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Background Drug–drug interactions (DDIs) are the reactions between drugs. They are compartmentalized into three types: synergistic, antagonistic and no reaction. As a rapidly developing technology, predicting DDIs-associated events is getting more and more attention and application in drug development and disease diagnosis fields. In this work, we study not only whether the two drugs interact, but also specific interaction types. And we propose a learning-based method using convolution neural networks to learn feature representations and predict DDIs. Results In this paper, we proposed a novel algorithm using a CNN architecture, named CNN-DDI, to predict drug–drug interactions. First, we extract feature interactions from drug categories, targets, pathways and enzymes as feature vectors and employ the Jaccard similarity as the measurement of drugs similarity. Then, based on the representation of features, we build a new convolution neural network as the DDIs’ predictor. Conclusion The experimental results indicate that drug categories is effective as a new feature type applied to CNN-DDI method. And using multiple features is more informative and more effective than single feature. It can be concluded that CNN-DDI has more superiority than other existing algorithms on task of predicting DDIs.

Background Previous genome-wide association studies (GWAS) have identified numerous risk genes for lacunar stroke, but it is challenging to decipher how they confer risk for the disease. We employed an integrative analytical pipeline to efficiently transform genetic associations to identify novel proteins for lacunar stroke. Methods We systematically integrated lacunar stroke genome-wide association study (GWAS) ( N =7338) with human brain proteomes ( N =376) to perform proteome-wide association studies (PWAS), Mendelian randomization (MR), and Bayesian colocalization. We also used an independent human brain proteomic dataset ( N =152) to annotate the new genes. Results We found that the protein abundance of seven genes ( ICA1L , CAND2 , ALDH2 , MADD , MRVI1 , CSPG4 , and PTPN11 ) in the brain was associated with lacunar stroke. These seven genes were mainly expressed on the surface of glutamatergic neurons, GABAergic neurons, and astrocytes. Three genes ( ICA1L , CAND2 , ALDH2 ) were causal in lacunar stroke ( P < 0.05/proteins identified for PWAS; posterior probability of hypothesis 4 ≥ 75 % for Bayesian colocalization), and they were linked with lacunar stroke in confirmatory PWAS and independent MR. We also found that ICA1L is related to lacunar stroke at the brain transcriptome level. Conclusions Our present proteomic findings have identified ICA1L , CAND2 , and ALDH2 as compelling genes that may give key hints for future functional research and possible therapeutic targets for lacunar stroke.

Soil detachment plays a central role in the formulation of soil erosion models, particularly for the simulation and prediction of erosion in cold climates during the thaw period. This research attempts to elucidate the mechanisms of soil detachment on spring thaw period slopes through comprehensive flume experiments, coupled with the application of rare earth element (REE) tracer, investigating the relationships between soil detachment rates, sediment concentration and sediment transport capacity. Observations indicate a nuanced response of soil detachment rate and sediment concentration to scour duration, characterized by an initial increase, subsequent decrease and eventual equilibrium. As thaw depth increases, the primary source of eroded sediment gradually shifts from the upper slope to the mid‐slope. Soil detachment rate was affected by sediment concentration, flow discharge, slope gradient, thawing depth, and slope positions. Furthermore, our analysis reveals a power function relationship (R2 = 0.846) between soil detachment rate, effective shear stress, and sediment transport rate and capacity. These results provide valuable insights into the modeling and prediction of soil erosion processes on brown soil slopes subjected to spring thaw period cycles. Key Points Soil detachment rate showed a significant power function relationship with sediment concentration The soil detachment rate showed a pattern of increasing, then decreasing, and finally stabilizing with time The soil detachment rate equation for spring thaw period slopes is nonlinear

Background Currently, the treatment and prevention of migraine remain highly challenging. Mendelian randomization (MR) has been widely used to explore novel therapeutic targets. Therefore, we performed a systematic druggable genome-wide MR to explore the potential therapeutic targets for migraine. Methods We obtained data on druggable genes and screened for genes within brain expression quantitative trait locis (eQTLs) and blood eQTLs, which were then subjected to two-sample MR analysis and colocalization analysis with migraine genome-wide association studies data to identify genes highly associated with migraine. In addition, phenome-wide research, enrichment analysis, protein network construction, drug prediction, and molecular docking were performed to provide valuable guidance for the development of more effective and targeted therapeutic drugs. Results We identified 21 druggable genes significantly associated with migraine (BRPF3, CBFB, CDK4, CHD4, DDIT4, EP300, EPHA5, FGFRL1, FXN, HMGCR, HVCN1, KCNK5, MRGPRE, NLGN2, NR1D1, PLXNB1, TGFB1, TGFB3, THRA, TLN1 and TP53), two of which were significant in both blood and brain (HMGCR and TGFB3). The results of phenome-wide research showed that HMGCR was highly correlated with low-density lipoprotein, and TGFB3 was primarily associated with insulin-like growth factor 1 levels. Conclusions This study utilized MR and colocalization analysis to identify 21 potential drug targets for migraine, two of which were significant in both blood and brain. These findings provide promising leads for more effective migraine treatments, potentially reducing drug development costs.

