Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation

The Gaussianity assumption has been consistently criticized as a main limitation of the Variational Autoencoder (VAE) despite its efficiency in computational modeling. In this paper, we propose a new approach that expands the model capacity (i.e., expressive power of distributional family) without sacrificing the computational advantages of the VAE framework. Our VAE model's decoder is composed of an infinite mixture of asymmetric Laplace distribution, which possesses general distribution fitting capabilities for continuous variables. Our model is represented by a special form of a nonparametric M-estimator for estimating general quantile functions, and we theoretically establish the relevance between the proposed model and quantile estimation. We apply the proposed model to synthetic data generation, and particularly, our model demonstrates superiority in easily adjusting the level of data privacy

Joint Distributional Learning via Cramer-Wold Distance

The assumption of conditional independence among observed variables, primarily used in the Variational Autoencoder (VAE) decoder modeling, has limitations when dealing with high-dimensional datasets or complex correlation structures among observed variables. To address this issue, we introduced the Cramer-Wold distance regularization, which can be computed in a closed-form, to facilitate joint distributional learning for high-dimensional datasets. Additionally, we introduced a two-step learning method to enable flexible prior modeling and improve the alignment between the aggregated posterior and the prior distribution. Furthermore, we provide theoretical distinctions from existing methods within this category. To evaluate the synthetic data generation performance of our proposed approach, we conducted experiments on high-dimensional datasets with multiple categorical variables. Given that many readily available datasets and data science applications involve such datasets, our experiments demonstrate the effectiveness of our proposed methodology

Causally Disentangled Generative Variational AutoEncoder

We present a new supervised learning technique for the Variational AutoEncoder (VAE) that allows it to learn a causally disentangled representation and generate causally disentangled outcomes simultaneously. We call this approach Causally Disentangled Generation (CDG). CDG is a generative model that accurately decodes an output based on a causally disentangled representation. Our research demonstrates that adding supervised regularization to the encoder alone is insufficient for achieving a generative model with CDG, even for a simple task. Therefore, we explore the necessary and sufficient conditions for achieving CDG within a specific model. Additionally, we introduce a universal metric for evaluating the causal disentanglement of a generative model. Empirical results from both image and tabular datasets support our findings

Dysregulation of the Wnt/β-catenin signaling pathway via Rnf146 upregulation in a VPA-induced mouse model of autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with impaired social behavior and communication, repetitive behaviors, and restricted interests. In addition to genetic factors, environmental factors such as prenatal drug exposure contribute to the development of ASD. However, how those prenatal factors induce behavioral deficits in the adult stage is not clear. To elucidate ASD pathogenesis at the molecular level, we performed a high-resolution mass spectrometry-based quantitative proteomic analysis on the prefrontal cortex (PFC) of mice exposed to valproic acid (VPA) in utero, a widely used animal model of ASD. Differentially expressed proteins (DEPs) in VPA-exposed mice showed significant overlap with ASD risk genes, including differentially expressed genes from the postmortem cortex of ASD patients. Functional annotations of the DEPs revealed significant enrichment in the Wnt/β-catenin signaling pathway, which is dysregulated by the upregulation of Rnf146 in VPA-exposed mice. Consistently, overexpressing Rnf146 in the PFC impaired social behaviors and altered the Wnt signaling pathway in adult mice. Furthermore, Rnf146-overexpressing PFC neurons showed increased excitatory synaptic transmission, which may underlie impaired social behavior. These results demonstrate that Rnf146 is critical for social behavior and that dysregulation of Rnf146 underlies social deficits in VPA-exposed mice. © 2023, The Author(s).TRU

Hyperglycemia and Hypoglycemia Are Associated with In-Hospital Mortality among Patients with Coronavirus Disease 2019 Supported with Extracorporeal Membrane Oxygenation

