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Strain- and temperature effects on structure–property relationships in nanocrystalline tungsten thin films
Phenolic compound-mediated covalent crosslinking as a universal mechanism for cellulase deactivation during lignocellulose saccharification
Vertical profile of ambient VOCs in background region of Southwest China from Mt. Fanjing observation
Abstract Atmospheric circulation and local meteorology strongly influence the transport and evolution of volatile organic compounds (VOCs) in mountainous environments, yet their vertical distribution and controlling factors in southwest China remain poorly characterized. Here, we conducted synchronous online measurements of VOCs and related air pollutants at the foot (550 m a.s.l.) and top (2119 m a.s.l.) of Mt. Fanjing during autumn and winter 2024, providing a rare side-by-side characterization of VOCs under contrasting atmospheric conditions in the background region of the Yunnan–Guizhou Plateau. The mean VOC concentration at the mountain top was 14.1 ± 4.8 ppbv, 17.5% lower than that at the mountain foot. Source apportionment showed that vehicles, biomass burning, and industrial-related sources together contributed more than 50% of VOCs at the foot site, whereas the relative contributions of aged traffic plume and solvent evaporation increased at the mountain top, reflecting the combined influence of transport and atmospheric processing. Elevated VOC species at both sites were mainly associated with emissions from the border region of Guizhou, Hunan, and Chongqing, in agreement with the emission inventory analysis. Mt. Fanjing was primarily affected by easterly and westerly background winds. Under westerly background conditions, air masses passing through the Sichuan Basin, Chongqing, and Yunnan enhanced the vertical gradient of VOCs at Mt. Fanjing and increased near-surface VOC accumulation, particularly during periods with pronounced diurnal evolution of local circulation. These results demonstrate that the vertical contrast in VOCs over Mt. Fanjing is jointly controlled by regional transport, background wind regimes, and thermally driven local circulation. The findings highlight that elevated mountain sites cannot be regarded simply as clean background environments and have important implications for regional air-quality assessment, ozone and secondary aerosol formation, and pollutant exposure in mountainous and ecologically sensitive areas.
Natural selection, genetic drift, and trait correlation shaped hominin midfoot evolution
The eGFR difference between creatinine-based and cystatin-C-based equations is also important in an Asian population within the J-CKD-DB-Ex
Corylin promotes healthy aging via RAGA–mTOR suppression and sex-dependent activation of SIRT3
Analgesic effects of human placental hydrolysate on capsaicin-induced hyperalgesia in rats
Stabilizing–sensing synergistic geogrid for high-speed railways
Construction of Shape-Persistent All-sp <sup>2</sup> Square Macrocycles via the Formation of Multiple Imine Bonds
Research on risk assessment method of wireline logging sticking and analysis of sensitive factors
Nitrate-reducing bacteria bridge nitrogen cycling and antibiotic resistance in river ecosystems
Federated deep reinforcement learning for privacy-preserving offloading in vehicular edge computing
Abstract With the rapid development of Internet of Vehicles (IoV) applications, the demand for serving computation-intensive and delay-sensitive tasks, which are executed in a dynamic mobility environment, continues to grow, while the embedded computing power carried by vehicles remains limited, and they are facing strict requirements in terms of latency. In vehicular edge computing, to offload computation to the nearby roadside units (RSUs) and to enable centralized learning-based offloading, mobility, task, channel, and resource information at the whole system needs to be gathered at a central learner, leading to a higher communication overhead and raw data exposure. This study introduces a privacy-aware federated deep reinforcement learning (FDRL) framework for vehicular edge computing task offloading with RSU assistance. The novelty of the proposed framework does not lie in the common usage of federated learning and deep reinforcement learning (DRL), but rather in the compactness of four coupled mechanisms: generation of a hybrid action representation of federated binary offloading decision and continuous resource allocation for RSUs, a personalized federated aggregation mechanism for non-IID vehicular observations collected on the RSU, a task-criticality-aware deadline reliability model with class-dependent violation penalties, and a handover-aware multi-RSU model that incorporates signaling delay, service-context transfer delay, and processing/authentication delay. In the proposed framework, the model parameters of the local SAC policies are provided to the federated coordinator rather than the locally observed information, such as raw vehicular trajectories, which can instead be used for local training of the SAC-based policies. Controlled simulation experiments are conducted to compare the proposed method with both local execution and edge-offloading methods, two centralized DRL baselines (DQN and DDPG), and three federated DRL baselines (FedAvg-DQN, centralized-SAC, and federated-MADRL). The results indicate that under the adopted simulation settings, the proposed FDRL framework achieves competitive and/or better system cost, delay, energy, deadline-violation performance, and communication overhead compared with other schemes. This privacy usefulness really means having less raw data exposed when federated training is used, and does not mean any formal privacy guarantee against inference attacks against model updates.
