278 Matching Annotations
  1. Last 7 days
  2. Dec 2020
    1. The more I think about this, the more I think that using the context API (for all the stores — page, preloading and session) is the most regret-proof approach, using the proposal above

      Looks like this is the approach that they went with

    1. Just realised this doesn't actually work. If store is just something exported by the app, there's no way to prevent leakage. Instead, it needs to be tied to rendering, which means we need to use the context API. Sapper needs to provide a top level component that sets the store as context for the rest of the app. You would therefore only be able to access it during initialisation, which means you couldn't do it inside a setTimeout and get someone else's session by accident:
    1. mars. One parameter, whose positive value is instantiated in languages with rich agreement such as Italian and Spanish, is the Null Pronoun Parameter, in which the null subject (i.e., pro) is identified through agreement. Another parameter, whose positive value is instantiated in languages without agreement such as Chinese and Japanese, is the Discourse Oriented Parameter (DOP). Such discourse-oriented languages allow null arguments in both subject and object positions, and the null argument is identified by a discourse topic or through co-indexation with a c-commanding nominal. A language such as English instantiates the negative value with respect to both the DOP and the Null Pronoun Parame

      Examples of situations in which adults can omit subjects.

    1. According to the best estimates from the Centers for Disease Control and Prevention, 99.997 percent of individuals aged 19 and younger who contract coronavirus make a full recovery, 99.98 percent of those aged 20 to 49 make a full recovery, and 99.5 percent aged 50 to 69 fully recover.

      The takeaway: >99% of people age 0-69 infected with SARS-CoV-2 survive COVID based on the CDC's current best estimate of infection fatality ratio. A subset of those infected will suffer from continued symptoms even though they did not die from COVID.

      The claim: Greater than 99% of people age 0-69 fully recover from COVID-19.

      The evidence: This numbers align with the CDC's current best estimate of the infection fatality ratio (1). Infection fatality ratio is the number of people that die from a disease divided by the number of people who get the disease. These numbers do not account for people with symptoms such as lung damage, chronic fatigue, and mental illness which may follow a COVID infection (2, 3).

      In a study of 143 hospitalized patients from Italy after an average of 60.3 days, only 12.6% were symptom free (4). Per Mayo Clinic guidelines, long term effects can occur in those with mild symptoms but most often occur in severe cases (5). Mental health problems were diagnosed 14-90 days after COVID in 18.1% of COVID patients studied (3).

      A more accurate estimate of the number of people that fully recover may be obtained if the number of people who recovered without hospitalization is used. The numbers presented are the CDC's current best estimate of the number of people that survive COVID not the number of people that fully recover.


      1) https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html

      2) https://www.nature.com/articles/d41586-020-02598-6

      3) https://www.thelancet.com/journals/lanpsy/article/PIIS2215-0366(20)30462-4/fulltext

      4) https://jamanetwork.com/journals/jama/fullarticle/2768351/

      5) https://www.mayoclinic.org/diseases-conditions/coronavirus/in-depth/coronavirus-long-term-effects/art-20490351

    1. Clusters of infections within families living in Bnei Brak were identified and investigated. The parents were asked regarding the first case of the infection in the family and regarding the pre-sumed source of the infection.In addition, household members underwent polymerase chain reaction (PCR) testing whether they were symptomatic or not

      testing regime: exhaustive (whether symptomatic or not)

    2. Thirteen family clusters were investigated; all families reside in the city of Bnei Brak.

      N=13 families

    3. Mayenei Hayeshuah Medical center is located in the city of Bnei Brak, Central Israel.Bnei Brak is a “young” city. Children of 0–19 years of age comprise almost 50% of its 200,000 population, and the average number of children in a family is 4.57

      setting: central Israel

    4. SARS-CoV-2 positive PCR was documented in the different age groups as follows:1. In 21 of 36 adults (>18 years) (58.3%).2. In 13 of 40 children, 5–17 years (32.5%), (P = 0.037 for the difference between group 1 and group 2, risk ratio: 0.61, 95% confidence interval [CI]: 0.39–0.96).3. In 2 of 18 children, 0–4 (<5) years of age (11.8%), (P < 0.002 for the difference between group 1 and group 3, risk ratio: 0.47, 95% CI: 0.30–0.71)

      main result: children ~half as likely to get infected given equivalent exposure in same household

  3. Nov 2020
    1. The recommendation to wear surgical masks to supplement other public health measures did not reduce the SARS-CoV-2 infection rate among wearers by more than 50% in a community with modest infection rates, some degree of social distancing, and uncommon general mask use.

      The takeaway: While minimal protection occurs when a mask is worn in a place where many others are not wearing a mask, community masking is associated with a reduction in COVID cases.

      The claim: In a community with modest infection rates, some social distancing, and most people not wearing masks, wearing a surgical mask did not reduce the SARS-CoV-2 infection rate by more than 50%.

      The evidence: This study showed that wearing a mask in a community where most people did not wear a mask, did not reduce the risk of getting infected by 50%. Fewer COVID infections were reported in the mask group than in the unmasked group. This study agrees with a meta analysis which showed that masks resulted in a decrease in infections but did not prevent all infections (1) According to the CDC, seven studies have shown community level benefit when masking recommendations were made (2).

      When most in the community are not wearing masks, social distancing, and washing hands, wearing a mask alone provides minimal protection to the mask wearer. Community wide masking is associated with a reduction in COVID cases (2).


      1) https://pubmed.ncbi.nlm.nih.gov/29140516/

      2) https://www.cdc.gov/coronavirus/2019-ncov/more/masking-science-sars-cov2.html

    1. Gov. Kristi Noem defended her hands-off approach to managing the deadly COVID-19 pandemic while addressing lawmakers earlier this week and called mandatory stay-at home orders "useless" in helping lower the spread.

      Take away: Lower COVID-19 spread occurred after stay-at home orders were issued. Room for debate exists on how restrictive lockdowns should be.

      The claim: Mandatory stay-at home orders are "useless" in helping lower the spread of SARS-CoV-2.

      The evidence: Two publications showed that lower COVID-19 spread occurred after stay-at home orders were issued (1, 2). Hospitalizations were lower than predicted exponential growth rates after implementation of stay-at home orders (3). Some caveats to consider include that it is impossible to tease apart the effects of the stay-at home orders from other measure implemented simultaneously with stay-at home orders such as increased hygiene measures, social distancing guidelines, and school closures. It is also impossible to conclusively state that the effect is from the stay-at home order and not the natural progression of the disease.

      The comparison between Illinois with stay-at home orders and Iowa without stay-at home orders resulted in an estimated 217 additional COVID-19 cases in Iowa over the course of a month (2). This small number raises the question, "are stay-at home orders worth it?" It is important to remember that comparison of Iowa and Illinois is the comparison of two social distancing strategies. Stay-at home orders close everything and then write the exceptions that can remain open. Iowa took the approach of leaving everything open except what the government choose to close (4). Some businesses in Iowa were still closed and many federal guidelines were still followed. A negative control showing disease progression without any mitigation measures does not exist in published literature.


      1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7246016/

      2 https://pubmed.ncbi.nlm.nih.gov/32413112/

      3 https://www.desmoinesregister.com/story/news/2020/04/07/iowa-equivalent-stay-at-home-order-coronavirus-kim-reynolds/2961810001/

      4 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7254451/

    1. Schleiss says a better analogy for COVID-19 is the mumps. For more than 45 years, we’ve had a very effective vaccine for measles, mumps, and rubella (which are also RNA viruses).

      The takeaway: Even though mutations happen in all virus, vaccines still work. Current evidence about SARS-CoV-2 indicates that an effective COVID-19 vaccine can be obtained, and that it should be able to provide immunity against the virus.

      The claim: A better analogy for COVID-19 is the mumps. For more than 45 years, we’ve had a very effective vaccine for measles, mumps, and rubella (which are also RNA viruses).

      The evidence: We are all imperfect and we all make mistakes. For a virus, a mistake means the introduction of a mutation in its sequence, and RNA viruses (like the flu, mumps, measles virus, and SARS-CoV-2) have the highest error rates in nature. Mutations are indispensable for viral survival and evolution; this property is believed to benefit the viral population, allowing it to adapt and respond to different complex environments encountered during spread between hosts, within organs and tissues, and in response to the pressure of the host immune response [1]. How fast a virus is changing can be estimated by measuring its mutation rate, and then they can be classified as changing fast – high mutation rate – like HIV or Influenza, or as stable, like measles or mumps virus. SARS-CoV-2 has a mutation rate three times slower than the flu virus [2], but it's still changing faster than the mumps virus (the mutation rate of influenza is more than 10 times higher than mumps) [3]. Of course, how fast a virus can change has implications in the efficacy of treatments and vaccines, but it's not the only determinant. Even though mutations happen in all viruses, vaccines still work. A great example is the measles virus, as the antigenic composition of the vaccine (the molecules that “wake up” the immune system) used to prevent it has remained efficient since it was developed, in the 1960s, and confers protection against the 24 circulating genotypes [4]. The same is true for the mumps virus, with a vaccine that has been efficient for many decades [5]. Sequencing data suggest that coronaviruses change more slowly than most other RNA viruses, probably because of a viral ‘proofreading’ activity that corrects all the copying mistakes [6]. Taken together, all this evidence indicates that an effective COVID-19 vaccine can be obtained, and that it should be able to provide lasting immunity against the virus.

      Sources:<br> 1

      2 SARS-CoV-2 mutation rate: 1.26 x 10-3 substitutions/site/year

      3 Influenza (flu-virus) mutation rate: 3.68 x 10-3 substitutions/site/year. Mumps mutation rate: 2.98 × 10−4 substitutions/site/year




    1. Anxiety From Reactions to Covid-19 Will Destroy At Least Seven Times More Years of Life Than Can Be Saved by Lockdowns

      Take away: Though the number of COVID deaths prevented and the exact number of years lost due directly to decreases in mental health from lockdowns is at best a rough estimate, several facts are known. Lockdowns decrease mental health, and a decrease in mental health shortens lives too.

      The claim: Anxiety from reactions to COVID-19 will destroy at least seven times more years of life than can be saved by lockdowns.

