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Basolateral amygdala dopamine transmits emotional salience
Influence of microwave excitation mode and antenna-sample orientation on rebar detection using microwave infrared thermography
Reducing P-O bond covalency in polyanionic groups to increase the intrinsic electronic conductivity in sodium positive electrodes
Collagen-derived biomarkers reveal distinct fibrotic responses among cancer-associated fibroblasts
Recognition of non-standard base pairs by triplex-forming oligonucleotides containing an expanded genetic alphabet
Abstract The sequence-specific recognition of double-stranded DNA by biocompatible molecules is fundamental to molecular medicine and synthetic biology. Triplex-forming oligonucleotides (TFOs) enable programmable major groove recognition via Hoogsteen base pairing; however, the limited repertoire of natural nucleobases imposes strict constraints on target sequences and parallel motif triplexes require acidic conditions for stability. Here, we have expanded the triplex recognition space using nucleobases from an artificially expanded genetic information system (AEGIS). Through a systematic evaluation of 120 base triad combinations, we identify at least 12 modular triads that can be combined interchangeably to target duplex DNA containing standard, damaged, or synthetic base pairs with nanomolar affinity at neutral pH. We further demonstrate the versatility of this expanded recognition code by detecting oxidative lesions or AEGIS base pairs in enzymatically assembled duplex constructs using both chemically and enzymatically synthesized TFOs. This generalized framework provides a robust platform for precision gene-targeting, molecular sensing, and nucleic acid nanotechnology.
Identification of cannabichromevarin as a potent stabilizer of the measles virus prefusion F protein: structural insights from long-timescale molecular dynamics
Rethinking the use of deep learning methods for photovoltaic power forecasting
Abstract Accurate photovoltaic power forecasting is critical for grid stability, but remains challenged by weather uncertainties and difficulty integrating historical observations with forward-looking forecasts. We re-evaluate existing architectural choices in deep-learning models for time-series forecasting and demonstrate the importance of full encoder-decoder architectures and channel dependence modeling when both weather forecasts and historical data are available. Based on this insight, we propose Cross-Unet, a Transformer-based architecture featuring multi-scale temporal encoding, correlation-aware channel attention, and hierarchical cross-attention decoding to fuse historical generation data with weather forecasts. Evaluated on open-source datasets from four utility-scale plants in northern China and one aggregated plant in central Australia, using three types of forward-looking inputs: numerical weather prediction, satellite-derived irradiance, and AI-based weather model forecasts. Across the majority of evaluated configurations spanning five photovoltaic power stations, five forecasting horizons (4 hours to 7 days), and three forecast sources, Cross-Unet outperforms ten deep learning baselines and traditional operational benchmarks. By integrating advanced forecasting systems, such as modern AI weather models, into an end-to-end forecasting pipeline, Cross-Unet enables operational 15-minute-resolution predictions over 4-hour to 7-day horizons, supporting grid scheduling and energy trading.
ViT-ConvGAN: a hybrid model for spatiotemporal action recognition using video transformer and 3D CNN
Relief of GbDEAH12G repression on the GbNAC072C-GbHOX3 axis improves lint percentage in Gossypium barbadense
Depressive symptoms hopelessness self-efficacy and COVID-19-related stressors among pregnant women in Hungary across two pandemic waves
Abstract Depression and hopelessness during pregnancy may be intensified by public health crises, yet less is known about how pregnancy- and pandemic-related stressors differ across successive COVID-19 waves. This study examined depressive symptoms, hopelessness, self-efficacy, and COVID-19-related concerns among pregnant women in Hungary during two independent cross-sectional pandemic waves. A total of 692 pregnant women participated in Wave 1 and 459 in Wave 2. Participants completed the Edinburgh Postnatal Depression Scale, Beck Hopelessness Scale, General Self-Efficacy Scale, and the study-specific „Pregnancy during the coronavirus” questionnaire. EPDS categories did not differ significantly between waves; 11.8% of participants in Wave 1 and 10.5% in Wave 2 fell into the major depressive symptom range. Mild hopelessness was reported by 31.1% and 26.4%, and moderate/severe hopelessness by 3.2% and 3.3%, respectively. Confinement-related difficulties and daily disruption, as well as perceived support and positive reappraisal, were higher in Wave 1 than Wave 2, both p < 0.001. In Wave 2, unplanned or unwanted pregnancy was associated with higher depressive symptoms, hopelessness, and financial/housing concerns. Emotional distress and impaired coping was the strongest predictor of EPDS and BHS scores, while self-efficacy was a consistent negative predictor. These findings support pregnancy-sensitive mental health screening during public health crises.
