SVision: a deep learning approach to resolve complex structural variants.

Complex structural variants (CSVs) encompass multiple breakpoints and are often missed or misinterpreted. We developed SVision, a deep-learning-based multi-object-recognition framework, to automatically detect and haracterize CSVs from long-read sequencing data. SVision outperforms current callers at identifying the internal structure of complex events and has revealed 80 high-quality CSVs with 25 distinct structures from an individual genome. SVision directly detects CSVs without matching known structures, allowing sensitive detection of both common and previously uncharacterized complex rearrangements.

MESPool: Molecular Edge Shrinkage Pooling for hierarchical molecular representation learning and property prediction.

Identifying task-relevant structures is important for molecular property prediction. In a graph neural network (GNN), graph pooling can group nodes and hierarchically represent the molecular graph. However, previous pooling methods either drop out node information or lose the connection of the original graph; therefore, it is difficult to identify continuous subtructures. Importantly, they lacked interpretability on molecular graphs. To this end, we proposed a novel Molecular Edge Shrinkage Pooling (MESPool) method, which is based on edges (or chemical bonds). MESPool preserves crucial edges and shrinks others inside the functional groups and is able to search for key structures without breaking the original connection. We compared MESPool with various well-known pooling methods on different benchmarks and showed that MESPool outperforms the previous methods. Furthermore, we explained the rationality of MESPool on some datasets, including a COVID-19 drug dataset.

Proposal and validation of a new approach in tele-rehabilitation with 3D human posture estimation: a randomized controlled trial in older individuals with sarcopenia.

OBJECTIVE: Through a randomized controlled trial on older adults with sarcopenia, this study compared the training effects of an AI-based remote training group using deep learning-based 3D human pose estimation technology with those of a face-to-face traditional training group and a general remote training group. METHODS: Seventy five older adults with sarcopenia aged 60-75 from community organizations in Changchun city were randomly divided into a face-to-face traditional training group (TRHG), a general remote training group (GTHG), and an AI-based remote training group (AITHG). All groups underwent a 3-month program consisting of 24-form Taichi exercises, with a frequency of 3 sessions per week and each session lasting 40 min. The participants underwent Appendicular Skeletal Muscle Mass Index (ASMI), grip strength, 6-meter walking pace, Timed Up and Go test (TUGT), and quality of life score (QoL) tests before the experiment, during the mid-term, and after the experiment. This study used SPSS26.0 software to perform one-way ANOVA and repeated measures ANOVA tests to compare the differences among the three groups. A significance level of p < 0.05 was defined as having significant difference, while p < 0.01 was defined as having a highly significant difference. RESULTS: (1) The comparison between the mid-term and pre-term indicators showed that TRHG experienced significant improvements in ASMI, 6-meter walking pace, and QoL (p < 0.01), and a significant improvement in TUGT timing test (p < 0.05); GTHG experienced extremely significant improvements in 6-meter walking pace and QoL (p < 0.01); AITHG experienced extremely significant improvements in ASMI, 6-meter walking pace, and QoL (p < 0.01), and a significant improvement in TUGT timing test (p < 0.05). (2) The comparison between the post-term and pre-term indicators showed that TRHG experienced extremely significant improvements in TUGT timing test (p < 0.01); GTHG experienced significant improvements in ASMI and TUGT timing test (p < 0.05); and AITHG experienced extremely significant improvements in TUGT timing test (p < 0.01). (3) During the mid-term, there was no significant difference among the groups in all tests (p > 0.05). The same was in post-term tests (p > 0.05). CONCLUSION: Compared to the pre-experiment, there was no significant difference at the post- experiment in the recovery effects on the muscle quality, physical activity ability, and life quality of patients with sarcopenia between the AI-based remote training group and the face-to-face traditional training group. 3D pose estimation is equally as effective as traditional rehabilitation methods in enhancing muscle quality, functionality and life quality in older adults with sarcopenia. TRIAL REGISTRATION: The trial was registered in ClinicalTrials.gov (NCT05767710).

Body landmarks and genetic algorithm-based approach for non-contact detection of head forward posture among Chinese adolescents: revitalizing machine learning in medicine.

