Cognitive Bias in Forensic Pathology Decisions was published today by Journal of Forensic Sciences

With @franklowery our new study on attributes of T cells that contribute to successful cell therapy in cancer patients in @ScienceMagazine today w/ colleagues @slgoff_SB, @NCI_CCR_SB @theNCI a TL;DR thread on key findings with caveats :) 1/10

has a long history of using tumor infiltrating T cells (TILs) to treat cancers since well.. before I was even born. We analyzed our most successful melanoma ACT trial for cell surface phenotypes in TIL infusion products of patients (aPD1/immunotherapy naive) /2

Surprisingly we found a CD39- TIL subset (CD39-CD69-, DN) associated with ACT-response. TBH we were expecting the opposite (CD39+). We only included CD39 bcz multiple groups (e.g. Simoni et al, 2018) had reported CD39+ as enriching for anti-tumor/neoantigen reactive T cells. /3

CD39- DN TILs RNA/epigenetics resemble stem-like memory progenitors, and in vitro were able to self-renew, and give rise to other CD39+ subsets. OTOH the most dominant subset of patient infusion products were CD39+ CD69+ (DP) and these guys were terminally differentiated.. /4

So, to clarify we specifically analyzed tumor-specific mutation-reactive Tcells. Turns out, ACT-responders had pool of neoantigen-reactive TILs in the CD39- phenotype, while non-responders did not (despite other irrelevant CD39- Tcells) -> not all CD39- T cells are bystanders /5

Announcing the 2021 Summer Institutes in Computational Social Science. #SICSS is for grad students, post-docs & beginning faculty. Free for participants. @chris_bail and I are happy to tell you about all 20 locations. https://t.co/wolhtZIMc5 [thread]

SICSS brings together social scientists & data scientists interested in computational social science for 1-2 week events with intensive study & collaborative research.

Topics covered include automated text analysis, web scraping, non-probability sampling, digital experiments, ethics & much more.

All education materials created for SICSS are available open-source so that people who are unable to participate can still learn. You can also use them in your teaching. Here are materials from previous years:

Because of COVID, all SICSS locations will be online only in 2021.

(THREAD CONTINUED)
As mentioned in the above videos human cloning has been confirmed and many of the elites you are now seeing are in fact clones or doubles standing in for themselves. The reason for these clones is because humanity has not yet awakened enough to handle knowing

all the crimes these sick disgusting Deep State Freemasonic Illuminati Cabal Globalist Satanists are truly involved in. The majority of society would literally go mentally insane if they learned all of this information at once.

Ct values can be used to estimate epidemic dynamics UPDATE! Ct values are expected to change depending on whether the epidemic is growing or declining, and we harness this to estimate the epidemic trajectory. Lots of cool new analyses and methods!

Highlights:
- Cts from symptom-based surveillance change over time, but the effect is weaker
- Methods to infer incidence using single cross-sections of Cts
- Unbiased by changing testing coverage
- Gaussian process (wiggly line) model for incidence tracking using Ct values
2/12

This work is *JOINTLY led* with @LeekShaffer and PI’d by @michaelmina_lab. Thank you also to the ever insightful @mlipsitch and to coauthors @SanjatKanjilal @gabriel_stacey and @nialljlennon. 3/12

Premise: times since infection depend on the epidemic trajectory. Distributions of randomly sampled viral loads proxy times since infection. With calibration, Ct values can estimate growth rate. We focus on qPCR in SARS-CoV-2, but the principle applies to any outbreak. 4/12

Result 1: viral loads are shifted higher (Cts lower) during epidemic growth and lower (Cts higher) during decline when individuals are sampled *based on the onset of symptoms*. We simulated linelist data under symptom-based surveillance and looked at TSI and Cts over time. 5/12