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Background Identifying drug–target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are expensive and time consuming. Effective computational methods to predict DTIs are useful to narrow the searching scope of potential drugs and speed up the process of drug discovery. There are a variety of non-negativity matrix factorization based methods to predict DTIs, but the convergence of the algorithms used in the matrix factorization are often overlooked and the results can be further improved. Results In order to predict DTIs more accurately and quickly, we propose an alternating direction algorithm to solve graph regularized non-negative matrix factorization with prior knowledge consistency constraint (ADA-GRMFC). Based on known DTIs, drug chemical structures and target sequences, ADA-GRMFC at first constructs a DTI matrix, a drug similarity matrix and a target similarity matrix. Then DTI prediction is modeled as the non-negative factorization of the DTI matrix with graph dual regularization terms and a prior knowledge consistency constraint. The graph dual regularization terms are used to integrate the information from the drug similarity matrix and the target similarity matrix, and the prior knowledge consistency constraint is used to ensure the matrix decomposition result should be consistent with the prior knowledge of known DTIs. Finally, an alternating direction algorithm is used to solve the matrix factorization. Furthermore, we prove that the algorithm can converge to a stationary point. Extensive experimental results of 10-fold cross-validation show that ADA-GRMFC has better performance than other state-of-the-art methods. In the case study, ADA-GRMFC is also used to predict the targets interacting with the drug olanzapine, and all of the 10 highest-scoring targets have been accurately predicted. In predicting drug interactions with target estrogen receptors alpha, 17 of the 20 highest-scoring drugs have been validated.

We present JBrowse 2, a general-purpose genome annotation browser offering enhanced visualization of complex structural variation and evolutionary relationships. It retains core features of JBrowse while adding new views for synteny, dotplots, breakpoints, gene fusions, and whole-genome overviews. It allows users to share sessions, open multiple genomes, and navigate between views. It can be embedded in a web page, used as a standalone application, or run from Jupyter notebooks or R sessions. These improvements are enabled by a ground-up redesign using modern web technology. We describe application functionality, use cases, performance benchmarks, and implementation notes for web administrators and developers.

Audiovisual working memory (WM) plays a critical role in multisensory cognitive processing, yet its structural neural correlates remain insufficiently understood. This study employed an audiovisual dual n-back task paradigm and voxel-based morphometry (VBM) to investigate gray matter volume (GMV) associations with behavioral performance in 60 healthy individuals. Behavioral results revealed a significant visual dominance effect under high cognitive load: visual performance remained stable across conditions, whereas auditory performance declined. Structural analyses showed modality-specific GMV correlations. Visual performance was positively associated with GMV in the insula, posterior cingulate, hippocampus, and inferior frontal regions, while auditory performance was negatively correlated with GMV in the angular and middle occipital gyri. Notably, the left cuneus exhibited a strong positive correlation with the Δd prime (visual–auditory) difference under high load, suggesting its potential role in cross-modal resource allocation. Furthermore, cognitive overload appeared to disrupt the structure–behavior associations observed under lower load, highlighting a load-dependent dissociation within executive control and sensory integration regions. These findings underscore the distinct anatomical substrates supporting audiovisual WM and the neural basis of visual dominance, offering structural markers for targeted cognitive training and clinical intervention.

Visual working memory (VWM) is a critical area of study in cognitive neuroscience, yet the neural and genetic foundations of individual differences in VWM remain unclear. This study investigates individual differences in VWM performance across four types of visual stimuli (Body, Face, Place, Tool) under 0-back and 2-back conditions by integrating gene expression data and spatiotemporal brain function metrics. First, multiple spatiotemporal brain function metrics were extracted, and Sequential Backward Selection (SBS) and Leave-One-Subject-Out Cross-Validation (LOSO-CV) linear regression were applied to predict behavioral performance under VWM conditions. Model performance was evaluated using RMSE. Next, the Working Memory Individual Differences Map (WMIDM) was constructed based on Pearson correlation coefficients between actual and predicted behavioral performance. Finally, WMIDM was integrated with Allen Human Brain Atlas (AHBA) gene expression data to explore its genetic underpinnings. Notably, the gene analysis is exploratory, providing a preliminary framework for future investigations into the molecular basis of working memory. The results demonstrated that under the 2 vs. 0-back condition, spatiotemporal metrics outperformed static metrics (rspa=0.40,q=8.9×10−28,RMSE=0.928 vs. rsta=0.28,q=2.7×10−14,RMSE=0.966). Brain regions contributing to the WMIDM were primarily located in the frontal lobe. Furthermore, genes associated with WMIDM were significantly enriched in pathways linked to intellectual disability and mental disorders, as well as related biological processes and cell types. This study highlights the neural and potential genetic foundations of individual differences in working memory through the lens of spatiotemporal multidimensional brain function and gene expression. These findings provide valuable insights for future neuroscience research and pave the way for personalized cognitive interventions. •Integrated spatiotemporal metrics to reveal individual differences in VWM.•Constructed a working memory individual differences map primarily represented by the frontal lobe.•Revealed correlations between gene expression (e.g., ZFHX2, DRD2) and individual difference in VWM.•Exploratory gene analysis links WMIDM-related genes to intelligence disorders and synaptic organization.