Addressing the current complexities, costs, and adherence issues in the detection of forward head posture (FHP), our study conducted an exhaustive epidemiologic investigation, incorporating a comprehensive posture screening process for each participant in China. This research introduces an avant-garde, machine learning-based non-contact method for the accurate discernment of FHP. Our approach elevates detection accuracy by leveraging body landmarks identified from human images, followed by the application of a genetic algorithm for precise feature identification and posture estimation. Observational data corroborates the superior efficacy of the Extra Tree Classifier technique in FHP detection, attaining an accuracy of 82.4%, a specificity of 85.5%, and a positive predictive value of 90.2%. Our model affords a rapid, effective solution for FHP identification, spotlighting the transformative potential of the convergence of feature point recognition and genetic algorithms in non-contact posture detection. The expansive potential and paramount importance of these applications in this niche field are therefore underscored.

CRISPR/Cas9 Gene Editing of NtAITRs, a Family of Transcription Repressor Genes, Leads to Enhanced Drought Tolerance in Tobacco.

Tobacco is a cash crop throughout the world, and its growth and development are affected by abiotic stresses including drought stress; therefore, drought-tolerant breeding may help to improve tobacco yield and quality under drought stress conditions. Considering that the plant hormone ABA (abscisic acid) is able to regulate plant responses to abiotic stresses via activating ABA response genes, the characterization of ABA response genes may enable the identification of genes that can be used for molecular breeding to improve drought tolerance in tobacco. We report here the identification of NtAITRs (Nicotiana tabacum ABA-induced transcription repressors) as a family of novel regulators of drought tolerance in tobacco. Bioinformatics analysis shows that there are a total of eight NtAITR genes in tobacco, and all the NtAITRs have a partially conserved LxLxL motif at their C-terminus. RT-PCR results show that the expression levels of at least some NtAITRs were increased in response to ABA and drought treatments, and NtAITRs, when recruited to the Gal4 promoter via a fused GD (Gal4 DNA-binding domain), were able to repress transcription activator LD-VP activated expression of the LexA-Gal4-GUS reporter gene. Roles of NtAITRs in regulating drought tolerance in tobacco were analyzed by generating CRISPR/Cas9 gene-edited mutants. A total of three Cas9-free ntaitr12356 quintuple mutants were obtained, and drought treatment assays show that drought tolerance was increased in the ntaitr12356 quintuple mutants. On the other hand, results of seed germination and seedling greening assays show that ABA sensitivity was increased in the ntaitr12356 quintuple mutants, and the expression levels of some ABA signaling key regulator genes were altered in the ntaitr12356-c3 mutant. Taken together, our results suggest that NtAITRs are ABA-responsive genes, and that NtAITRs function as transcription repressors and negatively regulate drought tolerance in tobacco, possibly by affecting plant ABA response via affecting the expression of ABA signaling key regulator genes.

Immune checkpoint inhibitors-related myocarditis in patients with cancer: an analysis of international spontaneous reporting systems.

BACKGROUND: Immune checkpoint inhibitors-induced myocarditis presents unique clinical challenges. Here, we assessed post-marketing safety of cytotoxic T-lymphocyte-associated protein-4 (CTLA-4), programmed cell death-1 (PD-1), and programmed death-ligand 1 (PD-L1) inhibitors by mining the real-world data reported in two international pharmacovigilance databases. METHODS: We analyzed immune checkpoint inhibitors (ICIs)-associated fatal adverse drug events (ADEs) reports from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) collected from July 1, 2014 to December 31, 2019 and data from EudraVigilance (EV) database accessed on February 29, 2020. Three different data mining approaches were used to detect the signal of fatal myocarditis caused by ICIs. RESULTS: Based on 7613 ICIs-related ADEs reported to the EV database and 5786 ICIs-associated ADEs submitted to the FAERS database, the most frequently reported ADE was ipilimumab-related colitis. For myocarditis, nivolumab-associated myocarditis was the most common. Among the five fatal toxic effects associated with ICIs, the lethality rate of myocarditis was the highest. Therefore, we further analyzed ICI-associated myocarditis and found that elderly patients and male patients were more likely to develop ICIs-related myocarditis. The results of signal detection showed that the risk signal of avelumab-related myocarditis detected by reporting odds ratio (ROR) method and proportional reporting ratios (PRR) method was the highest, whereas the signal strength of ipilimumab-related myocarditis detected by Bayesian confidence propagation neural networks (BCPNN) method was the strongest. CONCLUSION: The findings of this study indicated the potential safety issues of developing myocarditis when using ICIs, which were consistent with the results of previous clinical trials and could provide a reference for clinical workers when using ICIs.

Descriptor Selection via Log-Sum Regularization for the Biological Activities of Chemical Structure.

