Enhancing Tai Chi Training System: Towards Group-Based and Hyper-Realistic Training Experiences.

In this article, we propose a lightweight and flexible enhanced Tai Chi training system composed of multiple standalone virtual reality (VR) devices. The system aims to enable a hyper-realistic multi-user action training platform at low cost by displaying real-time action guidance trajectories, providing real-world impossible visual effects and functions, and rapidly enhancing movement precision and communication interest for learners. We objectively evaluate participants' action quality at different levels of immersion, including traditional coach guidance (TCG), VR, and mixed reality (MR), along with subjective measures like motion sickness, quality of interaction, social meaning, presence/immersion to comprehensively explore the system's feasibility. The results indicate VR performs the best in training accuracy, but MR provides superior social experience and relatively high accuracy. Unlike TCG, MR offers hyper-realistic hand movement trajectories and Tai Chi social references. Compared with VR, MR provides more realistic avatar companions and a safer environment. In summary, MR balances accuracy and social experience.

Self-Guided DMT: Exploring a Novel Paradigm of Dance Movement Therapy in Mixed Reality for Children with ASD.

Children diagnosed with Autism Spectrum Disorder (ASD) often exhibit motor disorders. Dance Movement Therapy (DMT) has shown great potential for improving the motor control ability of children with ASD. However, traditional DMT methods often lack vividness and are difficult to implement effectively. To address this issue, we propose a Mixed Reality DMT approach, utilizing interactive virtual agents. This approach offers immersive training content and multi-sensory feedback. To improve the training performance of children with ASD, we introduce a novel training paradigm featuring a self-guided mode. This paradigm enables the rapid creation of a virtual twin agent of the child with ASD using a single photo to embody oneself, which can then guide oneself during training. We conducted an experiment with the participation of 24 children diagnosed with ASD (or ASD propensity), recording their training performance under various experimental conditions. Through expert rating, behavior coding of training sessions, and statistical analysis, our findings revealed that the use of the twin agent for self-guidance resulted in noticeable improvements in the training performance of children with ASD. These improvements were particularly evident in terms of enhancing movement quality and refining overall target-related responses. Our study holds clinical potential in the field of medical treatment and rehabilitation for children with ASD.

Visuo-Haptic VR and AR Guidance for Dental Nerve Block Education.

The inferior alveolar nerve block (IANB) is a dental anesthetic injection that is critical to the performance of many dental procedures. Dental students typically learn to administer an IANB through videos and practice on silicone molds and, in many dental schools, on other students. This causes significant stress for both the students and their early patients. To reduce discomfort and improve clinical outcomes, we created an anatomically informed virtual reality headset-based educational system for the IANB. It combines a layered 3D anatomical model, dynamic visual guidance for syringe position and orientation, and active force feedback to emulate syringe interaction with tissue. A companion mobile augmented reality application allows students to step through a visualization of the procedure on a phone or tablet. We conducted a user study to determine the advantages of preclinical training with our IANB simulator. We found that in comparison to dental students who were exposed only to traditional supplementary study materials, dental students who used our IANB simulator were more confident administering their first clinical injections, had less need for syringe readjustments, and had greater success in numbing patients.

Breeze 2.0: an interactive web-tool for visual analysis and comparison of drug response data.

Functional precision medicine (fPM) offers an exciting, simplified approach to finding the right applications for existing molecules and enhancing therapeutic potential. Integrative and robust tools ensuring high accuracy and reliability of the results are critical. In response to this need, we previously developed Breeze, a drug screening data analysis pipeline, designed to facilitate quality control, dose-response curve fitting, and data visualization in a user-friendly manner. Here, we describe the latest version of Breeze (release 2.0), which implements an array of advanced data exploration capabilities, providing users with comprehensive post-analysis and interactive visualization options that are essential for minimizing false positive/negative outcomes and ensuring accurate interpretation of drug sensitivity and resistance data. The Breeze 2.0 web-tool also enables integrative analysis and cross-comparison of user-uploaded data with publicly available drug response datasets. The updated version incorporates new drug quantification metrics, supports analysis of both multi-dose and single-dose drug screening data and introduces a redesigned, intuitive user interface. With these enhancements, Breeze 2.0 is anticipated to substantially broaden its potential applications in diverse domains of fPM.

