Mol Cell | Computationally reconstructing cotranscriptional RNA folding from experimental data reveals rearrangement of non-native folding intermediates
J Chem Inf Model | Informatics for Chemistry, Biology, and Biomedical Sciences
PLoS Comp Bio | Ten simple rules for typographically appealing scientific texts
PLoS Comp Bio | Ten quick tips for making things findable
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Hopefully in 2020 I will read even more!
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The world came into a standstill in March, including this blog. Here’s to a restart.
Science | Genetic interaction mapping informs integrative structure determination of protein complexes
Chem Sci | Ligand design by targeting a binding site water
Sci. Rep | Statistics for the analysis of molecular dynamics simulations: providing P values for agonist-dependent GPCR activation
Science | Protein storytelling through physics Nice general MD review
J Med Chem | Chemists: AI Is Here; Unite To Get the Benefits
Sci. Rep | Moving targets in drug discovery
In the Pipeline | The New Mutations
It occurred to me that I’m trying to introduce mutations to my enzyme and here is the virus doing just that using the world as its test tube…What do you call this? Poignant? Amusing? Ironic?
The Atlantic | How COVID-19 Changes Our Sleep
The Atlantic | How Science Beat the Virus
Nature | The peanut snack that triggered a fresh approach to allergy prevention
How the novel coronavirus has evolved
Nature | Oxford COVID vaccine paper highlights lingering unknowns about results
PyCaret: Useful ML tool for chemoinformatics #chemoinformatics #RDKit #Machine learning
Nature | How to write a superb literature review
In the Pipeline | Get Ready for False Side Effects
Sci Am | The COVID Science Wars
Helen Berman: the crystallographer who pioneered the Protein Data Bank
Nautilus | Kim Stanley Robinson Holds Out Hope
Nature | Postdoc survey reveals disenchantment with working life
‘disenchantment’ is a curious diction… I suppose many of us have been charmed by the wizards of the ivory tower.
Life with purpose
Nature | Why your scientific presentation should not be adapted from a journal article
COVID-19 Molecular Structure and Therapeutics Hub
In the Pipeline | Vaccine Possibilities
SciAm | Mysteries of COVID Smell Loss Finally Yield Some Answers
Nature | Five rules for evidence communication
meta, biomedical research search engine
Nature | Science search engine links papers to grants and patents
C&EN | Covalent drugs go from fringe field to fashionable endeavor
In the Pipeline | Vaccine Efficacy Data!
How can climate be predictable if weather is chaotic?
brainpickings | Tenacity, the Art of Integration, and the Key to a Flexible Mind: Wisdom from the Life of Mary Somerville, for Whom the Word “Scientist” Was Coined
The New Yorker | How the Coronavirus Hacks the Immune System
Educated Fools: Why Democratic leaders still misunderstand the politics of social class
Beyond Tokyo and Jerusalem Shusaku Endo’s Silence review
Yo-Yo Ma, Kathryn Stott - Over the Rainbow (Official Video)
LARB | Of Course They Would: On Kim Stanley Robinson’s “The Ministry for the Future”
Thesis Whisperer | While you scream inside your heart, please keep working.
Do Elephants Have Souls?
Huawei, 5G, and the Man Who Conquered Noise
Wired | Remembrance
How to have a difficult conversation
Twitter magic realism bot
See a Stunningly Surreal Bookstore in China
Top 11 Github Repositories to Learn Python
Do you need a little darkness to get you going? –Mary Oliver
He adopted the following strategy: say what you know; what you don’t know; what you are doing to find out; what people can do in the meantime to be on the safe side; and that advice will change –Nature article
In my work as a mediator, I’ve learnt that successful conversations always involve what I call a ‘gem statement’. When two parties have listened long and hard to each other – have made the heroic effort to listen curiously and empathically even when they disagree strenuously – someone eventually unearths a glowing, priceless gem. It usually takes the form of a short, powerful statement –How to have a difficult conversation
aside from faith,
as far as you know,
you will never have another heart.
better to grow the one you were born with.
–Anahata, by Lenelle Moïse
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The term efficacy is not new for the drug industry people, but of course it has now entered the public consciousness thanks to the vaccine efforts. As Carl Zimmer pointed out, our intuitive understanding of “95% effectiveness” is that 95 out of 100 people who get the vaccine will be immune.
As you might already know, it’s not exactly that. That number means that out of certain number of symptomatic, PCR-positive participants, the placebo arm has 95% more than the vaccinated arm, where vaccine:placebo population is 1:1. Taking the numbers from the NY Times article, Pfizer waited until they had 170 cases (for statistical power): 162 in placebo, 8 in vaccine. If vaccine:placebo populations are exactly 1:1, the denominators all cancel out and efficacy = (162-8)/162 = 95%. Even if it is not exactly 1:1, usually it is close to that, so this is a good shortcut for quick calculation. For exact figure, just replace the absolute numbers as fraction in that arm instead – see how the denominators cancel out when a=b :
So, efficacy is a proxy for the eventual effectiveness. The former is in controlled clinical trial setting, while the latter is in messy real word setting. Some points on why there will be discrepancy between the 2:
- Clinical trial population is biased – only certain kind of people would volunteer for the trial. You can expect that they usually are healthy and do not have underlying conditions. Behaviour-wise, they would tend to be more cautious as well.
- Blinding is imperfect. Some people get mild reactions from the vaccine, which sort of tell you that you are in the vaccine arm.
- Asymptomatic cases are not accounted for. The participants are not tested regularly. Instead, they are only tested when they self-report symptoms. This is the case for Moderna trials. AZ-Oxford does weekly swabs so they have data about asymptomatic cases as well.
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I’m trying to learn Markov state models for my MD analysis. I will put thoughts and notes here.
- There are 2 popular Python modules, PyEMMA and MSMbuilder. I’m sticking to PyEMMA at the moment because I had trouble installing the other
- Intro to MSM by Frank Noé 1h+, volume is a bit low
- MSM for Simulation Analysis by Kyle Beauchamp ~20m
- PyEMMA Jupyter notebook tutorials
- Usually my eyes glaze over at statistical mechanics maths, but the PyEMMA paper is surprisingly very readable and easy to follow