The quantitative structure-activity relationship (QSAR) model searches for a reliable relationship between the chemical structure and biological activities in the field of drug design and discovery. (1) Background: In the study of QSAR, the chemical structures of compounds are encoded by a substantial number of descriptors. Some redundant, noisy and irrelevant descriptors result in a side-effect for the QSAR model. Meanwhile, too many descriptors can result in overfitting or low correlation between chemical structure and biological bioactivity. (2) Methods: We use novel log-sum regularization to select quite a few descriptors that are relevant to biological activities. In addition, a coordinate descent algorithm, which uses novel univariate log-sum thresholding for updating the estimated coefficients, has been developed for the QSAR model. (3) Results: Experimental results on artificial and four QSAR datasets demonstrate that our proposed log-sum method has good performance among state-of-the-art methods. (4) Conclusions: Our proposed multiple linear regression with log-sum penalty is an effective technique for both descriptor selection and prediction of biological activity.

FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test.

The maximum mean discrepancy (MMD) is a recently proposed test statistic for the two-sample test. Its quadratic time complexity, however, greatly hampers its availability to large-scale applications. To accelerate the MMD calculation, in this study we propose an efficient method called FastMMD. The core idea of FastMMD is to equivalently transform the MMD with shift-invariant kernels into the amplitude expectation of a linear combination of sinusoid components based on Bochner's theorem and Fourier transform (Rahimi & Recht, 2007). Taking advantage of sampling the Fourier transform, FastMMD decreases the time complexity for MMD calculation from O(N(2)d) to O(LN d), where N and d are the size and dimension of the sample set, respectively. Here, L is the number of basis functions for approximating kernels that determines the approximation accuracy. For kernels that are spherically invariant, the computation can be further accelerated to O(LN log d) by using the Fastfood technique (Le, Sarlós, & Smola, 2013). The uniform convergence of our method has also been theoretically proved in both unbiased and biased estimates. We also provide a geometric explanation for our method, ensemble of circular discrepancy, which helps us understand the insight of MMD and we hope will lead to more extensive metrics for assessing the two-sample test task. Experimental results substantiate that the accuracy of FastMMD is similar to that of MMD and with faster computation and lower variance than existing MMD approximation methods.

Multiview stereo and silhouette fusion via minimizing generalized reprojection error.

Accurate reconstruction of 3D geometrical shape from a set of calibrated 2D multiview images is an active yet challenging task in computer vision. The existing multiview stereo methods usually perform poorly in recovering deeply concave and thinly protruding structures, and suffer from several common problems like slow convergence, sensitivity to initial conditions, and high memory requirements. To address these issues, we propose a two-phase optimization method for generalized reprojection error minimization (TwGREM), where a generalized framework of reprojection error is proposed to integrate stereo and silhouette cues into a unified energy function. For the minimization of the function, we first introduce a convex relaxation on 3D volumetric grids which can be efficiently solved using variable splitting and Chambolle projection. Then, the resulting surface is parameterized as a triangle mesh and refined using surface evolution to obtain a high-quality 3D reconstruction. Our comparative experiments with several state-of-the-art methods show that the performance of TwGREM based 3D reconstruction is among the highest with respect to accuracy and efficiency, especially for data with smooth texture and sparsely sampled viewpoints.

Revisiting Nonlocal Self-Similarity from Continuous Representation.

Nonlocal self-similarity (NSS) is an important prior that has been successfully applied in multi-dimensional data processing tasks, e.g., image and video recovery. However, existing NSS-based methods are solely suitable for meshgrid data such as images and videos, but are not suitable for emerging off-meshgrid data, e.g., point cloud and weather data. In this work, we revisit the NSS from the continuous representation perspective and propose a novel Continuous Representation-based NonLocal method (termed as CRNL), which has two innovative features as compared with classical nonlocal methods. First, based on the continuous representation, our CRNL unifies the measure of self-similarity for on-meshgrid and off-meshgrid data and thus is naturally suitable for both of them. Second, the nonlocal continuous groups can be more compactly and efficiently represented by the coupled low-rank function factorization, which simultaneously exploits the similarity within each group and across different groups, while classical nonlocal methods neglect the similarity across groups. This elaborately designed coupled mechanism allows our method to enjoy favorable performance over conventional NSS methods in terms of both effectiveness and efficiency. Extensive multi-dimensional data processing experiments on-meshgrid (e.g., image inpainting and image denoising) and off-meshgrid (e.g., weather data prediction and point cloud recovery) validate the versatility, effectiveness, and efficiency of our CRNL as compared with state-of-the-art methods.

