is an excellent scientist and a responsible professional. She likely read the paper more carefully than most. She grasped some of its strengths and weaknesses that are not apparent from a cursory glance. Below, I will mention a few points some may have missed.
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Get a cup of coffee.

In this thread, we'll cover *State Quantities* and *Flow Quantities*.

This mental model can help us analyze many different entities -- from complex engineering systems to publicly traded companies.

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A "State Quantity" is something that's associated with a *specific point in time*.

For example, the number of followers a Twitter account has is a state quantity. This number can go up and down over time. But at any one *specific* time point, it's fixed.

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Similarly, the amount of cash a company has is a state quantity.

This cash balance can also rise and fall over time.

But pick any one *specific* time point -- and the company can only have one *specific* cash balance at that time point.

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By contrast, a "Flow Quantity" is something that's associated with a time *interval* rather than a time *point*.

For example, the number of followers gained by a Twitter account is a flow quantity. It's meaningful only when we specify a time interval (eg, the last 30 days).

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A simple analogy is that a State Quantity is like a photo -- a snapshot at a particular *point* in time.

But a Flow Quantity is like a video -- a record of events that take place during a particular time *interval*.

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