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Alcohol consumption and the incidence of hyperuricemia in Japanese men and women
High-fidelity collisional quantum gates with fermionic atoms
Abstract Quantum simulations of electronic structure and strongly correlated quantum phases are among the most promising applications of quantum computing. These computations benefit from native fermionic encodings 1,2 , enforcing fermionic statistics and conservation laws such as particle number and magnetization 3 independent of gate errors. While ultracold atoms in optical lattices have become established as powerful analogue simulators of strongly correlated fermionic matter 4–7 , neutral-atom platforms have concurrently emerged as versatile, scalable architectures for spin-based digital quantum computation 8 . Unifying these capabilities requires high-fidelity motionally coherent gates for fermionic atoms 9–11 , similar to collisional gates in bosonic systems 12,13 , paving the way for programmable fermionic quantum processors. Here we demonstrate collisional entangling gates with fidelities up to 99.75(6)% and Bell-state lifetimes exceeding 10 s, realized by means of controlled interactions of fermionic atoms in an optical superlattice. Using quantum gas microscopy 14 , we microscopically characterize spin-exchange and pair-tunnelling gates and realize a robust composite pair-exchange gate, a key building block for quantum chemistry simulations 3,15 . Our results establish controlled collisions in optical lattices as a competitive and complementary route to high entangling gate fidelities in neutral-atom quantum computers. Operating intrinsically with fermions, this capability naturally extends to many-qubit architectures, in which fermionic statistics become relevant, enabling complex state preparation and advanced readout 16–19 in scalable analogue–digital hybrid quantum simulators. Combined with local addressing 20,21 , these gates mark a crucial step towards a fully digital fermionic quantum computer based on controlled motion and entanglement of neutral atoms.
LGGC-Net: a local-global graph and color attention-based lightweight CNN for skin cancer classification
Abstract Developing clinically deployable AI systems for skin cancer classification remains challenging due to limited robustness, lack of interpretability and constrained computational resources in hospitals. Although many deep learning models report high accuracy, their large sizes, extensive training requirements and low generalizability hinder practical deployment. In this study, we propose LGGC-Net, a lightweight CNN that incorporates LGGC (Local, Global Graph, and Color) attention to enhance discriminative feature learning while maintaining computational efficiency. Experimental results demonstrate that LGGC attention consistently improves performance across all evaluated CNN backbones. The LGGC-Net model was assessed on external image sets with diverse skin tones to ensure robustness and generalizability under domain shift conditions. Ablation studies analyzed the contribution of individual attention components and explainability was examined using Gradient-weighted Class Activation Mapping++ (Grad-CAM++) and SHapley Additive exPlanations (SHAP). With only 0.81 million parameters, LGGC-Net achieved 88.05% accuracy in 50 epochs, corresponding to 1.761 accuracy per epoch and 108.7 accuracy per million parameters in binary classification. In multiclass settings, it attained 76.1% accuracy on the unseen HAM10000 dataset, with 1.52 accuracy per epoch and 94.0 accuracy per million parameters. In both cases, the area under the curve exceeded 0.93. LGGC-Net consistently outperformed existing methods on deployment-oriented metrics, maintaining stable accuracy. These results indicate that LGGC-Net is an effective, interpretable and potentially deployment-ready solution for practical skin cancer classification.
Scalable and energy efficient hybrid cryptographic framework for IoT security using advanced symmetric and asymmetric techniques
A CXCR4 targeting peptide delivered by silica nanoparticles eliminates migrating cancer stem cells in pancreatic ductal adenocarcinoma
Abstract Pancreatic ductal adenocarcinoma (PDAC) is among the most aggressive and metastatic malignancies worldwide. Migrating cancer stem cells (miCSCs), marked by CD133⁺CXCR4⁺ expression is a key driver of PDAC progression, which currently lack effective therapeutic targets. Activated pancreatic stellate cells (PSCs) within the tumor microenvironment secrete CXCL12, the ligand for CXCR4, thereby promoting stemness, epithelial-to-mesenchymal transition (EMT), and chemoresistance in miCSCs. Despite advances in understanding PDAC biology, clinically effective strategies that target CXCR4⁺ CSC populations remain limited. In order to investigate the molecular mechanisms sustaining miCSCs, we performed protein–protein interaction network analysis, which identified the transcription factor BMI1 as a key downstream effector of the CXCL12/CXCR4 axis. Functional studies using shRNA-mediated knockdown of CXCR4 and BMI1 were conducted to assess their roles in miCSC migration, EMT, and self-renewal. We further evaluated the therapeutic potential of the endogenous CXCR4 antagonist EPI-X4 and its optimized derivative JM#21 in PDAC cell lines. We addressed the peptide stability by encapsulating JM#21 into mesoporous silica nanoparticles (MSNs) designed for improved half-life and sustained release under physiological conditions. BMI1 was confirmed as a critical mediator of CXCL12/CXCR4-driven stemness and EMT. Knockdown of CXCR4 or BMI1 significantly impaired miCSC maintenance and migration towards CXCL12. Both EPI-X4 and JM#21 potently inhibited CXCL12-mediated signaling, reduced EMT and stemness markers, and suppressed miCSC migratory potential. JM#21 displayed superior efficacy and re-sensitized previously resistant PDAC cell lines to gemcitabine and paclitaxel. Functional assays demonstrated that nanoparticle-loaded JM#21 more effectively suppressed EMT markers and self-renewal than the free peptide, highlighting the advantage of nanoparticle delivery in therapeutic applications. Given their biocompatibility and modularity, silica nanoparticles offer a promising platform for stabilizing peptide drugs. Our findings reveal that tumor–stroma crosstalk via the CXCL12/CXCR4/BMI1 axis plays a central role in sustaining miCSC-driven metastasis and therapy resistance in PDAC. Targeting this signaling pathway with nanoparticle-stabilized JM#21 represents a novel and clinically promising therapeutic strategy to disrupt PDAC progression and improve the efficacy of existing combination treatments.
