Spatially resolved transcriptomics (SRT) allows for the comprehensive profiling of gene expression while preserving spatial context, advancing the study of tissue architecture. However, existing computational approaches still face key limitations, particularly the insufficient exploitation of histology information and the lack of cross-modal meaningful contrastive strategies for biological analyses. To overcome these challenges, we propose GCAST, a graph contrastive autoencoder framework for spatial transcriptomics that seamlessly integrates multimodal SRT data. GCAST adopts a self-supervised strategy to derive biologically meaningful representations directly from histology images when available. GCAST constructs dual graph views based on data augmentation and introduces a novel contrastive learning designed to leverage histology-weighted and gene-weighted features and improve biological interpretability. In addition, GCAST employs a block-diagonal graph construction to automatically align multiple datasets, achieving batch-effect correction without manual intervention. The framework not only captures spatial gene expression patterns to identify tissue domains but also adapts to datasets with or without histological images and supports the integration of multiple datasets for joint analyses. Overall, GCAST provides a unified and biologically informed framework that has the potential to facilitate deeper analyses of spatial transcriptomics.

The Rust programming language is lauded for enabling fearless concurrency with zero cost: detecting concurrency errors at compile time. Given the enduring difficulty of parallel programming in other languages, this implied panacea warrants analysis. In particular, the efficacy of Rust across types of parallelism remains unexplored. Is parallel programming always devoid of fear with Rust? We answer this question through a case study, porting 14 benchmarks with abundant regular and irregular parallelism from C++ to Rust and reporting our experience and observations. We find that Rust, with the Rayon library, indeed delivers fearlessness for program phases comprising only regular parallelism, e.g., prefix-sum. However, for applications with any irregular parallelism, the programmer must choose between unsafe code or high-overhead dynamic checks with errors that manifest at run time, leaving the arduous task of parallel programming as scary with Rust as with its predecessors.

Many irregular algorithms converge more quickly when they execute tasks in a specific order. When this order is discovered at run time, the algorithm demands a dynamic task scheduler. Scaling a priority scheduler to large systems with many cores is challenging and while many concurrent priority schedulers (CPS) have been proposed, a general classification of their design space is still lacking. We survey prior work and propose three dimensions for the design of CPSs: the degree of synchrony, the drift of priorities, and the underlying data structure. We use this taxonomy to classify existing schedulers and evaluate their strengths and weaknesses.

OBJECTIVE: This study aims to develop a prediction model based on non-contrast computed tomography (NCCT) images to differentiate uric acid stones from non-uric acid stones before treatment. METHODS: This study retrospectively enrolled 195 patients from Quzhou People's Hospital between 2022 and 2024 who underwent dual-energy CT scans with confirmed renal stone composition. The patients were randomly divided into a training set (156 cases) and a test set (39 cases) in an 8:2 ratio. Regions of interest (ROIs) were manually delineated slice-by-slice on NCCT images to extract radiomic features. Feature dimensionality reduction and selection were performed using intraclass correlation coefficient (ICC), Spearman rank correlation coefficients, and least absolute shrinkage and selection operator (LASSO) regression. Radiomics and clinical models were developed using logistic regression (LR), support vector machine (SVM), multilayer perceptron (MLP), ExtraTrees, and LightGBM algorithms. Finally, a combined model was constructed by integrating the selected radiomic features with clinically significant risk factors. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), while clinical utility was assessed through decision curve analysis (DCA). Model interpretability was examined using Shapley additive explanations (SHAP). RESULTS: A total of 1834 radiomic features were extracted from each ROI. After feature dimensionality reduction and selection, 6 radiomic features remained for characterizing stone composition. The clinical, radiomics, and combined models all demonstrated favorable discriminatory power for uric acid stones. The AUC values of the three models in the training set were 0.744 (95% CI: 0.666-0.822), 0.831 (95% CI: 0.766-0.897), and 0.886 (95% CI: 0.834-0.938), respectively, and 0.778 (95% CI: 0.631-0.925), 0.634 (95% CI: 0.454-0.777), and 0.805 (95% CI: 0.666-0.944) in the test set. DeLong's test indicated that in the training set, the performance of the combined model was significantly superior to both the clinical model and the radiomics model (0.886 vs. 0.744, p < 0.001; 0.886 vs. 0.831, p = 0.015). Decision curve analysis also demonstrated its potential clinical utility. SHAP analysis revealed that texture features were important factors in predicting uric acid stones. CONCLUSION: The combined model based on NCCT performs well in distinguishing uric acid stones and can provide effective references for treatment decisions. CLINICAL TRIAL NUMBER: Not applicable.

