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Influence of blade installation angle spanwise distribution on the energy characteristics of mining contra-rotating axial flow fan
Thermal biasing for lattice symmetry breaking and topological edge state imaging
Building rooftop extraction from high resolution aerial images using multiscale global perceptron with spatial context refinement
Rational design of precatalysts and controlled evolution of catalyst-electrolyte interface for efficient hydrogen production
Preliminary study demonstrating cancer cells detection at the margins of whole glioblastoma specimens with Raman spectroscopy imaging
A randomised controlled non-inferiority trial to compare the efficacy of ‘HPV screen, triage and treat’ with ‘HPV screen and treat’ approach for cervical cancer prevention among women living with HIV
Melanin from the fungus Gliocephalotrichum simplex protects seeds from the effects of exposure to gamma radiation
Non-invasive in vivo monitoring of PROTAC-mediated protein degradation using an environment-sensitive reporter
An intelligent prediction method for rock core integrity based on deep learning
Interpretable single-cell factor decomposition using sciRED
Abstract Single-cell RNA sequencing maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying signals in the data, such as gene expression programs, but the resulting factors must be manually interpreted. We developed Single-Cell Interpretable REsidual Decomposition (sciRED) to improve the interpretation of scRNA-seq factor analysis. sciRED removes known confounding effects, uses rotations to improve factor interpretability, maps factors to known covariates, identifies unexplained factors that may capture hidden biological phenomena, and determines the genes and biological processes represented by the resulting factors. We apply sciRED to multiple scRNA-seq datasets and identify sex-specific variation in a kidney map, discern strong and weak immune stimulation signals in a PBMC dataset, reduce ambient RNA contamination in a rat liver atlas to help identify strain variation and reveal rare cell type signatures and anatomical zonation gene programs in a healthy human liver map. These demonstrate that sciRED is useful in characterizing diverse biological signals within scRNA-seq data.
Modeling and control of functional electrical stimulation cycling training system
Analysis of NS2-dependent effects on influenza PB1 segment extends replication requirements beyond the canonical promoter
Abstract Influenza A virus encodes conserved promoter sequences. Using minimal replication assays—transfections with viral polymerase, nucleoprotein, and a genomic template—these sequences were identified as 13nt at the 5’ end of the genomic RNA (U13) and 12nt at the 3’ end (U12). Other than the fourth 3’ nucleotide, the U12 and U13 sequences are identical between all eight RNA molecules of the segmented influenza A genome. However, individual segments can exhibit different dynamics during infection. Influenza NS2, which modulates transcription and replication differentially between genomic segments, may provide an explanation. Here, we assess how internal sequences of two genomic segments, HA and PB1, contribute to NS2-dependent replication and map such interactions down to individual nucleotides in PB1. We find that the expression of NS2 significantly alters sequence requirements for efficient replication beyond the identical U12 and U13 sequences, providing a potential mechanism for segment-specific replication dynamics across the influenza genome.
Hybrid prediction method for solar photovoltaic power generation using normal cloud parrot optimization algorithm integrated with extreme learning machine
A rational multi-target combination strategy for synergistic improvement of non-ribosomal peptide production
Integrated bioinformatics investigation and experimental validation reveals the clinical and biological significance of chromobox family in breast cancer
The association between variation of neutrophil-to-lymphocyte ratio and post-thrombolysis early neurological outcomes in patients with stroke of different TOAST classification
Abstract Recent studies have shown that the neutrophil-to-lymphocyte ratio (NLR) can predict short-term and long-term outcomes in acute ischemic stroke (AIS) patients. However, the relationship of the variation of NLR (ΔNLR) with hemorrhage transformation (HT) and early neurological improvement (ENI) after intravenous thrombolysis (IVT) remains unclear. This study aimed to investigate the impact of ΔNLR on HT and ENI at 24 h post-IVT and its association with different TOAST classifications. AIS patients undergoing IVT between October 2021 and October 2023 were enrolled and classified by TOAST criteria. Patients were grouped based on the presence or absence of HT and ENI. Our study demonstrated that both HT and ENI were associated with ΔNLR, which was an independent influencing factor for HT and ENI following IVT. Specifically, the ΔNLR in the small artery occlusion (SAO) group was higher than that in the minor stroke of large artery atherosclerosis (LAA) subtype. Thus, ΔNLR may serve as a useful biomarker to assist in diagnosis and monitor the outcomes of thrombolytic therapy in AIS patients.