Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation

Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e.g., increasing the frame rate of the more dynamic portion of the video as well as handling missing video frames). To resolve the restricted nature of existing video generation models' ability to handle arbitrary timesteps, we propose continuous-time video generation by combining neural ODE (Vid-ODE) with pixel-level video processing techniques. Using ODE-ConvGRU as an encoder, a convolutional version of the recently proposed neural ODE, which enables us to learn continuous-time dynamics, Vid-ODE can learn the spatio-temporal dynamics of input videos of flexible frame rates. The decoder integrates the learned dynamics function to synthesize video frames at any given timesteps, where the pixel-level composition technique is used to maintain the sharpness of individual frames. With extensive experiments on four real-world video datasets, we verify that the proposed Vid-ODE outperforms state-of-the-art approaches under various video generation settings, both within the trained time range (interpolation) and beyond the range (extrapolation). To the best of our knowledge, Vid-ODE is the first work successfully performing continuous-time video generation using real-world videos.Comment: Accepted to AAAI 2021, 22 page

Deep Imbalanced Time-series Forecasting via Local Discrepancy Density

Time-series forecasting models often encounter abrupt changes in a given period of time which generally occur due to unexpected or unknown events. Despite their scarce occurrences in the training set, abrupt changes incur loss that significantly contributes to the total loss. Therefore, they act as noisy training samples and prevent the model from learning generalizable patterns, namely the normal states. Based on our findings, we propose a reweighting framework that down-weights the losses incurred by abrupt changes and up-weights those by normal states. For the reweighting framework, we first define a measurement termed Local Discrepancy (LD) which measures the degree of abruptness of a change in a given period of time. Since a training set is mostly composed of normal states, we then consider how frequently the temporal changes appear in the training set based on LD. Our reweighting framework is applicable to existing time-series forecasting models regardless of the architectures. Through extensive experiments on 12 time-series forecasting models over eight datasets with various in-output sequence lengths, we demonstrate that applying our reweighting framework reduces MSE by 10.1% on average and by up to 18.6% in the state-of-the-art model.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 202

Identification of Biomarkers That Distinguish Chemical Contaminants Based on Gene Expression Profiles

Background: High throughput transcriptomics profiles such as those generated using microarrays have been useful in identifying biomarkers for different classification and toxicity prediction purposes. Here, we investigated the use of microarrays to predict chemical toxicants and their possible mechanisms of action. Results: In this study, in vitro cultures of primary rat hepatocytes were exposed to 105 chemicals and vehicle controls, representing 14 compound classes. We comprehensively compared various normalization of gene expression profiles, feature selection and classification algorithms for the classification of these 105 chemicals into14 compound classes. We found that normalization had little effect on the averaged classification accuracy. Two support vector machine (SVM) methods, LibSVM and sequential minimal optimization, had better classification performance than other methods. SVM recursive feature selection (SVM-RFE) had the highest overfitting rate when an independent dataset was used for a prediction. Therefore, we developed a new feature selection algorithm called gradient method that had a relatively high training classification as well as prediction accuracy with the lowest overfitting rate of the methods tested. Analysis of biomarkers that distinguished the 14 classes of compounds identified a group of genes principally involved in cell cycle function that were significantly downregulated by metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and PXR mediators. Conclusions: Our results indicate that using microarrays and a supervised machine learning approach to predict chemical toxicants, their potential toxicity and mechanisms of action is practical and efficient. Choosing the right feature and classification algorithms for this multiple category classification and prediction is critical

IGG purity assay using a new high resolution SDS-GEL

Comunicaciones a congreso

Impaired decisional impulsivity in pathological videogamers

Abstract Background Pathological gaming is an emerging and poorly understood problem. Impulsivity is commonly impaired in disorders of behavioural and substance addiction, hence we sought to systematically investigate the different subtypes of decisional and motor impulsivity in a well-defined pathological gaming cohort. Methods Fifty-two pathological gaming subjects and age-, gender- and IQ-matched healthy volunteers were tested on decisional impulsivity (Information Sampling Task testing reflection impulsivity and delay discounting questionnaire testing impulsive choice), and motor impulsivity (Stop Signal Task testing motor response inhibition, and the premature responding task). We used stringent diagnostic criteria highlighting functional impairment. Results In the Information Sampling Task, pathological gaming participants sampled less evidence prior to making a decision and scored fewer points compared with healthy volunteers. Gaming severity was also negatively correlated with evidence gathered and positively correlated with sampling error and points acquired. In the delay discounting task, pathological gamers made more impulsive choices, preferring smaller immediate over larger delayed rewards. Pathological gamers made more premature responses related to comorbid nicotine use. Greater number of hours played also correlated with a Motivational Index. Greater frequency of role playing games was associated with impaired motor response inhibition and strategy games with faster Go reaction time. Conclusions We show that pathological gaming is associated with impaired decisional impulsivity with negative consequences in task performance. Decisional impulsivity may be a potential target in therapeutic management