Correction: Spectroscopic fingerprinting of extracellular vesicles from diverse cellular origins byATR-FTIR for vibrational biomarkers of vector–host interactions
The performance of FIT-based colorectal cancer screening: results from a population-based program
miR-181a-5p of MSCs-derived exosomes promote vascular formation and cell proliferation by PTEN/PI3K/AKT axis in HUVECs
Abstract Our previous studies have demonstrated that exosomes play a crucial role in promoting vaginal tissue reconstruction in rats. The present study aims to elucidate the molecular mechanisms through which human umbilical cord mesenchymal stem cell-derived exosomes (hUMSC-Exos), which carry microRNA-181a-5p (miR-181a-5p), promote vascularization and tissue regeneration, with particular focus on the involvement of the PTEN/PI3K/AKT signaling pathway. Human umbilical vein endothelial cells (HUVECs) served as a model for studying angiogenesis and cell proliferation, and the expression levels of miR-181a-5p, PTEN, phospho-PI3K (p-PI3K), and phospho-AKT (p-AKT) were analyzed. HUVECs were transfected with PTEN overexpression vector or a negative control vector, then treated with exosomes derived from mesenchymal stem cells (MSCs) transfected with either a miR-181a-5p mimic or an inhibitor. Cell proliferation and migration were assessed using the Cell Counting Kit-8 and scratch assay, respectively, while cell invasion was evaluated via Transwell assay. The StarBase tool was employed to predict binding sites between miR-181a-5p and its target gene, the phosphatase and tensin homolog (PTEN). This interaction was subsequently validated using a dual-luciferase reporter assay. In HUVECs, elevated miR-181a-5p levels were positively correlate with reduced PTEN expression. In vitro experiments demonstrate that hUMSC-Exos enhance HUVEC migration, proliferation, and tube formation. Furthermore, overexpression of PTEN partially counteracted these miR-181a-5p-mediated effects. Our findings indicate that hUMSC-Exos contain miR-181a-5p, which may enhance tube formation and proliferation in HUVECs by regulating PTEN expression, thereby influencing the PI3K/AKT pathway.
Wheat seed germination prediction in response to temperature, water potential, and salinity using an artificial neural network
Abstract Multi-layer perceptron (MLP) neural networks can be used to develop accurate models for quantifying plant responses to environmental factors. This study aimed to quantify wheat seed germination in response to temperature, water potential, and salinity using an ANN. Results indicated that the MLP model could predict total germination percentage with high model accuracy, including R 2 (0.99), MSE (0.342), RMSE (0.585), and MAE (2.166) for the test data. Time to 50% germination (T50) was also accurately estimated using the MLP model (R 2 = 0.97, MSE = 26.2, RMSE = 5.11, MAE = 5.70). Water potential was identified as the most significant variable affecting total seed germination and T50, followed by salinity and temperature. Seed germination was maximum at 20.5 °C and decreased at higher and lower temperatures. The optimal temperature for T50 was 25.3 °C. Higher salinity and more negative water potential led to lower total seed germination. The results of this study can be used to develop process-based models of crop growth and development and predict total seed germination and germination time under different conditions of temperature, water potential, and salinity.
