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Exploring the structure and properties of $$\alpha$$-sheet based bilayer borophenes
Abstract Recent experimental realizations of bilayer boron materials motivated us to study the structure and properties of $$\alpha$$ -sheet-based bilayer borophenes with interlayer covalent bonds. As shown here, at least three stacking variations are possible: AA, AB, and $$\hbox {AB}'$$ . The on-top AA-stacking has been obtained experimentally supported on a metallic substrate. The AB-stacking is the most stable among neutral freestanding structures, whereas the AA and $$\hbox {AB}'$$ stacking sequences are very close in energy, both for neutral and negatively charged cases. The studied bilayer borophenes exhibit extraordinarily high electric conductivity with values as high as $${\sim } 10^7\mathrm {~S}/\textrm{m}$$ for the experimentally observed AA-stacking. The highly stable AB-stacking bilayer, reported here for the first time, exhibits an anisotropic conductivity with an average value of $$6.0 \times 10^6~\mathrm {~S}/\textrm{m}$$ . Contrary to the AA-stacking bilayer that retains the 6-fold fold rotational symmetry of the $$\alpha$$ -sheet, the AB-stacking structure has 2-fold symmetry, which leads to the anisotropic transport properties.
Perioperative ultrasound screening of lower extremity veins is effective in the prevention of fatal pulmonary embolism in orthopedic patients
STAT1 regulates immune-mediated intestinal stem cell proliferation and epithelial regeneration
AbstractThe role of the immune system in regulating tissue stem cells remains poorly understood, as does the relationship between immune-mediated tissue damage and regeneration. Graft vs. host disease (GVHD) occurring after allogeneic bone marrow transplantation (allo-BMT) involves immune-mediated damage to the intestinal epithelium and its stem cell compartment. To assess impacts of T-cell-driven injury on distinct epithelial constituents, we have performed single cell RNA sequencing on intestinal crypts following experimental BMT. Intestinal stem cells (ISCs) from GVHD mice have exhibited global transcriptomic changes associated with a substantial Interferon-γ response and upregulation of STAT1. To determine its role in crypt function, STAT1 has been deleted within murine intestinal epithelium. Following allo-BMT, STAT1 deficiency has resulted in reduced epithelial proliferation and impaired ISC recovery. Similarly, epithelial Interferon-γ receptor deletion has also attenuated proliferation and ISC recovery post-transplant. Investigating the mechanistic basis underlying this epithelial response, ISC STAT1 expression in GVHD has been found to correlate with upregulation of ISC c-Myc. Furthermore, activated T cells have stimulated Interferon-γ-dependent epithelial regeneration in co-cultured organoids, and Interferon-γ has directly induced STAT1-dependent c-Myc expression and ISC proliferation. These findings illustrate immunologic regulation of a core tissue stem cell program after damage and support a role for Interferon-γ as a direct contributor to epithelial regeneration.
Extended PROMETHEE method with (p,q)-rung linear Diophantine fuzzy sets for robot selection problem
Development and validation of a prognostic nomogram model for severe osteomyelitis patients
Many-body van der Waals interactions in multilayer structures studied by atomic force microscopy
Heterozygous mutation in BRCA2 induces accelerated age-dependent decline in sperm quality with male subfertility in rats
Exosomes derived from hUC-MSCs exhibit ameliorative efficacy upon previous cesarean scar defect via orchestrating β-TrCP/CHK1 axis
Radiocarbon monoxide indicates increasing atmospheric oxidizing capacity
Data mining and analysis of adverse events of Vedolizumab based on the FAERS database
Genome-scale evolution in local populations of wild chimpanzees
Chemical looping synthesis of amines from N2 via iron nitride as a mediator
Study on the evolution characteristics and influencing factors of frost heave on kaolin clay during the horizontal freezing process
Validity and reliability of the step test to estimate maximal oxygen consumption in pediatric population
Deep learning and genome-wide association meta-analyses of bone marrow adiposity in the UK Biobank
Abstract Bone marrow adipose tissue is a distinct adipose subtype comprising more than 10% of fat mass in healthy humans. However, the functions and pathophysiological correlates of this tissue are unclear, and its genetic determinants remain unknown. Here, we use deep learning to measure bone marrow adiposity in the femoral head, total hip, femoral diaphysis, and spine from MRI scans of approximately 47,000 UK Biobank participants, including over 41,000 white and over 6300 non-white participants. We then establish the heritability and genome-wide significant associations for bone marrow adiposity at each site. Our meta-GWAS in the white population finds 67, 147, 134, and 174 independent significant single nucleotide polymorphisms, which map to 54, 90, 43, and 100 genes for the femoral head, total hip, femoral diaphysis, and spine, respectively. Transcriptome-wide association studies, colocalization analyses, and sex-stratified meta-GWASes in the white participants further resolve functional and sex-specific genes associated with bone marrow adiposity at each site. Finally, we perform a multi-ancestry meta-GWAS to identify genes associated with bone marrow adiposity across the different bone regions and across ancestry groups. Our findings provide insights into BMAT formation and function and provide a basis to study the impact of BMAT on human health and disease.