PROTOCOL: In‐person interventions to reduce social isolation and loneliness: An evidence and gap map

Abstract This is the protocol for an evidence and gap map. The objectives are as follows: This EGM aims to map available evidence on the effects of in‐person interventions to reduce social isolation and/or loneliness across all age groups in all settings

Comparison of transcriptional responses in liver tissue and primary hepatocyte cell cultures after exposure to hexahydro-1, 3, 5-trinitro-1, 3, 5-triazine

BACKGROUND: Cell culture systems are useful in studying toxicological effects of chemicals such as Hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), however little is known as to how accurately isolated cells reflect responses of intact organs. In this work, we compare transcriptional responses in livers of Sprague-Dawley rats and primary hepatocyte cells after exposure to RDX to determine how faithfully the in vitro model system reflects in vivo responses. RESULTS: Expression patterns were found to be markedly different between liver tissue and primary cell cultures before exposure to RDX. Liver gene expression was enriched in processes important in toxicology such as metabolism of amino acids, lipids, aromatic compounds, and drugs when compared to cells. Transcriptional responses in cells exposed to 7.5, 15, or 30 mg/L RDX for 24 and 48 hours were different from those of livers isolated from rats 24 hours after exposure to 12, 24, or 48 mg/Kg RDX. Most of the differentially expressed genes identified across conditions and treatments could be attributed to differences between cells and tissue. Some similarity was observed in RDX effects on gene expression between tissue and cells, but also significant differences that appear to reflect the state of the cell or tissue examined. CONCLUSION: Liver tissue and primary cells express different suites of genes that suggest they have fundamental differences in their cell physiology. Expression effects related to RDX exposure in cells reflected a fraction of liver responses indicating that care must be taken in extrapolating from primary cells to whole animal organ toxicity effects

In‐person interventions to reduce social isolation and loneliness: An evidence and gap map

BackgroundSocial isolation and loneliness can occur in all age groups, and they are linked to increased mortality and poorer health outcomes. There is a growing body of research indicating inconsistent findings on the effectiveness of interventions aiming to alleviate social isolation and loneliness. Hence the need to facilitate the discoverability of research on these interventions.ObjectivesTo map available evidence on the effects of in-person interventions aimed at mitigating social isolation and/or loneliness across all age groups and settings.Search MethodsThe following databases were searched from inception up to 17 February 2022 with no language restrictions: Ovid MEDLINE, Embase, EBM Reviews—Cochrane Central Register of Controlled Trials, APA PsycInfo via Ovid, CINAHL via EBSCO, EBSCO (all databases except CINAHL), Global Index Medicus, ProQuest (all databases), ProQuest ERIC, Web of Science, Korean Citation Index, Russian Science Citation Index, and SciELO Citation Index via Clarivate, and Elsevier Scopus.Selection CriteriaTitles, abstracts, and full texts of potentially eligible articles identified were screened independently by two reviewers for inclusion following the outlined eligibility criteria.Data Collection and AnalysisWe developed and pilot tested a data extraction code set in Eppi-Reviewer. Data was individually extracted and coded. We used the AMSTAR2 tool to assess the quality of reviews. However, the quality of the primary studies was not assessed.Main ResultsA total of 513 articles (421 primary studies and 92 systematic reviews) were included in this evidence and gap map which assessed the effectiveness of in-person interventions to reduce social isolation and loneliness. Most (68%) of the reviews were classified as critically low quality, while less than 5% were classified as high or moderate quality. Most reviews looked at interpersonal delivery and community-based delivery interventions, especially interventions for changing cognition led by a health professional and group activities, respectively. Loneliness, wellbeing, and depression/anxiety were the most assessed outcomes. Most research was conducted in high-income countries, concentrated in the United States, United Kingdom, and Australia, with none from low-income countries. Major gaps were identified in societal level and community-based delivery interventions that address policies and community structures, respectively. Less than 5% of included reviews assessed process indicators or implementation outcomes. Similar patterns of evidence and gaps were found in primary studies. All age groups were represented but more reviews and primary studies focused on older adults (≥60 years, 63%) compared to young people (≤24 years, 34%). Two thirds described how at-risk populations were identified and even fewer assessed differences in effect across equity factors for populations experiencing inequities