Assessing vision and autism spectrum disorder-relevant social interaction phenotypes in Dscam mice.

The generation of anchor-style product promotion videos presents promising opportunities in e-commerce, advertising, and consumer engagement. Despite advancements in pose-guided human video generation, creating product promotion videos remains challenging. In addressing this challenge, we identify the integration of human-object interactions (HOI) into pose-guided human video generation as a core issue. To this end, we introduce AnchorCrafter, a novel diffusion-based system designed to generate 2D videos featuring a target human and a customized object, achieving high visual fidelity and controllable interactions. Specifically, we propose two key innovations: the HOI-appearance perception, which enhances object appearance recognition from arbitrary multi-view perspectives and disentangles object and human appearance, and the HOI-motion injection, which enables complex human-object interactions by overcoming challenges in object trajectory conditioning and inter-occlusion management. Extensive experiments show that our system improves object appearance preservation by 7.5%, and achieves the best video quality compared to existing state-of-the-art approaches. It also outperforms existing approaches in maintaining human motion consistency and high-quality video generation.

Development of visual acuity and spatial interactions for luminance- and contrast-modulated symbols, pictures and letters.

Driven by advances in supercomputing, the scale of scientific simulation data has grown dramatically. In fields such as cosmology, particle data have become a common representation, with state-of-the-art simulations now exceeding the trillion-particle mark. Consequently, the challenge of visually analyzing such massive datasets has become increasingly urgent. The traditional visual analysis workflow typically follows a "compression $\rightarrow$→ storage $\rightarrow$→ reconstruction $\rightarrow$→ visualization" pipeline. However, this process is hampered by an extremely time-consuming reconstruction stage, which severely impedes real-time interactive visualization. Moreover, in multi-time-step analyses, the enormous volume of reconstructed data creates significant I/O bottlenecks. In this work, we draw inspiration from 3D Gaussian splatting and compress the simulation data using Gaussian Mixture Models (GMMs), treating the resulting Gaussian kernels as fundamental rendering primitives. Our method renders billion-scale particles for each timestep in approximately 32 ms, requiring only 645 MB of GPU memory per timestep - nearly 20× smaller than the original 12 GB raw data. This eliminates costly reconstruction, accelerates the visual analysis pipeline, and overcomes I/O bottlenecks in multi-time-step analysis. Extensive experiments and comparisons across multiple datasets validate the effectiveness of our method.

ABSTRACT The interactions between scalar and vector properties is vital in oceanic phenomena and energy/material circulation processes. This study integrates scalar and vector fields in ocean visualization to clarify their interplay and proposes a standardized approach for designing transfer functions that simplify ocean feature extraction. An interactive collaborative transfer function operational mode was introduced to visualise the scalar and vector fields, effectively capturing the dynamic characteristics of oceanic phenomena under mutual influence. Using the high-resolution GLORYS12V1 marine dataset, we visualized thermohaline fields and currents. Results show that the collaborative visualisation method and standardized transfer function effectively extract morphological outlines of ocean fronts and mesoscale eddies. Furthermore, the real-time collaborative transfer function reveals high spatial and directional consistency between ocean fronts (extracted via a 0.05 °C/km temperature gradient threshold) and Kuroshio axes (identified using a 0.1 m/s flow velocity threshold), which highlights the barrier effect of ocean fronts on meridional heat exchange. Additionally, we revealed the dynamic entrainment and transport capability of energy and material in mesoscale eddies. The above results demonstrate that the proposed synergistic transfer function in this study can effectively uncover the key role of scalar-vector field interactions in the evolution of oceanic phenomena.

We present a visualization design study of creating a collaborative virtual reality (VR) system for radiation treatment planning, with an emphasis on proton therapy. The goal is to support teams of dosimetrists, physicians, and medical physicists as they review and compare multiple possible patient-specific treatment plans, which requires analyzing complex 3D spatial relationships between a radiation dosage volume and anatomical structures. The approach is a novel combination and refinement of interactive visualization techniques including: networked multi-user immersive visualization, interactive volume rendering and slicing with 3D widgets and gestures, superimposed surface rendering with GPU-accelerated curvature-directed lines, smart cursors, teleporting, and avatars. These features are integrated within a workflow that supports three complementary modes of visual data comparison (juxtaposition, interchangeable, and explicit encoding). Results and feedback from multi-year iterative development with users and a summative field deployment in the form of a mock plan-review meeting reveal several advantages relative to current clinical practice and suggest directions for future work.