Quantum-inspired analysis of neural network vulnerabilities: the role of conjugate variables in system attacks.

Neural networks demonstrate vulnerability to small, non-random perturbations, emerging as adversarial attacks. Such attacks, born from the gradient of the loss function relative to the input, are discerned as input conjugates, revealing a systemic fragility within the network structure. Intriguingly, a mathematical congruence manifests between this mechanism and the quantum physics' uncertainty principle, casting light on a hitherto unanticipated interdisciplinarity. This inherent susceptibility within neural network systems is generally intrinsic, highlighting not only the innate vulnerability of these networks, but also suggesting potential advancements in the interdisciplinary area for understanding these black-box networks.

Simulating learning methodology (SLeM): an approach to machine learning automation.

This paper introduces a 'simulating learning methodology' (SLeM) approach for the learning methodology determination in general and for Auto6 ML in particular, and reports the SLeM framework, approaches, algorithms and applications.

Spatial Prior-Guided Bi-Directional Cross-Attention Transformers for Tooth Instance Segmentation.

Tooth instance segmentation of dental panoramic X-ray images represents a task of significant clinical importance. Teeth demonstrate symmetry within the upper and lower jawbones and are arranged in a specific order. However, previous studies frequently overlook this crucial spatial prior information, resulting in misidentifications of tooth categories for adjacent or similarly shaped teeth. In this paper, we propose SPGTNet, a spatial prior-guided transformer method, designed to both the extracted tooth positional features from CNNs and the long-range contextual information from vision transformers for dental panoramic X-ray image segmentation. Initially, a center-based spatial prior perception module is employed to identify each tooth's centroid, thereby enhancing the spatial prior information for the CNN sequence features. Subsequently, a bi-directional cross-attention module is designed to facilitate the interaction between the spatial prior information of the CNN sequence features and the long-distance contextual features of the vision transformer sequence features. Finally, an instance identification head is employed to derive the tooth segmentation results. Extensive experiments on three public benchmark datasets have demonstrated the effectiveness and superiority of our proposed method in comparison with other state-of-the-art approaches. The proposed method demonstrates the capability to accurately identify and analyze tooth structures, thereby providing crucial information for dental diagnosis, treatment planning, and research.

Improve Noise Tolerance of Robust Loss via Noise-Awareness.

Robust loss minimization is an important strategy for handling robust learning issue on noisy labels. Current approaches for designing robust losses involve the introduction of noise-robust factors, i.e., hyperparameters, to control the trade-off between noise robustness and learnability. However, finding suitable hyperparameters for different datasets with noisy labels is a challenging and time-consuming task. Moreover, existing robust loss methods usually assume that all training samples share common hyperparameters, which are independent of instances. This limits the ability of these methods to distinguish the individual noise properties of different samples and overlooks the varying contributions of diverse training samples in helping models understand underlying patterns. To address above issues, we propose to assemble robust loss with instance-dependent hyperparameters to improve their noise tolerance with theoretical guarantee. To achieve setting such instance-dependent hyperparameters for robust loss, we propose a meta-learning method which is capable of adaptively learning a hyperparameter prediction function, called noise-aware-robust-loss-adjuster (NARL-Adjuster). Through mutual amelioration between hyperparameter prediction function and classifier parameters in our method, both of them can be simultaneously finely ameliorated and coordinated to attain solutions with good generalization capability. Four SOTA robust loss functions are attempted to be integrated with our algorithm, and comprehensive experiments substantiate the general availability and effectiveness of the proposed method in both its noise tolerance and performance. Meanwhile, the explicit parameterized structure makes the meta-learned prediction function ready to be transferrable and plug-and-play to unseen datasets with noisy labels. Specifically, we transfer our meta-learned NARL-Adjuster to unseen tasks, including several real noisy datasets, and achieve better performance compared with conventional hyperparameter tuning strategy, even with carefully tuned hyperparameters.

Rotation Equivariant Proximal Operator for Deep Unfolding Methods in Image Restoration.

