text corpus mapped to graph

search - google - high resolution addressing of disaggregated text corpus mapped to graph - https://www.google.com/search?q=high+resolution+addressing+of+disaggregated+text+corpus+mapped+to+graph&oq=high+resolution+addressing+of+disaggregated+text+corpus+mapped+to+graph&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQIRigATIHCAIQIRigAdIBCTMzNjEzajBqN6gCALACAA&sourceid=chrome&ie=UTF-8

to - search results of interest - high resolution addressing of disaggregated text corpus mapped to graph - A New Method for Graph-Based Representation of Text in - The use of a new text representation method to predict book categories based on the analysis of its content resulted in accuracy, precision, recall and an F1- ... - https://hyp.is/H9UAbk46Ee-PT_vokcnTqA/www.mdpi.com/2076-3417/10/12/4081 - Encoding Text Information with Graph Convolutional Networks - According to our understanding, this is the first personality recognition study to model the entire user text information corpus as a heterogeneous graph and ... - https://hyp.is/H9UAbk46Ee-PT_vokcnTqA/www.mdpi.com/2076-3417/10/12/4081

for - from - search - google - high resolution addressing of disaggregated text corpus mapped to graph

from - search - google - high resolution addressing of disaggregated text corpus mapped to graph - https://hyp.is/ch_J9k43Ee-lGzfOapoCvQ/www.google.com/search?q=high+resolution+addressing+of+disaggregated+text+corpus+mapped+to+graph&oq=high+resolution+addressing+of+disaggregated+text+corpus+mapped+to+graph&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQIRigATIHCAIQIRigAdIBCTMzNjEzajBqN6gCALACAA&sourceid=chrome&ie=UTF-8

is this correct? it can be derived from the two wind components?

for - further research - common cliques in graphs - question - relevance to disaggregating text corpus into sub-sentence graph nodes?

for - from - search - google - high resolution addressing of disaggregated text corpus mapped to graph

from - search - google - high resolution addressing of disaggregated text corpus mapped to graph - https://hyp.is/ch_J9k43Ee-lGzfOapoCvQ/www.google.com/search?q=high+resolution+addressing+of+disaggregated+text+corpus+mapped+to+graph&oq=high+resolution+addressing+of+disaggregated+text+corpus+mapped+to+graph&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQIRigATIHCAIQIRigAdIBCTMzNjEzajBqN6gCALACAA&sourceid=chrome&ie=UTF-8

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

I didn't know this was a myth, I've seen so many of these where celebs would take, or where it would be used in an investigation. But I understand why it would be a myth since I would also consider myself one of those people who would fall anxious when answering questions. https://tenor.com/view/the-simpsons-lie-detector-yes-x-files-mulder-and-scully-gif-20366565

He misses the point of wisdom. Wisdom is about mindset and uplifting each other, to care and empathize... It's not about objective correctness; truth or false, this is science... Nor is it about the correctness of living life, that is ethics and morality...

Wisdom is thus about mindset and empathy.

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NBdca

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When someone wants to tell you how they are feeling they will tell you with their face. There are many emotions you can tell people by the way your face looks. This is called facial expression. So when your face looks angry it will tell other people who see it that you are angry.

DOI: 10.1016/j.celrep.2021.108892

Resource: RRID:Addgene_12259

Curator: @Naa003

SciCrunch record: RRID:Addgene_12259

What is this?

DOI: 10.1016/j.celrep.2021.108875

Resource: (DSHB Cat# 4F3 anti-discs large, RRID:AB_528203)

Curator: @Naa003

SciCrunch record: RRID:AB_528203

What is this?

DOI: 10.1038/s41419-024-06923-z

Resource: (IZSLER Cat# BS CL 129, RRID:CVCL_0045)

Curator: @evieth

SciCrunch record: RRID:CVCL_0045

What is this?

DOI: 10.1038/s41419-024-06923-z

Resource: (BCRJ Cat# 0125, RRID:CVCL_0367)

Curator: @evieth

SciCrunch record: RRID:CVCL_0367

What is this?

DOI: 10.1038/s41419-024-06923-z

Resource: (DSMZ Cat# ACC-625, RRID:CVCL_1966)

Curator: @evieth

SciCrunch record: RRID:CVCL_1966

What is this?

DOI: 10.1038/s41419-024-06923-z

Resource: (DSMZ Cat# ACC-83, RRID:CVCL_0244)

Curator: @evieth

SciCrunch record: RRID:CVCL_0244

What is this?

DOI: 10.1038/s41419-024-06923-z

Resource: (RRID:CVCL_8277)

Curator: @scibot

SciCrunch record: RRID:CVCL_8277

What is this?

