Biology subjects

Wang, X. T.

Publications and source records attributed to Wang, X. T..

2 recordsLinked to original sources

bioRxiv2026

A canonical four-state macro-architecture governs multimodal fear-goal arbitration across learning and relapse

When defensive behavior declines, has the individual recovered? Addressing this translational question requires testing whether exploration and goal pursuit also return, and whether those gains survive time and context. We adapted rat fear-conditioning, extinction, delay, and contextual relapse paradigms to this problem. Male rats first explored the chambers under safer conditions and learned to press for food; aversive conditioning then reorganized the repertoire around danger. Extending tri-reference-point (TRP) theory from risky choice, we framed the repertoire around danger, the current condition, and goals. Across 47,004 five-second windows from 29 animals and 213 subject-sessions, we aligned freezing, 22- and 50-kHz calls, and lever presses. Subject-grouped, discrete-aware validation selected a diagonal Gaussian hidden Markov model at theory-guided K=4, decoding recurrent Fear/Danger, Quiescent, Exploratory, and Goal-directed modes. K=8 predicted held-out data better and subdivided these functional regions. Conditioning shifted occupancy from Exploratory toward Fear/Danger. By the final decile of initial extinction, Fear/Danger had fallen to 2.7%, yet Quiescent occupied 95.8%, with Exploratory at 0.6% and Goal-directed at 1.0%: fear reduction without broader functional restoration. Across related cohorts, later tests revealed exploratory-rich and renewed-defense trajectories. Goal-to-Fear transitions were depleted under two null models, and Fear/Danger occupancy was associated with fecal boli after session adjustment (bias-reduced p=0.036). Count-tailored models preserved temporal dependence but regrouped behavior, showing that state identity depends partly on representation. Together, multimodal measurement, TRP-derived landmarks, and temporal modeling make functional recovery testable as the history-dependent restoration of an individual's behavioral repertoire, not merely the suppression of defense.

animal behavior and cognition↗

bioRxiv2022

A continental scale analysis reveals widespread root bimodality

Summary paragraphRecent studies of plant fine roots have greatly advanced our understanding of their geometric properties and symbiotic relationships, but knowledge of how these roots are spatially distributed across the soil matrix lags far behind. An improved understanding of broad-scale variability in root vertical distribution is critical for understanding plant-soil-atmosphere interactions and their influence on the land carbon sink. Here we analyze a continental-scale dataset of plant roots reaching 2-meters depth, spanning 19 ecoclimatic domains ranging from Alaskan tundra to Puerto Rican neotropical forest. Contrary to the common expectation that fine root abundance decays exponentially with increasing soil depth, we found surprising root bimodality at ~20% of 44 field sites --a secondary peak of fine root biomass far beneath the soil surface. All of the secondary root peaks were observed deeper than 60cm (with 33% below 1m), far deeper than the sampling depth commonly used in ecosystem studies and forestry surveys. We demonstrate that root bimodality is more likely in places with relatively low total fine root biomass, and is more frequently associated with shrubland vegetation but less with grassland. Further statistical analyses revealed that the secondary peak of root biomass coincided with unexpected high soil nitrogen contents at depth. By linking roots and nutrient distributions, we further demonstrate that deep soil nutrients tend to be underexploited by plant rooting systems, yet root bimodality offers a unique mechanism by which fine roots can tap into soil resources in the deep. Our findings suggest that empirical practices have often systematically overlooked root dynamics in deep soils, and as a result the current-generation global climate and vegetation models have relied on overly simplistic assumptions for plant rooting distribution.

ecology↗