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Defining functional puberty in female C57BL/6J mice using endocrine, cytological and morphological markers
Ribosomal allostery as a potential regulator of bacterial dormancy
Abstract Ribosomes are central to protein synthesis but also serve as dynamic hubs that integrate cellular stress responses. Here, we investigate how ribosomal protein L11 regulates ribosome conformational dynamics and long-distance coupling. Long-timescale molecular dynamics simulations of wild-type and L11-deleted (ΔL11) ribosomes reveal that L11 functions as a global allosteric regulator coordinating communication between the ribosomal stalk and the peptidyl transferase center. The absence of L11 disrupts long-distance couplings involving RelA and Obg and rigidifies the hibernation-promoting factor site, suggesting altered hibernation dynamics that could affect ribosome persistence under stress. To examine the physiological implications of these computational predictions, we construct a ΔL11 Bacillus subtilis strain and quantify its sporulation behavior. The ΔL11 variant exhibits delayed entry into and exit from dormancy, consistent with a breakdown in stress-adaptive ribosomal regulation. Overall, these results highlight the role of L11 in ribosomal allostery, suggesting how local perturbations propagate through the ribosome to influence global physiological outcomes and bacterial survival under environmental stress.
A pretrained biomedical large language model for Persian biomedical text mining
Direct Asymmetric α-C Conjugate Addition of Aminomethylphosphonate Enabled by Carbonyl/Iminium Double Organoactivation
Fast surface reconstruction of human brain MRI: benchmarking deep-learning based morphometry tools
Abstract Time efficient and reliable pipelines for quantitative evaluation of structural brain MRI are essential to utilize the potential of morphometry tools for large scale research projects as well as to pave the path towards future clinical applications. In our work, we have explored this idea by evaluating three deep learning models for brain segmentation and cortex parcellation (DeepSCAN, FastSurferCNN and QuickNAT) as input for an 11-min surface reconstruction pipeline adapted from the well studied open source software package FreeSurfer. Performance was assessed using both, large publicly available human MRI datasets and a synthetic dataset with known metrics and reference surfaces. Evaluation criteria included closeness to the surface reconstruction by FreeSurfer’s full pipeline, reproducibility within same-session rescans, performance stability across a wide age range, sensitivity to variations of the grey-white contrast in the MRI and accuracy regarding metrics of synthetic surfaces. Metrics derived from the DeepSCAN-based pipeline demonstrated the highest agreement with FreeSurfer in the human data and the greatest fidelity to the expected metrics in the synthetic dataset. Our findings identify the DeepSCAN-based surface reconstruction pipeline as a rapid, yet reliable alternative to established research-grade structural MRI processing. Time expenditure and reliability suggest it is suitable for research applications with high-throughput requirements. This is an essential first step towards necessary subsequent studies aimed at evaluating robustness, pathological variability, and utility in the context of clinical diagnostics.
Energy-translation coupling limits anaerobic yeast growth
Development and characterization of mineral oil–neem oil ester mixed insulation for transformer applications
pH-dependent activation of the Na+/H+ antiporter NhaA and conformational dynamics of its N-terminus
Abstract Na⁺/H⁺ antiporters are vital for regulating intracellular pH and sodium ion levels across all domains of life. In Escherichia coli , NhaA is the principal Na⁺/H⁺ antiporter, exhibiting strong pH sensitivity and rapid turnover, yet the structural transitions underlying its activation and substrate recognition have remained obscure. Here, we use single-particle cryo-electron microscopy to determine the conformational ensemble of NhaA across a physiological pH range and in the presence of Na⁺, complemented by constant-pH molecular dynamics simulations. High-resolution structures of apo and Na⁺-bound NhaA reconstituted in lipid nanodiscs reveal progressive opening of the cytoplasmic funnel with increasing pH. We also visualize the previously unresolved N-terminal tail, which forms a dynamic plug at the cytoplasmic entrance under low-pH conditions and disengages at alkaline pH, coinciding with activation. The Na⁺-bound structure captures Na⁺ coordination at the ion-binding site, and simulations suggest potential roles for the conserved charged residues. Together, these findings illuminate how pH sensing, N-terminal gating, and substrate binding are structurally coordinated in NhaA, providing a framework for understanding Na⁺/H⁺ antiporter activation and regulation, and the basis for targeting clinical important antiporters.
Hybrid cooperative complex treatment is associated with reduced facial pore size in a pilot study
Abstract Enlarged facial pores are a common aesthetic concern associated with aging and reduced dermal support. Hybrid Cooperative Complex (HCC), a stabilized hyaluronic acid formulation, has been proposed to improve skin quality through bioremodeling. This prospective observational pilot study evaluated changes in facial pore size after treatment with Hybrid Cooperative Complex (HCC) using standardized photography, clinical assessment, and validated rating scales. Ten healthy adult participants received two treatment sessions according to the Bio Aesthetic Points protocol, and outcomes were assessed at baseline and on Days 30, 60, 120, and 180. Visible improvement in facial pore size and skin texture was observed after treatment. Peak pore refinement was noted at Day 120 in most participants, while partial regression was observed in many cases by Day 180, although skin quality remained improved compared with baseline. HCC may be associated with visible improvement in facial pore size and skin quality in this pilot cohort. Changes in pore morphology may represent a practical clinical marker for estimating maintenance treatment timing, although larger controlled studies are needed.
Efficacy of Pam3CSK4 as a cross-species adjuvant for polysaccharide vaccines in humanized mouse and non-human primate models
Abstract Polysaccharide-based vaccines are essential for preventing bacterial infections, but their effectiveness is limited by weak antibody responses and lack of suitable adjuvants. TLR4 agonists enhance polysaccharide-specific antibody responses through B cell–intrinsic TLR4-MyD88 signaling in mice, but this mechanism is not conserved in primates, prompting the search for alternative MyD88-activating agonists. In vitro, the TLR1/2 agonist Pam3CSK4 synergizes with strong BCR crosslinking to enhance activation and antibody secretion by mouse and human B cells. In vivo, Pam3CSK4 in squalene emulsion increases protective pneumococcal polysaccharide–specific antibody responses in both immunocompetent and humanized mice. Although a dual TLR2/7 agonist shows strong in vitro activity, it fails to enhance polysaccharide-specific IgG responses in vivo, consistent with antagonism observed when Pam3CSK4 and TLR7 agonists are combined. In contrast, incorporating Pam3CSK4 into an adjuvant containing a TLR4 agonist, synthetic cord factor, and squalene emulsion further enhances memory B cell generation and protective antibody responses in mice and restores adjuvant activity in non-human primates, supporting Pam3CSK4-based formulations as promising adjuvants for polysaccharide vaccines.
Luminescent heparin-functionalized carbon dots with potential applications in nanoparticle-protein interactions and cell imaging
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.