Browse Articles

Influence of temperature and excitation density on carrier dynamics in CdZnTe quantum dots

Using graph neural network and symbolic regression to model disordered systems

Abstract The key to modeling disordered systems lies in accurately simulating atomic trajectories, typically achieved through molecular dynamic (MD) simulation. The accuracy of MD simulations depends on the precision of the interatomic potential function, which dictates the calculations of atom movements. Traditionally, deriving interatomic potential function relies on extensive prior physical knowledge and high computational cost. This study introduces a novel approach that integrates machine learning with molecular dynamic methods to provide precise interatomic potential energy calculations for disordered systems.

Effects of inflammation on hypoxic renal tubular epithelial cell injury under high-glucose conditions and the regulatory role of miR-125b

Effect of nano liposomal herbal extracts against biofilm formation and adherence of streptococcus mutans

Knowledge attitudes and practices of healthcare professionals regarding diabetes self management education and support

Enterprise fission path optimization and dynamic capability construction based on the soft actor-critic algorithm

Integrated analysis identifies key genes underlying the bidirectional association between depression and renal failure

Abstract Depression is a common psychiatric comorbidity in individuals with end-stage renal disease (ESRD). However, the underlying biological mechanisms and the precise relationship between depression and renal failure remain unclear. While interventions such as cognitive behavioral therapy and exercise have been shown to alleviate symptoms, the interplay between these conditions and their molecular pathways is poorly understood. An integrated analysis was conducted combining bioinformatics approaches and data from the UK Biobank (UKB) cohort. The UKB study revealed a significant association between renal failure and depression. Gene expression data from the Gene Expression Omnibus (GEO) database were analyzed to identify key co-expression modules using Weighted Gene Co-expression Network Analysis (WGCNA). Protein-protein interaction (PPI) networks were constructed using the STRING database, and immune cell infiltration was assessed with the CIBERSORT tool. UKB data confirmed a robust association between renal failure and depression. Bioinformatics analyses highlighted significant enrichment in pathways related to the acute inflammatory response, specific granule lumen, and immune receptor activity. PPI network analysis identified 23 hub genes, including CYP4F2, KCNA3, KISS1R, LILRA5, and ZC3H12D, as key players in the shared pathophysiology of ESRD and depression. Validation studies further emphasized the roles of LILRA5, CYP4F2, and KISS1R in these mechanisms. This study reveals novel insights into the molecular and immune interactions underlying the comorbidity of renal failure and depression. By combining cohort and bioinformatics analyses, we identify potential therapeutic targets and pathways that may inform innovative treatment strategies.

Preparation and property evaluation of oral colon targeted protein delivery system with sodium alginate and chitosan

Wave convergence principles of agricultural carbon emission efficiency: a multi-level urban agglomeration study

Contribution of glutamatergic projections to neurons in the nonhuman primate substantia nigra pars reticulata for reactive inhibition

The basal ganglia play a crucial role in action selection by facilitating desired movements and suppressing unwanted ones. The substantia nigra pars reticulata (SNr), a key output nucleus, facilitates movement through disinhibition of the superior colliculus (SC). However, its role in action suppression, particularly in primates, remains less clear. We investigated whether individual SNr neurons in three male macaque monkeys bidirectionally modulate their activity to both facilitate and suppress actions and examined the role of glutamatergic inputs in suppression. Monkeys performed a sequential choice task, selecting or rejecting visually presented targets. Electrophysiological recordings showed that SNr neurons decreased firing rates during target selection and increased firing rates during rejection, demonstrating bidirectional modulation. Pharmacological blockade of glutamatergic inputs to the lateral SNr disrupted saccadic control and impaired suppression of reflexive saccades, providing causal evidence for the role of excitatory input in behavioral inhibition. These findings suggest that glutamatergic projections, potentially originating from sources including the subthalamic nucleus, contribute to the increased SNr activity during action suppression. Our results highlight conserved basal ganglia mechanisms across species and offer insights into the neural substrates of action selection and suppression in primates, with implications for understanding disorders such as Parkinson’s disease.

RNA triphosphatase-mediated mRNA capping is essential for maintaining transcript homeostasis and the survival of Toxoplasma gondii

Different tumour-resident memory T-cell subsets regulate responses to anti-PD-1 and anti-CTLA-4 cancer immunotherapies

MassCube improves accuracy for metabolomics data processing from raw files to phenotype classifiers

Abstract Nontargeted peak detection in LC-MS-based metabolomics must become robust and benchmarked. We present MassCube, a Python-based open-source framework for MS data processing that we systematically benchmark against other algorithms and different types of input data. From raw data, peaks are detected by constructing mass traces through signal clustering and Gaussian-filter assisted edge detection. Peaks are then grouped for adduct and in-source fragment detection, and compounds are annotated by both identity- and fuzzy searches. Final data tables undergo quality controls and can be used for metabolome-informed phenotype prediction. Peak detection in MassCube achieves 100% signal coverage with comprehensive reporting of chromatographic metadata for quality assurance. MassCube outperforms MS-DIAL, MZmine3 or XCMS for speed, isomer detection, and accuracy. It supports diverse numerical routines for MS data analysis while maintaining efficiency, capable for handling 105 GB of Astral MS data on a laptop within 64 min, while other programs took 8–24 times longer. MassCube automatically detected age, sex and regional differences when applied to the Metabolome Atlas of the Aging Mouse Brain data despite batch effects. MassCube is available at https://github.com/huaxuyu/masscube for direct use or implementation into larger applications in omics or biomedical research.