      The evidence: This article references many studies detailing the anxiety surrounding COVID-19 (1-4). These studies indicate that many people have increased stress due to COVID. Nature Public Health Emergency Collection reports that the mental health cost of widespread lockdowns may negate the lives saved by this policy (5). This article lists many articles which describe the effect of stay-at-home orders on mental health. Additionally, the effect of poor mental health on physical outcomes is well-defined. Poor mental health shortens lives. Other factors with COVID such as negative media coverage and dealing with job loss and death are also described as negatively affecting mental health. It is unclear how much of the negative mental health outcomes is directly related to lockdowns and what is contributed to the disease, job loss, future uncertainty, and continuous media coverage.

      Several supporting facts used in this article are now outdated or could use clarification. Many assumptions are detailed in this article to estimate the number of years lost due to mental harm caused by lockdowns. One example is the authors used a survey of 1,266 patients to estimate the number of people in the United States who have suffered mental harm from lockdowns. These estimates are challenging to conclusively verify. The authors did choose the conservative estimate for each of their numbers. One example of an outdated number is the predicted number of deaths was 114,228 by August 4th. The actual number of deaths per Johns Hopkins was 157,500 (6).

      Based on the facts, anxiety and mental disorders can be deadly. Lockdowns result in an increase in poor mental health. The exact number of years lost due to poor mental health directly resulting from lockdowns is less clear. Poor mental health may also result from constant media coverage, loss of loved ones and fear of the future.

      The sources:

      1) https://www.psychiatry.org/newsroom/news-releases/new-poll-covid-19-impacting-mental-well-being-americans-feeling-anxious-especially-for-loved-ones-older-adults-are-less-anxious

      2) https://www.kff.org/health-reform/report/kff-health-tracking-poll-early-april-2020/

      3) https://www.bsgco.com/post/coronavirus-and-americans-mental-health-insights-from-bsg-s-pulse-of-america-poll

      4) https://www.kff.org/report-section/kff-health-tracking-poll-late-april-2020-economic-and-mental-health-impacts-of-coronavirus/

      5) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431738/#

      6) https://coronavirus.jhu.edu/us-map

    1. This module should not be used in other npm modules since it modifies the default require behavior! It is designed to be used for development of final projects i.e. web-sites, applications etc.
    1. The first benefit of working this way is that you become interruption-proof. Because you rarely even attempt to load the entire project into your mind all at once, there’s not much to “unload” if someone interrupts you. It’s much easier to pick up where you left off, because you’re not trying to juggle all the work-in-process in your head.

      The intermittent packet approach makes you more resilient towards interruptions

      Because you're not loading an entire project in your mind at once, you're not losing as much context when you get interrupted.

    1. On every measure — new infections, hospitalizations, and deaths — the U.S. is headed in the wrong direction

      The takeaway: Though COVID-19 cases are at a record high, the number of deaths from COVID-19 has not followed the steep rise in cases. An increase in the number of deaths may be reported later as deaths lag cases by several weeks.

      The claim: On every measure - new infections, hospitalizations, and deaths - the U.S. is headed in the wrong direction.

      The evidence: New COVID infections in the US are the highest they have ever been with a 7-day moving average of 104,417 cases/day (1). The number of deaths in the US is similar to the number of deaths in August, lower than the number of deaths in the spring and higher than the number of deaths in the summer (2). A slight increase was seen in the number of deaths for the first two weeks in October followed by a slight decline which may change as more data is added (3). The number of emergency department visits for coronavirus like symptoms is on an upward trajectory nationwide (4). The CDC states "At least one indicator used to monitor COVID-19 activity is increasing in each of the ten HHS regions, and many regions are reporting increases in multiple indicators" (3).

      Though COVID-19 cases are at a record high, the number of deaths from COVID-19 has not followed the steep rise in cases. An increase in the number of deaths may be reported later as deaths lag cases by several weeks.


      1) https://covid.cdc.gov/covid-data-tracker/#trends_dailytrendscases

      2) https://covid.cdc.gov/covid-data-tracker/#trends_dailytrendsdeaths

      3) https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

      4) https://covid.cdc.gov/covid-data-tracker/#ed-visits

    1. We have designed a dimeric lipopeptide fusion inhibitor that blocks this critical first step of infection for emerging coronaviruses and document that it completely prevents SARS-CoV-2 infection in ferrets.

      The takeaway: Dimeric lipopeptide fusion inhibitor prevented SARS-CoV-2 infection in all six ferrets tested. Much more work is needed before this could be used in humans.

      The claim: Treatment of ferrets with a dimeric lipopeptide fusion inhibitor completely prevents SARS-CoV-2 infection in ferrets.

      The evidence: Per Figure 3, SARS-CoV-2 was detected in all three animals inoculated with the virus, all six animals treated with a placebo, and none of the animals treated with the dimeric lipopeptide fusion inhibitor (1). Animals treated with dimeric lipopeptide fusion inhibitor did not mount an immune response to SARS-CoV-2 while an immune response was seen in inoculated animals and placebo treated animals (Figure 4).

      More research is needed before this treatment can be used in humans. This preliminary study showed that in a small sample of animals which do not typically show COVID symptoms, SARS-CoV-2 infection was blocked by the dimeric lipopeptide fusion inhibitor. This paper describes the first step in a long journey. Before a new treatment is approved for use in humans, Phase I, II and III clinical trials must be completed (2) which includes showing that a treatment does no harm to healthy humans and proving that it works in humans. This work also needs peer-review in a published journal which may occur with time.


      1) https://www.biorxiv.org/content/10.1101/2020.11.04.361154v1.full.pdf

      2) https://www.fda.gov/patients/drug-development-process/step-3-clinical-research

    1. The coronavirus pandemic is expected to take the U.S. national debt to levels not seen since World War II.

      The takeaway: The debt to GDP ratio after coronavirus relief spending is higher than it has ever been.

      The claim: The coronavirus pandemic is expected to take the U.S. national debt to levels not seen since World War II.

      The evidence: A number of COVID-19 spending acts and executive orders include: Coronavirus Preparedness and Response Supplemental Appropriations Act, 2020, Families First Coronavirus Response Act (FFCRA), CARES Act, Paycheck Protection Program and Health Care Enhancement Act, and President Trump's Executive Actions (1). Prior to these bills and executive actions, the fiscal year 2020 federal deficit was predicted to be $3.1 trillion (1). The total cost of the coronavirus relief measures is $2,607,000,000,000 (1). The debt to GDP ratio in 2020 at the end of quarter 2 is 136% (2). The debt had previously peaked in 1946 after WWII at 118% debt to GDP ratio (2).


      1) https://www.forbes.com/sites/robertberger/2020/10/18/5-big-numbers-reveal-the-unsettling-scope-of-stimulus-spending/?sh=26ae8057142b

      2) https://www.thebalance.com/national-debt-by-year-compared-to-gdp-and-major-events-3306287

  4. Oct 2020
    1. Take away: Even though mini-lungs (and mini-organs) are extremely valuable tools for scientist to study disease and prospective therapeutics, results obtained with these models are hardly generalizable and normally need to be validated in animal models and clinical studies.

      The claim: Based on our model we can tackle many unanswered key questions, such as understanding genetic susceptibility to SARS-CoV-2, assessing relative infectivity of viral mutants, and revealing the damage processes of the virus in human alveolar cells. Most importantly, it provides the opportunity to develop and screen potential therapeutic agents against SARS-CoV-2 infection.

      The evidence: Regardless of their name, mini-organs are hardly real miniature organs, these clumps of cells resemble organs in many ways, but they lack certain features that allow real organs to function and grow. For now, mini-organs don’t develop beyond tiny and simplistic models of organs, and remain hard to produce in the large, consistent batches needed for drug screening and other efforts. But, in spite of their limitations, they still are a giant step up from 2D cultures of cells that scientists have long grown in the lab. In particular, studies of SARS-CoV-2 in mini-organs have limitations because they do not reflect the crosstalk between organs and systems that happens in the body. Here for example, the mini-organs do not produce the full cellular spectrum present in the adult alveoli. Also, the mini-lungs in this study cannot mimic an interaction with the immune system, which likely influences how the disease develops. Some groups are beginning to test existing drugs against SARS-CoV-2 in mini-organs in a small scale, but we will only know at the end of this process what the predictive value of these systems are for testing drug efficacy.

      Source: https://www.cell.com/cell-stem-cell/fulltext/S1934-5909(20)30498-7 https://www.nature.com/articles/ncb3312 https://www.biorxiv.org/content/10.1101/2020.06.10.144816v1 https://www.biorxiv.org/content/10.1101/2020.05.25.115600v2

    1. We find that COVID-19 has likely become the leading cause of death (surpassing unintentional overdoses) among young adults aged 25-44 in some areas of the United States during substantial COVID-19 outbreaks.

      The takeaway: During the peak of infections during large outbreaks, COVID-19 deaths in age group 25-44 is higher than drug overdose deaths.

      The claim: COVID-19 has likely become the leading cause of death in age group 25-44.

      The evidence: This article compares COVID-19 deaths to opioid deaths during 2018. When the hardest hit areas are combined and areas not hit are excluded, the number of COVID-19 deaths is five deaths more than the opioid deaths during the same period in 2018. Unintentional injuries are the leading cause of death in the age group 25-44 (1-2). In 2018, opioid overdose resulted in 24,253 deaths in the age group of 25-44 in the United States (3). Transportation fatal injuries for the age group 25-44 in 2018 was 12,904 (4). In 2020, deaths from all causes for age group 25-44 is 124,736 with 5,911 directly attributable to COVID-19 (5, accessed 10/28/2020).

      COVID-19 was briefly the leading cause of death in the hardest hit areas during the peak of the epidemic for age group 25-44 if unintentional injuries is broken into subcategories.

      Sources: 1 https://www.cdc.gov/injury/wisqars/animated-leading-causes.html

      2 https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_06-508.pdf

      3 https://www.cdc.gov/mmwr/volumes/69/wr/mm6911a4.ht m

      4 https://webappa.cdc.gov/sasweb/ncipc/mortrate.html

      5 https://www.cdc.gov/nchs/nvss/vsrr/covid_weekly/index.htm

    1. The idea of the hermeneutic circle is to envision a whole in terms how the parts interact with each other, and how they interact with the whole. That may sound a little bit out there, so let’s have a look at a concrete example.

