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The Australian red claw crayfish Cherax quadricarinatus, an emerging species within the freshwater aquaculture trade, is not only an ideal species for commercial production due to its high fecundity, fast growth, and physiological robustness but also notoriously invasive. Investigating the reproductive axis of this species has been of great interest to farmers, geneticists, and conservationists alike for many decades; however, aside from the characterisation of the key masculinising insulin-like androgenic gland hormone (IAG) produced by the male-specific androgenic gland (AG), little remains known about this system and the downstream signalling cascade involved. This investigation used RNA interference to silence IAG in adult intersex C. quadricarinatus (Cq-IAG), known to be functionally male but genotypically female, successfully inducing sexual redifferentiation in all individuals. To investigate the downstream effects of Cq-IAG knockdown, a comprehensive transcriptomic library was constructed, comprised of three tissues within the male reproductive axis. Several factors known to be involved in the IAG signal transduction pathway, including a receptor, binding factor, and additional insulin-like peptide, were found to not be differentially expressed in response to Cq-IAG silencing, suggesting that the phenotypic changes observed may have occurred through post-transcriptional modifications. Many downstream factors displayed differential expression on a transcriptomic level, most notably related to stress, cell repair, apoptosis, and cell proliferation. These results suggest that IAG is required for sperm maturation, with necrosis of arrested tissue occurring in its absence. These results and the construction of a transcriptomic library for this species will inform future research involving reproductive pathways as well as biotechnological developments in this commercially and ecologically significant species. 

Climate change is reducing snowpack across temperate regions with negative consequences for human and natural systems. Because forest canopies create microclimates that preserve snowpack, managing forests to support snow refugia—defined here as areas that remain relatively buffered from contemporary climate change over time that sustain snow quality, quantity, and/or timing appropriate to the landscape—could reduce climate change impacts on snow cover, sustaining the benefits of snow. We review the current understanding of how forest canopies affect snow, finding that while closed‐conifer forests and snow interactions have been extensively studied in western North America, there are knowledge gaps for deciduous and mixed forests with dormant season leaf loss. We propose that there is an optimal, intermediate zone along a gradient of dormant season canopy cover (DSCC; the proportion of the ground area covered by the canopy during the dormant season), where peak snowpack depth and the potential for snow refugia will be greatest because the canopy‐mediated effects of snowpack sheltering (which can preserve snowpack) outweigh those of snowfall interception (which can limit snowpack). As an initial test of our hypothesis, we leveraged snowpack measurements in the northeastern United States spanning the DSCC gradient (low, <25% DSCC; medium, 25%–50% DSCC; and high, >50% DSCC), including from 2 sites in Old Town, Maine; 12 sites in Acadia National Park, Maine; and 30 sites in the northern White Mountains of New Hampshire. Medium DSCC forests (typically mature mixed coniferous–deciduous forests) exhibited the deepest peak snowpacks, likely due to reduced snowfall interception compared to high DSCC forests and reduced snowpack loss compared to low DSCC forests. Many snow accumulation or snowpack studies focus on the contrast between coniferous and open sites, but our results indicate a need for enhanced focus on mixed canopy sites that could serve as snow refugia. Measurements of snowpack depth and timing across a wider range of forest canopies would advance understanding of canopy–snow interactions, expand the monitoring of changing winters, and support management of forests and snow‐dependent species in the face of climate change. 