Harnessing CRISPR interference to resensitize laboratory strains and clinical isolates to last resort antibiotics
AbstractThe global race against antimicrobial resistance requires novel antimicrobials that are not only effective in killing specific bacteria, but also minimize the emergence of new resistances. Recently, CRISPR/Cas-based antimicrobials were proposed to address killing specificity with encouraging results. However, the emergence of target sequence mutations triggered by Cas-cleavage was identified as an escape strategy, posing the risk of generating new antibiotic-resistance gene (ARG) variants. Here, we evaluated an antibiotic re-sensitization strategy based on CRISPR interference (CRISPRi), which inhibits gene expression without damaging target DNA. The resistance to four antibiotics, including last resort drugs, was significantly reduced by individual and multi-gene targeting of ARGs in low- to high-copy numbers in recombinant E. coli. Escaper analysis confirmed the absence of mutations in target sequence, corroborating the harmless role of CRISPRi in the selection of new resistances. E. coli clinical isolates carrying ARGs of severe clinical concern were then used to assess the robustness of CRISPRi under different growth conditions. Meropenem, colistin and cefotaxime susceptibility was successfully increased in terms of MIC (up to > 4-fold) and growth delay (up to 11 h) in a medium-dependent fashion. ARG repression also worked in a pathogenic strain grown in human urine, as a demonstration of CRISPRi-mediated re-sensitization in host-mimicking media. This study laid the foundations for further leveraging CRISPRi as antimicrobial agent or research tool to selectively repress ARGs and investigate resistance mechanisms.
Study on discrete prediction model for mechanical behavior of buried pipelines under the influence of differential frost heave
One-year mortality and re-admission rate by disease etiology in National Heart Failure Registry of India
Innovative machine learning approaches for indoor air temperature forecasting in smart infrastructure
AbstractEfficient energy management and maintaining an optimal indoor climate in buildings are critical tasks in today’s world. This paper presents an innovative approach to surrogate modeling for predicting indoor air temperature (IAT) in buildings, leveraging advanced machine learning techniques. At the core of this study is the application of Long Short-Term Memory (LSTM) networks for time-series modeling, which significantly enhances the capture of temporal dependencies in temperature predictions. The proposed LSTM with RWCV (Rolling Window Cross-Validation) offers significant advantages over a usual LSTM in time-series tasks, particularly due to its ability to adapt to new data trends through the rolling window mechanism. It provides more robust and generalizable forecasts in dynamic environments, prevents overfitting through dropout and cross-validation, and improves model evaluation with temporal integrity. In contrast, traditional LSTM models are better suited for static, non-evolving datasets and may not handle dynamic time-series data effectively. To rigorously assess model performance, a comprehensive evaluation framework is developed, incorporating metrics such as mean square error (MSE) and the coefficient of determination (R²). Additionally, a novel cumulative error analysis method is introduced enabling real-time monitoring and model adjustment to maintain predictive accuracy over time. Test results demonstrate that model losses on the test dataset are only marginally higher than those on the training dataset, indicating robust generalization capabilities. Loss values range from 0.0004709 to 0.02819861, depending on building operating conditions. A comparative analysis reveals that Adaboost and Gradient Boosting models outperform linear regression, highlighting their potential for achieving energy-efficient and comfortable indoor climate management in buildings. The findings underscore the efficacy of the proposed approach for IAT prediction and point towards further research possibilities in dataset expansion and model optimization to enhance building climate management and energy conservation.