Liquid-liquid phase separation (LLPS) underpins the formation of membrane-less organelles (MLOs), particularly those involving intrinsically disordered proteins, and is increasingly recognized as a fundamental mechanism of cellular organization. Emulating such behavior in synthetic inorganic systems remains a central challenge in materials science. Herein, we demonstrate that atomically precise gold nanoclusters, Au22(SG)18 (where -SG represents glutathione), undergo LLPS in the presence of a macromolecular crowder, poly(ethylene glycol). Extended structural motifs present in the structure of Au22(SG)18 promote condensation under crowding conditions, yielding "nanoparticle condensates" with aggregation-induced emission characteristics that permit real-time visualization and fluorescence recovery after photobleaching (FRAP) analysis. These condensates exhibit hallmark features of biomolecular condensates, including liquid-like dynamicity and reversibility. In protein-rich environments, Au22(SG)18 displays a spectrum of phase behaviors: independent phase separation with mucin, partial co-condensation with γ-globulin, and robust heterotypic LLPS with bovine serum albumin (BSA), lysozyme, and β-lactoglobulin. Confocal laser scanning microscopy (CLSM) imaging and FRAP analysis reveal that protein co-condensation can modulate condensate diffusivity, with shared compartments dampening dynamics and distinct ones enhancing them. Our findings highlight atomically precise nanoclusters as a powerful alternative luminescent analogue for dissecting biomolecular LLPS and elucidating nanobio interactions.

Imaging Mass Cytometry (IMC) enables highly multiplexed, spatially resolved single-cell proteomics, providing simultaneous measurement of dozens of protein markers while preserving tissue architecture. Despite its analytical power, IMC data analysis remains fragmented across multiple software environments, requiring researchers to combine independent tools for visualization, preprocessing, segmentation, feature extraction, phenotyping, batch correction, and spatial analysis. This fragmentation increases technical barriers, complicates reproducibility, and limits accessibility for non-computational users. We developed OpenIMC, an open-source platform that integrates the major stages of IMC analysis within a unified graphical and command-line framework. OpenIMC supports image visualization, quality control, preprocessing, segmentation, feature extraction, dimensionality reduction, batch effect correction, clustering, phenotyping, and spatial analysis while maintaining interoperability with established community tools. The platform incorporates automated provenance tracking, records analytical parameters and software versions, and enables export and sharing of complete analytical sessions. Benchmarking demonstrated deterministic behavior across repeated runs, complete concordance between graphical and command-line workflows, and strong agreement with established IMC analysis pipelines. OpenIMC additionally provides support for high-resolution IMC workflows, including signal attenuation modeling and image deconvolution. We apply OpenIMC to two datasets of circulating cells and breast tissue to demonstrate the platform's ability to support integrated single-cell and spatial proteomics analysis. OpenIMC reduces the complexity of IMC data analysis by providing a unified, reproducible, and extensible framework for common IMC workflows. By combining interactive visualization with scalable computational analysis, OpenIMC lowers technical barriers and facilitates reproducible single-cell and spatial proteomics research.

The article deals with the creation and implementation of the Digital Analytical Platform of the Federal State Statistics Service (Rosstat DAP) as the core of the National Data Management System in the context of the digitalization of the economics. The authors have revealed and systematized the problems of the national statistics: fragmented data, high bur den on small and medium-sized businesses, lack of efficiency and evidence of information. The analysis of the normative evolution is used to formulate priorities of the development of statistics up to 2023: the transition to predictive analytics, ensuring trust in data, integration of sources based on the “one-stop shop” principle, personalization of services, staff development, international harmonization, transparency. The authors have described functional components of DAP: sample constructor, interactive visualization, statistical and network analysis, machine learning-based forecasting, application programming interface. It has been shown that the platform ensures the interaction of the state, business, science and society in a single digital environment on the principles of interdepartmental automation using the System of Inter departmental Electronic Interaction (SIEI), Unifi ed Identification and Authentication System (ESIA) and the Gostech platform. The results will be useful for the government authorities, large business and small and medium-sized enterprises, researchers and teachers.

