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High-throughput characterization of transcription factors that modulate UV damage formation and repair at single-nucleotide resolution
Abstract Genomic studies revealed elevated DNA damage and mutation rates at transcription factor (TF) binding sites in UV-linked cancers. While TFs can promote UV-induced mutagenesis by altering both damage formation and repair, these mechanisms have not been systematically characterized across TFs at high resolution. Using genome-wide UV damage maps from skin fibroblasts, we develop a scalable statistical framework to analyze TF-mediated mutagenic mechanisms across hundreds of TFs. We identify numerous previously unreported TFs that significantly enhance or suppress UV damage formation within their binding sites. A systematic survey of TF-DNA complexes reveals that damage modulation often coincides with TF-induced DNA distortions that either protect against or promote photodimer formation. Additionally, we analyze repair efficiency in TF binding sites at high resolution, identifying TFs likely to compete with repair. Comparisons with skin cancer mutations distinguish mutation enrichment driven by increased damageability versus attenuated repair, revealing the highly contextual nature of TF-mediated mutagenesis.
Interpretable coal mine fault detection via orthogonal Kolmogorov–Arnold networks
Navigating chemical-linguistic sharing space with heterogeneous molecular encoding
Characteristics of ST11-K64 carbapenem-resistant hypervirulent Klebsiella pneumoniae in Central-South China
Energy intensity, offshoring and the illusion of decarbonization
Evolutionary gene number variation and functional diversification of retinal EAATs are reflected in expression pattern adaptation
Abstract Excitatory amino acid transporters (EAATs; SLC1 family) are expressed in neurons and glial cells and are essential for glutamatergic signaling and neuroprotection in the vertebrate retina. However, lineage-specific genome duplications, especially in teleosts, and eaat gene losses during vertebrate evolution, raise the question of whether retinal EAAT functions are conserved across species. In our study, we combine retinal expression mapping, transgenic reporter assays, and electrophysiological characterization to examine the evolutionary diversification of EAAT expression and function of some key species across different vertebrate clades. The gene loss of eaat6 and eaat7 in eutherian mammals results in a pronounced shift in retinal expression of eaat1 to eaat5 when compared to non-therian vertebrates. While in the mouse retina Eaat1 ( Glast-1 ) is expressed in Muller glia cells, Eaat2 ( Glt-1 ) transcripts are predominantly found in bipolar cells. In contrast to this, Muller glia cells of zebrafish ( Danio rerio ) predominantly express eaat2a and transcripts for neither of the two zebrafish eaat1 paralogs are present in the retina. On the other hand, the predominant neuronal eaat transcripts in the zebrafish retina are eaat2b and eaat7 . A transgenic zebrafish line expressing GFP under the control of mouse Eaat1 regulatory elements demonstrates that mouse Eaat1 cis-regulatory sequences drive neuronal, rather than glial, expression in zebrafish, indicating evolutionary divergence of regulatory logic. Electrophysiological analyses of recombinant EAAT proteins reveal that these expression changes are paralleled by differences in biophysical properties. Glial EAATs display a low ratio of uncoupled anion conductances to glutamate transport currents, consistent with a primary role in glutamate clearance, whereas neuronally expressed EAATs exhibit disproportionately large anion currents, supporting a role in modulating membrane excitability. Taken together, our findings demonstrate that retinal eaat expression patterns and functional specializations have been extensively reshaped during vertebrate evolution, advising caution against a direct extrapolation of mouse retinal glutamate handling to non-mammalian species.
c-JUN controls microbial colonization via selective phagocytosis in the sea anemone Nematostella
Abstract Innate immunity is traditionally viewed as a broad defense system with limited specificity. However, increasing evidence suggests that innate immune cells can discriminate between distinct microbial partners. How such specificity arises in early-diverging animals remains unclear. Here, we identify in the sea anemone Nematostella vectensis a selective host innate immune mechanism mediated by nematosomes, motile multicellular bodies that differentially process bacterial cells. Nematosomes preferentially engulf non-native Vibrio isolates while showing reduced uptake of native host-associated strains. We identify the transcription factor cJUN as a key regulator of this process. CRISPR/Cas9-mediated knockout of cJUN reduces nematosome abundance, impairs lysosomal response, alters microbiome assembly, and increases susceptibility to bacterial infection. These results link immune gene function to microbial selectivity and demonstrate that even early-diverging animals exhibit sophisticated innate immunity mechanisms for microbiome regulation. Our findings support the idea that immune specificity can arise through repurposing deeply conserved pathways and may have deep evolutionary origin.
Pair approximation of the biased-independence q-voter model for innovation diffusion in organizational networks
Abstract Collective adaptation, including innovation adoption, pro-environmental change, and organizational change, emerges from the interplay between individual decisions and social influence. We study the biased-independence q -voter model, in which agents choose between adoption and non-adoption under conformity and independent choice. Independent choice is governed by an engagement parameter inspired by earlier models of eco-innovation diffusion. For engagement equal to 0.5, the model reduces to the standard q -voter model with independence; otherwise, the symmetry between the two options is broken. This asymmetry generates discontinuous phase transitions and irreversible hysteresis, reflecting path-dependent adoption dynamics. We first review variants of the asymmetric q -voter model and then analyze the model using mean-field approximation, pair approximation, and Monte Carlo simulations on artificial and empirical organizational networks. We derive, for the first time, a pair approximation for an asymmetric q -voter model and test how well it reproduces simulations on empirical networks. Pair approximation captures the dependence on network density and systematically improves upon mean-field approximation. However, while the improvement is substantial for random graphs, it is moderate for empirical networks. These results demonstrate the need to test analytical approximations directly in realistic settings, as such tests may show that, in some cases, a simple mean-field approach is sufficient.