•Reaction time (RTs) were significantly longer under choose conditions compared to follow conditions.•Gray matter volume (GMV) predicted decision-related RTs in choose but not follow conditions.•Activation in the right superior temporal gyrus and left mid-cingulate cortex showed a negative correlation with RTs.•Dynamic network reconfiguration supports stage-specific decision-making. Large-scale brain networks are well-established in resting-state research and are increasingly being used in task-based functional magnetic resonance imaging (fMRI) studies. However, the mechanisms by which brain networks dynamically reorganize across the various stages of decision-making remain unclear. Here, we investigated the neural basis of decision-making by integrating voxel-based morphometry and fMRI within a modified “Wheel of Fortune” gambling task. Stage-specific brain activation was characterized using the Yeo-7 network atlas to delineate large-scale network dynamics across task stages. We found that: (1) Reaction time (RTs) were significantly longer during choose conditions compared to follow conditions; (2) Gray matter volume correlated with individual variability in RT and predicted RT during choose conditions using multivariate pattern analysis with a Kernel Ridge Regression model, effects absent during follow conditions; (3) A negative correlation was observed between RT and activation in the right superior temporal gyrus and left mid-cingulate cortex; (4) Choice stage involved more extensive network engagement than the result and rating stages, with the rating stage showing the lowest overall activation. Network-specific fractional contributions revealed dominant engagement of the ventral attention network, default mode network, and somato-motor network during the choice stage; the frontoparietal network (FPN), dorsal attention network (DAN), and visual network during the result stage; and the DAN and FPN during the rating stage. These findings provide structural and functional explanations for individual differences in decision speed within a gambling paradigm, revealing the distinct and dynamic roles of brain networks across decision stages and offering mechanistic insights into the neural architecture of this process.

Geochemical evidence continues to challenge giant impact models, which predict that the Moon formed from both proto-Earth and impactor material. Analyses of lunar samples reveal isotopic homogeneity in titanium, a highly refractory element, suggesting lunar material was derived predominantly from the mantle of the proto-Earth. A giant impact between the proto-Earth and a Mars-sized impactor named Theia is the favoured scenario for the formation of the Moon 1 , 2 , 3 . Oxygen isotopic compositions have been found to be identical between terrestrial and lunar samples 4 , which is inconsistent with numerical models estimating that more than 40% of the Moon-forming disk material was derived from Theia 2 , 3 . However, it remains uncertain whether more refractory elements, such as titanium, show the same degree of isotope homogeneity as oxygen in the Earth–Moon system. Here we present 50 Ti/ 47 Ti ratios in lunar samples measured by mass spectrometry. After correcting for secondary effects associated with cosmic-ray exposure at the lunar surface using samarium and gadolinium isotope systematics, we find that the 50 Ti/ 47 Ti ratio of the Moon is identical to that of the Earth within about four parts per million, which is only 1/150 of the isotopic range documented in meteorites. The isotopic homogeneity of this highly refractory element suggests that lunar material was derived from the proto-Earth mantle, an origin that could be explained by efficient impact ejection, by an exchange of material between the Earth’s magma ocean and the protolunar disk, or by fission from a rapidly rotating post-impact Earth.

Graph theory and machine learning have been shown to be effective ways of classifying different stages of Alzheimer's disease (AD). Most previous studies have only focused on inter-subject classification with single-mode neuroimaging data. However, whether this classification can truly reflect the changes in the structure and function of the brain region in disease progression remains unverified. In the current study, we aimed to evaluate the classification framework, which combines structural Magnetic Resonance Imaging (sMRI) and resting-state functional Magnetic Resonance Imaging (rs-fMRI) metrics, to distinguish mild cognitive impairment non-converters (MCInc)/AD from MCI converters (MCIc) by using graph theory and machine learning. With the intra-subject (MCInc vs. MCIc) and inter-subject (MCIc vs. AD) design, we employed cortical thickness features, structural brain network features, and sub-frequency (full-band, slow-4, slow-5) functional brain network features for classification. Three feature selection methods [random subset feature selection algorithm (RSFS), minimal redundancy maximal relevance (mRMR), and sparse linear regression feature selection algorithm based on stationary selection (SS-LR)] were used respectively to select discriminative features in the iterative combinations of MRI and network measures. Then support vector machine (SVM) classifier with nested cross-validation was employed for classification. We also compared the performance of multiple classifiers (Random Forest, K-nearest neighbor, Adaboost, SVM) and verified the reliability of our results by upsampling. We found that in the classifications of MCIc vs. MCInc, and MCIc vs. AD, the proposed RSFS algorithm achieved the best accuracies (84.71, 89.80%) than the other algorithms. And the high-sensitivity brain regions found with the two classification groups were inconsistent. Specifically, in MCIc vs. MCInc, the high-sensitivity brain regions associated with both structural and functional features included frontal, temporal, caudate, entorhinal, parahippocampal, and calcarine fissure and surrounding cortex. While in MCIc vs. AD, the high-sensitivity brain regions associated only with functional features included frontal, temporal, thalamus, olfactory, and angular. These results suggest that our proposed method could effectively predict the conversion of MCI to AD, and the inconsistency of specific brain regions provides a novel insight for clinical AD diagnosis.