      This is a general concept, the rest of the article extrapolates the idea to the act of reading. This may be a stretch, since it implies that whatever can be broken into parts will belong to the hermeneutic circle, while this only applies to interpreting (text)

    2. As objective you may try to be, interpreting a text doesn’t happen in a vacuum. The hermeneutic circle captures the complex interaction between an interpreter and a text.

      This is the only useful idea in the text. Whatever we read has the context in which it was written and the context in whcih it is being read. Is this a hermeneutic circle as described earlier? Don't think so.

    1. The events did not seem to trigger spikes in infections

      The takeaway: An increase in COVID-19 infections occurred nationwide in the time following protests. Due to simultaneous occurrence of non-uniform lifting of stay-at home orders, Memorial Day, and Black Lives Matter protests, it is not possible to conclusively determine the exact cause of the nationwide COVID-19 case spike after June 9, 2020.

      The claim: Black Lives Matter protests did not seem to trigger a spike of COVID-19 infections.

      The evidence: This statement is based on an article written in IZA Institute of Labor Economics discussion paper series (1). The article, titled “Black Lives Matter Protests, Social Distancing, and COVID-19” states that overall, stay-at home orders were better followed during protests based on cell phone data. Yet it still shows a steady increase in COVID-19 cases (Figure 6, 1). Additionally, The data from this report stops after June 9th while riots continued and COVID-19 cases across the country spiked (2, 3). As other factors such as Memorial Day weekend, and opening of economies occurred in a non-uniform fashion during the same time as protests, it is not possible to determine the exact cause of the nationwide spike in COVID-19 cases.

      The abstract of the IZA report was updated August 2020 to read: "We conclude that predictions of population-level spikes in COVID-19 cases from Black Lives Matter protests were too narrowly conceived because of failure to account for non-participants’ behavioral responses to large gatherings." (4). The non-participant response was explained by this statement in the abstract: "Event-study analyses provide strong evidence that net stay-at-home behavior increased following protest onset." To put this in plain language: non-protestors stayed home more during protests which resulted in a steady increase in COVID-19 instead of a spike. The effect of mask wearing by protestors was not mentioned in the report.

      Only anecdotal evidence and one small study (20 participants) were found showing protestors wearing masks (5-9). No scientific publications with the direct effect of the masks on the spread of COVID-19 during protests were found.

      Valentine et al examined eight cities with tens of thousands of protestors (1, 10). Cities were chosen which had economies open at least 30 days prior to the protests to control for an expected spike when economies open. They found that six out of eight cities examined had significant abnormal positive growth of COVID-19 infection rate following the Black Lives Matter protests (10). All cities studied had abnormal positive infection rate growth.

      Protests resulted in abnormal positive infection growth rates in all eight cities with stay at home orders lifted for at least 30 days prior to protests (10). A spike in COVID-19 cases nationwide happened after June 9th (3). Due to simultaneous occurrence of non-uniform lifting of stay-at home orders, Memorial Day, and Black Lives Matter protests, it is not possible to conclusively determine the exact cause of the nationwide COVID-19 case spike after June 9, 2020.


      1 http://ftp.iza.org/dp13388.pdf

      2 https://www.theguardian.com/us-news/2020/jun/07/george-floyd-protests-enter-third-week

      3 https://covid.cdc.gov/covid-data-tracker/#trends_dailytrendscases

      4 https://www.nber.org/papers/w27408

      5 https://www.npr.org/sections/coronavirus-live-updates/2020/06/24/883017035/what-contact-tracing-may-tell-about-cluster-spread-of-the-coronavirus

      6 https://www.vox.com/2020/6/26/21300636/coronavirus-pandemic-black-lives-matter-protests

      7 https://news.northeastern.edu/2020/08/11/racial-justice-protests-were-not-a-major-cause-of-covid-19-infection-surges-new-national-study-finds/

      8 https://www.geekwire.com/2020/testing-shows-no-big-spike-covid-19-infections-due-protests-wear-mask/

      9 https://assets.researchsquare.com/files/rs-68862/v1/79db6827-52c3-4e94-afa0-679d15a89049.pdf

      10 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7454741/

    1. Encoding is dependent on the type of output - which means that for example a string, which will be used in a JavaScript variable, should be treated (encoded) differently than a string which will be used in plain HTML.
    1. trusktr herman willems • 2 years ago Haha. Maybe React should focus on a template-string syntax and follow standards (and provide options for pre-compiling in Webpack, etc).

      Well anywho, there's other projects now like hyperHTML, lit-html, etc, plus some really fast ones: https://www.stefankrause.ne...

      React seems a little old now (and the new Hooks API is also resource heavy).

      • Share ›  Michael Calkins trusktr • 4 years ago • edited That's a micro optimization. There isn't a big enough difference to matter unless you are building a game or something extraordinarily odd.

      • Share › −  trusktr Michael Calkins • 2 years ago True, it matters if you're re-rendering the template at 60fps (f.e. for animations, or for games). If you're just changing views one time (f.e. a URL route change), then 100ms won't hurt at all.

    1. Wouldn't it make more sense contextually to put this in the Sapper docs site; https://sapper.svelte.dev? We could also have a repo for the more complex examples but then it would need to be maintained etc.
    1. Context can only store a single value, not an indefinite set of values each with its own consumers.
    1. The architecture of the platform where I published allowed authorial control of content but could not control context collapse or social interactions.

      These are pieces which the IndieWeb should endeavor to experiment in and attempt to fix. Though I will admit that pieces of the IndieWeb layers on top of platforms like WordPress can help to mitigate some context collapse and aggregate social interactions better. (eg: reply context and POSSE)

    1. On my blog it has context. You can see all the other eat/drink posts on thier own or mixed in with everything else. I can include links to the place where I bought it, who makes it, or related posts.Instagram's context is its a photo with an optional description. It doesn't matter what it's of. It won't contain links to anything.
    1. The author and literary critic Sam Anderson has written: “Twitter is basically electronic marginalia on everything in the world: jokes, sports, revolutions.”

      I like their idea about Twitter being an annotation tool and to some extent it is, and a good one at that. However, we still need to address the distribution mechanism and the fact that Tweets like this are often bereft of context and cause context collapse.

      Quote tweets and dunking mechanisms would be interesting to study in this context, particularly in a world where people often delete tweets (dunked or not) which means the original context is gone or missing and we're only left with an orphaned annotation.

      Other cultural examples of missing context include commentary for live sporting or cultural events like the Super Bowl, World Series, World Cup Soccer, or the Academy Awards. Watchers will comment on something in real time (often even without an identifying or contextualizing hashtag, eg: #Oscars19), supposing an implied context from their audience, but later generations will be at odds to find or re-complete the original context.

    2. Social media has come to define an era in which we annotate texts every day, we easily share this commentary across contexts, and in doing so we iteratively define who we are.

      But are we also sometimes falsely defining ourselves because of context collapse within these structures?

      Isn't context collapse a root cause of a lot of the toxicity of our communications within platforms like Twitter and Facebook?

    1. Incidentally, teens and twenty-somethings, more so than the middle-aged and elderly, tend to juggle more identities. In middle and high school, kids have to maintain an identity among classmates at school, then another identity at home with family. Twenty-somethings craft one identity among coworkers during the day, then another among their friends outside of work. Often those spheres have differing status games, and there is some penalty to merging those identities. Anyone who has ever sent a text meant for their schoolmates to their parents, or emailed a boss or coworker something meant for their happy hour crew knows the treacherous nature of context collapse.
    1. If you look long enough you can find my early terrible writing. You can find blog posts in which I am an idiot. I’ve had a lot of uninformed and passionate opinions on geopolitical issues from Ireland to Israel. You can find tweets I thought were witty, but think are stupid now. You can find opinions I still hold that you disagree with. I’m going to leave most of that stuff up. In doing so, I’m telling you that you have to look for context if you are seeking to understand me. You don’t have to try, I’m not particularly important, but I am complicated. When I die, I’m going to instruct my executors to burn nothing. Leave the crap there, because it’s part of my journey, and that journey has a value. People who came from where I did, and who were given the thoughts I was given, should know that the future can be different from the past.
    1. A scientific review of the science behind lockdown concludes the policy was a MISTAKE & will have caused MORE deaths from Covid-19

      Take Away: The new scientific paper confirms earlier modeling work and should not be interpreted as a detailed prediction for future deaths due to the ongoing pandemic.

      The Claim: "A scientific review of the science behind lockdown concludes the policy was a MISTAKE & will have caused MORE deaths from Covid-19"

      The Evidence: The scientific process involves replication and confirmation of experiments and studies. A new paper replicates and expands on an early modeling study of the COVID-19 pandemic in England (1). Their findings support the earlier results. However, there are limitations to the replication paper, which does not accurately reflect the current state of the pandemic response and does not make detailed predictions for a second wave of infections and deaths.

      A recent expert response to the paper further explains (2):

      "It needs to be stressed that all the simulations assume that interventions are only in place for 3 months (18th April – 18th July) and then completely relaxed. This gives rise to a strange set of scenarios where a second wave is allowed to progress in an uncontrolled manner."

      “It is this that leads to the counter-intuitive headline finding “that school closures would result in more overall covid-19 deaths than no school closures” – actually what the authors find is that a short period of intense lock-down (including the closure of schools) leads to a large second wave if it is allowed to run with no controls. To be fair the authors do highlight this in the paper, but it is not in the reported press release." -Prof Matt Keeling, Professor of Populations and Disease, University of Warwick


      (1) https://www.bmj.com/content/371/bmj.m3588

      (2) https://www.sciencemediacentre.org/expert-reaction-to-reanalysis-of-model-used-for-imperial-report-9-and-impact-of-school-closures/

    1. The model predicted that school closures and isolation of younger people would increase the total number of deaths, albeit postponed to a second and subsequent waves. The findings of this study suggest that prompt interventions were shown to be highly effective at reducing peak demand for intensive care unit (ICU) beds but also prolong the epidemic, in some cases resulting in more deaths long term. This happens because covid-19 related mortality is highly skewed towards older age groups. In the absence of an effective vaccination programme, none of the proposed mitigation strategies in the UK would reduce the predicted total number of deaths below 200 000.