High temperature Nb-Si alloys using data science: optimization of fracture toughness and high-temperature strength
Abstract High temperature Nb-Si based alloys face a critical challenge: achieving adequate room-temperature fracture toughness ( > 18 MPa·m 1/2 ) for processing while maintaining high-temperature strength, properties that typically compete with each other. Here, we overcome this inherent trade-off through machine learning-guided alloy design, employing a three-step feature screening strategy to identify 6 key descriptors from 200 initial features. SHAP analysis reveals how melting enthalpy and atomic radius mismatch control property outcomes, enabling targeted multi-objective optimization via NSGA-II algorithm. The optimized Nb-12.26Si-21.35Ti-1.98Al-1.96Cr-0.51Hf-4.34Zr-4.35 V alloy achieves an as-cast fracture toughness of 18.92 MPa·m 1/2 while maintaining 322 MPa strength at 1250 °C, surpassing all reported as-cast Nb-Si alloys. Microstructural analysis shows that the superior properties originate from the dispersed distribution of nanoscale γ′-Nb 5 Si 3 phase and crack deflection at phase boundaries with 67.6% lattice mismatch. Our results demonstrate that combining machine learning techniques with mechanistic understanding can accelerate the discovery of high temperature materials.
Accurate orange yield estimation using a novel dataset, fine-tuned deep learning models, and vision-LLM benchmarking
Structural evolution of iron oxides melts at Earth’s outer-core pressures
Abstract Oxygen and other light elements comprise up to 5 wt% of the Earth’s outer-core, and may significantly influence its physical properties and the operation of the geodynamo. Here we report in situ X-ray diffraction measurements of Fe, Fe + 4.5 FeO (atomic proportion), and Fe 2 O 3 melts at 177-440 GPa, achieved using laser-driven shock compression at an x-ray free-electron laser. The melts exhibit Fe-O coordination numbers between 4.0(0.4) and 4.5(0.4), indicating predominantly four-fold coordination environments. These coordination states are significantly smaller than those of Fe-bearing lower-mantle phases such as bridgmanite and ferropericlase. Shorter Fe-Fe interatomic distances in compressed iron oxide melts drive the denser packing relative to ambient melts, while the structural differences between Fe + 4.5 FeO and Fe 2 O 3 melts under shock indicate that the oxidation state modulates oxygen solubility in liquid Fe. At 177 GPa ( ~ 380 km below the core-mantle boundary) and 3800 K, Fe 2 O 3 melts exhibit higher Fe-O coordination, suggesting that local variations in oxygen content could contribute to the stratification in the uppermost outer-core inferred from seismological and geomagnetic observations.
Federated generative adversarial network with hybrid transformer-GRU and explainable AI for financial fraud detection
Reversible hydrogen storage in reactive hydride composites under 400 K
Abstract Hydrogen storage remains a key challenge for widescale adoption of hydrogen as an energy vector. Lightweight complex hydrides offer high storage densities but suffer from hydrogen release/cyclability above the temperatures required for practical use. Here, we report on discoveries in ternary Reactive Hydride Composites (RHCs). We systematically tuned the LiBH₄ content in the well-established Mg(NH₂)₂ - LiH framework, achieving reversible hydrogen release at temperatures starting below 393 K and a capacity of 3.1 wt%; a decrease of 100 K compared to the Mg(NH₂)₂ - LiH system.This is a crucial step towards the use of complex hydride-based hydrogen carriers for stationary and onboard hydrogen storage applications. We demonstrate a reversible RHC within the utilisation range of low-grade waste heat from a fuel cell, alongside offering insight into the reaction pathways in these RHCs to inform the design of future materials.
Fuzzy robust model predictive fault tolerant control in wind turbine based on ST-SMO and PMIO
Abstract Conventional wind turbine maintenance relies on post-failure diagnosis, yet sustained degraded operation remains critical. This paper proposes a fault-tolerant model predictive control (MPC) framework with switching between a super-twisting sliding mode observer (ST-SMO) and a proportional multiple integral (PMI) dual observer system. In the observation layer, the PMI observer estimates unmeasured states and various faults, while the ST-SMO specifically compensates for high-order pitch angle faults. A Takagi-Sugeno (T-S) fuzzy logic based on the $$\nu$$ -gap metric coordinates the two observers. Adaptive penalty terms in the MPC layer compensate deviations via a linear parameter-varying model. Multi-scenario case studies validate the framework under load-range transitions and simultaneous multi-fault conditions. Compared with single-observer FTC methods and conventional MPC, the proposed framework improves maximum power tracking accuracy by 18% under sensor faults, suppresses drivetrain torsional torque fluctuation by 50% under cross-load switching, and reduces tower bending moment damage equivalent load (DEL) by 19.63% under multi-fault conditions. It innovatively integrates dual observers with T-S fuzzy logic and hard-soft combined LPV switching, achieving synergistic optimization of fault tolerance, fatigue load mitigation, and active power maximization for megawatt-class wind turbines under multi-fault coupling and cross-load transitions.