Cut-Based Graph Learning Networks to Discover Compositional Structure of Sequential Video Data

Conventional sequential learning methods such as Recurrent Neural Networks (RNNs) focus on interactions between consecutive inputs, i.e. first-order Markovian dependency. However, most of sequential data, as seen with videos, have complex dependency structures that imply variable-length semantic flows and their compositions, and those are hard to be captured by conventional methods. Here, we propose Cut-Based Graph Learning Networks (CB-GLNs) for learning video data by discovering these complex structures of the video. The CB-GLNs represent video data as a graph, with nodes and edges corresponding to frames of the video and their dependencies respectively. The CB-GLNs find compositional dependencies of the data in multilevel graph forms via a parameterized kernel with graph-cut and a message passing framework. We evaluate the proposed method on the two different tasks for video understanding: Video theme classification (Youtube-8M dataset) and Video Question and Answering (TVQA dataset). The experimental results show that our model efficiently learns the semantic compositional structure of video data. Furthermore, our model achieves the highest performance in comparison to other baseline methods.Comment: 8 pages, 3 figures, Association for the Advancement of Artificial Intelligence (AAAI2020). arXiv admin note: substantial text overlap with arXiv:1907.0170

Serum alanine aminotransferase levels are closely associated with metabolic disturbances in apparently healthy young adolescents independent of obesity

Purpose Liver metabolism plays a pivotal role in the development of metabolic disorders. We aimed to investigate the clinical and laboratory risk factors associated with alanine aminotransferase (ALT) levels in young adolescents from an urban population in Korea. Methods A population of 120 apparently healthy adolescents aged 12–13 years was included in the cross-sectional design study; 58 were overweight or obese and 62 were of normal weight. We estimated anthropometric and laboratory measurements, including waist-to-height ratio, blood pressure, insulin sensitivity, aspartate aminotransferases (AST), ALT, and lipid profiles. Results The mean ages of the overweight or obese and normal weight participants were 12.9±0.3 and 13.0±0.3 years, respectively. Height, weight, body mass index, waist circumference, waist-to-height ratio, systolic and diastolic blood pressure, AST, ALT, total cholesterol, low-density lipoprotein-cholesterol, triglyceride, insulin, and the homeostatic model assessment of insulin resistance (HOMA-IR) score were significantly higher and the high-density lipoprotein-cholesterol and quantitative insulin-sensitivity check index were significantly lower in the overweight/obese participants in comparison to the normal-weight participants (all P<0.05). In multivariate linear regression analysis, waist-to-height ratio, systolic blood pressure, and HOMA-IR score were independently and positively associated with serum ALT levels. Conclusion Screening for ALT levels in adolescents may help to differentiate those at risk of metabolic abnormalities and thus prevent disease progression at an early age

Role of Diffusion Tensor Imaging in Prognostication and Treatment Monitoring in Niemann-Pick Disease Type C1

Niemann-Pick Disease, type C1 (NPC1) is a rapidly progressive neurodegenerative disorder characterized by cholesterol sequestration within late endosomes and lysosomes, for which no reliable imaging marker exists for prognostication and management. Cerebellar volume deficits are found to correlate with disease severity and diffusion tensor imaging (DTI) of the corpus callosum and brainstem, which has shown that microstructural disorganization is associated with NPC1 severity. This study investigates the utility of cerebellar DTI in clinical severity assessment. We hypothesize that cerebellar volume, fractional anisotropy (FA) and mean diffusivity (MD) negatively correlate with NIH NPC neurological severity score (NNSS) and motor severity subscores. Magnetic resonance imaging (MRI) was obtained for thirty-nine NPC1 subjects, ages 1–21.9 years (mean = 11.1, SD = 6.1). Using an atlas-based automated approach, the cerebellum of each patient was measured for FA, MD and volume. Additionally, each patient was given an NNSS. Decreased cerebellar FA and volume, and elevated MD correlate with higher NNSS. The cognition subscore and motor subscores for eye movement, ambulation, speech, swallowing, and fine motor skills were also statistically significant. Microstructural disorganization negatively correlated with motor severity in subjects. Additionally, Miglustat therapy correlated with lower severity scores across ranges of FA, MD and volume in all regions except the inferior peduncle, where a paradoxical effect was observed at high FA values. These findings suggest that DTI is a promising prognostication tool

Laboratory information management system for COVID-19 non-clinical efficacy trial data

