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ABSTRACT The abundance of various cell types can vary significantly among patients with varying phenotypes and even those with the same phenotype. Recent scientific advancements provide mounting evidence that other clinical variables, such as age, gender, and lifestyle habits, can also influence the abundance of certain cell types. However, current methods for integrating single-cell-level omics data with clinical variables are inadequate. In this study, we propose a regularized Bayesian Dirichlet-multinomial regression framework to investigate the relationship between single-cell RNA sequencing data and patient-level clinical data. Additionally, the model employs a novel hierarchical tree structure to identify such relationships at different cell-type levels. Our model successfully uncovers significant associations between specific cell types and clinical variables across three distinct diseases: pulmonary fibrosis, COVID-19, and non-small cell lung cancer. This integrative analysis provides biological insights and could potentially inform clinical interventions for various diseases. 

Abstract The field of spatially resolved transcriptomics (SRT) has greatly advanced our understanding of cellular microenvironments by integrating spatial information with molecular data collected from multiple tissue sections or individuals. However, methods for multi-sample spatial clustering are lacking, and existing methods primarily rely on molecular information alone. This paper introduces BayeSMART, a Bayesian statistical method designed to identify spatial domains across multiple samples. BayeSMART leverages artificial intelligence (AI)-reconstructed single-cell level information from the paired histology images of multi-sample SRT datasets while simultaneously considering the spatial context of gene expression. The AI integration enables BayeSMART to effectively interpret the spatial domains. We conducted case studies using four datasets from various tissue types and SRT platforms, and compared BayeSMART with alternative multi-sample spatial clustering approaches and a number of state-of-the-art methods for single-sample SRT analysis, demonstrating that it surpasses existing methods in terms of clustering accuracy, interpretability, and computational efficiency. BayeSMART offers new insights into the spatial organization of cells in multi-sample SRT data. 

ABSTRACT Recent breakthroughs in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive molecular characterization at the spot or cellular level while preserving spatial information. Cells are the fundamental building blocks of tissues, organized into distinct yet connected components. Although many non-spatial and spatial clustering approaches have been used to partition the entire region into mutually exclusive spatial domains based on the SRT high-dimensional molecular profile, most require an ad hoc selection of less interpretable dimensional-reduction techniques. To overcome this challenge, we propose a zero-inflated negative binomial mixture model to cluster spots or cells based on their molecular profiles. To increase interpretability, we employ a feature selection mechanism to provide a low-dimensional summary of the SRT molecular profile in terms of discriminating genes that shed light on the clustering result. We further incorporate the SRT geospatial profile via a Markov random field prior. We demonstrate how this joint modeling strategy improves clustering accuracy, compared with alternative state-of-the-art approaches, through simulation studies and 3 real data applications. 

Abstract Current clustering analysis of spatial transcriptomics data primarily relies on molecular information and fails to fully exploit the morphological features present in histology images, leading to compromised accuracy and interpretability. To overcome these limitations, we have developed a multi-stage statistical method called iIMPACT. It identifies and defines histology-based spatial domains based on AI-reconstructed histology images and spatial context of gene expression measurements, and detects domain-specific differentially expressed genes. Through multiple case studies, we demonstrate iIMPACT outperforms existing methods in accuracy and interpretability and provides insights into the cellular spatial organization and landscape of functional genes within spatial transcriptomics data. 

Abstract Technologies to enhance the survivability of wave energy converters (WECs) in harsh ocean environment and reduce the difficulty and cost of deployment and operation are important. Traditional two-body point absorber with a rigid Power Take-off (PTO) may result in two essential problems on the deployment and operation. This study proposes a novel a two-body self-reactive point absorber with a flexible tether drive PTO. This flexible PTO design can avoid the request of supporting structures on the WEC to constrain the motion and harvest energy from multiple degree of freedoms (DOFs) motion without requirement of a taut mooring. System dynamics considering 4-DOF with the proposed flexible PTO system are formulated. A scaled prototype is designed, fabricated, and tested in a wave tank. Results show that the proposed flexible PTO can greatly increase the power absorption and add a reactive peak in the frequency domain. This study reveals that the proposed PTO is desirable for the two-body point absorber and thus holding the advantages of fast and easy deployment with slack mooring and good survivability under large wave conditions. 

This paper presents a systematic approach to designing digital forensics instructional materials to address the severe shortage of active learning materials in the digital forensics community. The materials include real-world scenario-based case studies, hands-on problem-driven labs for each case study, and an integrated forensic investigation environment. In this paper, we first clarify some fundamental concepts related to digital forensics, such as digital forensic artifacts, artifact generators, and evidence. We then re-categorize knowledge units of digital forensics based on the artifact generators for measuring the coverage of learning outcomes and topics. Finally, we utilize a real-world cybercrime scenario to demonstrate how knowledge units, digital forensics topics, concepts, artifacts, and investigation tools can be infused into each lab through active learning. The repository of the instructional materials is publicly available on GitHub. It has gained nearly 600 stars and 22k views within several months. Index Terms 

Abstract Soft, worm-like robots show promise in complex and constrained environments due to their robust, yet simple movement patterns. Although many such robots have been developed, they either rely on tethered power supplies and complex designs or cannot move external loads. To address these issues, we here introduce a novel, maggot-inspired, magnetically driven “mag-bot” that utilizes shape memory alloy-induced, thermoresponsive actuation and surface pattern-induced anisotropic friction to achieve locomotion inspired by fly larvae. This simple, untethered design can carry cargo that weighs up to three times its own weight with only a 17% reduction in speed over unloaded conditions thereby demonstrating, for the first time, how soft, untethered robots may be used to carry loads in controlled environments. Given their small scale and low cost, we expect that these mag-bots may be used in remote, confined spaces for small objects handling or as components in more complex designs.