Impact of parental depression on pharmacotherapy and psychotherapy amongst children and adolescents with depression diagnoses

OBJECTIVE: Parental depression is considered amongst the greatest risk factors for the diagnosis of depression in children and adolescents. Less is known about how and to what extent parental depression influences their depressed children’s depression treatment. Building on Bronfenbrenner’s ecological model and Andersen’s model, this study aimed to assess the impact of parental depression on depressed children and adolescents’ depression treatment. DATA/STUDY POPULATION: Children and adolescents aged 3 to 17 diagnosed with depression in MarketScan Claims and Encounters Database (2010–2014) METHOD: Psychotherapy, pharmacotherapy, and combination treatment were examined. Study covariates were described. Chi-square tests with Bonferroni correction were conducted to examine the difference in study covariates between types of depression treatment. Generalized estimating equation models were used to examine the effect of parental depression on the likelihood of receiving depression treatment, controlling for study covariates, while accounting for the clustering of children and adolescents in the same household. RESULT: Children and adolescents with depressive parents were less likely to use psychotherapy (aOR: 0.80, 95% CI: (0.74, 0.86)), and more likely to use antidepressant (aOR: 1.41, 95% CI: (1.28, 1.56)) and combination treatment (aOR: 1.55, 95%CI: (1.43, 1.68)), compared to those without. Depressed children and adolescents living with depressed siblings in the same family were significantly less likely to use any depression treatment (aOR: 0.47, 95% CI: (0.41, 0.55)). CONCLUSION: The significant relationship between parental depression and their depressed children and adolescents’ treatment indicates that consideration should be given to parents’ decision-making and engagement in the treatment

Understanding the Digital Gap Among US Adults With Disability: Cross-Sectional Analysis of the Health Information National Trends Survey 2013

BACKGROUND: Disabilities affect more than 1 in 5 US adults, and those with disabilities face multiple barriers in accessing health care. A digital gap, defined as the disparity caused by differences in the ability to use advanced technologies, is assumed to be prevalent among individuals with disabilities. OBJECTIVE: This study examined the associations between disability and use of information technology (IT) in obtaining health information and between trust factors and IT use. We hypothesized that compared to US adults without disabilities, those with disabilities are less likely to refer to the internet for health information, more likely to refer to a health care provider to obtain health information, and less likely to use IT to exchange medical information with a provider. Additionally, we hypothesized that trust factors, such as trust toward health information source and willingness to exchange health information, are associated with IT use. METHODS: The primary database was the 2013 Health Information National Trends Survey 4 Cycle 3 (N=3185). Disability status, the primary study covariate, was based on 6 questions that encompassed a wide spectrum of conditions, including impairments in mobility, cognition, independent living, vision, hearing, and self-care. Study covariates included sociodemographic factors, respondents\u27 trust toward the internet and provider as information sources, and willingness to exchange medical information via IT with providers. Study outcomes were the use of the internet as the primary health information source, use of health care providers as the primary health information source, and use of IT to exchange medical information with providers. We conducted multivariate logistic regressions to examine the association between disability and study outcomes controlling for study covariates. Multiple imputations with fully conditional specification were used to impute missing values. RESULTS: We found presence of any disability was associated with decreased odds (adjusted odds ratio [AOR] 0.65, 95% CI 0.43-0.98) of obtaining health information from the internet, in particular for those with vision disability (AOR 0.27, 95% CI 0.11-0.65) and those with mobility disability (AOR 0.51, 95% CI 0.30-0.88). Compared to those without disabilities, those with disabilities were significantly more likely to consult a health care provider for health information in both actual (OR 2.21, 95% CI 1.54-3.18) and hypothetical situations (OR 1.80, 95% CI 1.24-2.60). Trust toward health information from the internet (AOR 3.62, 95% CI 2.07-6.33), and willingness to exchange via IT medical information with a provider (AOR 1.88, 95% CI 1.57-2.24) were significant predictors for seeking and exchanging such information, respectively. CONCLUSIONS: A potential digital gap may exist among US adults with disabilities in terms of their recent use of the internet for health information. Trust toward health information sources and willingness play an important role in people\u27s engagement in use of the internet for health information. Future studies should focus on addressing trust factors associated with IT use and developing tools to improve access to care for those with disabilities

MiRNA-145 increases therapeutic sensibility to gemcitabine treatment of pancreatic adenocarcinoma cells.