Background : As the number of large-scale studies involving multiple organizations producing data has steadily increased, an integrated system for a common interoperable format is needed. In response to the coronavirus disease 2019 (COVID-19) pandemic, a number of global efforts are underway to develop vaccines and therapeutics. We are therefore observing an explosion in the proliferation of COVID-19 data, and interoperability is highly requested in multiple institutions participating simultaneously in COVID-19 pandemic research. Results : In this study, a laboratory information management system (LIMS) approach has been adopted to systemically manage various COVID-19 non-clinical trial data, including mortality, clinical signs, body weight, body temperature, organ weights, viral titer (viral replication and viral RNA), and multiorgan histopathology, from multiple institutions based on a web interface. The main aim of the implemented system is to integrate, standardize, and organize data collected from laboratories in multiple institutes for COVID-19 non-clinical efficacy testings. Six animal biosafety level 3 institutions proved the feasibility of our system. Substantial benefits were shown by maximizing collaborative high-quality non-clinical research. Conclusions : This LIMS platform can be used for future outbreaks, leading to accelerated medical product development through the systematic management of extensive data from non-clinical animal studies.This research was supported by the National research foundation of Korea(NRF) grant funded by the Korea government(MSIT) (2020M3A9I2109027 and 2021M3H9A1030260)

The Effect of a Horse-Riding Simulator with Virtual Reality on Gross Motor Function and Body Composition of Children with Cerebral Palsy: Preliminary Study

This study aimed to evaluate the effect of a horse-riding simulator (HRS) with virtual reality (VR) on gross motor function, balance control, and body composition in children with spastic cerebral palsy (CP). Seventeen preschool and school-aged children with spastic CP were included; 10 children in the intervention group (HRS group) received 30 min of HRS with VR training twice a week for a total of 16 sessions in addition to conventional physiotherapy. Seven children in the control group were instructed to perform home-based aerobic exercises twice a week for 8 weeks in addition to conventional physiotherapy. Gross motor function measure (GMFM) and body composition were evaluated before the first session and after the last session. Before and after the 2-month intervention, Pediatric Balance Scale and Timed Up and Go test were evaluated for the HRS group. GMFM scores and body composition changed significantly in the HRS group (p &lt; 0.05). However, no significant differences were observed in the control group. Changes in the GMFM total scores, GMFM dimension D scores, and skeletal muscle mass significantly differed between the HRS and control groups (p &lt; 0.05). HRS with VR may be an effective adjunctive therapeutic approach for the rehabilitation of children with CP

Cut-Based Graph Learning Networks to Discover Compositional Structure of Sequential Video Data

Conventional sequential learning methods such as Recurrent Neural Networks (RNNs) focus on interactions between consecutive inputs, i.e. first-order Markovian dependency. However, most of sequential data, as seen with videos, have complex dependency structures that imply variable-length semantic flows and their compositions, and those are hard to be captured by conventional methods. Here, we propose Cut-Based Graph Learning Networks (CB-GLNs) for learning video data by discovering these complex structures of the video. The CB-GLNs represent video data as a graph, with nodes and edges corresponding to frames of the video and their dependencies respectively. The CB-GLNs find compositional dependencies of the data in multilevel graph forms via a parameterized kernel with graph-cut and a message passing framework. We evaluate the proposed method on the two different tasks for video understanding: Video theme classification (Youtube-8M dataset (Abu-El-Haija et al. 2016)) and Video Question and Answering (TVQA dataset(Lei et al. 2018)). The experimental results show that our model efficiently learns the semantic compositional structure of video data. Furthermore, our model achieves the highest performance in comparison to other baseline methods

Projecting the prevalence of obesity in South Korea through 2040: a microsimulation modelling approach

Objective To project the prevalence of obesity in 2040 among individuals 19 years and older in South Korea.Design, setting, and participants Using the ‘Population Health Model-body mass index’ (BMI) microsimulation model, the prevalence of obesity in Korean adults 19 years and older was projected until 2040. The model integrated individual survey data from the Korea Health Panel Survey of 2011 and 2012, population statistics based on resident registration, population projections and complete life tables categorised by sex and age. Birth rate, life expectancy and international migration were based on a medium growth scenario. The base population of Korean adults in 2012, devised through data aggregation, was 39 842 730. The prediction equations were formulated using BMI as the dependent variable; the individual’s sex, age, smoking status, physical activity and preceding year’s BMI were used as predictive factors.Outcome measure BMI categorised by sex.Results The median BMI for Korean adults in 2040 was expected to be 23.55 kg/m2 (23.97 and 23.17 kg/m2 for men and women, respectively). According to the Korean BMI classification, 70.05% of all adults were expected to be ‘preobese’ (ie, have BMIs 23–24.9 kg/m2) by 2040 (81.23% of men and 59.07% of women) and 24.88% to be ‘normal’.Conclusions We explored the possibility of applying and expanding on the concept of microsimulation in the field of healthcare by combining data sources available in Korea and found that more than half of the adults in this study population will be preobese, and the proportions of ‘obesity’ and ‘normal’ will decrease compared with those in 2012. The results of our study will aid in devising healthy strategies and spreading public awareness for preventing this condition