Changing Aesthetics in Biomolecular Graphics.

Aesthetics for the visualization of biomolecular structures have evolved over the years according to technological advances, user needs, and modes of dissemination. In this article, we explore the goals, challenges, and solutions that have shaped the current landscape of biomolecular imagery from the overlapping perspectives of computer science, structural biology, and biomedical illustration. We discuss changing approaches to rendering, color, human-computer interface, and narrative in the development and presentation of biomolecular graphics. With this historical perspective on the evolving styles and trends in each of these areas, we identify opportunities and challenges for future aesthetics in biomolecular graphics that encourage continued collaboration from multiple intersecting fields.

FraudAuditor: A Visual Analytics Approach for Collusive Fraud in Health Insurance.

Collusive fraud, in which multiple fraudsters collude to defraud health insurance funds, threatens the operation of the healthcare system. However, existing statistical and machine learning-based methods have limited ability to detect fraud in the scenario of health insurance due to the high similarity of fraudulent behaviors to normal medical visits and the lack of labeled data. To ensure the accuracy of the detection results, expert knowledge needs to be integrated with the fraud detection process. By working closely with health insurance audit experts, we propose FraudAuditor, a three-stage visual analytics approach to collusive fraud detection in health insurance. Specifically, we first allow users to interactively construct a co-visit network to holistically model the visit relationships of different patients. Second, an improved community detection algorithm that considers the strength of fraud likelihood is designed to detect suspicious fraudulent groups. Finally, through our visual interface, users can compare, investigate, and verify suspicious patient behavior with tailored visualizations that support different time scales. We conducted case studies in a real-world healthcare scenario, i.e., to help locate the actual fraud group and exclude the false positive group. The results and expert feedback proved the effectiveness and usability of the approach.

A Predictive Visual Analytics System for Studying Neurodegenerative Disease Based on DTI Fiber Tracts.

Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain. We introduce a predictive visual analytics system for studying patient groups based on their labeled DTI fiber tract data and corresponding statistics. The system's machine-learning-augmented interface guides the user through an organized and holistic analysis space, including the statistical feature space, the physical space, and the space of patients over different groups. We use a custom machine learning pipeline to help narrow down this large analysis space and then explore it pragmatically through a range of linked visualizations. We conduct several case studies using DTI and T1-weighted images from the research database of Parkinson's Progression Markers Initiative.

Visualization Display System of Gannan Hakka Paper-Cut Works Based on Computer Graphics Algorithm.

Today, computer graphics and graphic image processing techniques have been widely used in daily life and industrial production. Due to the development of computers, computer graphics has brought more convenience to our daily life. In order to give full play to the value of computers, this paper takes the Hakka paper-cut art with local characteristics as the starting point, first of all its development history, artistic characteristics, compositional forms, expression techniques, cultural connotations, Hakka paper-cut patterns, and the symbolic meaning of folk customs, and then we design a visualization system for the paper-cut works of Gannan Hakka based on computer graphics. In addition, the system provides a solution for the integration of Gannan Hakka paper-cut art and Jiangxi native product packaging design and provides a reference for the theory and practice of modern native product packaging design.

Preclinical validation of therapeutic targets predicted by tensor factorization on heterogeneous graphs.

Incorrect drug target identification is a major obstacle in drug discovery. Only 15% of drugs advance from Phase II to approval, with ineffective targets accounting for over 50% of these failures1-3. Advances in data fusion and computational modeling have independently progressed towards addressing this issue. Here, we capitalize on both these approaches with Rosalind, a comprehensive gene prioritization method that combines heterogeneous knowledge graph construction with relational inference via tensor factorization to accurately predict disease-gene links. Rosalind demonstrates an increase in performance of 18%-50% over five comparable state-of-the-art algorithms. On historical data, Rosalind prospectively identifies 1 in 4 therapeutic relationships eventually proven true. Beyond efficacy, Rosalind is able to accurately predict clinical trial successes (75% recall at rank 200) and distinguish likely failures (74% recall at rank 200). Lastly, Rosalind predictions were experimentally tested in a patient-derived in-vitro assay for Rheumatoid arthritis (RA), which yielded 5 promising genes, one of which is unexplored in RA.

