Person:

Katz, Yarden

Microbes growing in animal host environments face fluctuations that have elements of both randomness and predictability. In the mammalian gut, fluctuations in nutrient levels and other physiological parameters are structured by the animal host’s behavior, diet, health and microbiota composition. Microbial cells that are able to anticipate these fluctuations by exploiting this structure would likely gain a fitness advantage, by adapting their internal state in advance. We propose that the problem of adaptive growth in these structured changing environments can be viewed as probabilistic inference. We analyze environments that are “meta-changing”: where there are changes in the way the environment fluctuates, governed by a mechanism unobservable to cells. We develop a dynamic Bayesian model of these environments and show that a real-time inference algorithm (particle filtering) for this model can be used as a microbial growth strategy implementable in molecular circuits. The growth strategy suggested by our model outperforms heuristic strategies, and points to a class of algorithms that could support real-time probabilistic inference in natural or synthetic cellular circuits.

Intestinal epithelial cells (IECs) absorb nutrients, respond to microbes, provide barrier function and help coordinate immune responses. We profiled 53,193 individual epithelial cells from mouse small intestine and organoids, and characterized novel subtypes and their gene signatures. We showed unexpected diversity of hormone-secreting enteroendocrine cells and constructed their novel taxonomy. We distinguished between two tuft cell subtypes, one of which expresses the epithelial cytokine TSLP and CD45 (Ptprc), the pan-immune marker not previously associated with non-hematopoietic cells. We also characterized how cell-intrinsic states and cell proportions respond to bacterial and helminth infections. Salmonella infection caused an increase in Paneth cells and enterocytes abundance, and broad activation of an antimicrobial program. In contrast, Heligmosomoides polygyrus caused an expansion of goblet and tuft cell populations. Our survey highlights new markers and programs, associates sensory molecules to cell types, and uncovers principles of gut homeostasis and response to pathogens.

While the term "Artificial Intelligence" (AI) was coined in the 1950s, in recent years AI has become a focus of attention in mainstream media. Yet the forces behind AI's revival have been unclear. I argue here that the "AI" label has been rebranded to promote a contested vision of world governance through big data. Major tech companies have played a key role in the rebranding, partly by hiring academics that work on big data (which has been effectively relabeled "AI") and helping to create the sense that super-human AI is imminent. However, I argue that the latest AI systems are premised on an old behaviorist view of intelligence that's far from encompassing human thought. In practice, the confusion around AI's capacities serves as a pretext for imposing more metrics upon human endeavors and advancing traditional neoliberal policies. The revived AI, like its predecessors, seeks intelligence with a "view from nowhere" (disregarding race, gender and class)---which can also be used to mask institutional power in visions of AI-based governance. Ultimately, AI's rebranding showcases how corporate interests can rapidly reconfigure academic fields. It also brings to light how a nebulous technical term (AI) may be exploited for political gain.

Researchers and research institutes are increasingly being evaluated using metrics (from bibliometrics to patent counts), which are core instruments of a longstanding effort to quantify scientific productivity and worth. Here, we examine the relationship between commonly used metrics and funding levels for investigators funded by the National Institutes of Health, the largest public funder of biomedical research in the United States, in the years 1985-2015. We find that funding inequality has been rising since 1985, with a small segment of investigators and institutes getting an increasing proportion of funds, and that investigators who start in the top funding ranks tend to stay there (which results in stasis, or lack of mobility). Furthermore, funding levels are a strong quantitative predictor of the interrelated set of metrics frequently used by economists and policy makers to evaluate scientific research. Our results suggest that the widespread system of metrics favors a minority of elite, highly funded researchers and institutes. Current attempts to “optimize” science are inextricably linked to the concentration of funds in the biomedical research system and are likely to further reduce diversity in the research community.