The collection of individual GPS data, as a substitute for traditional travel surveys, is hindered by low response rates and high costs. Meanwhile, passive mobile phone data, such as Location-Based Service (LBS) data, has high user penetration but remains unexplored for travel behavior analysis. The irregular frequency of data collection results in distorted features and non-uniformly distributed information of trajectories in the Travel Mode Identification (TMI) task. The diversity of trajectories also poses challenges for TMI models. To fill the gap, we propose a TMI framework named Trajectory-as-a-Sequence for Non-uniform data (TaaSN). Specifically, we incorporate GIS features to address the sparsity of motion features. Then, we design a model structure that accounts for time gaps, capturing non-uniform information of trajectory due to irregular frequency. To further enhance model’s generalizability to diverse trajectory data, we propose a trajectory point dropout training strategy. The experimental results demonstrate that the TaaSN framework can greatly exploit the potential of LBS data in travel behavior mining. The proposed model achieves high accuracy on both non-uniform and uniform trajectories. On pseudo-LBS data, the accuracy reaches 84.9% when applied to the trajectories of existing travelers, and achieves 83.5% when applied to the trajectories of new travelers. On uniform data, it reaches 86.8% and 85.4%, respectively. Furthermore, we carry out comprehensive experiments and conclude valuable insights for future TMI framework design.

The pathogenesis of many human diseases is unclear, but many studies have shown that lncRNAs are deeply involved in the development of diseases. However, the exploration of lncRNA-disease associations in the laboratory requires a lot of time and financial resources, and computational-based methods have obvious advantages and become a promising research direction. But few experiments consider the relationship between other biological factors and lncRNAs and diseases. In this paper, a novel lncRNA-disease association prediction method, MANBNR, is proposed. MiRNAs are introduced by MANBNR to construct a lncRNA-miRNA-disease multilayer association network (MAN). The main innovation of MANBNR is to mine potential lncRNA-disease association information based on miRNA information. For any lncRNA-disease pair, lncRNA-associated miRNAs and disease-associated miRNAs are sorted into two sets, respectively. The association of this lncRNA-disease pair is judged by comparing the number of miRNAs shared in the two sets. This solves the problem that the known lncRNA-disease association matrices are too sparse. Finally, the bipartite network recommendation (BNR) algorithm was used to accurately predict potential lncRNA-disease association. The performance of MANBNR is better than that of many advanced methods at present. Case studies of breast cancer and lung cancer further demonstrate that MANBNR is an effective and reliable method for LDAs prediction.

Predicting disease-related long non-coding RNAs (lncRNAs) can help reveal the genetic mechanisms of complex diseases. Accurately identifying disease-associated lncRNAs is crucial for human diagnosis and therapeutics of complex diseases. However, most computational models ignore the noise of the data and the interference of redundant information. In this study, we build heterogeneous networks by integrating three different data sources of lncRNAs, miRNAs and diseases, and then propose an efficient computational model called MHILDA. MHILDA selects the most helpful features to train the model by Lasso's feature extraction. MHILDA is evaluated by five-fold cross-validation and performs well both on the benchmark dataset and on the independent test set. To further evaluate the performance of MHILDA, two types of case studies are implemented. The experimental results show that MHILDA can predict lncRNAs for unknown diseases.

Neutral-point (NP) voltage oscillation amplitude, THD of output voltage and the switching frequency of the inverter are the three key performance indices in three-level neutral-point-clamped (NPC) inverter. Every element connects with each other and restricts each other, which indicates that to keep the three key performance indices balanced is the research focus of the modulation strategy for three-level NPC inverter. Hybrid space vector PWM strategy, which is proposed in this paper, combines NV and non-NV, synthesizes the advantages of the two methods, reduces NP low-frequency oscillation and also decreases switching frequency and output voltage THD. Taking NTV (the most typical method of NV) and NTV (the most typical method of non-NV) as examples, A new hybrid SVPWM method, based on the NP current maximum minimum (MM) method is proposed in this paper. This new hybrid space vector PWM method applies NTV to uncontrolled balancing area and minimizes the switching frequency and THD of output voltage within the range of the allowable NP oscillation amplitude. Meanwhile, the impact on the output voltage produced by NP oscillation is researched in this paper. The experimental results show the validity of the proposed strategy.

Torque ripples due to cogging torque, current measurement errors and non-sinusoidal flux distribution restrict application of PMSM in direct drive system. In this paper, a method based on PI type iterative learning control (ILC) is presented to suppress the ripple. Tuning method of the controller is given in detail, which ensures stability and convergence of the system. Effect of controller parameters on its performance is discussed. Compared with the existing ILC compensatory strategy, the proposed method needs no estimation or close loop control of the electromagnetic torque, which simplifies structure of the controller. Simulating and experimental results verify that ripple of q axis current is reduced obviously, which consequently leads to desired torque ripple restriction performance.