      Take away: This model excludes the possibility of vaccination. As many vaccines are in stage three clinical trials, the conclusion that more people will die from closing schools, etc. will most likely not be realized.

      The claim: School closures and isolation of younger people will increase total number of deaths from second and subsequent waves of COVID-19 when restrictions are lifted.

      The evidence: This model predicts more deaths from the combination of place closures such as schools, case isolations, household quarantine, and social distancing of over 70s than for the combination of case isolation, household quarantine, and social distancing for over 70s. The majority of the deaths for the combination of place closures, case isolations, household quarantine, and social distancing of over 70s occur once the restrictions are lifted. This model excludes the possibility of a vaccine reducing the size of the second wave.

      At least ten companies have a COVID-19 vaccine in the final stage (Phase III) of clinical trials (1). Therefore a model which excludes vaccination will most likely not be accurate to reality once a vaccine is widely administered.


      1 https://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines

    1. For every one confirmed case, Redfield said, the CDC estimates that 10 more people have been infected.

      Take away: While this estimate may be the most accurate at the time there are several reasons (addressed below) why any estimate provided at this time may be imprecise. As more data is accrued, including information on the immunological dynamics of the SARS-CoV2 antibodies, we should expect to see a more accurate estimate.

      The claim: For every one confirmed case the CDC estimates that another 10 more people have been infected.

      The evidence: This estimate was revealed in a press briefing with CDC Director Robert Redfield on June, 25, 2020. It is important to emphasize that this is an estimate extrapolated from the collective data of numerous seroprevalence surveys (antibody tests) performed in different locations across the U.S. While it is most definitely true that the reported case numbers are lower than the actual, given the prevalence of asymptomatic individuals that do not visit medical centers to be tested, the actual figure may be lower or higher than the estimate presented here due to a variety of factors including:

      1) Areas surveyed: Indeed, it is known that the number of cases vary disproportionately across different areas of the U.S. According to the CDC, three types of seroprevalence surveys are commonly performed: large-scale geographic, community-level, and special populations. It is important to note that each survey may or may not be completely representative of the specific area yet alone the U.S. as a whole.

      2)Type of antibody testing: The FDA reported on the performance of numerous EUA authorized serology tests. The conclusion is that each test has varying levels of accuracy and confidence intervals. As the estimate provided by Redfield was most likely obtained from data derived from the specific test used at each individual surveillance site, the figure may be further skewed by the accuracy of each test.

      3)Origin of blood samples: The type of individuals from which the blood samples tested originated may have a significant effect on the Redfield’s estimate. For example, if certain surveillance sites are exclusively testing samples from sick patients, the estimate may be an overestimate as a population presenting COVID symptoms is more likely to test positive than a healthy-looking population. Therefore, a detailed characterization of the individuals from which the blood was obtained would be needed in order to uphold accuracy.

      4)Time of tests: As the advent of antibodies can occur a week or longer post-infection, individuals who have recently been infected may not have detectable levels of antibodies and may come up as false negatives. It is also possible for an individual to simply not produce enough antibodies to be detectable by a given serology test. Furthermore, a recent paper published in medrxiv suggests that certain antibodies have reduced titers within 50 days of symptom onset.

      Take away: While this estimate may be the most accurate at the time there are several reasons (addressed below) why any estimate provided at this time may be imprecise. As more data is accrued, including information on the immunological dynamics of the SARS-CoV2 antibodies, we should expect to see a more accurate estimate.

      The claim: For every one confirmed case the CDC estimates that another 10 more people have been infected.

      The evidence: This estimate was revealed in a press briefing with CDC Director Robert Redfield on June, 25, 2020. It is important to emphasize that this is an estimate extrapolated from the collective data of numerous seroprevalence surveys (antibody tests) performed in different locations across the U.S. While it is most definitely true that the reported case numbers are lower than the actual, given the prevalence of asymptomatic individuals that do not visit medical centers to be tested, the actual figure may be lower or higher than the estimate presented here due to a variety of factors including:

      1) Areas surveyed: Indeed, it is known that the number of cases vary disproportionately across different areas of the U.S. According to the CDC, three types of seroprevalence surveys are commonly performed: large-scale geographic, community-level, and special populations (1). It is important to note that each survey may or may not be completely representative of the specific area yet alone the U.S. as a whole.

      2)Type of antibody testing: The FDA reported on the performance of numerous EUA authorized serology tests (2). The conclusion is that each test has varying levels of accuracy and confidence intervals. As the estimate provided by Redfield was most likely obtained from data derived from the specific test used at each individual surveillance site, the figure may be further skewed by the accuracy of each test.

      3)Origin of blood samples: The type of individuals from which the blood samples tested originated may have a significant effect on the Redfield’s estimate. For example, if certain surveillance sites are exclusively testing samples from sick patients, the estimate may be an overestimate as a population presenting COVID symptoms is more likely to test positive than a healthy-looking population. Therefore, a detailed characterization of the individuals from which the blood was obtained would be needed in order to uphold accuracy.

      4)Time of tests: As the advent of antibodies can occur a week or longer post-infection, individuals who have recently been infected may not have detectable levels of antibodies and may come up as false negatives. It is also possible for an individual to simply not produce enough antibodies to be detectable by a given serology test. Furthermore, a recent paper published in medrxiv suggests that certain antibodies have reduced titers within 50 days of symptom onset (3).

      To conclude, while this estimate may be the most accurate at the time given the available data, many factors can affect the figure and, in some instances, more information is needed as it is unclear exactly how this number was obtained from the information provided in the press briefing.

      Sources: 1) https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/about-serology-surveillance.html

      2) https://www.fda.gov/medical-devices/coronavirus-disease-2019-covid-19-emergency-use-authorizations-medical-devices/eua-authorized-serology-test-performance

      3) https://www.medrxiv.org/content/10.1101/2020.07.09.20148429v1

    1. The CDC summarized it succinctly, “For 6% of the deaths, COVID-19 was the only cause mentioned.

      The takeaway: >50% of the adult US population has at least one chronic condition. Therefore exclusion of deaths from people with comorbidity will not predict how COVID-19 affects >50% of the adult US population.

      The claim: Only 6% of deaths were caused by COVID-19 alone.

      The evidence: The CDC website does state that "For 6% of the deaths, COVID-19 was the only cause mentioned." (1) The same web page also states "Data during the period are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more."

      Additionally, in the USA 6 out of 10 adults have one chronic disease and 4 out of 10 adults have two or more chronic conditions (2). Based on this data, COVID-19 deaths in people with chronic conditions should not be excluded because >50% of the adult population has at least one chronic condition.


      1 https://www.cdc.gov/nchs/nvss/vsrr/covid_weekly/index.htm?fbclid=IwAR2-muRM3tB3uBdbTrmKwH1NdaBx6PpZo2kxotNwkUXlnbZXCwSRP2OmqsI

      2 https://www.cdc.gov/chronicdisease/resources/infographic/chronic-diseases.htm

    1. The positivity rate — the percentage of tests with positive results — is 6.5%, well below the 10% recorded recently, he said.

      This is a particularly important metric for any testing program, because it gives a sense of whether enough testing is being done to accurately capture the true positive rate. For instance, the WHO recommends that to ensure adequate testing, the % positive rate should be at or below 5% for at least 14 consecutive days: https://coronavirus.jhu.edu/testing/testing-positivity

    1. Not so novel coronavirus?

      Take away: More research is needed before the conclusion can be reached that T-cells from common cold coronaviruses are protective against SARS-CoV-2.

      The claim: A significant part of the population may be immune to SARS-CoV-2 due to cross-reactivity to T-cells from HCo infections (“common cold viruses”).

      The evidence: T- cell cross reactivity between common cold coronaviruses and SARS-CoV-2 occurred in 20-50% of people not exposed to SARS-CoV-2 (1-4). This cross-reactivity from T-cells led to the speculative hypothesis that cross-reactivity explains why children and young adults are not affected as badly as older adults (1). Additional research is needed to conclude that the presence of cross-reactive T-cells leads to less severe COVID-19 disease and does not result in the cytokine storm which is harmful instead of helpful in recovery from COVID-19 (2, 4). Significant cross-reactivity between SARS-CoV-2 and HCo antibodies was not observed with 1064 serum samples when tested with ELISA (5). A small study found some cross-reactivity between SARS-CoV-2 and HCo using rapid immunochromatographic antibody test which tests the ability of antibodies to react with SARS-CoV-2 (6).

      The immune system uses multiple components to rid the body of an infection. Innate immunity includes inflammation, fever, and cells which non-specifically destroy infectious/toxic particles (7). The adaptive immune system includes cells which adapt to the specific pathogen it is attacking (8). The adaptive immune system includes B cells and T cells. B cells produce antibodies. Antibodies bind and neutralize toxins/infectious particles. T cells kill infected human cells which present antigens, infectious particle identifiers, which specific T cells recognize. A summary of the immune system's interaction with SARS-CoV-2 is written (9). Additional discussion can be found (10, 11).

      In conclusion, T-cell cross reactivity was shown to occur (1-4). More research is needed to conclusively determine whether the presence of HCo cross-reactive T-cells leads to prevention or less severe infection by SARS-CoV-2.


      1 https://www.nature.com/articles/s41577-020-0389-z

      2 https://www.cell.com/cell/fulltext/S0092-8674(20)30610-3?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867420306103%3Fshowall%3Dtrue

      3 https://pubmed.ncbi.nlm.nih.gov/32766111/

      4 https://www.nature.com/articles/s41586-020-2550-z

      5 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7417941/

      6 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7381928/

      7 https://www.ncbi.nlm.nih.gov/books/NBK26846/

      8 https://www.ncbi.nlm.nih.gov/books/NBK21070/

      9 https://www.cell.com/trends/pharmacological-sciences/pdf/S0165-6147(20)30130-9.pdf?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0165614720301309%3Fshowall%3Dtrue

      10 https://blogs.sciencemag.org/pipeline/archives/2020/07/07/more-on-t-cells-antibody-levels-and-our-ignorance

      11 https://twitter.com/EricTopol/status/1278400526716211200?s=20

  5. Sep 2020
    1. It lives in a context="module" script — see the tutorial — because it's not part of the component instance itself; instead, it runs before the component is created, allowing you to avoid flashes while data is fetched.
    1. setContext / getContext can only be used once at component init, so how do you share your API result through context? Related: how would you share those API results if the call was made outside of a Svelte component, where setContext would be even more out of the question (and the API call would arguably be better located, for separation of concerns matters)? Well, put a store in your context.
    2. setContext must be called synchronously during component initialization. That is, from the root of the <script> tag
    1. Table 3. WBGT exposed levels in °C at different work intensities and rest/ work periods for an average worker with light clothing.

      worker productivity relation to the WBGT heat stress levels using work intensity and rest relation

    2. Table 2. Reference values for WBGT (°C) at corresponding work intensity.

      foundational research to base heat stress on productivity analysis: reference values for WBGT to work intensity

    3. 1.1. Physiological response to heat exposure

      divided into sub-sections of each variable that is affected by heat exposure

    1. Researchers offer first proof that Ultraviolet C light with a 222 nm wavelength — which is safer to use around humans — effectively kills the SARS-CoV-2 virus.

