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Gap junction communication (GJC) mediated by connexins is critical for heart function. To gain insight into the causal relationship of molecular mechanisms of disease pathology, it is important to understand which mechanisms contribute to impairment of gap junctional communication. Here, we present an update on the known modulators of connexins, including various interaction partners, kinases, and signaling cascades. This gap junction network (GJN) can serve as a blueprint for data mining approaches exploring the growing number of publicly available data sets from experimental and clinical studies.

AbstractStudies of sex chromosome systems at early stages of divergence are key to understanding the initial process and underlying causes of recombination suppression. However, identifying signatures of divergence in homomorphic sex chromosomes can be challenging due to high levels of sequence similarity between the X and the Y. Variations in methodological precision and underlying data can make all the difference between detecting subtle divergence patterns or missing them entirely. Recent efforts to test for X-Y sequence differentiation in the guppy have led to contradictory results. Here we apply different analytical methodologies to the same dataset to test for the accuracy of different approaches in identifying patterns of sex chromosome divergence in the guppy. Our comparative analysis reveals that the most substantial source of variation in the results of the different analyses lies in the reference genome used. Analyses using custom-made de novo genome assemblies for the focal species successfully recover a signal of divergence across different methodological approaches. By contrast, using the distantly related Xiphophorus reference genome results in variable patterns, due to both sequence evolution and structural variations on the sex chromosomes between the guppy and Xiphophorus. Changes in mapping and filtering parameters can additionally introduce noise and obscure the signal. Our results illustrate how analytical differences can alter perceived results and we highlight best practices for the study of nascent sex chromosomes.

Summary Understanding how to find targets with very limited information is a topic of interest in many disciplines. In ecology, such research has often focused on the development of two movement models: (i) the Lévy walk and (ii) the composite correlated random walk and its associated area‐restricted search behaviour. Although the processes underlying these models differ, they can produce similar movement patterns. Due to this similarity and because of their disparate formulation, current methods cannot reliably differentiate between these two models. Here, we present a method that differentiates between the two models. It consists of likelihood functions, including one for a hidden Markov model, and associated statistical measures that assess the relative support for and absolute fit of each model. Using a simulation study, we show that our method can differentiate between the two search models over a range of parameter values. Using the movement data of two polar bears (Ursus maritimus), we show that the method can be applied to complex, real‐world movement paths. By providing the means to differentiate between the two most prominent search models in the literature, and a framework that could be extended to include other models, we facilitate further research into the strategies animals use to find resources.

Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the underlying intrinsic geometry of data, diffusion embeddings are inherently limited due to their discrete nature. To this end, we propose neural FIM, a method for computing the Fisher information metric (FIM) from point cloud data - allowing for a continuous manifold model for the data. Neural FIM creates an extensible metric space from discrete point cloud data such that information from the metric can inform us of manifold characteristics such as volume and geodesics. We demonstrate Neural FIM's utility in selecting parameters for the PHATE visualization method as well as its ability to obtain information pertaining to local volume illuminating branching points and cluster centers embeddings of a toy dataset and two single-cell datasets of IPSC reprogramming and PBMCs (immune cells). Comment: 13 pages, 11 figures, 1 table

There is much evidence that high-capacity reasoners perform better on a variety of reasoning tasks (Stanovich, 1999), a phenomenon that is normally attributed to differences in either the efficacy or the probability of deliberate (Type II) engagement (Evans, 2007). In contrast, we hypothesized that intuitive (Type I) processes may differentiate high- and low-capacity reasoners. To test this hypothesis, reasoners were given a reasoning task modeled on the logic of the Stroop Task, in which they had to ignore one dimension of a problem when instructed to give an answer based on the other dimension (Handley, Newstead, & Trippas, 2011). Specifically, in Experiment 1, 112 reasoners were asked to give judgments consistent with beliefs or validity for 2 different types of deductive reasoning problems. In Experiment 2, 224 reasoners gave judgments consistent with beliefs (i.e., stereotypes) or statistics (i.e., base-rates) on a base rate task; half responded under a strict deadline. For all 3 problem types and regardless of the deadline, high-capacity reasoners performed better for logic/statistics than did belief judgments when the 2 conflicted, whereas the reverse was true for low-capacity reasoners. In other words, for high-capacity reasoners, statistical information interfered with their ability to make belief-based judgments, suggesting that, for them, probabilities may be more intuitive than stereotypes. Thus, at least part of the accuracy-capacity relationship observed in reasoning may be because of intuitive (Type I) processes. (PsycINFO Database Record

Superoscillations are band-limited functions with the counterintuitive property that they can vary arbitrarily faster than their fastest Fourier component, over arbitrarily long intervals. Modern studies originated in quantum theory, but there were anticipations in radar and optics. The mathematical understanding - still being explored - recognises that functions are extremely small where they superoscillate; this has implications for information theory. Applications to optical vortices, sub-wavelength microscopy and related areas of nanoscience are now moving from the theoretical and the demonstrative to the practical. This Roadmap surveys all these areas, providing background, current research, and anticipating future developments.

Current constraints on spatial curvature show that it is dynamically negligible: $|\Omega_{\rm K}| \lesssim 5 \times 10^{-3}$ (95% CL). Neglecting it as a cosmological parameter would be premature however, as more stringent constraints on $\Omega_{\rm K}$ at around the $10^{-4}$ level would offer valuable tests of eternal inflation models and probe novel large-scale structure phenomena. This precision also represents the "curvature floor", beyond which constraints cannot be meaningfully improved due to the cosmic variance of horizon-scale perturbations. In this paper, we discuss what future experiments will need to do in order to measure spatial curvature to this maximum accuracy. Our conservative forecasts show that the curvature floor is unreachable - by an order of magnitude - even with Stage IV experiments, unless strong assumptions are made about dark energy evolution and the $\Lambda$CDM parameter values. We also discuss some of the novel problems that arise when attempting to constrain a global cosmological parameter like $\Omega_{\rm K}$ with such high precision. Measuring curvature down to this level would be an important validation of systematics characterisation in high-precision cosmological analyses. Comment: 9 pages, 1 figure. Updated to match version published in Phys. Rev. D

The Cambrian explosion (CE) and the great Ordovician biodiversification event (GOBE) are the two most important radiations in Paleozoic oceans. We quantify the role of bioturbation and bioerosion in ecospace utilization and ecosystem engineering using information from 1367 stratigraphic units. An increase in all diversity metrics is demonstrated for the Ediacaran-Cambrian transition, followed by a decrease in most values during the middle to late Cambrian, and by a more modest increase during the Ordovician. A marked increase in ichnodiversity and ichnodisparity of bioturbation is shown during the CE and of bioerosion during the GOBE. Innovations took place first in offshore settings and later expanded into marginal-marine, nearshore, deep-water, and carbonate environments. This study highlights the importance of the CE, despite its Ediacaran roots. Differences in infaunalization in offshore and shelf paleoenvironments favor the hypothesis of early Cambrian wedge-shaped oxygen minimum zones instead of a horizontally stratified ocean. Biogenic reworking played a major role in early Paleozoic oceans, and its study helps to constrain paleo-oxygenation models.