Pancreatic adenocarcinoma is one of the most leading causes of cancer-related deaths worldwide. Although recent advances provide various treatment options, pancreatic adenocarcinoma has poor prognosis due to its late diagnosis and ineffective therapeutic multimodality. Gemcitabine is the effective first-line drug in pancreatic adenocarcinoma treatment. However, gemcitabine chemoresistance of pancreatic adenocarcinoma cells has been a major obstacle for limiting its treatment effect. Our study found that p70S6K1 plays an important role in gemcitabine chemoresistence. MiR-145 is a tumor suppressor which directly targets p70S6K1 for inhibiting its expression in pancreatic adenocarcinoma, providing new therapeutic scheme. Our findings revealed a new mechanism underlying gemcitabine chemoresistance in pancreatic adenocarcinoma cells

Attention-Based Dense Point Cloud Reconstruction From a Single Image

Article proposes a two-stage training dense point cloud generation network

Methodological Challenges for Epidemiologic Studies of Deprescribing at the End of Life

Purpose of Review: To describe approaches to measuring deprescribing and associated outcomes in studies of patients approaching end of life (EOL). Recent Findings: We reviewed studies published through 2020 that evaluated deprescribing in patients with limited life expectancy and approaching EOL. Deprescribing includes reducing the number of medications, decreasing medication dose(s), and eliminating potentially inappropriate medications. Tools such as STOPPFrail, OncPal, and the Unnecessary Drug Use Measure can facilitate deprescribing. Outcome measures vary and selection of measures should align with the operationalized deprescribing definition used by study investigators. Summary: EOL deprescribing considerations include medication appropriateness in the context of patient goals for care, expected benefit from medication given life expectancy, and heightened potential for medication-related harm as death nears. Additional data are needed on how EOL deprescribing impacts patient quality of life, caregiver burden, and out-of-pocket medication-related costs to patients and caregivers. Investigators should design deprescribing studies with this information in mind

MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation

We propose the first joint audio-video generation framework that brings engaging watching and listening experiences simultaneously, towards high-quality realistic videos. To generate joint audio-video pairs, we propose a novel Multi-Modal Diffusion model (i.e., MM-Diffusion), with two-coupled denoising autoencoders. In contrast to existing single-modal diffusion models, MM-Diffusion consists of a sequential multi-modal U-Net for a joint denoising process by design. Two subnets for audio and video learn to gradually generate aligned audio-video pairs from Gaussian noises. To ensure semantic consistency across modalities, we propose a novel random-shift based attention block bridging over the two subnets, which enables efficient cross-modal alignment, and thus reinforces the audio-video fidelity for each other. Extensive experiments show superior results in unconditional audio-video generation, and zero-shot conditional tasks (e.g., video-to-audio). In particular, we achieve the best FVD and FAD on Landscape and AIST++ dancing datasets. Turing tests of 10k votes further demonstrate dominant preferences for our model. The code and pre-trained models can be downloaded at https://github.com/researchmm/MM-Diffusion.Comment: Accepted by CVPR 202

A Comprehensive Survey on Deep Graph Representation Learning

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Human gesture control multiple quadcopter drones

Quadcopter drones are popular hobbyist aerial vehicles. The current input methods such as remote control and phone apps can control only one drone, and are too cumbersome and un-intuitive for one to learn quickly. [1st Award

Computational cognition for big data analytics

The machine learning algorithms like Projection Based Learning algorithm in Meta-cognitive Radial Basis Function Network (PBL-McRBFN) look up to human brain in attempts to make its computation more efficient. However, the performance of this algorithm is limited by the nature of sequential learning. In this project we bring Big Data-era technology Apache Spark cluster computing platform to use with this algorithm in an attempt to achieve collaborative distributed learning. Amidst the various difficulties, the project achieved a partial success while discovering other potential issues in using Spark to achieve the goal.Bachelor of Engineering (Computer Science