      Take Away: Most germicidal ultraviolet (UV) lamps emit a wavelength of around 254 nm. While these are very effective means of sterilization, they are also damaging to human skin and eyes and therefore are used in unoccupied spaces. However, a recent study has shown that a safer form of UV light at a wavelength of 222 nm is effective in killing SARS-CoV-2 virus in vitro.

      The Claim: Researchers offer first proof that Ultraviolet C light with a 222 nm wavelength — which is safer to use around humans — effectively kills the SARS-CoV-2 virus.

      The Evidence: The authors reference the safety of 222 nm UV light, and there are many studies to support this claim. 222 nm UV light has been shown to not cause DNA damage or skin lesions even at higher doses and for longer exposure times than used here (1, 2).

      In the study referenced, researchers at Hiroshima University exposed SARS-CoV-2 virus to low dosage 222 nm UV light and subsequently measured the amount of viable virus (3). They found that exposure of 0.1 mW/cm^2 for 30 seconds reduced the amount of viable virus by 99.7%. However, as the authors note, this study was performed using virus plated on a dish in a hood, and translation of these results to a public setting is unclear. For instance, in a hospital, there are many different types of surfaces and direct/consistent exposure to the UV light might not be feasible. While this study is promising, additional studies need to be done before promoting this as a safe and effective means of killing SARS-CoV-2 in an occupied environment.

      Sources: 1) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6059456/

      2) https://onlinelibrary.wiley.com/doi/abs/10.1111/php.13269

      3) https://www.sciencedirect.com/science/article/pii/S0196655320308099#:~:text=Results,after%20a%205%2Dmin%20irradiation.

    1. He added that while it would not be possible to check every test to see whether there was active virus, the likelihood of false positive results could be reduced if scientists could work out where the cut-off point should be.

      Take Away: This is an incorrect usage of the term "false positive." A positive PCR test result from a recovered infection is a valid and true positive.

      Claim: PCR tests for SARS-CoV-2 give false positive results when there is no active virus.

      Evidence: The diagnostic PCR tests currently in widespread use are designed to detect the presence of the SARS-CoV-2 viral RNA in a clinical sample. The RNA is only a part of the complete virus and is not infectious on its own. Research has shown that viral RNA can be detected in some samples up to 12 weeks after onset of symptoms (1). In other words, this is like testing if an oven is warmer than the room temperature - it could be hot even after it has been turned off.

      By definition, in the context of SARS-CoV-2 PCR tests, a "false positive" means that a test result is deemed positive when in reality there was no viral RNA in the sample. If a person is recovering from an infection, gets tested, and then is given a positive test result, that is a true positive regardless of whether they are infectious or not.

      Sources: 1) https://www.cdc.gov/coronavirus/2019-ncov/hcp/duration-isolation.html

    1. If you were infected with the novel coronavirus, a new study suggests that your immunity to the virus could decline within months.

      Take away: Waning antibodies don’t necessarily mean that immunity will also decrease, because other components of the immune system retain “memory” for an infection and can combat invaders even after antibody counts have gone down.

      The claim: “If you were infected with the novel coronavirus, a new study suggests that your immunity to the virus could decline within months.”

      The evidence: This study [1], along with others [2], does indeed show evidence for declining neutralizing antibodies within a few months after infection; however, antibody counts alone are not enough to predict whether a patient will have durable immunity to a virus. Neutralizing antibodies are generated by B cells, a type of immune cell that patrols the body looking for their molecular targets. Some B cells carry “memory,” a quality that allows them to respond quickly when they see a virus or pathogen that they have encountered before, which allows them to pump out large quantities of antibody rapidly to fight the infection [3]. It’s actually normal in many viral infections for antibody levels within the blood to wane over time; the real concern is whether there are enough memory B cells to generate new antibodies at a moment’s notice.

      In addition to B cells, a second type of immune cell known as a “T cell” is critical for predicting durable immunity. Like B cells, some T cells carry “memory” and can patrol the body for years looking for their targets. Some T cells play a role in helping B cells produce antibodies quickly, and other T cells can actually target the infection directly [4]. Studies have now shown that T cell responses can persist after SARS-CoV-2 infection, and some patients even have T cells that can react to SARS-CoV-2 due to “cross-reactivity,” likely from preexisting immunity from common cold viruses that share some characteristics of SARS-CoV-2. While this cross-reactivity does not guarantee immunity, the presence of robust B and T cell responses is important, and could be more predictive than presence of antibodies alone.

      This article, written by a two well-known immunologists and COVID-19 experts at Yale University, provides a nice summary of the data that puts these claims in context [6].

    1. COVID-19 Can Wreck Your Heart, Even if You Haven’t Had Any Symptoms

      Take Away: SARS-CoV-2 infection has been clearly linked to heart muscle injury in those with severe COVID-19 illness. However, at present, there is insufficient data to determine the impact of mild or asymptomatic COVID-19 on the hearts of previously healthy individuals.

      The Claim: COVID-19 can wreck your heart, even if you haven’t had any symptoms.

      The Evidence: Several articles, including this August 31st piece (1), have raised the alarm about dangerous effects of mild or even asymptomatic cases of COVID-19 on the heart of infected individuals.

      In support of this argument, there have been numerous reports, some of which are cited in the article above, documenting severe heart inflammation (myocarditis) and injury (e.g. cardiomyopathy and/or heart failure) in patients with COVID-19. However, most of these documented cases were in individuals with severe cases of COVID-19. At present, the evidence for clinically significant heart injury (requiring treatment or special precautions) from mild or asymptomatic COVID-19, is much less clear, especially in those with no prior evidence of heart disease.

      One recent study reported that 78% of patients from an unselected cohort (including patients with asymptomatic, mild, and severe cases) had evidence of myocarditis (via MRI or blood testing) following COVID-19 infection (2). This study clearly demonstrated the link between COVID-19 and myocarditis by examining tissue from biopsies of the heart (the gold standard definitive diagnosis of myocarditis) of patients with the most severe cases. The study went on to show that, on average, patients who were treated for COVID-19 at the hospital (presumably more severe cases) and patients who were treated at home (presumably asymptomatic to moderate cases) both had blood test levels or MRI findings suggesting elevated myocarditis compared to non-COVID-19 infected patients with similar health profiles.

      A key limitation here is “average”. The study was not designed or powered to look for the rate of myocarditis in only previously healthy patients with mild or asymptomatic COVID-19. This study included asymptomatic patients in the analysis, but without knowing their prior health or comparing their findings to other healthy non-COVID patients, it is not possible to infer the risk of myocarditis to this population. To their credit, the authors of the study discuss this limitation in their conclusions.

      Despite this, the study was widely covered as evidence that ”COVID-19 can wreck your heart, even if you haven’t had any symptoms.“ In order to answer that question, we need research looking selectively healthy patients with mild or asymptomatic COVID-19 as outlined above.

      Until that research is conducted, we might look at COVID within the same context as a number of other well studied viruses, many of which generally cause mild illness, that have also been shown to lead to heart injury and inflammation (3).

      Disclaimer: This content is not intended as a substitute for professional medical advice. Always seek the advice of a qualified health provider with any questions regarding a medical condition.


      1. https://www.scientificamerican.com/article/covid-19-can-wreck-your-heart-even-if-you-havent-had-any-symptoms/
      2. https://jamanetwork.com/journals/jamacardiology/fullarticle/2768916
      3. https://www.ahajournals.org/doi/full/10.1161/CIRCULATIONAHA.108.766022
    1. Take away: People are infectious for only part of the time they test positive. The tests for COVID-19 were granted emergency status by the FDA so some debate concerning the most ideal number of cycles is to be expected. It is worth noting that the FDA has the disclaimer "Negative results do not preclude 2019-nCoV infection and should not be used as the sole basis for treatment or other patient management decisions. Negative results must be combined with clinical observations, patient history, and epidemiological information (2)."

      The claim: Up to 90 percent of people diagnosed with coronavirus may not be carrying enough of it to infect anyone else

      The evidence: Per Walsh et al. (1), SARS-CoV-2 virus (COVID-19) is most likely infectious if the number of PCR cycles is <24 and the symptom onset to test is <8 days. RT-PCR detects the RNA, not the infectious virus. Therefore, setting the cycle threshold at 37-40 cycles will most likely result in detecting some samples with virus which is not infectious. As the PCR tests were granted emergency use by the FDA (samples include 2-9), it is not surprising that some debate exists currently about where the cycle threshold should be. Thresholds need to be set and validated for dozens of PCR tests currently in use. If identifying only infectious individuals is the goal, a lower cycle number may be justified. If detection of as many cases as possible to get closer to the most accurate death rate is the goal, setting the cycle threshold at 37-40 makes sense. A lower threshold will result in fewer COVID-19 positive samples being identified. It is worth noting that the emergency use approval granted by the FDA includes the disclaimer that a negative test does not guarantee that a person is not infected with COVID-19. RNA degrades easily. If samples are not kept cold or properly processed, the virus can degrade and result in a false negative result.

