High performance mode (de)multiplexer assisted with a microring resonator on the lithium niobate-on-insulator platform.

The high extinction ratio mode (de)multiplexer is a pivotal component in high capacity mode-division multiplexing data communication and nascent on-chip intermodal acousto-optic modulators. Up to now, high performance on-chip mode (de)multiplexers are still lacking for integrated AOMs on the lithium niobate-on-insulator platform. In this paper, we propose and demonstrate an innovative scheme to achieve high extinction ratio signal routing for acousto-optic modulation, by leveraging a two-mode (de)multiplexer in conjunction with a high- racetrack microring resonator.

Artificial Peptides as Autophagy Modulators.

Autophagy is a highly conserved lysosomal degradation pathway for maintaining cellular homeostasis. Its dysregulation is implicated in various diseases, including cancer, neurodegeneration, and infections, making precise modulation of autophagy a potentially promising therapeutic strategy. Artificial peptide-based autophagy modulators have emerged as a promising alternative to conventional small molecules, offering several advantages in terms of specificity, biocompatibility, and functional versatility.

Mannose-B from Codonopsis pilosula modulates LAMB1 expression to enhance trophoblast function and alleviate subchorionic hematoma.

This study investigates the active component Mannose-B from Codonopsis pilosula and its effect on human trophoblast cell function, particularly focusing on the regulation of Laminin Subunit Beta 1 (LAMB1) expression and its implications in subchorionic hematoma (SCH). Key genes involved in SCH pathology were identified through RNA sequencing and bioinformatics analysis. Network pharmacology was utilized to screen active components in Codonopsis pilosula and their critical targets.

Pure Deep Red Electroluminescence in Mixed Phase Quasi-2D Tin Iodide Perovskite Heterostructure.

Mixed cation quasi-2D tin iodide perovskites are promising materials for deep-red to NIR emitting light-emitting diode (LED) applications. However, phase purity remains the major concern to avoid mixed emissions from two or more wavelengths. This work presents the evolution of pure deep red electroluminescence (EL) centred at ≈700 nm corresponding to the emission of pure n = 2 2D crystalline phase.

Effects of Apigenin on the apoptosis index of atherosclerosis.

Recently, Apigenin has been widely studied for its anti-atherosclerosis properties, but its mechanism remains to be further elucidated.This study aims to evaluate the expression of LOX-1, Bcl-2 and Bax in apigenin-treated atherosclerotic rats and to explore whether they affect the expression of apoptotic genes.We analyzed the relationship between these three genes and atherosclerosis based on bioinformatics methods and conducted animal experiments on them.

Hybrid path planning algorithm for underactuated AUV based on RRT star and APF.

To address the kinematic constraints and real-time requirements of autonomous underwater vehicle (AUV), this paper proposes a novel path planning algorithm: Directional Cone and Goal-Biased Dynamic Artificial Potential Field RRT* (DCGB-DAPF-RRT*). The algorithm integrates four key techniques-Directional Cone sampling, Goal-Biased sampling, Adaptive Step Length, and a Dynamic Artificial Potential Field-to improve sampling efficiency and path quality over conventional RRT-based methods. In addition, redundant node pruning and cubic non-uniform B-spline interpolation are employed to enhance trajectory smoothness and ensure kinematic feasibility.

Interpretable Graph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification using Multi-Omics Data.

The integration of heterogeneous multi-omics datasets at a systems level remains a central challenge for developing analytical and computational models in precision cancer diagnostics. This paper introduces Multi-Omics Graph Kolmogorov-Arnold Network (MOGKAN), a deep learning framework that utilizes messenger-RNA, micro-RNA sequences, and DNA methylation samples together with Protein-Protein Interaction (PPI) networks for cancer classification across 31 different cancer types. The proposed approach combines differential gene expression with DESeq2, Linear Models for Microarray (LIMMA), and Least Absolute Shrinkage and Selection Operator (LASSO) regression to reduce multi-omics data dimensionality while preserving relevant biological features.

Interpretable graph Kolmogorov-Arnold networks for multi-cancer classification and biomarker identification using multi-omics data.

The integration of heterogeneous multi-omics datasets at a systems level remains a central challenge for developing analytical and computational models in precision cancer diagnostics. This paper introduces Multi-Omics Graph Kolmogorov-Arnold Network (MOGKAN), a deep learning framework that utilizes messenger-RNA, micro-RNA sequences, and DNA methylation samples together with Protein-Protein Interaction (PPI) networks for cancer classification across 31 different cancer types. The proposed approach combines differential gene expression with DESeq2, Linear Models for Microarray (LIMMA), and Least Absolute Shrinkage and Selection Operator (LASSO) regression to reduce multi-omics data dimensionality while preserving relevant biological features.