Metabolic abnormalities, such as preexisting diabetes or hyperglycemia or hypoglycemia during hospitalization aggravated the severity of COVID-19. We evaluated whether diabetes history, hyperglycemia before and during extracorporeal membrane oxygenation (ECMO) support, and hypoglycemia were risk factors for mortality in patients with COVID-19. This study included data on 195 patients with COVID-19, who were aged >= 19 years and were treated with ECMO. The proportion of patients with diabetes history among nonsurvivors was higher than that among survivors. Univariate Cox regression analysis showed that in-hospital mortality after ECMO support was associated with diabetes history, renal replacement therapy (RRT), and body mass index (BMI) < 18.5 kg/m(2). Glucose at admission >200 mg/dL and glucose levels before ventilator >200 mg/dL were not associated with in-hospital mortality. However, glucose levels before ECMO >200 mg/dL and minimal glucose levels during hospitalization 200 mg/dL before ECMO and minimal glucose <70 mg/dL during hospitalization remained risk factors for in-hospital mortality after adjustment for age, BMI, and RRT. In conclusion, glucose >200 mg/dL before ECMO and minimal glucose level <70 mg/dL during hospitalization were risk factors for in-hospital mortality among COVID-19 patients who underwent ECMO.N

Genome-wide copy number variation in Hanwoo, Black Angus, and Holstein cattle

Hanwoo, Korean native cattle, is indigenous to the Korean peninsula. They have been used mainly as draft animals for about 5,000 years; however, in the last 30 years, their main role has been changed to meat production by selective breeding which has led to substantial increases in their productivity. Massively parallel sequencing technology has recently made possible the systematic identification of structural variations in cattle genomes. In particular, copy number variation (CNV) has been recognized as an important genetic variation complementary to single-nucleotide polymorphisms that can be used to account for variations of economically important traits in cattle. Here we report genome-wide copy number variation regions (CNVRs) in Hanwoo cattle obtained by comparing the whole genome sequence of Hanwoo with Black Angus and Holstein sequence datasets. We identified 1,173 and 963 putative CNVRs representing 16.7 and 7.8 Mbp from comparisons between Black Angus and Hanwoo and between Holstein and Hanwoo, respectively. The potential functional roles of the CNVRs were assessed by Gene Ontology enrichment analysis. The results showed that response to stimulus, immune system process, and cellular component organization were highly enriched in the genic-CNVRs that overlapped with annotated cattle genes. Of the 11 CNVRs that were selected for validation by quantitative real-time PCR, 9 exhibited the expected copy number differences. The results reported in this study show that genome-wide CNVs were detected successfully using massively parallel sequencing technology. The CNVs may be a valuable resource for further studies to correlate CNVs and economically important traits in cattle

Hyperglycemia and Hypoglycemia Are Associated with In-Hospital Mortality among Patients with Coronavirus Disease 2019 Supported with Extracorporeal Membrane Oxygenation

Metabolic abnormalities, such as preexisting diabetes or hyperglycemia or hypoglycemia during hospitalization aggravated the severity of COVID-19. We evaluated whether diabetes history, hyperglycemia before and during extracorporeal membrane oxygenation (ECMO) support, and hypoglycemia were risk factors for mortality in patients with COVID-19. This study included data on 195 patients with COVID-19, who were aged ≥19 years and were treated with ECMO. The proportion of patients with diabetes history among nonsurvivors was higher than that among survivors. Univariate Cox regression analysis showed that in-hospital mortality after ECMO support was associated with diabetes history, renal replacement therapy (RRT), and body mass index (BMI) < 18.5 kg/m2. Glucose at admission >200 mg/dL and glucose levels before ventilator >200 mg/dL were not associated with in-hospital mortality. However, glucose levels before ECMO >200 mg/dL and minimal glucose levels during hospitalization <70 mg/dL were associated with in-hospital mortality. Multivariable Cox regression analysis showed that glucose >200 mg/dL before ECMO and minimal glucose <70 mg/dL during hospitalization remained risk factors for in-hospital mortality after adjustment for age, BMI, and RRT. In conclusion, glucose >200 mg/dL before ECMO and minimal glucose level <70 mg/dL during hospitalization were risk factors for in-hospital mortality among COVID-19 patients who underwent ECMO