Modeling Spatial Relations of Human Body Parts for Indexing and Retrieving Close Character Interactions

Retrieving pre-captured human motion for analyzing and synthesizing virtual character movement have been widely used in Virtual Reality (VR) and interactive computer graphics applications. In this paper, we propose a new human pose representation, called Spatial Relations of Human Body Parts (SRBP), to represent spatial relations between body parts of the subject(s), which intuitively describes how much the body parts are interacting with each other. Since SRBP is computed from the local structure (i.e. multiple body parts in proximity) of the pose instead of the information from individual or pairwise joints as in previous approaches, the new representation is robust to minor variations of individual joint location. Experimental results show that SRBP outperforms the existing skeleton-based motion retrieval and classification approaches on benchmark databases

Effect of real-time computer-aided polyp detection system (ENDO-AID) on adenoma detection in endoscopists-in-training: a randomized trial

Background The effect of computer-aided polyp detection (CADe) on adenoma detection rate (ADR) among endoscopists-in-training remains unknown. Methods We performed a single-blind, parallel-group, randomized controlled trial in Hong Kong between April 2021 and July 2022 (NCT04838951). Eligible subjects undergoing screening/surveillance/diagnostic colonoscopies were randomized 1:1 to receive colonoscopies with CADe (ENDO-AID(OIP-1), Olympus Co., Japan) or not (control) during withdrawal. Procedures were performed by endoscopists-in-training with <500 procedures and <3 years’ experience. Randomization was stratified by patient age, sex, and endoscopist experience (beginner vs intermediate-level, <200 vs 200-500 procedures). Image enhancement and distal attachment devices were disallowed. Subjects with incomplete colonoscopies or inadequate bowel preparation were excluded. Treatment allocation was blinded to outcome assessors. The primary outcome was ADR. Secondary outcomes were ADR for different adenoma sizes and locations, mean number of adenomas, and non-neoplastic resection rate. Results 386 and 380 subjects were randomized to CADe and control groups, respectively. The overall ADR was significantly higher in CADe than control group (57.5% vs 44.5%, adjusted relative risk 1.41, 95%CI 1.17-1.72, p<0.001). The ADRs for <5mm (40.4% vs 25.0%) and 5-10mm adenomas (36.8% vs 29.2%) were higher in CADe group. The ADRs were higher in CADe group in both right (42.0% vs 30.8%) and left colon (34.5% vs 27.6%), but there was no significant difference in advanced ADR. The ADRs were higher in CADe group among beginners (60.0% vs 41.9%) and intermediate-level endoscopists (56.5% vs 45.5%). Mean number of adenomas (1.48 vs 0.86) and non-neoplastic resection rate were higher in CADe group (52.1% vs 35.0%). Conclusions Among endoscopists-in-training, the use of CADe during colonoscopies was associated with increased overall ADR. (ClinicalTrials.gov: NCT04838951

FMR1 premutation and full mutation molecular mechanisms related to autism

Fragile X syndrome (FXS) is caused by an expanded CGG repeat (>200 repeats) in the 5′ un-translated portion of the fragile X mental retardation 1 gene (FMR1) leading to a deficiency or absence of the FMR1 protein (FMRP). FMRP is an RNA-binding protein that regulates the translation of a number of other genes that are important for synaptic development and plasticity. Furthermore, many of these genes, when mutated, have been linked to autism in the general population, which may explain the high comorbidity that exists between FXS and autism spectrum disorders (ASD). Additionally, premutation repeat expansions (55 to 200 CGG repeats) may also give rise to ASD through a different molecular mechanism that involves a direct toxic effect of FMR1 mRNA. It is believed that RNA toxicity underlies much of the premutation-related involvement, including developmental concerns like autism, as well as neurodegenerative issues with aging such as the fragile X-associated tremor ataxia syndrome (FXTAS). RNA toxicity can also lead to mitochondrial dysfunction, which is common in older premutation carriers both with and without FXTAS. Many of the problems with cellular dysregulation in both premutation and full mutation neurons also parallel the cellular abnormalities that have been documented in idiopathic autism. Research regarding dysregulation of neurotransmitter systems caused by the lack of FMRP in FXS, including metabotropic glutamate receptor 1/5 (mGluR1/5) pathway and GABA pathways, has led to new targeted treatments for FXS. Preliminary evidence suggests that these new targeted treatments will also be beneficial in non-fragile X forms of autism