Clinical efficacy of meridian differentiation acupuncture in the treatment of intractable insomnia based on objective data: a protocol for a multicentre, single-blind, randomised controlled trial.

Introduction: Insomnia, a prevalent sleep disorder, significantly impacts individuals' physical and mental well-being. Despite conventional treatments, some patients experience persistent insomnia. Acupuncture, a safe complementary therapy, has been demonstrated to be effective in treating insomnia.

Stable, Efficient, and Scalable Multicolor Lasing from Colloidal Quantum Dots in Liquids.

Liquid-state optical gain media are desired for high-power lasing applications due to their easier heat management than solid-state media, as well as for the emerging field of optofluidics. Colloidal quantum dots (QDs) are solution-processed materials that carry many attractive properties suitable for liquid lasing. To date, however, their lasing action is often achieved in close-packed films, as a high volume fraction of QDs is required for stimulated emission to outpace the ultrafast Auger decay of gain-active multiexciton states.

Efficacy and safety of different acupuncture therapies in treating sleep apnea syndrome: a systematic review and network meta-analysis.

Objective: To evaluate and rank the efficacy and safety of different acupuncture (AP) therapies in treating sleep apnea syndrome (SAS).

Methods: We searched PubMed, Web of Science, Cochrane Library, Embase, Wanfang Data, CNKI, CBM, and VIP for randomized controlled trials (RCTs) studying AP for SAS until April 25, 2024. Effectiveness required a >25% decrease in apnea-hypopnea index (AHI) and a >1/3 reduction in symptom scores after treatment.

SPP1+ Tumor-associated Macrophages Drive Immunotherapy Resistance via CD8+ T-Cell Dysfunction in Clear Cell Renal Cell Carcinoma.

Tumor-associated macrophages (TAMs) are key regulators of tumor immunity. With advances in single-cell analyses, SPP1+ TAMs have been observed across multiple tumor sites. However, their clinical relevance and phenotypic characteristics in clear cell renal cell carcinoma (ccRCC) have not been comprehensively delineated.

Ribociclib derivative Rib-CA suppresses breast cancer progression via p53-dependent apoptosis.

Breast cancer is a major global health challenge with high incidence and mortality. CDK4/6 inhibitors like Ribociclib have shown clinical benefits, but drug resistance remains a limitation. We designed and synthesized a novel Ribociclib derivative, Rib-CA, and evaluated its antitumor effects using in vitro assays (MTT, colony formation, EdU incorporation, cell adhesion, Transwell invasion, and wound healing) and in vivo models (CAM assay, xenografts).

Analysis of the Causes and Treatment of Surgical Site Infection in Unilateral Biportal Endoscopy for Lumbar Disc Herniation.

Background: Degenerative lumbar spine pathologies are a major cause of chronic low back pain and radiculopathy. In recent years, unilateral biportal endoscopic (UBE) technology has been applied more in the treatment of lumbar disc herniation. However, there are few studies on UBE postoperative infection.

Temperature-Responsive Polymer Grafted Carbon Nanotubes for Active Control of Mineral Scaling.

Scaling presents a major challenge in water treatment industries, reducing operational efficiency and shortening the service life of membranes and plumbing systems. Frequent membrane replacement imposes a substantial economic burden. Traditional scale removal methods, including physical cleaning and chemical treatments, often cause membrane damage, environmental hazards, and additional costs.

Do Sharpness-Based Optimizers Improve Generalization in Medical Image Analysis?

Effective clinical deployment of deep learning models in healthcare demands high generalization performance to ensure accurate diagnosis and treatment planning. In recent years, significant research has focused on improving the generalization of deep learning models by regularizing the sharpness of the loss landscape. Among the optimization approaches that explicitly minimize sharpness, Sharpness-Aware Minimization (SAM) has shown potential in enhancing generalization performance on general domain image datasets.

Aleatoric-Uncertainty-Aware Maximum Intensity Projection-Based GAN for 7T-Like Generation From 3T TOF-MRA.

Time-of-flight magnetic resonance angiography (TOF-MRA) is a prevalent vascular imaging technique for assessing cerebrovascular diseases. Compared to routine 3T TOF-MRA, 7T TOF-MRA provides vascular structures with a higher signal-to-noise ratio (SNR) and better vessel contrast, revealing greater vascular details. However, the inaccessibility of 7T scanners and specific physiological and technical concerns limit its clinical application.

