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Abstract Automated, data-driven construction and evaluation of scientific models and theories is a long-standing challenge in artificial intelligence. We present a framework for algorithmically synthesizing models of a basic part of human language: morpho-phonology, the system that builds word forms from sounds. We integrate Bayesian inference with program synthesis and representations inspired by linguistic theory and cognitive models of learning and discovery. Across 70 datasets from 58 diverse languages, our system synthesizes human-interpretable models for core aspects of each language’s morpho-phonology, sometimes approaching models posited by human linguists. Joint inference across all 70 data sets automatically synthesizes a meta-model encoding interpretable cross-language typological tendencies. Finally, the same algorithm captures few-shot learning dynamics, acquiring new morphophonological rules from just one or a few examples. These results suggest routes to more powerful machine-enabled discovery of interpretable models in linguistics and other scientific domains. 

Seven new coumarinolignans, walthindicins A–F (1a, 1b, 2–5, 7), along with five known analogs (6, 8–11), were isolated from the roots of Waltheria indica. The structures of the new compounds are determined by detailed nuclear magnetic resonance (NMR), circular dichroism (CD) with extensive computational support, and mass spectroscopic data interpretation. Compounds were tested for their antioxidant activity in Human Cervical Cancer cells (HeLa cells). Compounds 1a and 6 showed higher reactive oxygen species (ROS) inhibitory activity at 20 μg/mL when compared with other natural compound-based antioxidants such as ascorbic acid. Considering the role of ROS in nuclear-factor kappa B (NF-κB) activation, compounds 1a and 6 were evaluated for NF-κB inhibitory activity and showed a concentration-dependent inhibition in Human Embryonic Kidney 293 cells (Luc-HEK-293). 

Mycobacterium tuberculosis ( Mtb ) has complex and dynamic interactions with the human host, and subpopulations of Mtb that emerge during infection can influence disease outcomes. This study implicates zinc ion (Zn 2+ ) availability as a likely driver of bacterial phenotypic heterogeneity in vivo . Zn 2+ sequestration is part of “nutritional immunity”, where the immune system limits micronutrients to control pathogen growth, but this defense mechanism seems to be ineffective in controlling Mtb infection. Nonetheless, Zn 2+ -limitation is an environmental cue sensed by Mtb , as calprotectin triggers the zinc uptake regulator (Zur) regulon response in vitro and co-localizes with Zn 2+ -limited Mtb in vivo . Prolonged Zn 2+ limitation leads to numerous physiological changes in vitro , including differential expression of certain antigens, alterations in lipid metabolism and distinct cell surface morphology. Furthermore, Mtb enduring limited Zn 2+ employ defensive measures to fight oxidative stress, by increasing expression of proteins involved in DNA repair and antioxidant activity, including well described virulence factors KatG and AhpC, along with altered utilization of redox cofactors. Here, we propose a model in which prolonged Zn 2+ limitation defines a population of Mtb with anticipatory adaptations against impending immune attack, based on the evidence that Zn 2+ -limited Mtb are more resistant to oxidative stress and exhibit increased survival and induce more severe pulmonary granulomas in mice. Considering that extracellular Mtb may transit through the Zn 2+ -limited caseum before infecting naïve immune cells or upon host-to-host transmission, the resulting phenotypic heterogeneity driven by varied Zn 2+ availability likely plays a key role during early interactions with host cells.