Molecular subtyping is essential for precision oncology, enabling the classification of tumors into biologically and clinically relevant categories. DNA methylation has emerged as a promising biomarker for cancer subtyping, yet its application remains limited by high dimensionality, batch effects, and the lack of automated, user-friendly analytical tools. Here, we present CancerSubtyper, an end-to-end computational framework for deep learning-based cancer subtyping using DNA methylation data, which is accessible through an intuitive web interface designed to support interactive exploration and downstream analysis. CancerSubtyper integrates two complementary models: a semi-supervised classifier for cancers with well-established subtypes, and a hybrid framework that integrates supervised and unsupervised learning to identify novel subtypes. The framework automatically performs preprocessing, feature selection, batch correction, and cancer subtyping while offering interactive visualization for subtype exploration and validation. By providing an automated, end-to-end workflow accessible through a user-friendly web interface, CancerSubtyper lowers the barrier to large-scale methylation analysis and provides a powerful tool for molecular subtyping and precision oncology research. The framework is freely accessible at https://github.com/ycheung5/cancersubtyper/.

Monitoring novel psychoactive substance trends on social media: Analysis of discussions and dashboard implementation.

Structure-based virtual screening (VS) via molecular docking is a pivotal approach for hit identification. Many artificial intelligence (AI)-powered protein-ligand docking and scoring methods have demonstrated impressive speed and accuracy. Retrospective benchmarking studies using enrichment rate and computational efficiency on curated datasets have corroborated their potential for discovering bioactive compounds. However, determining which method suits a specific application and implementing it efficiently remains challenging. Here we present the Comprehensive VS Platform with AI Engine (CVSP-AIE) for drug discovery from compound libraries. It integrates three AI models: KarmaDock, a fast docking model that directly updates atomic coordinates; CarsiDock, an accurate docking model that predicts protein-ligand distances and reconstructs binding poses; and RTMScore, an accurate scoring model that learns residue-atom distance distributions for affinity prediction. Their hierarchical application enables dynamical balances in screening speed and accuracy. CVSP-AIE is available as an online web server ( https://cadd.zju.edu.cn/cvsp/ ) and a local software package. Users can efficiently initiate drug screening by uploading a protein and a known binder that defines the binding pocket. The following workflow involves (1) preprocessing, including protein structure repair and molecule standardization, (2) binding pose and affinity prediction powered by KarmaDock, CarsiDock and RTMScore and (3) postprocessing, comprising protein-ligand interaction calculation and visualization. It takes 30-45 min to hierarchically screen 100,000 compounds, and the output is a ranked list of molecules with predicted binding scores, intermolecular interaction profiles and interactive chemical space analysis. Users can also install locally the hierarchical screening module through command-line package for arbitrary-scale screening.

The optimal timing of a treatment within therapy lines can maximize its benefit while minimizing risks and side effects. Because patients differ in clinical and biological characteristics, personalized treatment planning can support physicians' decision-making. However, multistate disease progression complicates the comparison of treatment strategies, motivating the development of a user-friendly web tool for multistate disease progression simulation and treatment decision support. Microsimulation is a natural approach for comparing prespecified treatment-timing strategies, where it generates disease trajectories for a large number of virtual patients matched to an individual patient's observed data. In our framework, microsimulation is performed using a multistate model to capture state-to-state transition hazards. We develop TxMicroSim, a web-based interface built with R Shiny, to compare treatment-timing strategies by estimating model-projected restricted mean survival times (RMSTs). The application consists of three main steps: defining the multistate structure, building the multistate model, and performing microsimulation. First, the user specifies the multistate structure, with the treatment of interest as one of the states and an absorbing state representing the time-to-event endpoint. The user also identifies transitions that lead into the treatment state, defining the strategy-driven transitions used to compare treatment-timing strategies. Next, to build clock-reset transition-specific flexible parametric proportional hazards models, the user can either upload data for model fitting or manually enter covariate hazard ratios and baseline hazard parameters. Two baseline hazard specifications are supported: a piecewise constant function and a natural cubic spline. Finally, the user enters patient-specific information, including covariates, the initial disease state at diagnosis, and the treatment start time for strategy-driven transitions. Microsimulation is used to compute RMST up to a prespecified time horizon under each strategy. Throughout the app, interactive visualizations are provided for the multistate structure, estimated hazard ratios and baseline hazards, and RMST comparisons across treatment strategies. TxMicroSim is a flexible web tool that compares treatment-timing strategies and quantifies survival impact using interpretable summaries and interactive visualizations.

