30 Matching Annotations
  1. Sep 2020
  2. Aug 2020
  3. Jul 2020
  4. Jun 2020
    1. the lowest copy number sample points impacted faster and sronger, then you just need to stabilize by adding a neutral nucleic acid background in your standard curve. I usually use water containig 10ng/microl yeast tRNA to perform serial dilutions. This will first create a reaction background similar to your RTQPCR reaction, but also stabilize your DNA copies; I can freeze and thaw (min 20C) more than 50 times the same standard curve sample without any loss in Cts, from 10E6 to 10E2 copies. When I tested the same standard curve but diluted in water only, then the 10E2 started to be slightly affected after one freeze and thaw and then crashed further; then higher copy numbers samples were also affected after 2 to 3 freeze and thaw.
  5. May 2020
    1. still took years before it paid off handsomely.

      In economics, this same concept is known as the J Curve (https://www.wikiwand.com/en/J_curve)

  6. Apr 2020
    1. Such languages may make it easier for a person without knowledge about the language to understand the code and perhaps also to learn the language.
    1. Did you expect the temp directory to get printed? In the last example, we saw the directories ./temp and ./C/temp got printed, but not now. This is the effect of the -print option. By default, the find command prints all the files matching the criteria. However, once the -print option is specified, it will print files only on explicit print instructions. In this find command, -print is associated in the other side of the OR condition, and hence nothing will get printed from the 1st part of the condition.
  7. Mar 2020
  8. Dec 2019
    1. Might be a little too low-level (even with GUIs) for some teams of users. GPG and Git both require some setup and experience in these tools, or the willingness to learn. Porting a GPG key from machine to machine is not trivial.
  9. Jul 2019
    1. Sidenote: Visually comparing estimated survival curves in order to assess whether there is a difference in survival between groups is usually not recommended, because it is highly subjective. Statistical tests such as the log-rank test are usually more appropriate.
    1. Note that, the confidence limits are wide at the tail of the curves, making meaningful interpretations difficult. This can be explained by the fact that, in practice, there are usually patients who are lost to follow-up or alive at the end of follow-up. Thus, it may be sensible to shorten plots before the end of follow-up on the x-axis (Pocock et al, 2002).
  10. Jan 2019
    1. The Grid is based around ideas familiar to Bitwig Studio

      The continuity between these new modular features and the rest of the DAW’s workflow probably has unexpected consequences. Before getting information about BWS3, one might have thought that the “Native Modular System” promised since the first version might still be an add-on. What the marketing copy around this “killer feature” makes clear, it’s the result of a very deliberate process from the start and it’ll make for a qualitatively different workflow.

    1. Some readers will also be surprised to find that The Bell Curve is not as controversial as its reputation would lead one to believe (and most of the book is not about race at all).

      I wrote this sentence. Two coauthors, three peer reviewers, and an editor all read it multiple times. No one ever asked for it to be changed.

  11. Jul 2018
    1. I think this paper and these data could be extremely useful for psychologists, but I also think this paper needs at least one more analysis: estimating effect sizes by research area, controlling for publication bias.

      It's very hard to interpret these estimates given good evidence and arguments that researchers and journals select for p < .05. I think it's safe to assume that all these estimates reported in this preprint are bigger than the true averages (Simonsohn, Nelson, & Simmons, 2014).

      One approach to estimating "selection bias adjusted" effects would be to estimate the effect size for each research area using the code provided in the p-curve effect size paper supplements (http://www.p-curve.com/Supplement/). You could estimate confidence intervals or percentiles using bootstrapping procedures or write code to estimate the lower and upper bounds using the same methods to estimate the average effect.

      This approach assumes the p-values all test the hypothesis of interest and don't suffer from unique selection biases (see Selecting p Values in Simonsohn, Nelson, & Simmons, 2014, p. 540).

      Hope this helps make the paper better!