Adaptive shifts in amygdala–hippocampal theta coupling govern aversive learning and extinction
Abstract Adaptive behaviour relies on the flexible encoding and suppression of aversive associations often underpinned by amygdala-hippocampal interactions. Yet the spectral and directional dynamics underlying these interactions in humans remain poorly understood. Using intracranial EEG recordings from the amygdala and the hippocampus acquired during a two-day aversive learning and extinction task, we identified frequency-specific shifts: amygdala theta (3–8 Hz) and gamma (30–45 Hz) power increased during conditioning and decreased during extinction, while hippocampal alpha and gamma activity gave way to theta and gamma during extinction. Directional phase connectivity, results showed frequency-specific reversals: amygdala-to-hippocampus dominance at 3-5 Hz and hippocampus-to-amygdala predominance at 6-8 Hz, a reconfiguration validated by computational modelling. These findings uncover distinct theta sub-bands coordinating dynamic, bidirectional communication in the human amygdala–hippocampal circuit, elucidating a neural mechanism for the flexible regulation of emotional memory.
Tacticity-Regulated Electrochemical Properties of Poly(2,2,6,6-tetramethylpiperidinyloxy Methacrylate)
Robust rice-crab detection via receptive-field attention and feature reassembly in unstructured agricultural scenes
Synthetically designed anti-defense proteins overcome barriers to bacterial transformation and phage infection
In silico genomic analysis of resistome, virulome, and mobilome of β-lactamase-producing Klebsiella pneumoniae
SciPhy: A Bayesian phylogenetic framework using sequential genetic lineage tracing data
Abstract CRISPR-based lineage tracing offers a promising avenue to decipher single-cell lineage trees, especially in organisms not amenable to microscopy. Sequential genome editing records not only genetic edits but also the order in which they occur. To leverage this enriched information, we introduce SciPhy, a simulation and inference tool implemented in BEAST 2. SciPhy utilizes a Bayesian phylogenetic approach to jointly estimate time-scaled phylogenies and cell population parameters. After validation on simulated data, we use simulated and real data from a monoclonal cell culture to benchmark SciPhy against existing methods and find that it consistently reconstructs more accurate phylogenies. Compared to UPGMA, SciPhy additionally reports uncertainty and proliferation rates. Our second example applies SciPhy to murine gastruloids, demonstrating its ability to model time-varying population dynamics in early development. Together, these results establish a phylodynamic framework for the quantitative analysis of lineage tracing data. SciPhy’s codebase is publicly available at https://github.com/azwaans/SciPhy .
Thermodynamic coupling between cold and heat activations of TRPV2
Abstract The homotetrameric thermosensitive transient receptor potential vanilloid 2 (TRPV2) channel is a biological macromolecule with unique high temperature threshold and sensitivity. However, the underlying thermodynamic basis has not been well understood. In this computational study, the 3D cryo-EM structures of rat TRPV2 in response to various chemical perturbations at different sites at low temperatures were quantified at the tertiary and quaternary levels using a highly sensitive thermoring model. The results indicated that a putative stable pre-open closed state without a lipid at the well-known active vanilloid site exhibited at least three weakest tertiary noncovalent bridges on the protein surface as primary thermal sensors with matched thresholds for initial heat activation. Any chemical perturbation away from these sensors activated the channel but with lower cold sensitivity. In contrast, when the sensors were simultaneously exposed to a mild detergent, together with hydrolysis of nearby charged residues at the membrane surface, the channel could be opened with the unique high cold sensitivity similarly to mirror the initial heat sensation. Further, disrupting intersubunit interactions near the heat sensors was required for full channel opening at both upper and lower gates. Therefore, the heat capacity mechanism, once cross-examined, could be applied to elucidate the unique thermoring basis for the sharp heat response of thermosensitive TRPV2 above body temperature.