The protein design field is rapidly advancing, with frequent emergence of new models and pipelines for designing de novo proteins with tailored properties and functions not found in nature. However, the current tool landscape is fragmented, tools are hard to install and deploy, and require significant computational expertise to integrate into end-to-end, scalable pipelines. A particular challenge is managing many sequences, structures, and metrics for downstream testing and retrospective analysis of input parameters. To address this need, we introduce Ovo, an open-source de novo protein design ecosystem that consolidates models, workflows, data management, and interactive visualization into a scalable, infrastructure-agnostic platform. Ovo features Nextflow-based workflow orchestration, a storage layer, and both command-line and graphical interfaces that democratize scaffold design, binder design and diversification, and validation workflows. Ovo's novel ProteinQC module computes comprehensive sequence and structure descriptors, contextualizing designs against reference sets. Ovo plugins let the community add new workflows and user interfaces to accelerate adoption of emerging methods and facilitate community-driven benchmarking. Ovo lowers engineering barriers and demystifies the design process, allowing experts and non-technical users to design proteins at scale. With community-driven development, Ovo can accelerate de novo protein design and advance discovery in therapeutics and biotechnology.

Single-nuclei transcriptomics enables investigating ligand-receptor mediated cell-cell communication between different cell-types. However, current tools do not allow for non-programmatic means to access this analysis. Additionally, most methods can not account for molecular pathways involved in downstream receptor signaling in cell-cell communication. We developed scVizComm, a ShinyApp based web-portal that can be used to interactively visualize ligand-receptor networks across cell-types and different conditions. scVizComm can be used to host pathway centric pre-calculated cell-cell communication results. Using human kidney and heart single-nuclei transcriptomics data, we showcase the utility of scVizComm in understanding ligand-receptor interactions involved in fibrosis related pathways. Additionally using mouse anterior brain data, we demonstrate the major ligand-receptor expression exploration in space. scVizComm allows interactive visualization and pathway centric prioritization of cell-cell communication analyses.

The "Make in India" initiative has successfully transformed the nation into a burgeoning global manufacturing powerhouse, fostering an environment where industrial production is prioritized as a pillar of economic growth. However, this transition has revealed a significant divide, as numerous Small and Medium Enterprises (SMEs) find themselves struggling to integrate the advanced standards of Industry 4.0 into their legacy operations. These smaller entities frequently encounter insurmountable barriers, including the prohibitive costs associated with sophisticated proprietary software, a lack of specialized technical expertise, and the significant complexities inherent in digital transformation. Consequently, many SMEs operate with antiquated systems that fail to leverage the vast amounts of performance data generated on the factory floor, leaving them unable to compete with larger counterparts who have already optimized their workflows through digital adoption. To bridge this critical technological gap, the "MakeInIndia – Industrial Data Analytics Dashboard" has been developed as an intelligent, cost-effective platform specifically engineered to provide robust, real-time operational insights for mid-scale industries. By harnessing the transformative power of Artificial Intelligence (AI) and comprehensive Big Data analytics, this system serves as a bridge for companies that require high-level performance tracking without the financial burden of enterprise-level software. The platform functions by continuously monitoring vital production metrics, accurately predicting potential machine downtime before it occurs, and meticulously optimizing supply chain logistics to ensure a seamless flow of materials. By shifting from reactive maintenance and manual tracking to a proactive, data-driven methodology, the system ensures that SMEs can achieve levels of operational efficiency that were previously attainable only by industry giants. Beyond its technical capabilities, the platform features an intuitive, highly interactive visualization suite that serves as a bridge between complex raw information and informed human decision-making. By transforming fragmented, chaotic industrial data into clear, actionable intelligence, the dashboard empowers manufacturers to rapidly identify inefficiencies, minimize material waste, and significantly enhance overall productivity. This technological intervention not only democratizes access to sophisticated manufacturing tools but also aligns directly with national economic goals by fostering a more competitive, resilient, and digitized industrial sector. Ultimately, the implementation of this dashboard serves as a catalyst for sustainable industrial growth, reinforcing the "Make in India" vision by equipping smaller manufacturers with the intelligence needed to operate as modern, efficient, and highly innovative entities on the global stage.