      Source: 1 https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciaa638/5842165

      2 https://www.fda.gov/media/134922/download

      3 https://www.fda.gov/media/138150/download

      4 https://www.fda.gov/media/137120/download

      5 https://www.fda.gov/media/136231/download

      6 https://www.fda.gov/media/136472/download

      7 https://www.fda.gov/media/139279/download

      8 https://www.fda.gov/media/136314/download

      9 https://www.fda.gov/media/140776/download

    1. Your Coronavirus Test Is Positive. Maybe It Shouldn’t Be.

      Take Away: Diagnostic tests are most useful when they are both sensitive and rapid. The sensitivity of SARS-CoV-2 PCR tests is not the issue, but rather the time it takes to get a result. Additionally, the "90%" statistic is likely misleading due to the data source and not generalisable to all testing results.

      The Claim: The usual PCR diagnostic tests may be too sensitive and too slow, with up to 90% of positive cases due to trace amounts of virus.

      The Evidence: Polymerase Chain Reaction (PCR)-based tests, which are currently in the most widespread use for detection of SARS-CoV-2 RNA, involves a molecular process that amplifies target DNA sequences in repeated temperature-dependent cycles. The amount of target DNA is measured after each cycle and the number of the cycle when the target can be reliably detected is often referred to as the cycle threshold (Ct). The Ct value is proportional to the amount of starting DNA in the sample and can be used to estimate the viral load of a patient. In some ways this is like a teacher making photocopies of a chapter from a textbook until they have enough for all their students.

      However, Ct values are relative measurements and need to be directly compared to controls for every sample - a Ct value taken alone can be meaningless. For instance, consider an infected patient who is tested twice: the first time they are gently swabbed and the sample is relatively dilute, the second time they are vigorously swabbed and the sample is relatively concentrated. The resulting Ct values could be drastically different. Therefore, Ct values need to be considered carefully in the proper context for making medical or policy decisions. The FDA also recommends that a PCR result alone should not be used to determine infection status.

      Positive results are indicative of the presence of SARS-CoV-2 RNA; clinical correlation with patient history and other diagnostic information is necessary to determine patient infection status. (1)

      Current PCR test results are generally given as a binary positive/negative based on a cutoff value for Ct. The cutoff needs to be determined based on the performance of each individually developed SARS-CoV-2 test, of which there are currently over 160 that have been granted emergency use authorization by the FDA (2). Based on unpublished data from the CDC, setting a stringent Ct cutoff of 30 could return negative results in patients who are both infected and potentially infectious (3 Fig 5). Furthermore, a 30 cycle cutoff would return invalid results for samples which are too diluted. Based on the same CDC data, up to 30% of potentially infectious patients would get invalid results and need to be re-swabbed, thereby extending the time between getting infected and getting a positive result.

      The period of time when RNA from SARS-CoV-2 can be detected (and a positive PCR test result returned) may extend up to 12 weeks after recovery, with Ct values trending higher over time (3,4). According to The New York Times article, they looked at Ct values from people who tested positive in Massachusetts in July and found 85-90% of results had Ct values greater than 30. The epidemiology of COVID-19 is highly time and region dependent. Massachusetts had a peak in COVID-19 hospitalizations on April 21 (5), which is 9-12 weeks prior to the testing data analyzed by The NY Times. Therefore, the detection of a large proportion of people with lingering viral RNA is not surprising. These results are likely not universal and can not be applied to other regions, especially where community spread is still significant.


      (1) https://www.fda.gov/media/135900/download

      (2) https://www.fda.gov/medical-devices/coronavirus-disease-2019-covid-19-emergency-use-authorizations-medical-devices/vitro-diagnostics-euas

      (3) https://www.cdc.gov/coronavirus/2019-ncov/hcp/duration-isolation.html

      (4) Li N, Wang X, Lv T. Prolonged SARS-CoV-2 RNA Shedding: Not a Rare Phenomenon. J Med Virol 2020 Apr 29. doi: 10.1002/jmv.25952.

      (5) https://www.bostonherald.com/2020/05/22/massachusetts-finally-seeing-downward-coronavirus-trends/

    1. Stores are global state. While context is local state.
    2. Notice it's not related to components. Another crucial difference is that it's accessible from outside of components. And good way to determine where goes where is to ask yourself, can this particular state and functionality still makes sense outside of the displayed component?
    1. Detection of viruses using Polymerase Chain Reaction (PCR) is helpful so long as its accuracy can be understood: it offers the capacity to detect RNA in minute quantities, but whether that RNA represents infectious virus is another matter. RT-PCR uses enzymes called reverse transcriptase to change a specific piece of genetic material called RNA into a matching piece of genetic DNA. The test then amplifies this DNA exponentially; millions of copies of DNA can be made from a single viral RNA strand.

      Take away: The claim that virus can be detected for a long time but is not infectious needs further clarification. This claim was based on a Lancet article (1). Within the Lancet article, some of the studies cited detected RNA in stool/blood/seminal fluid samples instead of nasal swabs. Other studies cited did not test infectious nature of virus detected by PCR. It is several logic steps to travel from detecting virus in stool/blood/seminal fluid in Lancet article to concluding that PCR of nasal swabs for COVID-19 results in large numbers of false positives.

      The claim: RNA from coronavirus is present and can be detected for a long time but may not be infectious.

      The evidence: The Spectator article links to the article "SARS-CoV-2 shedding and infectivity" in the Lancet (1). This article cites seven articles to support the statement that RNA persists long after virus is not infectious. Of these articles, only one reports that virus was detected at ~30 days but could not be cultured beyond three weeks (2). This article also states that detection was easier in stool samples than nasal samples after the first five days. Several articles cited by source 1 did not report infectivity of virus detected (3, 4, 5, and 7). Of the two remaining articles, virus was detected in serum/blood (6 and 8). In the serum study, 58% of tested specimens were infectious (6).


      1 https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30868-0/fulltext

      2 https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(03)13412-5/fulltext

      3 https://pubmed.ncbi.nlm.nih.gov/15030700/

      4 https://pubmed.ncbi.nlm.nih.gov/27682053/

      5 https://pubmed.ncbi.nlm.nih.gov/29648602/

      6 https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(16)30243-1/fulltext

      7 https://pubmed.ncbi.nlm.nih.gov/28195756/

      8 https://pubmed.ncbi.nlm.nih.gov/22872860/

    1. Take away: Though many articles are referenced, additional context is needed because the conclusions of the publications do not always agree with the conclusion of the author of this article. Additionally, publications which conflict with the claims in this article are not presented.

      The claim: Masks are neither effective nor safe.

      The evidence: The data in the articles referenced here is inconclusive regarding whether masks are or are not effective. Though several studies referenced here did not see a statistically significant difference between those who wore masks and controls, several facts need to be considered. The sample size of people who became sick in the individual studies was small (often <10 people). Compliance in the mask group was not enforced. A number of the articles referenced are pre-prints lacking peer-review and validation. Some of the articles compare N95 masks to surgical masks but do not have a control no mask group. Additionally, the claim of the author of this article sometimes differs from the conclusions written by the authors of the publications cited. Publications which contradict the conclusions of the author are not presented (1, 2).


      1 https://pubmed.ncbi.nlm.nih.gov/32497510/

      2 https://pubmed.ncbi.nlm.nih.gov/27632416/

  6. Aug 2020
    1. Second, in the absence of any such attenuation, I think a practical and healthy thing that any user of social media can do when confronted with a free-floating cube of news is ask: how big is this, really? Does it matter to me and my community? Does it, in fact, matter anywhere except the particular place it happened? Sometimes, the answer is absolutely yes, but not always—and these platform don’t make it easy to judge.

      These are good prescriptive questions that social media users should frequently use. (Sadly most will not unless they're forced to by design.)

    1. I would shy away from using contexts as your application state. They're intended to be used sparingly. It's likely that some child is changing something in the context and as several items access that context, they're seeing that as a state change they have to care about. I'd highly recommend moving away from contexts as mini-stores and instead focus on how you structure your component tree and the state each one manages.
    1. Allows batch updates by silencing notifications while the fn is running. Example: form.batch(() => { form.change('firstName', 'Erik') // listeners not notified form.change('lastName', 'Rasmussen') // listeners not notified }) // NOW all listeners notified
    1. Although public health officials have warned that the presence of antibodies does not guarantee immunity from the disease, the common perception that this is the case makes the issue of bogus tests nothing short of a matter of life and death.

      Take away: COVID-19 infections result in antibodies in almost all cases. These antibodies probably give immunity to future infection for at least some time, although how long is still not known.

      The claim: The presence of antibodies to SARS-CoV2 does not guarantee future immunity from future COVID-19 infection.

      The evidence: COVID-19 has not been present in the human population long enough to know how long immunity will last. There is some evidence to suggest that having COVID-19 typically leads to antibodies will provide at least some immunity to future infections. The vast majority (>90%) of serious (1-3) and mild (4,5) COVID-19 infections do result in the production of antibodies and it has been found that neutralizing antibodies provide immunity to reinfection in monkeys (6). We do not know how long immunity lasts. The best evidence is from the related coronavirus infections SARS and MERS. SARS and MERS infections result in antibodies that last for at least 1-3 years (7-9).


      1. https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciaa344/5812996
      2. https://erj.ersjournals.com/content/early/2020/05/13/13993003.00763-2020.abstract
      3. https://www.nature.com/articles/s41591-020-0897-1)
      4. https://www.sciencedirect.com/science/article/pii/S2352396420302905
      5. https://www.medrxiv.org/content/10.1101/2020.07.11.20151324v1
      6. https://www.biorxiv.org/content/10.1101/2020.03.13.990226v2.abstract
      7. https://www.jimmunol.org/content/jimmunol/181/8/5490.full.pdf
      8. https://wwwnc.cdc.gov/eid/article/13/10/07-0576_article,
      9. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5512479/
    1. Asymptomatic spread of coronavirus is ‘very rare,’ WHO says

      Take away: Dr. Van Kerkhove appeared to refer to only “asymptomatic” individuals and not “presymptomatic” individuals in her statement. Clarification from the WHO, and public availability of the data leading to the claim, is needed for proper interpretation. At the current time, existing published data indicates that a significant amount of SARS-CoV-2 infections are due to individuals who did not have symptoms when they spread the virus.

      The claim: According to the WHO, asymptomatic spread of coronavirus is ‘very rare’.