Research on a denoising model for entity-relation extraction using hierarchical contrastive learning with distant supervision

Abstract Distant supervision is a technique that utilizes knowledge base information to automatically generate labels for text samples, enabling the large-scale creation of labeled data. However, this approach often encounters the issue of noisy labels in practice, which arises from inaccuracies in the alignment between the text and the knowledge base, leading to erroneous generated labels that adversely affect the model’s performance. In the task of relation extraction, such noise not only diminishes extraction accuracy but may also cause the model to favor the recognition of common relations while neglecting long-tail relations. To address these issues, this paper proposes an innovative hierarchical contrastive learning framework, specifically applied to the Uyghur language using pre-trained models for and CINO minority language modeling. This framework effectively integrates both global structural information and local fine-grained interactions to reduce noise within sentences. Specifically, a three-layer learning architecture is designed, which incorporates interactions at different levels and employs a multi-head self-attention mechanism to generate denoised context-aware representations, referred to as multi-granular re-contextualization. Additionally, a dynamic gradient adversarial perturbation data augmentation strategy is introduced to provide pseudo-positive samples for contrastive learning, further enhancing the model’s capabilities in recognizing rare relations. Experimental results demonstrate that the proposed framework significantly improves accuracy and robustness in the task of Uyghur relation extraction, validating its effectiveness and innovativeness. This research offers new perspectives and methodologies for the field of distant supervision in relation extraction, advancing further development in this area.

Genetic evidence suggests a causal relationship linking thyroid function to prospective memory and dementia and Parkinson’s disease

Loneliness is related to facial emotion perception in people with HIV

Characterizations of sulfate-reducing bacteria biofilm formed on N80 carbon steel in artificial shale gas field produced water

Abstract The corrosion of steel caused by sulfate-reducing bacteria (SRB) has been a big trouble resulting in the service failure of engineering equipment, and SRB biofilm is the direct reason leading to the corrosion acceleration. In this work, SRB biofilms formed on N80 carbon steel in an artificial shale gas field produced water with different test conditions were characterized carefully by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), fluorescence microscope, and three-dimensional stereoscopic microscope. Results demonstrate that test time, temperature, and initial SRB cell concentration can influence the growth and surface morphology of biofilm, and test time and temperature are primary factors. There is a highest corrosion rate of 0.100 ± 0.005 mm/y on the seventh day due to the high biological activity, and then corrosion rates gradually decline with time. The formed biofilms at different time have a similar morphology and the contents of elemental S in biofilms are high also suggesting SRB corrosion. Temperature can influence the biological activity of SRB, and then affect the formation of SRB biofilms. SRB has a higher biological activity at 20 and 37 °C than that of at 60 and 80 °C. The influence of initial SRB cell count differences on biofilm is weak.

Preparation and analysis of structural, morphological and optical properties of BaFeO2.67/Fe2O3 nanocomposite

Altered miRNA expression in the lesions of cutaneous leishmaniasis caused by L. major and L. tropica with insights into apoptosis regulation

Abstract Leishmaniasis is a vector-borne infectious disease that affects many subtropical countries. Leishmania (L.) major and L. tropica are among the old-world causative agents and cause cutaneous leishmaniasis (CL). The parasite utilizes various mechanisms to evade host immune responses and facilitate intracellular survival. Among these mechanisms, apoptosis inhibition through altering the host cell miRNA expression profile plays a significant role in parasite survival and infection progression. Based on the in-silico analysis through the miRDB database and literature review, miR-4795-3p, miR-6785-5p, miR-5011-5p, and miR-155-5p were selected as miRNAs regulating apoptosis-related genes. The expression of selected miRNAs was evaluated in the skin biopsy lesions collected from L. tropica and L. major-infected patients by qRT-PCR. Our results showed that miR-155-5p, miR-5011-5p, and miR-6785-5p were significantly upregulated (P < 0.05) in L. tropica-infected patients. Similar expression patterns for miR-155-5p and miR-6785-5p, but with a higher magnitude, were found in the lesions of CL patients infected with L. major. Additionally, miR-4795-3p expression level was downregulated in this group. The KEGG pathway analysis indicated that these miRNAs target several pathways that play key roles during leishmaniasis. The results underscore that further investigation is needed to better understand the regulatory roles of these miRNAs in CL infection.

Detecting heavy trucks from mobile phone trajectories using image-based behavioral representations and deep learning models