Browse Articles

New design solution for porous plug in the argon steel purging process

Iron pyrite nanocrystals as a sustainable nanozyme for colorimetric detection of ceftriaxone sodium in environmental samples

All-cause hospital admissions and incidence of type 2 diabetes among adolescents in Kazakhstan

SARS-CoV-2 infection promotes lung thrombosis by inducing integrinβ3 expression in vascular endothelial cells

Language models learn to represent antigenic properties of human influenza A(H3) virus

Long-wave infrared computational multispectral metasurface and spectral reconstruction method

Chemical and biological investigation of Plectranthus amboinicus essential oil in the control of Sitophilus oryzae L

The impact of ambient air pollution and meteorological factors on emergency hospital admissions of COPD patients in Poland (2012–2019)

Selective oxidative modification of tryptophan and cysteine residues using visible light responsive Rh doped SrTiO3 photocatalyst

Abstract In recent years, amino acids and peptides have attracted significant attention in food and medical fields due to their functionality. These functionalities largely depend on the chemical properties of their side chains, and efficient methods for selective side chain modification are desired. In this study, we investigated the selective modification of amino acids and peptides using rhodium-doped SrTiO3 (g-STO:Rh), a visible light-responsive photocatalyst. HPLC and LCMS analyses revealed that g-STO:Rh exhibited selective reactivity toward tryptophan and cysteine among the 20 protein-constituent amino acids. While cysteine was oxidatively dimerized to cystine, tryptophan underwent selective oxidation of its indole ring side chain, forming N-formylkynurenine. Furthermore, studies on dipeptides and tripeptides containing tryptophan have demonstrated that selective oxidation proceeds similarly to tryptophan residues within peptides.

Glycoproteomic profiling of serum-derived small extracellular vesicles enriched via ultracentrifugation and affinity-based techniques

Abstract Small extracellular vesicles (sEVs) are gaining recognition as potential biomarkers for diseases, including cancer, due to their involvement in key pathophysiological processes. However, the glycosylation of EVs and the specific roles of their glycans remain poorly understood. While several methods exist for isolating sEVs from complex biological samples, achieving sufficient purity and quantity for mass spectrometry-based glycoproteomic analysis remains a significant challenge. In this study, we compared two commonly used isolation methods, ultracentrifugation (UC) and immunoaffinity capture (MagCapture kit), across different starting volumes of human serum (200 µL and 500 µL) to evaluate their performance for downstream glycoproteomic analysis. While prior studies have examined protein content across isolation methods, our work uniquely investigates how isolation technique and sample volume affect glycoproteomic yield and quality. We show that UC, particularly at higher sample volumes, enables deeper glycoproteomic coverage, whereas MagCapture is advantageous when serum availability is limited. Notably, we report for the first time site-specific glycan microheterogeneity on sEV glycoproteins derived from human serum, including multiple glycoforms at the same glycosylation site. These findings highlight the complexity and biological relevance of glycosylation in sEV proteins and offer practical guidance for optimizing isolation protocols based on specific omics applications.

Real-time analysis of soccer ball–player interactions using graph convolutional networks for enhanced game insights

Abstract We present a sequential fusion-based real-time soccer video analytics approach designed to comprehensively understand ball–player interactions. Our approach leverages the power of deep computer vision models, employing a CSPDarknet53 backbone for detection and a Graph Convolutional Network (GCN) for predictive analytics. The proposed approach intricately analyzes ball–player interactions by evaluating metrics such as inter-player distances, proximity to the ball, and hierarchical sorting based on shortest distances to the ball. We also track and estimate each player’s total distance and speed covered throughout the game. Our method performs exceptionally well on both uni- and multi-directional player movements, uncovering unique patterns in soccer videos. Extensive experimental evaluations demonstrate the effectiveness of our approach, achieving 91% object detection accuracy, 90% tracking and action recognition accuracy, and 92% speed analysis accuracy on benchmark datasets. Furthermore, our approach outperforms existing GCN techniques, achieving accuracies of 92% in graph connectivity, 89% in node classification, 87% in player tracking, and 88% in event recognition. Here, we show that our method provides a robust and accurate solution for real-time soccer video analytics, offering valuable insights into player performance and team strategies.

Many-objective optimization scheduling of cascade reservoirs in small watersheds based on an evolutionary multitasking framework

Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase 18F-Florbetaben PET and clinical features

Enhancing the thermal performance of expanded polystyrene with radiant barrier foil boards for improved building insulation

Optical anisotropy of pristine and reduced V2O5(010)

Agroinfiltration technique for elucidating the functions of strawberry genes in Fragaria vesca

Abstract Unraveling gene function is crucial for enhancing quality traits in commercially cultivated strawberry (Fragaria × ananassa) through biotechnology. However, such analysis is hindered by the complexity of the octoploid genome (2n = 8x = 56) of F. × ananassa. In this study, we leveraged the ancestral wild strawberry species Fragaria vesca as an experimental model and developed an Agrobacterium tumefaciens–based leaf infiltration (agroinfiltration) technique to elucidate the functions of genes and regulatory sequences in strawberry. We transiently expressed genes encoding transcriptional regulators that govern floral organ development in F. vesca leaf cells, allowing us to investigate the underlying regulatory mechanisms through in planta gene expression and transactivation assays. Our agroinfiltration method serves as a practical tool for studying the activities of genes controlling economically important traits in strawberry.

Elevated triglyceride-glucose index, a surrogate marker of insulin resistance, is associated with a high risk of obstructive sleep apnea

Dusp1 in osteolytic diseases-mechanisms and therapeutic potential

Short-term effects of ambient air pollutants and meteorological factors on emergency department visits in tropical Haikou, China

Research and optimization of a multilevel fire detection framework based on deep learning and classical pattern recognition techniques