DOI: 10.3390/toxics12020102

Resource: Bloomington Drosophila Stock Center (RRID:SCR_006457)

Curator: @anisehay

SciCrunch record: RRID:SCR_006457

What is this?

DOI: 10.1016/j.celrep.2024.114538

Resource: (BCRC Cat# 60005, RRID:CVCL_0030)

Curator: @vtello

SciCrunch record: RRID:CVCL_0030

What is this?

DOI: 10.1016/j.celrep.2024.114538

Resource: (Rockland Cat# 18-8816-33, RRID:AB_2610848)

Curator: @scibot

SciCrunch record: RRID:AB_2610848

What is this?

DOI: 10.1016/j.celrep.2024.114538

Resource: (Rockland Cat# 18-8817-30, RRID:AB_2610849)

Curator: @scibot

SciCrunch record: RRID:AB_2610849

What is this?

DOI: 10.1016/j.celrep.2024.114538

Resource: (Jackson ImmunoResearch Labs Cat# 111-035-003, RRID:AB_2313567)

Curator: @scibot

SciCrunch record: RRID:AB_2313567

What is this?

DOI: 10.1016/j.celrep.2024.114538

Resource: (Yeasen Biotech Cat# 33201ES60, RRID:AB_10015289)

Curator: @scibot

SciCrunch record: RRID:AB_10015289

What is this?

DOI: 10.1016/j.celrep.2024.114538

Resource: (Molecular Probes Cat# A-31571, RRID:AB_162542)

Curator: @scibot

SciCrunch record: RRID:AB_162542

What is this?

DOI: 10.1016/j.celrep.2024.114538

Resource: (Thermo Fisher Scientific Cat# A-11012, RRID:AB_2534079)

Curator: @scibot

SciCrunch record: RRID:AB_2534079

What is this?

DOI: 10.1016/j.celrep.2024.114538

Resource: (Thermo Fisher Scientific Cat# A-11001, RRID:AB_2534069)

Curator: @scibot

SciCrunch record: RRID:AB_2534069

What is this?

DOI: 10.1016/j.celrep.2024.114538

Resource: (Santa Cruz Biotechnology Cat# sc-73614, RRID:AB_1131294)

Curator: @scibot

SciCrunch record: RRID:AB_1131294

What is this?

DOI: 10.1016/j.celrep.2024.114538

Resource: (ABclonal Cat# A19686, RRID:AB_2862735)

Curator: @scibot

SciCrunch record: RRID:AB_2862735

What is this?

DOI: 10.1016/j.celrep.2024.114538

Resource: (Sigma-Aldrich Cat# T5168, RRID:AB_477579)

Curator: @scibot

SciCrunch record: RRID:AB_477579

What is this?

DOI: 10.1016/j.ccell.2024.06.012

Resource: CVCL_2186

Curator: @vtello

SciCrunch record: RRID:CVCL_2186

What is this?

DOI: 10.1016/j.celrep.2024.114530

Resource: (ATCC Cat# CRL-2700, RRID:CVCL_G654)

Curator: @vtello

SciCrunch record: RRID:CVCL_G654

What is this?

DOI: 10.1016/j.celrep.2024.114530

Resource: (ATCC Cat# CRL-11268, RRID:CVCL_1926)

Curator: @vtello

SciCrunch record: RRID:CVCL_1926

What is this?

DOI: 10.1016/j.celrep.2024.114530

Resource: (RRID:CVCL_D615)

Curator: @vtello

SciCrunch record: RRID:CVCL_D615

What is this?

DOI: 10.1016/j.celrep.2024.114559

Resource: ZFIN_ZDB-GENO-150721-5

Curator: @vtello

SciCrunch record: RRID:ZFIN_ZDB-GENO-150721-5

What is this?

DOI: 10.1016/j.celrep.2024.114559

Resource: ZFIN_ZDB-GENO-980202-1296

Curator: @vtello

SciCrunch record: RRID:ZFIN_ZDB-GENO-980202-1296

What is this?

DOI: 10.1016/j.celrep.2024.114559

Resource: ZFIN_ZDB-GENO-190806-1

Curator: @vtello

SciCrunch record: RRID:ZFIN_ZDB-GENO-190806-1

What is this?

DOI: 10.1016/j.celrep.2024.114559

Resource: ZFIN_ZDB-GENO-141030-2

Curator: @vtello

SciCrunch record: RRID:ZFIN_ZDB-GENO-141030-2

What is this?

DOI: 10.14336/ad.2024.0224

Resource: (Santa Cruz Biotechnology Cat# sc-31984, RRID:AB_2167511)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2167511

What is this?