The deep unfolding approach has attracted significant attention in computer vision tasks, which well connects conventional image processing modeling manners with more recent deep learning techniques. Specifically, by establishing a direct correspondence between algorithm operators at each implementation step and network modules within each layer, one can rationally construct an almost "white box" network architecture with high interpretability. In this architecture, only the predefined component of the proximal operator, known as a proximal network, needs manual configuration, enabling the network to automatically extract intrinsic image priors in a data-driven manner. In current deep unfolding methods, such a proximal network is generally designed as a CNN architecture, whose necessity has been proven by a recent theory. That is, CNN structure substantially delivers the translational symmetry image prior, which is the most universally possessed structural prior across various types of images. However, standard CNN-based proximal networks have essential limitations in capturing the rotation symmetry prior, another universal structural prior underlying general images. This leaves a large room for further performance improvement in deep unfolding approaches. To address this issue, this study makes efforts to suggest a high-accuracy rotation equivariant proximal network that effectively embeds rotation symmetry priors into the deep unfolding framework. Especially, we deduce, for the first time, the theoretical equivariant error for such a designed proximal network with arbitrary layers under arbitrary rotation degrees. This analysis should be the most refined theoretical conclusion for such error evaluation to date and is also indispensable for supporting the rationale behind such networks with intrinsic interpretability requirements. Through experimental validation on different vision tasks, including blind image super-resolution, medical image reconstruction, and image de-raining, the proposed method is validated to be capable of directly replacing the proximal network in current deep unfolding architecture and readily enhancing their state-of-the-art performance. This indicates its potential usability in general vision tasks.

OSCNet: Orientation-Shared Convolutional Network for CT Metal Artifact Learning.

X-ray computed tomography (CT) has been broadly adopted in clinical applications for disease diagnosis and image-guided interventions. However, metals within patients always cause unfavorable artifacts in the recovered CT images. Albeit attaining promising reconstruction results for this metal artifact reduction (MAR) task, most of the existing deep-learning-based approaches have some limitations. The critical issue is that most of these methods have not fully exploited the important prior knowledge underlying this specific MAR task. Therefore, in this paper, we carefully investigate the inherent characteristics of metal artifacts which present rotationally symmetrical streaking patterns. Then we specifically propose an orientation-shared convolution representation mechanism to adapt such physical prior structures and utilize Fourier-series-expansion-based filter parametrization for modelling artifacts, which can finely separate metal artifacts from body tissues. By adopting the classical proximal gradient algorithm to solve the model and then utilizing the deep unfolding technique, we easily build the corresponding orientation-shared convolutional network, termed as OSCNet. Furthermore, considering that different sizes and types of metals would lead to different artifact patterns (e.g., intensity of the artifacts), to better improve the flexibility of artifact learning and fully exploit the reconstructed results at iterative stages for information propagation, we design a simple-yet-effective sub-network for the dynamic convolution representation of artifacts. By easily integrating the sub-network into the proposed OSCNet framework, we further construct a more flexible network structure, called OSCNet+, which improves the generalization performance. Through extensive experiments conducted on synthetic and clinical datasets, we comprehensively substantiate the effectiveness of our proposed methods. Code will be released at https://github.com/hongwang01/OSCNet.

Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery.

Since higher-order tensors are naturally suitable for representing multi-dimensional data in real-world, e.g., color images and videos, low-rank tensor representation has become one of the emerging areas in machine learning and computer vision. However, classical low-rank tensor representations can solely represent multi-dimensional discrete data on meshgrid, which hinders their potential applicability in many scenarios beyond meshgrid. To break this barrier, we propose a low-rank tensor function representation (LRTFR) parameterized by multilayer perceptrons (MLPs), which can continuously represent data beyond meshgrid with powerful representation abilities. Specifically, the suggested tensor function, which maps an arbitrary coordinate to the corresponding value, can continuously represent data in an infinite real space. Parallel to discrete tensors, we develop two fundamental concepts for tensor functions, i.e., the tensor function rank and low-rank tensor function factorization, and utilize MLPs to paramterize factor functions of the tensor function factorization. We theoretically justify that both low-rank and smooth regularizations are harmoniously unified in LRTFR, which leads to high effectiveness and efficiency for data continuous representation. Extensive multi-dimensional data recovery applications arising from image processing (image inpainting and denoising), machine learning (hyperparameter optimization), and computer graphics (point cloud upsampling) substantiate the superiority and versatility of our method as compared with state-of-the-art methods. Especially, the experiments beyond the original meshgrid resolution (hyperparameter optimization) or even beyond meshgrid (point cloud upsampling) validate the favorable performances of our method for continuous representation.

Deep Diversity-Enhanced Feature Representation of Hyperspectral Images.