Structural design of advanced cathodes is a promising strategy to suppress the shuttle effect for lithium‐sulfur batteries (LSBs). In this work, the carbon cloth covered with CoS2 nanoparticles (CC‐CoS2) is prepared to function as both three‐dimensional (3D) current collector and physicochemical barrier to retard migration of soluble lithium polysulfides. On the one hand, the CC‐CoS2 film works as a robust 3D current collector and host with high conductivity, high sulfur loading, and high capability of capturing polysulfides. On the other hand, the 3D porous CC‐CoS2 film serves as a multifunctional interlayer that exhibits efficient physical blocking, strong chemisorption, and fast catalytic redox reaction kinetics toward soluble polysulfides. Consequently, the Al@S/AB@CC‐CoS2 cell with a sulfur loading of 1.2 mg cm−2 exhibits a high rate capability (≈823 mAh g−1 at 4 C) and delivers excellent capacity retention (a decay of ≈0.021% per cycle for 1000 cycles at 4 C). Moreover, the sandwiched cathode of CC‐CoS2@S/AB@CC‐CoS2 is designed for high sulfur loading LSBs. The CC‐CoS2@S/AB@CC‐CoS2 cells with sulfur loadings of 4.2 and 6.1 mg cm−2 deliver high reversible capacities of 1106 and 885 mAh g−1, respectively, after 100 cycles at 0.2 C. The outstanding electrochemical performance is attributed to the sandwiched structure with active catalytic component. Sandwiched cathodes constructed from CoS2‐modified carbon clothes (CC‐CoS2) are designed for high sulfur loading lithium‐sulfur batteries. The sandwiched cathode not only offers three‐dimensional (3D) current collector and host with enough voids for volume expansion to maintain the structural stability, but also promotes the physical encapsulation, chemical entrapment and catalytic conversion of polysulfides species to suppress the shuttle effect.

Multi-step traffic forecasting has always been extremely challenging due to constantly changing traffic conditions. Advanced Graph Convolutional Networks (GCNs) are widely used to extract spatial information from traffic networks. Existing GCNs for traffic forecasting are usually shallow networks that only aggregate two- or three-order node neighbor information. Because of aggregating deeper neighborhood information, an over-smoothing phenomenon occurs, thus leading to the degradation of model forecast performance. In addition, most existing traffic forecasting graph networks are based on fixed nodes and therefore need more flexibility. Based on the current problem, we propose Dynamic Adaptive Deeper Spatio-Temporal Graph Convolutional Networks (ADSTGCN), a new traffic forecasting model. The model addresses over-smoothing due to network deepening by using dynamic hidden layer connections and adaptively adjusting the hidden layer weights to reduce model degradation. Furthermore, the model can adaptively learn the spatial dependencies in the traffic graph by building the parameter-sharing adaptive matrix, and it can also adaptively adjust the network structure to discover the unknown dynamic changes in the traffic network. We evaluated ADSTGCN using real-world traffic data from the highway and urban road networks, and it shows good performance.

Immunotherapy and sorafenib exert anti-tumor effects via ferroptosis, but reliable biomarkers for the individual treatment and prognosis prediction of hepatocellular carcinoma (HCC) based on the ferroptosis and immune status remain lacking. Ferroptosis-related genes (FRGs) were identified by downloading data from FerrDb and by searching and reading original articles from PubMed. Immune-related genes (IRGs) were downloaded from ImmPort. Prognostic FRGs and IRGs in the GSE14520 ( = 220) and The Cancer Genome Atlas (TCGA, = 365) datasets were identified. Least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were used for model construction. Ferroptosis expression profiles, the infiltration of immune cells, and the somatic mutation status were analyzed and compared. Twenty-seven prognostic ferroptosis- and immune-related signatures were included to construct a comprehensive index of ferroptosis and immune status (CIFI). A subgroup of patients was identified as having a high CIFI value, which was associated with a worse prognosis. This subgroup of patients had significantly up-regulated expressions of many suppressors of ferroptosis and higher fractions of immunosuppressive cells, such as cancer-associated fibroblasts (CAFs) and myeloid-derived suppressor cells (MDSCs). Notably, somatic mutation analysis indicated that high-CIFI patients had higher levels of tumor heterogeneity and higher mutation frequencies of genes like TP53. In this work, a novel prognostic classifier was developed based on ferroptosis- and IRGs in HCC, and this classifier could be used for prognostic prediction and the selection of patients for immunotherapies and targeted therapies.