      The evidence: This statement is attributed to WHO official Dr. Maria Van Kerkhove during a recent news conference. It deserves greater clarification from the WHO, but Dr. Van Kerkhove appears to make the distinction between “asymptomatic” and “pre-symptomatic” individuals during her comments. This distinction is essential for proper interpretation of her statement. “Asymptomatic” refers to persons who test positive, but who never display symptoms throughout the course of their SARS-CoV-2 infection. In contrast, “presymptomatic” individuals are those with confirmed infection, who do not currently display symptoms, but later go on to develop COVID-19 related symptoms (fever, cough, loss of taste/smell, etc).

      Importantly, the distinction between asymptomatic and presymptomatic can only be made retrospectively. From a clinical standpoint, if someone currently has no symptoms, but tests positive, there is no way of knowing at that time if they are “asymptomatic” or “presymptomatic”. Preliminary data estimates that around 20% of SARS-CoV-2 infections are truly “asymptomatic”.

      If “asymptomatic” individuals were rarely involved in transmission of the virus, this would be an important finding, but from a practical standpoint if “presymptomatic” individuals still spread the virus (as the data indicates), then the rationale for preventative measures still stands. Early studies [1] [2] have estimated that up to 40-60% of virus spread occurs when people don’t have symptoms. Preventative measures such as social distancing and universal mask wearing have been implemented to prevent the spread of virus from individuals not currently demonstrating symptoms.

    1. Though important, social distancing could be reduced to one metre instead of 2m

      Take away: As with most things in nature, there are always exceptions – transmission occurring at greater distances than 3 ft and evidence of aerosolization have been reported.

      Discussion: In scientific terms, this virus is still very new so the data supporting an optimal physical distance to prevent transmission remains scarce. In the absence of data, public health agencies have used what they understand about this virus and similar viruses to infer a “best” answer. Public health agencies try to simplify the recommendation to a single answer, but the reality is much more complex.

      According to reports the WHO bases their recommendation for 1 meter (~3 ft) distancing off of an understanding that SARS-CoV-2 behaves like similar respiratory viruses that are primarily transmitted via larger droplets (as opposed to smaller aerosols). Assuming most spread is via droplets, the WHO reportedly follows the results of a 1934 study indicating most respiratory droplets fall to the ground within 3 feet.

      However, as with most things in nature, there are always exceptions – transmission occurring at greater distances than 3 ft and evidence of aerosolization have been reported.

      The evidence basis for the CDCs guidance for 6 feet of separation is less clear, but probably reflects lower risk tolerance, or greater weight to evidence of aerosolization or wider droplet spread.

      Even with further study, there may never be a clear answer for optimal physical distancing. This is because, (1) the area of high risk for transmission is probably dependent on the specific conditions of the interaction (e.g. loud talking, windy environment), and (2) the “optimal” distance is based on risk tolerance. There is no single distance between individuals where risk of transmission drops off precipitously to zero.

      All evidence indicates that greater distances are safer but, for example, consider how restrictive a physical distancing recommendation of >50 ft would be. In the end, because we can’t control how far others stand away from us, we ask governments to consider these tradeoffs and deliver a “best” answer to guide their citizenry.

  7. Jul 2020
    1. The vaccine uses messenger RNA (mRNA), which are cells used to build proteins -- in this case, the proteins that are needed to build the coronavirus' spike protein, which the virus uses to attach itself to and infect human cells. Once the immune system learns to recognize this target -- thanks to the vaccine -- it can mount a response faster than if it encountered the virus for the first time due to an infection.

      This explanation is garbled and misstated. Genetic material is stored in DNA in the nucleus of the cell. Messenger RNA (mRNA) molecules carry the information stored within the DNA to the rest of the cell. Both DNA and RNA are a type of molecule called a "nucleic acid." Once outside the nucleus, the information in the messenger RNA can then be read, or "translated," to create proteins, such as the spike protein used by SARS-CoV-2. These proteins in turn carry out a wide variety of tasks that allow cells to function. This process is known as the "Central Dogma of Molecular Biology".

    1. and that is independent of the implementation of the encryptionscheme
    1. Если встретят они верующих, то [общаясь с ними] говорят: «Мы [так же, как и вы] уверовали». Когда же остаются наедине со своими дьяволами [оказываясь в привычной для них атмосфере бездуховности, неверия и порока], говорят: «Мы с вами [остаемся такими, какие есть]! Мы [над верующими] лишь насмехаемся (подшучиваем, издеваемся)» (Св. Коран, 2:14).
  8. Jun 2020
    1. I could get a lot more done in an 8-9 hour day with a PC and a desk phone than I get done now in a 9-10 hour day with a laptop /tablet / smartphone, which should allow me to be more a lot more productive but just interrupt me. I don't want the mobile flexibility to work anywhere. It sucked in management roles doing a full day then having dinner with friends and family then getting back to unfinished calls and mails. I much prefer to work later then switch off totally at home.
    1. The skill of writing is to create a context in which other people can think.— Edwin Schlossberg
  9. May 2020
    1. You can recommend articles based on the page customers are viewing, so they don't even have to search.
    1. One huge advantage to scaling up is that you’ll get far more feedback for your Insight through making process. It’s true that Effective system design requires insights drawn from serious contexts of use, but it’s possible to create small-scale serious contexts of use which will allow you to answer many core questions about your system.

      Even though a larger user base will increase your odds of getting more feedback, you can still get valuable contextual feedback with less users.

    1. In Flare 2020, context-sensitive help identifiers can now be associated with micro content phrases. Since micro content is intended to be short bits of content, this makes it ideal for field-level or embedded help within apps.
    1. The Microsoft Calculator program uses the former in its standard view and the latter in its scientific and programmer views.
    1. In the context of first-order logic, a distinction is maintained between logical validities, sentences that are true in every model, and tautologies, which are a proper subset of the first-order logical validities. In the context of propositional logic, these two terms coincide.

      A distinction is made between the kind of logic (first-order logic) where this other distinction exists and propositional logic, where the distinction doesn't exist (the two terms coincide in that context).

    1. The qualifier of ‘certain circumstances’ is important to highlight here, because it’s often the context in which information exists that determines whether it can identify someone.
    1. no longer have the forest nor the heavens.

      Kostenko represents Chernobyl not only as an environmental catastrophe, but also as the cause of total alienation from spirituality and heaven. It is possible to read this poem as engaging with one well-known reading of Chernobyl, which interprets the disaster as an inevitable apocalyptic moment predicted in the Book of Revelation:

      Then the third angel sounded: And a great star fell from heaven, burning like a torch, and it fell on a third of the rivers and on the springs of water. The name of the star is Wormwood. A third of the waters became wormwood, and many men died from the water, because it was made bitter. [1]

      In Ukrainian, the world "Chernobyl" is derived from two separate roots that combine to mean "black plant." The word "Chernobyl" refers to a specific species of Artemisia, which is a type of weed. [2] In English, Artemisia is translated as "wormwood," which has led many people to link the Chernobyl catastrophe to the "wormwood" mentioned in the Book of Revelation. However, as Michael Palij and William Fletcher note, "The coincidence is not quite so striking in the Ukrainian translation of the Bible, for there the name of the star is Polyn, the genus wormwood, rather than chornobyl', a species of wormwood." [2] Although the etymological relationship between Chernobyl and the Bible does not align perfectly, the religious reading of Chernobyl continues to resonate.



      [1] The Bible. King James Version. Christian Art Publishers, 2012.

      [2] Palij, Michael & William C. Fletcher. "Chornobyl: An Etymology." Ukrainian Quarterly, vol. 42 Spring-Summer 1986, p. 22-24.

      Image Credit:

      "Redstem Wormwood" by Moxxie is licensed by CC BY-SA 3.0. The image has not been modified in any way and falls under fair use.

    2. the robot could not         shut down the troubles,

      After the explosion at Chernobyl, the Soviet government officials tried to use robots to assist with the most dangerous aspects of the radiation cleanup. [1] However, almost all of the robots could not withstand the high radiation levels on the roof of the reactor. [1] As such, thousands of conscripted soldiers and workers from all over the Soviet Union had to clear the radioactive material off of the roof of the reactor with little protective gear. [1] The image below features the Monument to Those Who Saved the World in Ukraine, which is dedicated to the firefighters and liquidators who responded to Chernobyl.


      [1] Anderson, Christopher. "Soviet Official Admits that Robots Couldn't Handle Chernobyl Cleanup." The Scientist, 19 January 1990, https://www.the-scientist.com/news/soviet-official-admits-that-robots-couldnt-handle-chernobyl-cleanup-61583.

      Image Credit:

      "Memorial to Those Who Saved the World" by Jorge Franganillo is licensed under CC BY-2.0. The image has not been modified in any way and falls under fair use.

    3. Doctor Gale

      Doctor Gale refers to the American doctor Robert Gale, who is well-known for his controversial bone marrow transplants that he performed on the most severely irradiated victims of the Chernobyl catastrophe. Doctor Gale flew to Moscow shortly after the reactor exploded. He was publicly recognized by Gorbachev for his efforts to help mitigate the health effects of the disaster. [1] That being said, Soviet doctors with extensive experience with treating radiation sickness, such as Dr. Angelina Guskova, criticized Gale for carrying out ineffective bone marrow transplants. [1]

      Prior to his involvement in Chernobyl, Gale was investigated by the government for bypassing standard protocols and treating patients with unapproved drugs without approval. [2] At Chernobyl, he used an experimental drug on his bone marrow transplant patients that was not approved for testing. He used this drug as well at a radiation accident in Brazil, where he practiced medicine on a tourist visa without having been invited by the Brazilian authorities. [2]

      As historian Serhii Plokhy writes in Chernobyl: The History of a Nuclear Catastrophe, "Gale was a messenger of hope in a world divided by Cold War rivalries, which meant that Soviet and American governments alike presented his actions as heroic. [1] Due to his political importance as a symbol of humanitarian goodwill, he was protected from serious repercussions for his alleged actions.


      [1] Plokhy, Serhii. Chernobyl: The History of a Nuclear Catastrophe. Basic Books, 2018.

      [2] Roark, Anne C. "Chernobyl 'Hero': Dr Gale – Medical Maverick." Los Angeles Times, 5 May 1988, https://www.latimes.com/archives/la-xpm-1988-05-05-mn-3615-story.html.