DOI: 10.1016/j.immuni.2024.07.002

Resource: (RCB Cat# RCB1189, RRID:CVCL_0006)

Curator: @vtello

SciCrunch record: RRID:CVCL_0006

What is this?

DOI: 10.1002/ijc.35098

Resource: (ICLC Cat# HTL06001, RRID:CVCL_1511)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_1511

What is this?

DOI: 10.1101/2024.07.21.604463

Resource: (ATCC Cat# CRL-4001, RRID:CVCL_6573)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_6573

What is this?

DOI: 10.1101/2024.07.22.604482

Resource: (Millipore Cat# 06-863, RRID:AB_310268)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_310268

What is this?

DOI: 10.1101/2024.07.22.604482

Resource: (Cell Signaling Technology Cat# 9198, RRID:AB_2561044)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2561044

What is this?

DOI: 10.1101/2024.07.22.604482

Resource: (Cell Signaling Technology Cat# 3879, RRID:AB_2255011)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2255011

What is this?

DOI: 10.1101/2024.07.22.604482

Resource: (BCRC Cat# 60250, RRID:CVCL_0553)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0553

What is this?

DOI: 10.1101/2024.07.22.604482

Resource: (ECACC Cat# 86082104, RRID:CVCL_0332)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0332

What is this?

DOI: 10.1101/2024.07.22.604482

Resource: (ATCC Cat# CRM-HTB-26, RRID:CVCL_0062)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0062

What is this?

DOI: 10.1139/cjm-2024-0014

Resource: SCR_003070

Curator: @dhovakimyan1

SciCrunch record: RRID: SCR_003070

What is this?

DOI: 10.1101/2024.07.24.604995

Resource: pClamp (RRID:SCR_011323)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_011323

What is this?

DOI: 10.1101/2024.07.19.604274

Resource: (IMSR Cat# JAX_000664,RRID:IMSR_JAX:000664)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:000664

What is this?

DOI: 10.1128/msphere.00254-24

Resource: (KCB Cat# KCB 2010167, RRID:CVCL_0358)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0358

What is this?

DOI: 10.1101/2024.07.24.604946

Resource: (IMSR Cat# JAX_002810,RRID:IMSR_JAX:002810)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:002810

What is this?

DOI: 10.1101/2024.07.24.604946

Resource: (IMSR Cat# JAX_017320,RRID:IMSR_JAX:017320)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:017320

What is this?

DOI: 10.1101/2024.07.24.604946

Resource: (IMSR Cat# JAX_005628,RRID:IMSR_JAX:005628)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:005628

What is this?

DOI: 10.1101/2024.07.25.605169

Resource: Addgene_86244

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_86244

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_91794

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_91794

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_91792

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_91792

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: Addgene_116374

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_116374

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_12259

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_12259

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_12260

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_12260

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_1864

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_1864

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_90007

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_90007

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_90005

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_90005

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: Addgene_90006

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_90006

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_31355

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_31355

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: RRID:Addgene_31354

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_31354

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: Addgene_31353

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_31353

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: Addgene_32886

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_32886

What is this?

DOI: 10.1101/2024.07.25.605008

Resource: Addgene_31352

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_31352

What is this?

DOI: 10.1186/s13048-024-01479-0

Resource: (ATCC Cat# HTB-161, RRID:CVCL_0465)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0465

What is this?

DOI: 10.3390/antiox13070855

Resource: (Cell Signaling Technology Cat# 7074, RRID:AB_2099233)

Curator: @scibot

SciCrunch record: RRID:AB_2099233

What is this?

DOI: 10.3390/antiox13070855

Resource: (Cell Signaling Technology Cat# 7076, RRID:AB_330924)

Curator: @scibot

SciCrunch record: RRID:AB_330924

What is this?

DOI: 10.3390/antiox13070855

Resource: (Cell Signaling Technology Cat# 8690, RRID:AB_10999090)

Curator: @scibot

SciCrunch record: RRID:AB_10999090

What is this?

DOI: 10.3390/antiox13070855

Resource: (Cell Signaling Technology Cat# 9215, RRID:AB_331762)

Curator: @scibot

SciCrunch record: RRID:AB_331762

What is this?

DOI: 10.3390/antiox13070855

Resource: (Cell Signaling Technology Cat# 9102, RRID:AB_330744)

Curator: @scibot

SciCrunch record: RRID:AB_330744

What is this?

DOI: 10.3390/antiox13070855

Resource: (Cell Signaling Technology Cat# 4370, RRID:AB_2315112)

Curator: @scibot

SciCrunch record: RRID:AB_2315112

What is this?