Improving posture classification accuracy for depth sensor-based human activity monitoring in smart environments

Smart environments and monitoring systems are popular research areas nowadays due to its potential to enhance the quality of life. Applications such as human behaviour analysis and workspace ergonomics monitoring are automated, thereby improving well-being of individuals with minimal running cost. The central problem of smart environments is to understand what the user is doing in order to provide the appropriate support. While it is difficult to obtain information of full body movement in the past, depth camera based motion sensing technology such as Kinect has made it possible to obtain 3D posture without complex setup. This has fused a large number of research projects to apply Kinect in smart environments. The common bottleneck of these researches is the high amount of errors in the detected joint positions, which would result in inaccurate analysis and false alarms. In this paper, we propose a framework that accurately classifies the nature of the 3D postures obtained by Kinect using a max-margin classifier. Different from previous work in the area, we integrate the information about the reliability of the tracked joints in order to enhance the accuracy and robustness of our framework. As a result, apart from general classifying activity of different movement context, our proposed method can classify the subtle differences between correctly performed and incorrectly performed movement in the same context. We demonstrate how our framework can be applied to evaluate the user’s posture and identify the postures that may result in musculoskeletal disorders. Such a system can be used in workplace such as offices and factories to reduce risk of injury. Experimental results have shown that our method consistently outperforms existing algorithms in both activity classification and posture healthiness classification. Due to the low-cost and the easy deployment process of depth camera based motion sensors, our framework can be applied widely in home and office to facilitate smart environments

Interactive partner control in close interactions for real-time applications

This article presents a new framework for synthesizing motion of a virtual character in response to the actions performed by a user-controlled character in real time. In particular, the proposed method can handle scenes in which the characters are closely interacting with each other such as those in partner dancing and fighting. In such interactions, coordinating the virtual characters with the human player automatically is extremely difficult because the system has to predict the intention of the player character. In addition, the style variations from different users affect the accuracy in recognizing the movements of the player character when determining the responses of the virtual character. To solve these problems, our framework makes use of the spatial relationship-based representation of the body parts called interaction mesh, which has been proven effective for motion adaptation. The method is computationally efficient, enabling real-time character control for interactive applications. We demonstrate its effectiveness and versatility in synthesizing a wide variety of motions with close interactions

A multi-country analysis of COVID-19 hospitalizations by vaccination status

Background: Individuals vaccinated against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), when infected, can still develop disease&nbsp;that requires hospitalization. It remains unclear whether these patients differ from hospitalized unvaccinated patients with regard to presentation, coexisting comorbidities, and outcomes. Methods: Here, we use data from an international consortium to study this&nbsp;question and assess whether differences between these groups are&nbsp;context specific. Data from 83,163 hospitalized COVID-19 patients (34,843 vaccinated, 48,320 unvaccinated) from 38 countries were analyzed. Findings: While typical symptoms were more often reported in unvaccinated patients, comorbidities, including some associated with worse prognosis in previous studies, were more common in vaccinated patients. Considerable between-country variation in both in-hospital fatality risk and vaccinated-versus-unvaccinated difference in this outcome was observed. Conclusions: These findings will inform allocation of healthcare resources in future surges as well as design of longer-term international studies to characterize changes in clinical profile of hospitalized COVID-19 patients related to vaccination history. Funding: This work was made possible by the UK Foreign, Commonwealth and Development Office and Wellcome (215091/Z/18/Z, 222410/Z/21/Z, 225288/Z/22/Z, and 220757/Z/20/Z); the Bill&nbsp;&amp; Melinda Gates&nbsp;Foundation (OPP1209135); and the philanthropic support of the donors&nbsp;to the University of Oxford's COVID-19 Research Response Fund (0009109). Additional funders are listed in the "acknowledgments" section

An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients

Background: Whilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings. Methods: Here, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries. Results: Our analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61-0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population. Conclusions: Although clinical studies with matching viral genomic information should remain a priority, our approach combining publicly available data on variant frequency and a multi-country clinical characterisation dataset with more than 100,000 records allowed analysis of data from a wide range of settings and novel insights on real-world heterogeneity of COVID-19 presentation and clinical outcome