Guoqiang Wang

Over-representation analysis (ORA) is widely used to interpret selected-gene lists, including differentially expressed genes and cell-type marker genes. Unlike ranked-list gene-set enrichment analysis (GSEA), ORA tests whether selected genes are over-represented in predefined gene sets under an explicit background. Repeated analysis of large gene-set libraries, however, can be computationally costly. GPU-accelerated methods such as rapidGSEA mainly target ranked-list GSEA and do not address repeated exact-ORA processing. We developed paraORA to accelerate repeated ORA by preparing a gene-set library once and reusing its compressed representation across queries. In the GPU-exact path, overlap counting, exact hypergeometric testing, Benjamini–Hochberg correction, and odds-ratio calculation are performed on the GPU. Across 425 numerical validation cases on three GPU models, paraORA agreed with CPU-reference results within the prespecified tolerance while preserving statistical decisions and rankings. On an RTX 4090, the CPU/GPU runtime ratio for the complete in-memory workflow increased from 1.89 for one query to 60.10 for 100 queries against 141,002 gene sets, including one-time preparation but excluding file writing. In a separate complete file-to-file benchmark, GPU-exact was 2.55-fold faster than CPU execution. An exploratory limma reanalysis showed direction-specific Hallmark enrichment. paraORA is available as a web service and an open-source package.

Background/Objectives: Large language models and automated analytical tools show potential for biomedical text understanding, knowledge integration, and single-cell data interpretation, but interpretations of specific gene regulatory relationships still require empirical grounding. For TF–target relationships, one relevant constraint is whether curated regulatory edges show detectable expression-level statistical evidence in the single-cell data being interpreted, because such evidence may vary across cellular states, conditions, and regulatory mechanisms. We therefore evaluated the statistical visibility of known transcription factor (TF)–target relationships in single-cell expression space to aid the interpretation of regulatory inference and automated-tool outputs. Methods: We performed two complementary analyses: first, testing whether curated TF–target edges showed stronger pair-level associations than matched background gene pairs; and second, assessing whether high-scoring TF–gene pairs corresponded to existing regulatory knowledge and whether their target genes showed pathway coherence. Results: Across four peripheral blood mononuclear cell (PBMC) datasets, four adult tissues, and seven adult cell types, curated TF–target relationships showed weak but reproducible statistical visibility rather than strong separation. For all five association metrics, the mean area under the precision–recall curve (AUPRC) was only slightly above the random-ranking baseline of 0.5. High-scoring pairs were more often supported by curated resources, and their target genes showed context-specific Hallmark pathway coherence. Conclusions: Single-cell expression associations can provide useful but limited statistical clues for TF–target regulation and should be interpreted as complementary rather than definitive regulatory evidence.

To address the challenges of traditional photonic crystal fiber (PCF) sensors in fabrication technology and performance optimization, we propose a highly sensitive Au/TiO2 composite film-coated photonic crystal fiber (GT-PCF) refractive index(RI) sensor based on the external surface plasmon resonance (SPR) effect. The cladding air holes of this GT-PCF are distributed in a circular configuration. Without destroying its intrinsic structure, TiO2 and Au films are deposited successively on the outer surface of the fiber, which not only effectively solves the technical difficulty of coating the inner walls of the holes, but also achieves a significant improvement in sensing performance. The mode coupling properties of the proposed GT-PCF are systematically investigated using the finite element method (FEM), and the optimal structural parameters are identified through analyzing the impacts of these parameters on the sensor's performance. The simulation results demonstrate that across the RI range from 1.36 to 1.425 RIU, this sensor achieves a maximum wavelength sensitivity of 30000 nm/RIU and a maximum amplitude sensitivity of 2219.87 RIU⁻¹. The proposed GT-PCF sensor possesses the advantages of both high sensitivity and simple structure, making it a promising candidate for applications in fields such as biological detection, chemical analysis, and medical diagnosis.

For practical industrial production scenarios where collected vibration signals are easily interfered by environmental noise and bearing operating conditions are complex and variable, this paper proposes a Double Branch Lightweight Convolutional Neural Network (DBLCNN). The model adopts a dual-branch architecture: the one-dimensional branch enhances feature extraction capability under low signal-to-noise ratio conditions, while the two-dimensional branch improves feature representation while significantly reducing the number of parameters. The complementary fault features extracted by the dual branches effectively enhance the accuracy of fault diagnosis. Under varying operating conditions, the model achieves an average accuracy of 95.58%; with the addition of 0 dB Gaussian white noise, its average accuracy under varying conditions remains at 90.17%. This study demonstrates that, even based on raw vibration signals without cumbersome preprocessing, the model can achieve excellent diagnostic performance in noisy environments and under variable operating conditions.

In order to realize the efficient exploitation of coalbed methane (CBM) in the southern Qinshui basin in China, raw coal specimens with original fractures of the No.3 coal reservoir in the Sihe mine were selected to conduct compression tests with different confining pressures. During this loading process, the synchronous and staged real-time CT scanning tests were carried out by a high-precision X-ray scanner. The mechanical properties, the failure mode, and the evolution of fractures of coal samples due to the increased effective stress were analyzed from the microscopic point of view. The result showed that the original fractures in the coal specimens of the Sihe coal mine are relatively developed. Four mechanical stagescompaction, elastic, yield, and plastic failurewere consistently identified; image-based segmentation allowed real-time, precise quantification of crack initiation and growth. Within the tested confining pressure range, further confirmation through real experiments that compressive strength, peak strain, and elastic modulus rose with confinement, while residual strength was preserved by frictional resistance along fracture surfaces. New fractures tend to start cracking near the original fractures. Crack networks were more intricate under lower confinement (uniaxial tensile failure) and simplified yet more distributed under higher confinement (triaxial shear failure), indicating a transition from brittle to semibrittle/semiductile behavior. Quantitative CT metricsgray value, fracture volume, and fracture porosityproved reliable indicators of damage progression. Porosity is below 1% at the compaction and elasticity stage, twice the size of the original fractures at the initial damage stage. The integrated experimental–visualization workflow elucidates fracture propagation mechanisms in anthracite and offers theoretical guidance for CBM stimulation and ground-control strategies in the Qinshui Basin.