An egalitarian approach for sharing the cost of a spanning tree.

A minimum cost spanning tree problem analyzes the way to efficiently connect individuals to a source. Hence the question is how to fairly allocate the total cost among these agents. Our approach, reinterpreting the spanning tree cost allocation as a claims problem defines a simple way to allocate the optimal cost with two main criteria: (1) each individual only pays attention to a few connection costs (the total cost of the optimal network and the cost of connecting himself to the source); and (2) an egalitarian criteria is used to share costs. Then, using claims rules, we define an egalitarian solution so that the total cost is allocated as equally as possible. We show that this solutions could propose allocations outside the core, a counter-intuitive fact whenever cooperation is necessary. Then we propose a modification to get a core selection, obtaining in this case an alternative interpretation of the Folk solution.

Learning the Edit Costs of Graph Edit Distance Applied to Ligand-Based Virtual Screening.

BACKGROUND: Graph edit distance is a methodology used to solve error-tolerant graph matching. This methodology estimates a distance between two graphs by determining the minimum number of modifications required to transform one graph into the other. These modifications, known as edit operations, have an edit cost associated that has to be determined depending on the problem. OBJECTIVE: This study focuses on the use of optimization techniques in order to learn the edit costs used when comparing graphs by means of the graph edit distance. METHODS: Graphs represent reduced structural representations of molecules using pharmacophore-type node descriptions to encode the relevant molecular properties. This reduction technique is known as extended reduced graphs. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were used. RESULTS: In the experiments, the graph edit distance using learned costs performed better or equally good than using predefined costs. This is exemplified with six publicly available datasets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS. CONCLUSION: This study shows that the graph edit distance along with learned edit costs is useful to identify bioactivity similarities in a structurally diverse group of molecules. Furthermore, the target-specific edit costs might provide useful structure-activity information for future drug-design efforts.

Embedded Bio-Mimetic System for Functional Electrical Stimulation Controlled by Event-Driven sEMG.

The analysis of the surface ElectroMyoGraphic (sEMG) signal for controlling the Functional Electrical Stimulation (FES) therapy is being widely accepted as an active rehabilitation technique for the restoration of neuro-muscular disorders. Portability and real-time functionalities are major concerns, and, among others, two correlated challenges are the development of an embedded system and the implementation of lightweight signal processing approaches. In this respect, the event-driven nature of the Average Threshold Crossing (ATC) technique, considering its high correlation with the muscle force and the sparsity of its representation, could be an optimal solution. In this paper we present an embedded ATC-FES control system equipped with a multi-platform software featuring an easy-to-use Graphical User Interface (GUI). The system has been first characterized and validated by analyzing CPU and memory usage in different operating conditions, as well as measuring the system latency (fulfilling the real-time requirements with a 140 ms FES definition process). We also confirmed system effectiveness, testing it on 11 healthy subjects: The similarity between the voluntary movement and the stimulate one has been evaluated, computing the cross-correlation coefficient between the angular signals acquired during the limbs motion. We obtained high correlation values of 0.87 ± 0.07 and 0.93 ± 0.02 for the elbow flexion and knee extension exercises, respectively, proving good stimulation application in real therapy-scenarios.

Potential of using visual imagery to revolutionise measurement of emotional health.

Appropriate measurement of emotional health by all those working with children and young people is an increasing focus for professional practice. Most of the tools used for assessment or self-assessment of emotional health were designed in the mid-20th century using language and technology derived from pen and paper written texts. However, are they fit for purpose in an age of pervasive computing with increasingly rich audiovisual media devices being in the hands of young people? This thought piece explores how the increased use of visual imagery, especially forms that can be viewed or created on digital devices, might provide a way forward for more effective measuring of emotional health, including smiley faces, other emojis and other potential forms of visual imagery. The authors bring together perspectives from healthcare, counselling, youth advocacy, academic research, primary care and school-based mental health support to explore these issues.

Relating Instructional Design Components to the Effectiveness of Internet-Based Mindfulness Interventions: A Critical Interpretive Synthesis.