Abstract We present a strong lensing analysis of COOL J1241+2219, the brightest known gravitationally lensed galaxy atz≥ 5, based on new multiband Hubble Space Telescope (HST) imaging data. The lensed galaxy has a redshift ofz= 5.043, placing it shortly after the end of the “Epoch of Reionization,” and an AB magnitudez_AB= 20.47 mag (Khullar et al.). As such, it serves as a touchstone for future research of that epoch. The high spatial resolution of HST reveals internal structure in the giant arc, from which we identify 15 constraints and construct a robust lens model. We use the lens model to extract the cluster mass and lensing magnification. We find that the mass enclosed within the Einstein radius of thez= 1.001 cluster lens is $M(<5.″77)={1.079}_{-0.007}^{+0.023}\times {10}^{13}{M}_{☉}$, significantly lower than other known strong lensing clusters at its redshift. The average magnification of the giant arc is 〈μ_arc〉 = ${76}_{-20}^{+40}$, a factor of ${2.4}_{-0.7}^{+1.4}$greater than previously estimated from ground-based data; the flux-weighted average magnification is 〈μ_arc〉 = ${92}_{-31}^{+37}$. We update the current measurements of the stellar mass and star formation rate (SFR) of the source for the revised magnification to $\log \left(M_{\star }/M_{\odot }\right)=$9.7 ± 0.3 and SFR = ${10.3}_{-4.4}^{+7.0}$M_⊙yr⁻¹, respectively. The powerful lensing magnification acting upon COOL J1241+2219 resolves the source and enables future studies of the properties of its star formation on a clump-by-clump basis. The lensing analysis presented here will support upcoming multiwavelength characterization with HST and JWST data of the stellar mass assembly and physical properties of this high-redshift lensed galaxy. 

Three-dimensional eukaryotic genome organization provides the structural basis for gene regulation. In Drosophila melanogaster , genome folding is characterized by somatic homolog pairing, where homologous chromosomes are intimately paired from end to end; however, how homologs identify one another and pair has remained mysterious. Recently, this process has been proposed to be driven by specifically interacting ‘buttons’ encoded along chromosomes. Here, we turned this hypothesis into a quantitative biophysical model to demonstrate that a button-based mechanism can lead to chromosome-wide pairing. We tested our model using live-imaging measurements of chromosomal loci tagged with the MS2 and PP7 nascent RNA labeling systems. We show solid agreement between model predictions and experiments in the pairing dynamics of individual homologous loci. Our results strongly support a button-based mechanism of somatic homolog pairing in Drosophila and provide a theoretical framework for revealing the molecular identity and regulation of buttons. 

Abstract The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools to quantify natural animal behavior. While advances in deep learning and computer vision have enabled markerless pose estimation in individual animals, extending these to multiple animals presents unique challenges for studies of social behaviors or animals in their natural environments. Here we present Social LEAP Estimates Animal Poses (SLEAP), a machine learning system for multi-animal pose tracking. This system enables versatile workflows for data labeling, model training and inference on previously unseen data. SLEAP features an accessible graphical user interface, a standardized data model, a reproducible configuration system, over 30 model architectures, two approaches to part grouping and two approaches to identity tracking. We applied SLEAP to seven datasets across flies, bees, mice and gerbils to systematically evaluate each approach and architecture, and we compare it with other existing approaches. SLEAP achieves greater accuracy and speeds of more than 800 frames per second, with latencies of less than 3.5 ms at full 1,024 × 1,024 image resolution. This makes SLEAP usable for real-time applications, which we demonstrate by controlling the behavior of one animal on the basis of the tracking and detection of social interactions with another animal. 

Abstract Significant advances in computational ethology have allowed the quantification of behaviour in unprecedented detail. Tracking animals in social groups, however, remains challenging as most existing methods can either capture pose or robustly retain individual identity over time but not both.To capture finely resolved behaviours while maintaining individual identity, we built NAPS (NAPS is ArUco Plus SLEAP), a hybrid tracking framework that combines state‐of‐the‐art, deep learning‐based methods for pose estimation (SLEAP) with unique markers for identity persistence (ArUco). We show that this framework allows the exploration of the social dynamics of the common eastern bumblebee (Bombus impatiens).We provide a stand‐alone Python package for implementing this framework along with detailed documentation to allow for easy utilization and expansion. We show that NAPS can scale to long timescale experiments at a high frame rate and that it enables the investigation of detailed behavioural variation within individuals in a group.Expanding the toolkit for capturing the constituent behaviours of social groups is essential for understanding the structure and dynamics of social networks. NAPS provides a key tool for capturing these behaviours and can provide critical data for understanding how individual variation influences collective dynamics.