In this paper, we study the problem of efficiently and effectively embedding the high-dimensional spatio-spectral information of hyperspectral (HS) images, guided by feature diversity. Specifically, based on the theoretical formulation that feature diversity is correlated with the rank of the unfolded kernel matrix, we rectify 3D convolution by modifying its topology to enhance the rank upper-bound. This modification yields a rank-enhanced spatial-spectral symmetrical convolution set (ReS 3-ConvSet), which not only learns diverse and powerful feature representations but also saves network parameters. Additionally, we also propose a novel diversity-aware regularization (DA-Reg) term that directly acts on the feature maps to maximize independence among elements. To demonstrate the superiority of the proposed ReS 3-ConvSet and DA-Reg, we apply them to various HS image processing and analysis tasks, including denoising, spatial super-resolution, and classification. Extensive experiments show that the proposed approaches outperform state-of-the-art methods both quantitatively and qualitatively to a significant extent. The code is publicly available at https://github.com/jinnh/ReSSS-ConvSet.

Self-determined sequence exercise program for elderly with sarcopenia: A Randomized controlled trial with clinical assistance from explainable artificial intelligence.

To improve and even reverse sarcopenia in elderly people, this study developed a self-determined sequence exercise program consisting of strength training exercise, Yijinjing exercise (a traditional Chinese exercise), and hybrid strength training with Yijinjing exercise. Ninety-four community-dwelling older adults screened for sarcopenia using the Asian Working Group for Sarcopenia criteria were randomly assigned to 24 weeks of a control group (CG, n = 30), self-determined sequence exercise program group (SDSG, n = 34) or strength training group (STG, n = 30). The study examined the effects of three interventions on participantsL3 skeletal muscle fat density, L3 skeletal muscle fat area, L3 skeletal muscle density, L3 skeletal muscle area, muscle fat infiltration, relative skeletal muscle mass index, and grip strength using a repeated-measures ANOVA to evaluate the experimental data. To evaluate the real effect of this model in reversing sarcopenia after the intervention, nine classification models were trained. Significant interaction effects were observed with grip strength, RSMI, L3 SMD, and L3 SMA. At the 24th week, participants' grip strength, L3 SMFA, L3 SMA, and RSMI were improved significantly in the SDSG and STG. The SDSG achieved significantly greater RSMI and grip strength than the STG and CG after the intervention. The self-determined sequence exercise program exhibited better performance than the single type of exercise modality in reversing sarcopenia and improving older adults' skeletal muscle area. Consequently, the stacking model is feasible to make a prediction as to whether or not sarcopenia may be reversed in older adults.

Causal effect of physical activity and sedentary behavior on the risk of alcohol dependence: A bidirectional two-sample Mendelian randomization study.

BACKGROUND: Alcohol dependence, influenced by physical activity (PA) and sedentary behavior, lacks clear causal clarity. This study aims to clarify causal relationships by estimating these effects using bidirectional two-sample Mendelian randomization (MR). METHODS: A bidirectional multivariable two-sample MR framework was employed to assess the causal effects of PA and sedentary behavior on alcohol dependence. Summarized genetic association data were analyzed for four PA-related activity patterns-moderate to vigorous physical activity (MVPA), vigorous physical activity (VPA), accelerometer-based physical activity with average acceleration (AccAve), and accelerometer-based physical activity with accelerations greater than 425 milli-gravities (Acc425)-and three sedentary behavior patterns-sedentary, TV watching, and computer use. The study was expanded to include the examination of the relationship between sedentary behavior or PA and general drinking behavior, quantified as drinks per week (DPW). We obtained summarized data on genetic associations with four PA related activity patterns (MVPA, VPA, AccAve and Acc425) and three sedentary behavior related behavior patterns (sedentary, TV watching and computer use). RESULTS: MR analysis found AccAve inversely associated with alcohol dependence risk (OR: 0.87; 95% CI: 0.80-0.95; p < 0.001), MVPA positively associated (OR: 2.86; 95%CI: 1.45-5.66; p = 0.002). For sedentary behavior and alcohol dependence, only TV watching was positively associated with the risk of alcohol dependence (OR: 1.43; 95%CI: 1.09-1.88; p = 0.009). No causal links found for other physical or sedentary activities. Reverse analysis and sensitivity tests showed consistent findings without pleiotropy or heterogeneity. Multivariate MR analyses indicated that while MVPA, AccAve and TV watching are independently associated with alcohol dependence, DPW did not show a significant causal relationship. CONCLUSIONS: Our results suggest that AccAve is considered a protective factor against alcohol dependence, while MVPA and TV watching are considered risk factors for alcohol dependence. Conversely, alcohol dependence serves as a protective factor against TV watching. Only TV watching and alcohol dependence might mutually have a significant causal effect on each other.