DOI: 10.3390/antiox13070855

Resource: (Cell Signaling Technology Cat# 9252, RRID:AB_2250373)

Curator: @scibot

SciCrunch record: RRID:AB_2250373

What is this?

DOI: 10.3390/antiox13070855

Resource: (Thermo Fisher Scientific Cat# A-11001, RRID:AB_2534069)

Curator: @scibot

SciCrunch record: RRID:AB_2534069

What is this?

DOI: 10.3390/antiox13070855

Resource: (Thermo Fisher Scientific Cat# A-11073, RRID:AB_2534117)

Curator: @scibot

SciCrunch record: RRID:AB_2534117

What is this?

DOI: 10.3390/antiox13070855

Resource: (Thermo Fisher Scientific Cat# A-11012, RRID:AB_2534079)

Curator: @scibot

SciCrunch record: RRID:AB_2534079

What is this?

DOI: 10.3390/antiox13070855

Resource: (Sigma-Aldrich Cat# S5768, RRID:AB_477523)

Curator: @scibot

SciCrunch record: RRID:AB_477523

What is this?

DOI: 10.3390/antiox13070855

Resource: (Millipore Cat# AB2253, RRID:AB_1586992)

Curator: @scibot

SciCrunch record: RRID:AB_1586992

What is this?

DOI: 10.3390/antiox13070855

Resource: (Abcam Cat# ab104225, RRID:AB_10711153)

Curator: @scibot

SciCrunch record: RRID:AB_10711153

What is this?

DOI: 10.3390/antiox13070855

Resource: (Wako Cat# 019-19741, RRID:AB_839504)

Curator: @scibot

SciCrunch record: RRID:AB_839504

What is this?

DOI: 10.1091/mbc.E23-11-0421

Resource: RRID:Addgene_61838

Curator: @scibot

SciCrunch record: RRID:Addgene_61838

What is this?

DOI: 10.1091/mbc.E23-11-0421

Resource: Addgene_162276

Curator: @scibot

SciCrunch record: RRID:Addgene_162276

What is this?

DOI: 10.1091/mbc.E24-02-0093

Resource: RRID:Addgene_41393

Curator: @scibot

SciCrunch record: RRID:Addgene_41393

What is this?

DOI: 10.1186/s12974-024-03182-9

Resource: Colorado State University Laboratory Animal Resources Core Facility (RRID:SCR_022157)

Curator: @scibot

SciCrunch record: RRID:SCR_022157

What is this?

DOI: 10.1002/ctm2.1758

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @scibot

SciCrunch record: RRID:SCR_002798

What is this?

DOI: 10.1002/ctm2.1758

Resource: clusterProfiler (RRID:SCR_016884)

Curator: @scibot

SciCrunch record: RRID:SCR_016884

What is this?

DOI: 10.1002/ctm2.1758

Resource: IPython (RRID:SCR_001658)

Curator: @scibot

SciCrunch record: RRID:SCR_001658

What is this?

DOI: 10.1002/ctm2.1758

Resource: Addgene_188492

Curator: @scibot

SciCrunch record: RRID:Addgene_188492

What is this?

DOI: 10.1002/ctm2.1758

Resource: RRID:Addgene_11795

Curator: @scibot

SciCrunch record: RRID:Addgene_11795

What is this?

DOI: 10.1002/ctm2.1758

Resource: CRISPOR (RRID:SCR_015935)

Curator: @scibot

SciCrunch record: RRID:SCR_015935

What is this?

DOI: 10.1083/jcb.202308083

Resource: (LI-COR Biosciences Cat# 926-68073, RRID:AB_10954442)

Curator: @scibot

SciCrunch record: RRID:AB_10954442

What is this?

DOI: 10.1083/jcb.202308083

Resource: (LI-COR Biosciences Cat# 926-32212, RRID:AB_621847)

Curator: @scibot

SciCrunch record: RRID:AB_621847

What is this?

DOI: 10.1083/jcb.202308083

Resource: (Santa Cruz Biotechnology Cat# sc-1616, RRID:AB_630836)

Curator: @scibot

SciCrunch record: RRID:AB_630836

What is this?

DOI: 10.1083/jcb.202308083

Resource: (Millipore Cat# MABE343, RRID:AB_2566826)

Curator: @scibot

SciCrunch record: RRID:AB_2566826

What is this?

DOI: 10.1083/jcb.202308083

Resource: (Roche Cat# 11814460001, RRID:AB_390913)

Curator: @scibot

SciCrunch record: RRID:AB_390913

What is this?