Time series models are widely used to examine temporal dynamics and uncover patterns across diverse fields. A commonly employed approach for modeling such data is the (Vector) Autoregressive (AR/VAR) model, in which each variable is represented as a linear combination of its own and others’ lagged values. However, the traditional (V)AR framework relies on the key assumption of stationarity, that autoregressive coefficients remain constant over time, which is often violated in practice, especially in systems affected by structural breaks, seasonal fluctuations, or evolving causal mechanisms. To overcome this limitation, Time-Varying (Vector) Autoregressive (TV-AR/TV-VAR) models have been developed, enabling model parameters to evolve over time and thus better capturing non-stationary behavior. Conventional approaches to estimating such models, including generalized additive modeling and kernel smoothing techniques, often require strong assumptions about basis functions, which can restrict their flexibility and applicability. To address these challenges, we introduce a novel framework that leverages physics-informed neural networks (PINN) to model TV-AR/TV-VAR processes. The proposed method extends the PINN framework to time series analysis by reducing reliance on explicitly defined physical structures, thereby broadening its applicability. Its effectiveness is validated through simulations on synthetic data and an empirical study of real-world health-related time series.

Olives (Olea europaea L.) are a significant part of the agroindustry in China. Olive leaves, the most abundant by-products of the olive and olive oil industry, contain bioactive compounds that are beneficial to human health. The purpose of this study was to evaluate the phytochemical profiles and antioxidant capacities of olive leaves from 32 cultivars grown in China. A total of 32 phytochemical compounds were identified using high-performance liquid chromatography–electrospray ionization–tandem mass spectrometry, including 17 flavonoids, five iridoids, two hydroxycinnamic acids, six triterpenic acids, one simple phenol, and one coumarin. Specifically, olive leaves were found to be excellent sources of flavonoids (4.92–18.29 mg/g dw), iridoids (5.75–33.73 mg/g dw), and triterpenic acids (15.72–35.75 mg/g dw), and considerable variations in phytochemical content were detected among the different cultivars. All tested cultivars were classified into three categories according to their oil contents for further comparative phytochemicals assessment. Principal component analysis indicated that the investigated olive cultivars could be distinguished based upon their phytochemical profiles and antioxidant capacities. The olive leaves obtained from the low-oil-content (<16%) cultivars exhibited higher levels of glycosylated flavonoids and iridoids, while those obtained from high-oil-content (>20%) cultivars contained mainly triterpenic acids in their compositions. Correspondingly, the low-oil-content cultivars (OL3, Frantoio selection and OL14, Huaou 5) exhibited the highest ABTS antioxidant activities (758.01 ± 16.54 and 710.64 ± 14.58 mg TE/g dw, respectively), and OL9 (Olea europaea subsp. Cuspidata isolate Yunnan) and OL3 exhibited the highest ferric reducing/antioxidant power assay values (1228.29 ± 23.95 mg TE/g dw and 1099.99 ± 14.30 mg TE/g dw, respectively). The results from this study may be beneficial to the comprehensive evaluation and utilization of bioactive compounds in olive leaves.

Mounting evidence has linked changes in human gut microbiota to proton pump inhibitor (PPI) use. Accordingly, multiple studies have analyzed the gut microbiomes of PPI users, but PPI–microbe interactions are still understudied. Here, we performed a meta-analysis of four studies with available 16S rRNA gene amplicon sequencing data to uncover the potential changes in human gut microbes among PPI users. Despite some differences, we found common features of the PPI-specific microbiota, including a decrease in the Shannon diversity index and the depletion of bacteria from the Ruminococcaceae and Lachnospiraceae families, which are crucial short-chain fatty acid-producers. Through training based on multiple studies, using a random forest classification model, we further verified the representativeness of the six screened gut microbial genera and 20 functional genes as PPI-related biomarkers, with AUC values of 0.748 and 0.879, respectively. Functional analysis of the PPI-associated 16S rRNA microbiome revealed enriched carbohydrate- and energy-associated genes, mostly encoding fructose-1,6-bisphosphatase and pyruvate dehydrogenase, among others. In this study, we have demonstrated alterations in bacterial abundance and functional metabolic potential related to PPI use, as a basis for future studies on PPI-induced adverse effects.