Impact of gap anisotropy of Polar and Anderson-Brinkman-Morel p-wave superconductors on thermoelectric properties of quantum dot hybrids
Abstract We theoretically investigate the thermoelectric transport properties of a hybrid device consisting of a quantum dot (QD) coupled to a ferromagnetic lead and a p -wave, spin-triplet superconducting electrode. We focus on two distinct phases - the Polar and Anderson-Brinkman-Morel (ABM) - both having anisotropic gap structure and pure spin-triplet pairing. To capture the momentum-dependent tunneling between QD and the triplet superconductor (TSC), we introduce a phenomenological angle-dependent weighting of the QD-TSC coupling and analyze two configurations in which the superconducting symmetry axis is parallel or perpendicular to the tunneling axis. Employing the Keldysh Green’s function formalism in the linear response regime, we compute key transport coefficients - electrical and thermal conductance, thermopower, and the thermoelectric figure of merit - based on the anisotropy strength parameter, which is the central focus of this work rather than strong intra-dot correlation physics. Transport coefficients exhibit phase, geometry and anisotropy strength-sensitive behaviors, thereby making them potential probes of superconducting order parameter and nodal orientation. We demonstrate that neglecting anisotropy in modeling conceals important qualitative signatures. Our formulation allows to separately quantify the contribution of triplet Andreev reflection and quasiparticle tunneling and shows that, by mere rotation of the crystallographic axis of the superconductor, it is possible to obstruct or maximize the effective (triplet) Andreev reflection. In the ABM state, the origin of orientation-dependent suppression of Andreev reflection is traced to the azimuthal phase dependence. Moreover, the thermal conductance is enhanced by a few orders of magnitude compared to the conventional s -wave case. The results demonstrate that the anisotropic, orientation-dependent triplet gap strongly governs transport, offering experimentally accessible signatures of the superconducting phase and nodal structure.
The network neuropsychology of neighborhood deprivation in juvenile myoclonic epilepsy
Abstract Using network analytics, we sought to determine whether neighborhood disadvantage was associated with disruptions to the intrinsic cognitive networks of persons with Juvenile Myoclonic Epilepsy (JME). 62 participants with JME were categorized into high ( n = 18) and low ( n = 44) disadvantaged groups using the Area Deprivation Index and compared to 44 controls as to the network properties of their cognitive networks. The networks were interrogated using test metrics from a comprehensive neuropsychological test battery that assessed intellectual ability, language, visouperception/construction, learning and memory, executive function, and speed-dependent abilities. Network analyses demonstrated graded increases in overall connectivity and association strength across groups (controls < low-disadvantage JME< high-disadvantage JME), with the high-disadvantage JME group showing the greatest number of positive correlations and strongest inter-test associations. Community structure differed across groups, with reduced modularity and less differentiated cognitive networks in JME, particularly in the high-disadvantage group, suggesting over-integration and reduced network segregation. Regression analyses identified antiseizure medication load as a significant predictor of global efficiency in low-disadvantage JME only, with no significant clinical seizure predictors observed in the high-disadvantage JME group. The results indicate that structural (neighborhood) disadvantage is associated with detectable adverse effects on the underlying cognitive networks of persons with JME. While the cognitive status of JME has long been known to be adversely affected, the results reported here demonstrate that fundamental shifts in underlying cognitive network properties are associated with disadvantage, characterized by an abnormally highly integrated and less modular (differentiated) network structure.
Role of tumor markers before or during chemotherapy for digestive neuroendocrine carcinomas as an exploratory analysis of JCOG1213
New drugs take aim at one of cancer’s deadliest mutations
Integrated epidemiology and multi-assay evidence of anthelmintic resistance in ovine gastrointestinal nematodes of Northern India
HA-DETR: accelerating real-time object detection by replacing decoder self-attention
Foslevodopa–Foscarbidopa subcutaneous infusion in Parkinson’s disease: a cross-cultural/cross-racial international multicentre comparative tolerability study (CRIM-FOS study)
The relationship between abdominal obesity indices and the risk of metabolic dysfunction-associated fatty liver disease: a prospective cohort study
Breast tumor microbiome regulates anti-tumor immunity and T cell-associated metabolites
Abstract The breast tumor microbiome has emerged as a potential regulator of tumor immunity, yet its interactions with intratumoral lymphocytes and metabolites remain poorly defined. Here, we investigated relationships among CD8 + tumor-infiltrating lymphocytes (TILs), the breast tumor microbiome, and tumor metabolome. In a cohort of 46 breast cancer patients, Staphylococcus was the only bacterial genus whose intratumoral abundance positively correlated with cytotoxic CD8 + T cell markers and innate-like T cell signatures, including multiple KLR-family receptors. Several metabolites were significantly associated with CD8 + TILs, among which NADH, γ-glutamyltryptophan, and γ-glutamylglutamate differed between Staphylococcus -positive and Staphylococcus -negative tumors. Analysis of an independent cohort of 314 treatment-naïve patients further showed that the association between intratumoral Staphylococcus , heightened CD8 + T cell activity, and the KLR-associated innate-like T cell program was specific to triple-negative breast cancer (TNBC). In TNBC mouse models, direct intratumoral injection of Staphylococcus aureus depleted intratumoral NAD metabolites and suppressed tumor growth by activating CD8 + TILs. Together, these findings identify a link between low-biomass intratumoral bacteria and local anti-tumor immunity, and highlight Staphylococcus and TIL-associated metabolites as potential biomarkers and therapeutic targets for breast cancer immunotherapy.