    4. Von Mekk

      Countess Nadezhda von Mekk was one of the most important patrons of the famous composer Pyotr Ilyich Tchaikovsky. She was the widow of Karl von Mekk, who created over 14000 kilometers of railroads throughout the Russian Empire. [1]

      Nadezhda Von Mekk

      She fully funded Tchaikovsky's work for years, and they frequently exchanged deeply personal letters about art, music, and their personal lives. [1] Tchaikovsky dedicated his Fourth Symphony to her, among other works. [1] While they communicated extensively, they agreed to never meet in person. Their correspondence ended without explanation in 1890, at which point Nadezhda falsely told Tchaikovsky that she was bankrupt. There is no agreement among scholars regarding the circumstances surrounding the end of their relationship. [1]


      [1] Tommasini, Anthony. "Critic's Notebook: The Patroness Who Made Tchaikovsky Tchaikovsky." The New York Times, 2 September 1998, https://www.nytimes.com/1998/09/02/arts/critic-s-notebook-the-patroness-who-made-tchaikovsky-tchaikovsky.html.

      Image Credit:

      Music Division, The New York Public Library. "Nadezhda Filaretovna von Meck." The New York Public Library Digital Collections, http://digitalcollections.nypl.org/items/510d47e0-beb5-a3d9-e040-e00a18064a99.

      Image in the public domain.

  10. Apr 2020
    1. Theophanes the Greek.

      Theophanes the Greek is known for being one of the most influential and talented icon painters in Russia. In Russian Orthodox tradition, iconography is an art form that dates back to the year 988 AD, when Prince Vladimir introduced Christianity to Kiev and to the Rus' territory. [1] Theophanes the Greek came to Novgorod and Moscow from Constantinople in the 14th century, where he quickly began to excel as an icon painter and as an illuminator of manuscripts. [1] In Russia, he mentored the great icon painter Andrei Rublev and created some of Russia's most well-known icons, such as Our Lady of the Don, which is included below and is currently featured in the Tretyakov Gallery. [2]

      As art critic Simon Morley writes, "Nearly all icons are not only anonymously painted but also based on pre-existing prototypes, which in their turn are copies of the archetype – the subject itself." [3]That is to say, icon painters must follow a strict set of rules that guide both the design and painting process and the selection of scenes that will be depicted. The individual artist is expecte to learn from and emulate the work created by others.

      To the faithful, icons are sacred because they represent a spiritual "window" to the divine. [4] Therefore, it is very important that icons highlight the eyes of the religious figures that they depict, in order to allow the worshippers to see through these portals and communicate their prayers. It is common practice to physically interact with the icons by kissing them, touching them, and lighting candles in front of them as a form of veneration. [5]

      Under Stalin, many icons were destroyed and icon-painting was outlawed as a profession to support the official policy of atheism. The Yaroslavl Restoration Committee endeavored to save as many of these icons as possible by taking them out of churches and storing them independently, many of which were returned to churches after the fall of the Soviet Union. [6]


      [1] Sevcenko, Ihor. "The Christianization of Kievan Rus'." The Polish Review, vol. 5, no. 4, 1960, pp. 29-35.

      [2] Gorbatova, Anastasia. "Theophanes the Greek, Russia's First Great Master of Religious Art," Russia Beyond, 7 January 2015, Link.

      [3] Morley, Simon. "So Real They Scratched Out Their Eyes," The Independent, 12 November 2000, https://www.independent.co.uk/incoming/so-real-they-scratched-out-their-eyes-625288.html.

      [4] "About Icons and Iconography." Museum of Russian Icons, https://www.museumofrussianicons.org/about-icons/.

      [5] Espinola, Vera Beaver-Bricken. “Russian Icons: Spiritual and Material Aspects.” Journal of the American Institute for Conservation, vol. 31, no. 1, 1992, pp. 17–22.

      [6] "Destruction of Icons." The Museum of Russian Art, https://tmora.org/currentexhibitions/online-exhibitions/transcendent-art-icons-from-yaroslavl-russia/introduction-yaroslavl-city-of-the-bear/destruction-of-icons/.

      Image Credit:

      Theophanes the Greek. Our Lady of the Don. Image in the public domain in the United States. Wikimedia Commons, https://commons.wikimedia.org/wiki/File:Feofan_Donskaja.jpg.

    2. sarcophagus

      Immediately after Chernobyl, workers from across the Soviet Union subjected themselves to serious radiation risk to construct a concrete "sarcophagus" around the Chernobyl reactor #4. [1] However, due to the hastiness of the construction and the materials used, the containment structure started to leak. As a result, at the 1997 G-7 Summit, the European Commission and Ukraine created a plan for the New Safe Confinement structure. This structure covers the previously constructed sarcophagus and is expected to remain intact for up to 100 years. [2]

      New Safe Containment Structure


      [1] Petryna, Adriana. “Sarcophagus: Chernobyl in Historical Light.” Cultural Anthropology, vol. 10, no. 2, 1995, pp. 196–220.

      [2] "Background on Chernobyl Nuclear Power Plant Accident." NRC Library, United States Nuclear Regulatory Commission, 15 August 2018, https://www.nrc.gov/reading-rm/doc-collections/fact-sheets/chernobyl-bg.html#sarco.

      Image Credit:

      "The New Sarcophagus" by kdanecki is licensed by CC BY 2.0. The image has not been modified in any way and falls under fair use.

    3. parsec

      The word parsec is composed of the words parallax and arcsecond. Parsec is a unit used in astronomy to measure extraordinarily large spaces between astronomical objects outside of our Solar System. While the full explanation of this mathematical concept is beyond the scope of this project, a detailed description can be found in the source below. [1]


      [1] Bender, Stephanie. "What is a Parsec?" Universe Today, 14 November 2013, https://www.universetoday.com/32872/parsec/.

    4. He who extinguished the reactor

      Firefighters received the highest doses of radiation from the Chernobyl accident. As Serhii Plokhy notes in Chernobyl: The History of a Nuclear Catastrophe, thirty of the sickest firefighters were evacuated from Pripyat and sent to Moscow Hospital No. 6, where they were treated by Dr. Angelina Guskova. [1] Dr. Guskova had extensive experience treating victims of previous radiation-related incidents. Despite the efforts of Guskova's team to assist these patients, 29 firefighters died of acute radiation exposure in the immediate aftermath of the disaster. [2] Additional firefighters and first responders died in the months and years following Chernobyl of radiation-related causes. [3] For more contextual information about the medical response to Chernobyl, click here.


      [1] Plokhy, Serhii. Chernobyl: The History of a Nuclear Catastrophe. Basic Books, 2018.

      [2] Ritchie, Hannah. "What was the Death Toll from Chernobyl and Fukushima?" Our World in Data, 24 July 2017, https://ourworldindata.org/what-was-the-death-toll-from-chernobyl-and-fukushima.

      [3] Lanese, Nicoletta. "The Real Chernobyl: A&A With a Radiation Exposure Expert." UCSF, 16 July 2019, https://www.ucsf.edu/news/2019/07/414976/real-chernobyl-qa-radiation-exposure-expert.

    5. thousands of dead

      The Chernobyl death toll has been and continues to be highly contested by scientists and politicians. As Kate Brown discusses in her book Manual for Survival, the United States and the Soviet Union alike were concerned that studying the long-term effects of continuous exposure to low-dose radiation would result in public scrutiny of nuclear weapons testing and development in general. [1] In 1990, the International Atomic Energy Agency (IAEA) sent scientists to Belarus to investigate a growing number of Chernobyl-related health claims, but the relevant records in the Institute of Radiation Medicine in Minsk were stolen, thereby interfering with this research. [1] The IAEA's studies of Chernobyl's effects depended on incomplete data and did not occur over the timespan necessary to generate a comprehensive understanding of the relationship between radiation, mortality rates, and other genetic effects. [1] For a more in-depth analysis of the history and politicization of scientific investigation of the Chernobyl catastrophe, see Manual for Survival.

      As the graphic below illustrates, estimates of Chernobyl-related deaths vary widely, due to the logistical and political barriers that have interfered with reliable scientific investigation of Chernobyl's health effects. Without studies focused on the impact of low-level radiation exposures, it is difficult to determine the degree to which radiation from Chernobyl can be held responsible for cancer rates, birth defects, and other epidemiological implications.

      Chernobyl Death Estimates


      [1] Brown, Kate. Manual for Survival: A Chernobyl Guide to the Future. W. W. Norton and Company, 2019.

      Image Credit:

      "Deaths from Chernobyl [Estimates]" by Our World in Data is licensed under CC BY 4.0. The image has not been modified in any way and falls under fair use.

    6. Gorbachev speaks:

      This verse references Gorbachev's significantly delayed speech on television about the Chernobyl nuclear disaster, which took place on May 15, 1986. In this speech, he specifically thanked Dr. Robert Gale for his willingness to treat the victims of Chernobyl. [1] At the same time, he also condemned the United States' instrumentalization of the Chernobyl catastrophe as part of an "anti-Soviet campaign." [1] In doing so, Gorbachev drew attention to the United States' legacy of mishandling nuclear incidents within their own territory, such as the partial meltdown at Three Mile Island. [1]


      [1] "Excerpts from Gorbachev's Speech on Chernobyl Accident." The New York Times, 15 May 1986, https://www.nytimes.com/1986/05/15/world/excerpts-from-gorbachev-s-speech-on-chernobyl-accident.html.

    7. Sweden

      This line references the early detection of the Chernobyl catastrophe in Sweden. A worker at Forsmark Nuclear Power Plant in Sweden recognized that his shoes were flagged for excessively high levels of radiation. [1] After analyzing the radioactive materials and wind patterns, the Forsmark plant employees determined that the radiation came from the Chernobyl region. [1] As Swedish nuclear scientists had previously detected radiation spread from the Soviet Union's nuclear tests in the Arctic, they were equipped to locate the source of the Chernobyl radiation and alert the global community. [2] When Swedish officials asked Soviet authorities whether an accident had happened, it was initially denied until the Swedish diplomats threatened to notify the International Atomic Energy Authority. [3] According to scientific research conducted in Sweden after the disaster, the country received about 5% of the fallout from Chernobyl, which has contributed to environmental contamination and increased medical risk within the country. [4]


      [1] "Forsmark: How Sweden Alerted the World About the Danger of the Chernobyl Disaster." News: European Parliament, 15 May 2014, https://www.europarl.europa.eu/news/en/hea