BACKGROUND: Internet-based mindfulness interventions are a promising approach to address challenges in the dissemination and implementation of mindfulness interventions, but it is unclear how the instructional design components of such interventions are associated with intervention effectiveness. OBJECTIVE: The objective of this study was to identify the instructional design components of the internet-based mindfulness interventions and provide a framework for the classification of those components relative to the intervention effectiveness. METHODS: The critical interpretive synthesis method was applied. In phase 1, a strategic literature review was conducted to generate hypotheses for the relationship between the effectiveness of internet-based mindfulness interventions and the instructional design components of those interventions. In phase 2, the literature review was extended to systematically explore and revise the hypotheses from phase 1. RESULTS: A total of 18 studies were identified in phase 1; 14 additional studies were identified in phase 2. Of the 32 internet-based mindfulness interventions, 18 were classified as more effective, 11 as less effective, and only 3 as ineffective. The effectiveness of the interventions increased with the level of support provided by the instructional design components. The main difference between effective and ineffective interventions was the presence of just-in-time information in the form of reminders. More effective interventions included more supportive information (scores: 1.91 in phases 1 and 2) than less effective interventions (scores: 1.00 in phase 1 and 1.80 in phase 2), more part-task practice (scores: 1.18 in phase 1 and 1.60 in phase 2) than less effective interventions (scores: 0.33 in phase 1 and 1.40 in phase 2), and provided more just-in-time information (scores: 1.35 in phase 1 and 1.67 in phase 2) than less effective interventions (scores: 0.83 in phase 1 and 1.60 in phase 2). The average duration of more effective, less effective, and ineffective interventions differed for the studies of phase 1, with more effective interventions taking up more time (7.45 weeks) than less effective (4.58 weeks) or ineffective interventions (3 weeks). However, this difference did not extend to the studies of phase 2, with comparable average durations of effective (5.86 weeks), less effective (5.6 weeks), and ineffective (7 weeks) interventions. CONCLUSIONS: Our results suggest that to be effective, internet-based mindfulness interventions must contain 4 instructional design components: formal learning tasks, supportive information, part-task practice, and just-in-time information. The effectiveness of the interventions increases with the level of support provided by each of these instructional design components.

Multipurpose Virtual Reality Environment for Biomedical and Health Applications.

Virtual reality is a trending, widely accessible, and contemporary technology of increasing utility to biomedical and health applications. However, most implementations of virtual reality environments are tailored to specific applications. We describe the complete development of a novel, open-source virtual reality environment that is suitable for multipurpose biomedical and healthcare applications. This environment can be interfaced with different hardware and data sources, ranging from gyroscopes to fMRI scanners. The developed environment simulates an immersive (first-person perspective) run in the countryside, in a virtual landscape with various salient features. The utility of the developed VR environment has been validated via two test applications: an application in the context of motor rehabilitation following injury of the lower limbs and an application in the context of real-time functional magnetic resonance imaging neurofeedback, to regulate brain function in specific brain regions of interest. Both applications were tested by pilot subjects that unanimously provided very positive feedback, suggesting that appropriately designed VR environments can indeed be robustly and efficiently used for multiple biomedical purposes. We attribute the versatility of our approach on three principles implicit in the design: selectivity, immersiveness, and adaptability. The software, including both applications, is publicly available free of charge, via a GitHub repository, in support of the Open Science Initiative. Although using this software requires specialized hardware and engineering know-how, we anticipate our contribution to catalyze further progress, interdisciplinary collaborations and replicability, with regards to the usage of virtual reality in biomedical and health applications.

MRI imaging and histopathological study of brain iron overload of ß-thalassemic mice.

Brain iron overload is chronic and slow progressing and plays an important role in the pathogenesis of neurodegenerative disorders. Magnetic resonance imaging (MRI) is a useful noninvasive tool for determining liver iron content, but it has not been proven to be adequate for evaluating brain iron overload. We evaluated the usefulness of MRI-derived parameters to determine brain iron concentration in ß-thalassemic mice and the effects of the membrane permeable iron chelator, deferiprone. Sixteen ß-thalassemic mice underwent 1.5T MRI of the brain that included a multiecho T2*-weighted sequence. Brain T2* values ranged from 28 to 31ms for thalassemic mice. For the iron overloaded thalassemic mice, brain T2* values decreased, ranging from 8 to 12ms, which correlated with the iron overload status of the animals. In addition, brain T2* values increased in the group with the treatment of deferiprone, ranging from 18 to 24ms. Our results may be useful to understand brain pathology in iron overload. Moreover, data could lead to an earlier diagnosis, assist in following disease progression, and demonstrate the benefits of iron chelation therapy.

Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure.

Extended reduced graphs provide summary representations of chemical structures using pharmacophore-type node descriptions to encode the relevant molecular properties. Commonly used similarity measures using reduced graphs convert these graphs into 2D vectors like fingerprints, before chemical comparisons are made. This study investigates the effectiveness of a graph-only driven molecular comparison by using extended reduced graphs along with graph edit distance methods for molecular similarity calculation as a tool for ligand-based virtual screening applications, which estimate the bioactivity of a chemical on the basis of the bioactivity of similar compounds. The results proved to be very stable and the graph editing distance method performed better than other methods previously used on reduced graphs. This is exemplified with six publicly available data sets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were also used. In the experiments, our method performed better than other molecular similarity methods which use array representations in most cases. Overall, it is shown that extended reduced graphs along with graph edit distance is a combination of methods that has numerous applications and can identify bioactivity similarities in a structurally diverse group of molecules.

Statistical assessment and visualization of synergies for large-scale sparse drug combination datasets.

BACKGROUND: Drug combinations have the potential to improve efficacy while limiting toxicity. To robustly identify synergistic combinations, high-throughput screens using full dose-response surface are desirable but require an impractical number of data points. Screening of a sparse number of doses per drug allows to screen large numbers of drug pairs, but complicates statistical assessment of synergy. Furthermore, since the number of pairwise combinations grows with the square of the number of drugs, exploration of large screens necessitates advanced visualization tools. RESULTS: We describe a statistical and visualization framework for the analysis of large-scale drug combination screens. We developed an approach suitable for datasets with large number of drugs pairs even if small number of data points are available per drug pair. We demonstrate our approach using a systematic screen of all possible pairs among 108 cancer drugs applied to melanoma cell lines. In this dataset only two dose-response data points per drug pair and two data points per single drug test were available. We used a Bliss-based linear model, effectively borrowing data from the drug pairs to obtain robust estimations of the singlet viabilities, consequently yielding better estimates of drug synergy. Our method improves data consistency across dosing thus likely reducing the number of false positives. The approach allows to compute p values accounting for standard errors of the modeled singlets and combination viabilities. We further develop a synergy specificity score that distinguishes specific synergies from those arising with promiscuous drugs. Finally, we developed a summarized interactive visualization in a web application, providing efficient access to any of the 439,000 data points in the combination matrix ( http://www.cmtlab.org:3000/combo_app.html ). The code of the analysis and the web application is available at https://github.com/arnaudmgh/synergy-screen . CONCLUSIONS: We show that statistical modeling of single drug response from drug combination data can help determine significance of synergy and antagonism in drug combination screens with few data point per drug pair. We provide a web application for the rapid exploration of large combinatorial drug screen. All codes are available to the community, as a resource for further analysis of published data and for analysis of other drug screens.

DeepChemStable: Chemical Stability Prediction with an Attention-Based Graph Convolution Network.

In the drug discovery process, unstable compounds in storage can lead to false positive or false negative bioassay conclusions. Prediction of the chemical stability of a compound by de novo methods is complex. Chemical instability prediction is commonly based on a model derived from empirical data. The COMDECOM (COMpound DECOMposition) project provides the empirical data for prediction of chemical stability. Models such as the extended-connectivity fingerprint and atom center fragments were built from the COMDECOM data and used for estimation of chemical stability, but deficits in the existing models remain. In this paper, we report DeepChemStable, a model employing an attention-based graph convolution network based on the COMDECOM data. The main advantage of this method is that DeepChemStable is an end-to-end model, which does not predefine structural fingerprint features, but instead, dynamically learns structural features and associates the features through the learning process of an attention-based graph convolution network. The previous ChemStable program relied on a rule-based method to reduce the false negatives. DeepChemStable, on the other hand, reduces the risk of false negatives without using a rule-based method. Because minimizing the rate of false negatives is a greater concern for instability prediction, this feature is a major improvement. This model achieves an AUC value of 84.7%, recall rate of 79.8%, and 10-fold stratified cross-validation accuracy of 79.1%.