1,080 Matching Annotations
  1. Aug 2020
    1. Bangaru, S., Ozorowski, G., Turner, H. L., Antanasijevic, A., Huang, D., Wang, X., Torres, J. L., Diedrich, J. K., Tian, J.-H., Portnoff, A. D., Patel, N., Massare, M. J., Yates, J. R., Nemazee, D., Paulson, J. C., Glenn, G., Smith, G., & Ward, A. B. (2020). Structural analysis of full-length SARS-CoV-2 spike protein from an advanced vaccine candidate. BioRxiv, 2020.08.06.234674. https://doi.org/10.1101/2020.08.06.234674

    1. Sherrard-Smith, E., Hogan, A. B., Hamlet, A., Watson, O. J., Whittaker, C., Winskill, P., Ali, F., Mohammad, A. B., Uhomoibhi, P., Maikore, I., Ogbulafor, N., Nikau, J., Kont, M. D., Challenger, J. D., Verity, R., Lambert, B., Cairns, M., Rao, B., Baguelin, M., … Churcher, T. S. (2020). The potential public health consequences of COVID-19 on malaria in Africa. Nature Medicine, 1–6. https://doi.org/10.1038/s41591-020-1025-y

    1. Unterman, A., Sumida, T. S., Nouri, N., Yan, X., Zhao, A. Y., Gasque, V., Schupp, J. C., Asashima, H., Liu, Y., Cosme, C., Deng, W., Chen, M., Raredon, M. S. B., Hoehn, K., Wang, G., Wang, Z., Deiuliis, G., Ravindra, N. G., Li, N., … Cruz, C. S. D. (2020). Single-Cell Omics Reveals Dyssynchrony of the Innate and Adaptive Immune System in Progressive COVID-19. MedRxiv, 2020.07.16.20153437. https://doi.org/10.1101/2020.07.16.20153437

  2. Jul 2020
    1. Meyer, B., Torriani, G., Yerly, S., Mazza, L., Calame, A., Arm-Vernez, I., Zimmer, G., Agoritsas, T., Stirnemann, J., Spechbach, H., Guessous, I., Stringhini, S., Pugin, J., Roux-Lombard, P., Fontao, L., Siegrist, C.-A., Eckerle, I., Vuilleumier, N., & Kaiser, L. (2020). Validation of a commercially available SARS-CoV-2 serological immunoassay. Clinical Microbiology and Infection, 0(0). https://doi.org/10.1016/j.cmi.2020.06.024

  3. Jun 2020
    1. Chu, D. K., Akl, E. A., Duda, S., Solo, K., Yaacoub, S., Schünemann, H. J., Chu, D. K., Akl, E. A., El-harakeh, A., Bognanni, A., Lotfi, T., Loeb, M., Hajizadeh, A., Bak, A., Izcovich, A., Cuello-Garcia, C. A., Chen, C., Harris, D. J., Borowiack, E., … Schünemann, H. J. (2020). Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: A systematic review and meta-analysis. The Lancet, 0(0). https://doi.org/10.1016/S0140-6736(20)31142-9

  4. May 2020
    1. If it’s a live pet, you do a little threat modeling: is the cat cute and cuddly, or will it scratch the kid’s face off?
    1. While somewhat modest in size, the literature on chronic tolerance to nicotine in humans is reasonably consistent in showing clear evidence of tolerance to subjective mood effects but little or no tolerance to cardiovascular, performance or other nicotine effects

      This is what I'd expect for tobacco, but it tells me little about nicotine. Most of the subjective effects are not from tobacco, so It's still plausible that nicotine does not develop tolerance. Indeed, the effects that don't go away are the effects expected from nicotine.

    1. Drew, D. A., Nguyen, L. H., Steves, C. J., Menni, C., Freydin, M., Varsavsky, T., Sudre, C. H., Cardoso, M. J., Ourselin, S., Wolf, J., Spector, T. D., Chan, A. T., & Consortium§, C. (2020). Rapid implementation of mobile technology for real-time epidemiology of COVID-19. Science. https://doi.org/10.1126/science.abc0473

  5. Apr 2020
    1. Jefferson, T., Jones, M., Al Ansari, L. A., Bawazeer, G., Beller, E., Clark, J., Conly, J., Del Mar, C., Dooley, E., Ferroni, E., Glasziou, P., Hoffman, T., Thorning, S., & Van Driel, M. (2020). Physical interventions to interrupt or reduce the spread of respiratory viruses. Part 1 - Face masks, eye protection and person distancing: Systematic review and meta-analysis [Preprint]. Public and Global Health. https://doi.org/10.1101/2020.03.30.20047217

    1. Sumner, P., Vivian-Griffiths, S., Boivin, J., Williams, A., Bott, L., Adams, R., Venetis, C. A., Whelan, L., Hughes, B., & Chambers, C. D. (2016). Exaggerations and Caveats in Press Releases and Health-Related Science News. PLOS ONE, 11(12), e0168217. https://doi.org/10.1371/journal.pone.0168217

    1. Ferres, L., Schifanella, R., Perra, N., Vilella, S., Bravo, L., Paolotti, D., Ruffo, G., & Sacasa, M. (n.d.). Measuring Levels of Activity in a Changing City. 11.

    1. It might be contrary to traditional thinking, but writing unique passwords down in a book and keeping them inside your physically locked house is a damn sight better than reusing the same one all over the web. Just think about it - you go from your "threat actors" (people wanting to get their hands on your accounts) being anyone with an internet connection and the ability to download a broadly circulating list Collection #1, to people who can break into your house - and they want your TV, not your notebook!
    1. “Even if experts are saying it’s really not going to make a difference, a little [part of] people’s brains is thinking, well, it’s not going to hurt. Maybe it’ll cut my risk just a little bit, so it’s worth it to wear a mask,” she says.
  6. Mar 2020
    1. One MailChimp user tweeted this week that it seems the EU has "effectively killed newsletter with GDPR." He said he sent "get consent" emails through MailChimp and reported these numbers: 100 percent delivery rate, 37 percent open rate, 0 percent given consent.
    1. Factors that affect power

      Factors that affect power.

    2. Cohen’s recommendations:  Jacob Cohen has many well-known publications regarding issues of power and power analyses, including some recommendations about effect sizes that you can use when doing your power analysis.  Many researchers (including Cohen) consider the use of such recommendations as a last resort, when a thorough literature review has failed to reveal any useful numbers and a pilot study is either not possible or not feasible.  From Cohen (1988, pages 24-27):

      Recommendations from Cohen about choosing the effect size when doing a power analysis.

    3. Obtaining the necessary numbers to do a power analysis

      Obtaining the necessary numbers to do a power analysis

    4. Power is the probability of detecting an effect, given that the effect is really there.  In other words, it is the probability of rejecting the null hypothesis when it is in fact false.  For example, let’s say that we have a simple study with drug A and a placebo group, and that the drug truly is effective; the power is the probability of finding a difference between the two groups.  So, imagine that we had a power of .8 and that this simple study was conducted many times.  Having power of .8 means that 80% of the time, we would get a statistically significant difference between the drug A and placebo groups.  This also means that 20% of the times that we run this experiment, we will not obtain a statistically significant effect between the two groups, even though there really is an effect in reality.

      Power analysis definition