The rapid advancement in single-cell, spatial omics, imaging, and genomic technologies requires robust analytical and visualisation platforms capable of managing complex biological data. Tools such as Multi-Dimensional Viewer (MDV) offer comprehensive interfaces for data exploration, but often require advanced computational expertise and manual configuration to generate visualisation outputs, limiting accessibility for many users. We present ChatMDV, a natural language interface integrated with MDV that enables users to generate high-quality, interactive visualisations and analyses through natural language commands. ChatMDV employs a retrieval-augmented generation (RAG) pipeline in combination with large language models (LLMs) to translate user queries into executable, reproducible Python code and interactive output. This conversational layer facilitates both exploratory and targeted analyses in diverse biological domains. We demonstrate ChatMDV's capabilities using three datasets of increasing complexity: the Peripheral Blood Mononuclear Cells 3K (PBMC3K) single-cell RNA-sequencing (scRNA-seq) dataset, the lung cancer atlas scRNA-seq dataset included in the Human Cell Atlas and the longitudinal TAURUS study scRNA-seq dataset. Across all use cases, ChatMDV produced high-quality, reproducible visualisations from simple natural language queries, achieving a high semantic success rate between 79% and 97% when visualising the datasets. By bridging the gap between natural language processing and bioinformatics visualisation, ChatMDV reduces technical barriers, enhances reproducibility, and supports more inclusive scientific inquiry. Its modular design and adherence to Findability, Accessibility, Interoperability, and Reuse (FAIR) principles make it a scalable and adaptable framework for accelerating biological data analysis.

High-throughput genomic and proteomic technologies are used to study biological systems by performing differential expression analysis across various experimental conditions. Geneset Ordinal Association Test (GOAT) is an analytic method recently introduced to statistically evaluate the differential expression of a defined set of genes or proteins. Despite the availability of numerous enrichment tools, many lack accessibility for users without programming expertise, provide limited continuation beyond listing top enriched terms, and offer little support for interactive visual exploration or hypothesis generation. Moreover, existing web-based platforms rarely support multi-contrast comparisons and generally omit gene-level or network-based context for pathway analysis. To address these limitations, we present Geneset Ordinal Association Test Enrichment Analysis (GOATEA), an R/Shiny application that implements and extends the GOAT algorithm with interactive visualization, multi-contrast comparison, and integrated gene- and network-based context for bottom-up pathway analysis, enabling comprehensive enrichment analysis. GOATEA supports independent analysis of transcriptomic and proteomic data. To demonstrate its capability to integrate matched modalities, we applied it to the Colameo dataset containing paired mass spectrometry and RNA sequencing data. This proof-of-concept example highlights the tool's strength in enabling multi-omics analyses and simultaneous comparison of multiple contrasts. An interactive overlap analysis identified 458 shared genes for focused enrichment and network exploration. By integrating these results in a gene- and network-based context for bottom-up pathway analysis, GOATEA applies a stringent interaction confidence threshold to emphasize qualitative protein-protein interactions, highlighting topic-relevant associations for further hypothesis generation. GOATEA streamlines enrichment analysis workflows by combining the GOAT algorithm with interactive visualizations in a user-friendly graphical interface. It facilitates exploratory analysis and hypothesis generation for researchers with or without programming expertise. GOATEA is available as an open-source tool, with full documentation, including usage vignettes (https://mauritsunkel.github.io/goatea/).

In the experience economy, user attention in product interaction has shifted from functional performance toward emotional and perceptual experience, making Kansei factors increasingly important in product form design (PFD). However, the identification and prioritization of customer requirements (CRs) often rely on subjective judgment, and form design decisions remain constrained by designers’ intuition. This study proposes a hybrid human-centered Kansei Engineering (KE) framework that integrates explainable machine learning with visual interaction analysis. Online user reviews are utilized to objectively extract and evaluate affective requirements, while visual sequence features derived from eye-tracking experiments capture users’ perceptual and attentional responses to product forms. Key CRs are identified using Latent Dirichlet Allocation, and sentiment information is analyzed through BERT and SnowNLP. An ‌eXtreme Gradient Boosting‌ (XGBoost) model combined with Shapley Additive Explanations (SHAP) is employed to perform Kano-based requirement classification and importance weighting. Subsequently, a Transformer-BiLSTM model maps Kansei factors to form features based on users’ visual sequence data, enabling the prediction of form attribute combinations aligned with user affective expectations. A Bluetooth speaker design case study demonstrates the applicability of the proposed framework. Results indicate that integrating affective requirement modeling with visual attention analysis effectively supports data-driven and human-centered product form design, contributing to research on visual perception and interaction in human-computer interaction contexts.

Evaluating interactive 2D visualization as a sample selection strategy for biomedical time-series data annotation.