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Visuomotor experience is necessary for the development of normal function of visual cortex (Attinger et al., 2017) and likely establishes a balance between movement-related predictions and sensory signals (Jordan and Keller, 2020). This process depends at least in part on plasticity in visual cortex (Widmer et al., 2022). Key signals involved in driving this plasticity are visuomotor prediction errors (Keller et al., 2012; Keller and Mrsic-Flogel, 2018). Ideally however, the amount of plasticity induced by an error signal should be a function of several variables - including the total prediction error across all of cortex at that moment, the animal's experience in the current environment or task, stability of the current environment, and task engagement - for optimal computational performance. Candidates for regulators of visuomotor prediction error driven plasticity are the three major neuromodulatory systems that innervate visual cortex in the mouse: acetylcholine, noradrenaline, and serotonin. While visuomotor mismatch acutely triggers activity in noradrenaline (Jordan and Keller, 2023) but not acetylcholine (Yogesh and Keller, 2023) axons in visual cortex, how serotonergic axons in cortex respond to visuomotor mismatch is unknown. Here, we characterized the activity of serotonergic axons in visual cortex (V1) and in area A24b, a motor cortical area in anterior cingulate cortex (ACC), of awake head-fixed mice using two-photon calcium imaging. Our results reveal cortical region-specific responses to visuomotor stimuli in serotonergic axons, but no evidence of a response to visuomotor mismatch. However, average activity in serotonergic axons was modulated by the recent history of visuomotor coupling. We speculate that serotonin could function to regulate visuomotor plasticity as a function of the predictability of the environment with a slow integration time constant.

The vaginal microbiota is known to affect women's health. Yet, there is a notable paucity of high-resolution follow-up studies lasting several months, which would be required to interrogate the long-term dynamics and associations with demographic and behavioural covariates. Here, we present a high-resolution longitudinal cohort study of 125 women, followed for a median duration of 8.6 months, with a median of 11 samples collected per woman. Using a hierarchical Bayesian Markov model, we characterised the patterns of vaginal microbiota community persistence and transition, simultaneously estimated the impact of 16 covariates and quantified individual variability among women. We showed that "optimal" (Community State Type (CST) I, II, and V) and "sub-optimal" (CST III) communities are more stable over time than "non-optimal" (CST IV) ones. Furthermore, we found that some covariates - most notably alcohol consumption - impacted the probability of shifting from one CST to another. We performed counterfactual simulations to confirm that alterations of key covariates, such as alcohol consumption, could shape the prevalence of different microbiota communities in the population. Finally, our analyses indicated that there is a relatively canalised pathway leading to the deterioration of vaginal microbiota communities, whereas the paths to recovery can be highly individualised among women. In addition to providing one of the first insights into vaginal microbiota dynamics over a year, our study showcases a novel application of a hierarchical Bayesian Markov model to clinical cohort data with many covariates. Our findings pave the way for an improved mechanistic understanding of microbial dynamics in the vaginal environment and the development of novel preventative and therapeutic strategies to improve vaginal health.

Domestication is a process marked by complex interactions between demographic changes and selective pressures, which together shape genetic diversity. While the phenotypic outcomes of domestication are well documented, its genetic basis-particularly the dynamics of selection-remain less well understood. To investigate these dynamics, we performed simulations designed to approximate the demographic history of large domestic mammals. These simulations used selection coefficients as a modeling tool to represent changes in selection pressures, recognizing that such coefficients are abstractions rather than direct representations of biological reality. Specifically, we analyzed site frequency spectra (SFS) under varying distributions of fitness effects (DFE) and proportions of mutations with divergent selective pressures. Our results show that the discretized deleterious DFE can be reliably inferred from the SFS of a single population, but reconstructing the beneficial DFE and demographic history remains challenging, even when using the joint SFS of both populations. We further developed a novel joint DFE inference model to estimate the proportion of mutations with divergent selection coefficients (p _c), although we found that signals of classic hard sweeps can mimic increases in p _c, complicating interpretation. These findings underscore both the utility and limitations of DFE inference and highlight the need for caution when interpreting demographic histories in domesticated populations based on such modeling assumptions.