A Simple AMC Antenna for Liquid Monitoring in an Infusion Bag.

Running-out detection of the liquids in an infusion bag is important for medical treatment. This paper proposed a simple low-cost sensing scheme with an artificial magnetic conductor (AMC) antenna for liquid-running-out detection in infusion bags. The proposed antenna consists of a dipole antenna supported by an AMC layer.

Characterization and Inverse Design of Stochastic Mechanical Metamaterials Using Neural Operators.

Machine learning (ML) is emerging as a transformative tool for the design of mechanical metamaterials, offering properties that far surpass those achievable through lab-based trial-and-error methods. However, a major challenge in current inverse design strategies is their reliance on extensive computational and/or experimental datasets, which becomes particularly problematic for designing micro-scale stochastic architected materials that exhibit nonlinear mechanical behaviors. Here, a comprehensive end-to-end scientific ML framework, leveraging deep neural operators (including DeepONet and its variants) is introduced, to directly learn the relationship between the complete microstructure and mechanical response of architected metamaterials from sparse but high-quality in situ experimental data.

Boron carbide nanoparticles for boron neutron capture therapy.

Boron agent is widely accepted as one of the most important factors in boron neutron capture therapy (BNCT). In this study, boron carbide (BC) nanoparticles were subjected to chemical modification, with the folic acid moiety linked to the surface of the particles by varying the segments of the covalent linker polyethylene glycol (PEG) through γ-aminopropyltriethoxysilane (APTES) functionalization. The resultant products were three boron agents, termed as BC-APTES-FA, BC-APTES-PEG2K-FA, and BC-APTES-PEG5K-FA.

EGF-Upregulated lncRNA ESSENCE Promotes Colorectal Cancer Growth through Stabilizing CAD and Ferroptosis Defense.

Epidermal growth factor receptor/mitogen-activated protein kinase (EGFR/MAPK) signaling is highly activated in various types of cancer. The long noncoding RNAs induced by this pathway and their roles in colorectal cancer (CRC) have not been fully elucidated. In this study, based on the profiling of long noncoding RNAs triggered by EGFR/MAPK signaling, we identified that ESSENCE (EGF [epidermal growth factor] Signal Sensing CAD's Effect; ENST00000415336), which is mediated by the transcription factor early growth response factor 1, functions as a potent oncogenic molecule that predicts poor prognosis in CRC.

Differential impact of cerebral small vessel disease on thalamic regulation of anxiety: insights from 7T MRI.

Little is known about how thalamic vascular patterns interact with small vessel diseases (SVDs) to influence affective symptoms. Here we collected 7-Telsa magnetic resonance imaging (MRI) data from 84 individuals with SVD and analyzed the influences of thalamic vascular pattern on affective symptoms, aiming to elucidate the underlying mechanisms driven by brain structure and function in the context of SVD. Subjects with a combined arterial supply by tuberothalamic and paramedian arteries to the right thalamus exhibited a lower Hamilton Anxiety scale (HAMA) score.

Flaw-size-dependent mechanical interlayer coupling and edge-reconstruction embrittlement in van der Waals materials.

Van der Waals (vdW) materials consisting of two-dimensional (2D) building blocks have strong in-plane covalent bonding and weak interlayer interactions. While monolayer 2D materials exhibit impressive fracture resistance, as demonstrated in hexagonal boron nitride (h-BN), preserving these remarkable properties in vdW materials remains a challenge. Here we reveal an anomalous mechanical interlayer coupling that involves interlayer-friction toughening and edge-reconstruction embrittlement during the fracture of multilayer h-BN.

Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification.

Recent studies on integrating multiple omics data highlighted the potential to advance our understanding of the cancer disease process. Computational models based on graph neural networks and attention-based architectures have demonstrated promising results for cancer classification due to their ability to model complex relationships among biological entities. However, challenges related to addressing the high dimensionality and complexity in integrating multi-omics data, as well as in constructing graph structures that effectively capture the interactions between nodes, remain active areas of research.

BioArchLinux: community-driven fresh reproducible software repository for life sciences.

Motivation: The BioArchLinux project was initiated to address challenges in bioinformatics software reproducibility and freshness. Relying on Arch Linux's user-driven ecosystem, we aim to create a comprehensive and continuously updated repository for life sciences research.

Results: BioArchLinux provides a PKGBUILD-based system for seamless software packaging and maintenance, enabling users to access the latest bioinformatics tools across multiple programming languages.