Affected by global warming, the permafrost in Northeast China (NEC) has been continuously degrading in recent years. Many researchers have focused on the spatial and temporal distribution characteristics of permafrost in NEC, however, few studies have delved into the field scale. In this study, based on the Optimal Parameters-based Geographical Detector (OPGD) model and Receiver Operating Characteristic (ROC) test, the spatial stratified heterogeneity of permafrost distribution and the indicating performance of environmental variables on permafrost in NEC at the field scale were analyzed. Permafrost spatial distribution data were obtained from the Engineering Geological Investigation Reports (EGIR) of six highways located in NEC and a total of 19 environmental variables related to heat transfer, vegetation, soil, topography, moisture, and ecology were selected. The H-factors (variables with the highest contribution in factor detector results and interaction detector results): slope position (γ), surface frost number (SFN), elevation (DEM), topographic diversity (TD), and annual snow cover days (ASCD) were found to be the major contributors to the distribution of permafrost at the field scale. Among them, γ has the highest contribution and is a special explanatory variable for permafrost. In most cases, interaction can improve the impact of variables, especially the interaction between H-factors. The risk of permafrost decreases with the increase of TD, RN, and SBD, and increases with the increase of SFN. The performance of SFN to indicate permafrost distribution was found to be the best among all variables (AUC = 0.7063). There is spatial heterogeneity in the distribution of permafrost on highways in different spatial locations. This study summarized the numerical and spatial location between permafrost and different environmental variables at the field scale, and many results were found to be informative for environmental studies and engineering construction in NEC.

With the aggravation of climate warming, unstable soil slopes are more and more common in permafrost regions. The long-term monitoring of a slow earthflow (K178 + 530 landslide) in the Xiao Xing’an Mountains permafrost area in Northeast China was carried out. The deformation characteristics and occurrence mechanism of the landslide were studied using field investigation, on-site drilling, sensor monitoring, laboratory test, Google satellite image, unmanned aerial vehicle photogrammetry, and high-density resistivity. To analyze the variation laws of pore water pressure and effective stress and their influence on slope deformation, a coupled hydro-thermo-mechanical model was established to reconstruct the deformation process of the slope. The results show that the groundwater recharge from the permafrost degradation and surface infiltration reduces the soil cohesion and internal friction angle near the main scarp and increases the soil gravity, thus providing dynamic and mechanical conditions for slope deformation. The melting of the continuous segregation ice in the active layer and surface infiltration reduces the soil strength of the sliding surface and provides deformation conditions for the start of the landslide. The combination of these two factors finally led to the occurrence of the landslide. According to its deformation mechanism, it can be judged that the landslide is a thrust-type landslide. In addition, after the melting of the segregation ice, the upper soil slides along the slope under the action of gravity, causing the sliding surface to be parallel to the slope surface. The soil near the main scarp slides downward and accumulates near the toe to form several transverse ridges. The instability of the transverse ridges produces secondary sliding which causes the toe to advance continuously. The numerical simulation results can intuitively reflect the stage deformation characteristics of the slope, pore water pressure changes, and effective stress distributions, which provides a supplement for further understanding the formation mechanism and deformation process of the landslide.

Reporter viruses provide powerful tools for both basic and applied virology studies, however, the creation and exploitation of reporter influenza A viruses (IAVs) have been hindered by the limited tolerance of the segmented genome to exogenous modifications. Interestingly, our previous study has demonstrated the underlying mechanism that foreign insertions reduce the replication/transcription capacity of the modified segment, impairing the delicate balance among the multiple segments during IAV infection. In the present study, we developed a “balance compensation” strategy by incorporating additional compensatory mutations during initial construction of recombinant IAVs to expand the tolerance of IAV genome. As a proof of concept, promoter-enhancing mutations were introduced within the modified segment to rectify the segments imbalance of a reporter influenza PR8-NS-Gluc virus, while directed optimization of the recombinant IAV was successfully achieved. Further, we generated recombinant IAVs expressing a much larger firefly luciferase (Fluc) by coupling with a much stronger compensatory enhancement, and established robust Fluc-based live-imaging mouse models of IAV infection. Our strategy feasibly expands the tolerance for foreign gene insertions in the segmented IAV genome, which opens up better opportunities to develop more versatile reporter IAVs as well as live attenuated influenza virus-based vaccines for other important human pathogens.