Abstract Phenological responses to climate warming are well-documented, yet limited data come from low latitudes. Because phenological sensitivity may vary with latitude, studies in low-latitude regions are essential to understand global patterns. To fill this gap, we analysed acoustic recordings collected daily over 15 years on Iriomote Island, in Japan's transitional zone between subtropical and tropical biomes. We applied the BirdNET deep learning model to quantify the breeding phenology of seven frog species. Seasonal calling patterns differed among species, with peaks occurring in various seasons while one species called nearly year-round. Among six species analysed for phenological trends, a winter-breeding tree-frog (Zhangixalus owstoni) showed a statistically significant shift in median calling date (3.8 days later per decade), whereas the other species showed no significant shifts of varying magnitude and direction. Sliding time window analysis indicated late-summer temperature—which increased during the study period—best predicted median calling dates in Z. owstoni, suggesting that climate warming delayed the phenology of a winter breeder even where year-round reproduction is possible. The absence of significant phenological advancement in our dataset contrasts with spring advancement commonly reported in temperate regions. These findings reinforce the importance of examining long-term phenological shifts across more sites in low-latitude regions.
Abstract Human sodium-glucose cotransporter 2 (hSGLT2) is a membrane-embedded renal transporter and an established therapeutic target for type 2 diabetes. Hyperoside, myricetin, cyanidin-3-O-glucoside and isoliquiritigenin were investigated as a chemically diverse panel of natural scaffolds using a multi-scale computational workflow combining redocking-validated molecular docking, reconstruction of an unresolved transporter segment, molecular dynamics (MD) in an explicit POPC bilayer, molecular mechanics/Poisson–Boltzmann surface area (MM/PBSA) calculations, principal component and free-energy landscape analyses and density functional theory (DFT). The membrane simulations preserved the global hSGLT2 fold while revealing distinct ligand-dependent binding behaviours. Myricetin and cyanidin-3-O-glucoside exhibited limited positional deviations; hyperoside achieved persistent accommodation following an initial adjustment, whereas isoliquiritigenin underwent a marked late rearrangement. Ensemble-based energetic analysis prioritized cyanidin-3-O-glucoside, while hyperoside and myricetin formed an energetically comparable intermediate group and isoliquiritigenin displayed the least favourable profile. Electronic-structure analysis further characterized cyanidin-3-O-glucoside as the most responsive ligand within the DFT subset. Collectively, these findings indicate that glycone-assisted polar anchoring, aromatic complementarity and restricted conformational flexibility promote persistent hSGLT2 recognition. Direct hSGLT2 inhibition and hSGLT1 counter-screening assays remain necessary to establish potency and selectivity.
AbstractInsect cuticles are covered by a complex mixture of cuticular hydrocarbons (CHCs) that are essential for desiccation resistance and chemical communication, yet the physical structure of CHC layers remains poorly understood. We extracted CHCs from five insect species (desert locusts, house crickets, American cockroaches and two ant species) using solvents of differing polarity and extraction durations, and from insects reared under different temperature regimes. Extractions with n-hexane were enriched in relatively solid CHCs (n-alkanes and terminally branched monomethylalkanes), while extractions with dichloromethane and dichloromethane–methanol yielded proportionally more fluid compounds (internally branched monomethyl and dimethylalkanes, unsaturated hydrocarbons). Solid-phase CHCs were proportionally more abundant in shorter extractions, whereas fluid-phase compounds were proportionally more abundant in prolonged or sequential extractions. These patterns are consistent with a model of cohesive solid domains surrounded by more accessible fluid regions, though this interpretation remains indirect and alternative explanations (e.g. differential solubility and diffusion kinetics) cannot be excluded. CHCs of similar phase behaviour covaried across treatments, indicating that they aggregate within shared domains. Warm acclimatization increased the proportion of solid compounds, whereas fluctuating temperatures enhanced CHCs with intermediate phase behaviour. Our results suggest spatial and chemical heterogeneity in the CHC layer, with patterns of differential extractability consistent with clustering into phase-distinct domains, though direct imaging is required to confirm spatial topology. This potential heterogeneity may facilitate CHC multi-functionality, with the phase behaviour and domain membership of each CHC depending on its biophysical properties relative to the individual's overall CHC profile.
AbstractZoonotic disease emergence at the wildlife–domestic animal–human interface results from a complex interplay of ecological, physiological and anthropogenic factors. Here, we have developed a multi-environment, multi-host compartmental model to identify key drivers of spillover risk across urban, rural and natural environments. The framework incorporates heterogeneous host populations, inter-species contacts and host movement, and includes wildlife chronic stress as one factor influencing susceptibility, transmissibility and recovery. Sensitivity analyses show that domestic animal populations at agricultural interfaces strongly amplify spillover risk through their recovery dynamics and per-contact transmission probabilities, while within-wildlife transmission across environments also shapes system dynamics. Chronic stress effects can further increase spillover risk, and the model indicates that reducing wildlife stress alongside improving domestic animal health and management can markedly lower the likelihood of cross-species transmission. These findings highlight the importance of maintaining ecological conditions that reduce chronic physiological stress in wildlife populations and integrating physiological and ecological data into epidemiological modelling. Such models can inform both bottom-up interventions targeting domestic animals and top-down strategies that sustain resilient wildlife communities and, thereby, mitigate zoonotic spillover.
AbstractConflict fatalities tend to follow heavy-tailed statistical distributions. A 2005 fusion–fission theory predicts that, when armed groups operate through dynamically forming and fragmenting clusters, the number of fatalities per event should be approximated by a power-law distribution with exponent α near 2.5, with deviations reflecting the relative robustness of larger versus smaller fighter clusters. We use Armed Conflict Location and Event Data (ACLED) event-level fatality data to estimate α in recent Yemen and Syria conflict periods during 2023–2025. These conflict event datasets are small, incomplete and imperfect and only produce very crude approximations to a power law; nonetheless, the best-fit α values occupy the range 2.5<α<3.5 across most periods, which is broadly consistent with predominantly fragmented or weakly consolidated armed-group organization. Temporary reductions in α are broadly consistent with episodes in which larger coordinated clusters become more consequential. More rigorous testing will require improved datasets to become available in the future.