Engineering human PEG10-based nanoparticles for RNA self-packaging, delivery and cancer therapy
Temporal dynamics of ammonia and nitrous oxide emissions following green crop residue recycling in soils
Abstract The recycling of green crop residues is promoted for enhancing soil organic carbon sequestration and health, but can also stimulate emissions of nitrous oxide (N₂O) and ammonia (NH₃). Soil tillage can lead to different residue distributions which affect contact with decomposers and local gas and solute diffusion. This study aims to quantify N₂O, NH₃, and carbon dioxide emissions from N-rich red clover residue subjected to three different distributions in the soil—surface, layered, and mixed—across three contrasting agricultural soils. The incubations were performed under laboratory conditions for 50 days at 15 °C, with a water-filled pore space of 60%. Gas fluxes as well as soil ammonium and nitrate contents were measured. Results showed that N₂O emissions were strongly influenced by soil type and residue placement, with sandy loam producing the highest cumulative fluxes, particularly in the mixed (17.4 kg N ha⁻ 1 ) and layered (11.5 kg N ha⁻ 1 ) treatments. NH₃ volatilization occurred almost exclusively from surface residues, peaking at 10.2 kg N ha⁻ 1 in sandy loam soil. Emission for N 2 O were significantly higher than previously reported for residues. These findings highlight that both residue placement and soil properties critically determine gaseous emission patterns.
Adaptive kNN graph model
Evolutionary transition from single-cell to multi-cell organisms: the role of surface waters and their hydrodynamics
Abstract The evolutionary transition from unicellular to multicellular organisms exhibited a sharp increase in metabolic rates, yet the physical conditions supporting this transition remain poorly understood. Here we propose that wide-spread hydrodynamic enhancement of nutrient delivery to the microorganisms in the Neoproterozoic Era constituted a necessary enabling condition for early multicellularity, complementing the effect of rising oxygen levels occurring at the same time. Along with conceptual arguments in support of this hypothesis, we define three testable hydrodynamic mechanisms (viscous-diffusive, viscous-convective, and inertial-convective) controlling nutrient uptake and largely applicable for both bed-attached and free-floating microorganisms. We also highlight a flow-bed interface in surface streams as potentially important hotbed for multicellularity breeding, complementing the mainstream perception on the defining role of early oceans. Overall, our results emphasize hydrodynamics as under-represented physical component in existing evolutionary frameworks and generate testable predictions linking organism size, flow conditions, and habitat type.
Bidirectional integrin β1 activation synergizes neurovascular coupling and enhances bone regeneration
Abstract Reconstruction of large segmental bone defects remains challenging because current grafting strategies often fail to coordinate angiogenesis, neurogenesis, and osteogenesis. Here we developed a functional scaffold (peptide/Talin1 plasmid/PLA–HA/GelMA, PTPG) capable of simultaneously delivering peptides and Talin1 plasmids. We hypothesized that this scaffold enables neurovascularized bone regeneration through bidirectional activation of integrin β1 (ITGB1). The REDV–IKVAV (Arg-Glu-Asp-Val-Gly-Gly-Gly-Ile-Lys-Val-Ala-Val) peptide triggers “outside-in” ITGB1 signaling in endothelial and Schwann cells, while Talin1 plasmid-mediated “inside-out” activation. This PTPG scaffold synergistically enhances cell proliferation, migration and secretion, which are eliminated by ITGB1 silencing. In vivo, PTPG scaffold promotes aligned neurovascular networks guiding bone deposition. Single-cell RNA sequencing demonstrates enrichment of endothelial H-type signatures and repair-associated Schwann cell phenotypes, with activation of ITGB1–focal adhesion kinase–paxillin signaling. Collectively, this scaffold integrates structural support with peptide and genetic cues to promote coordinated angiogenesis, neurogenesis, and osteogenesis, offering a promising strategy for functional bone regeneration.