Spray-assisted assembly of thin-film composite membranes in one process

Spray coating has been exploited to fabricate and tailor the morphologies of various components in thin film composite membranes separately. For the first time, here we exploit this technology to construct and assemble both the selective layer and porous support of a thin-film composite membrane in a single process. In our approach, spray-assisted non-solvent induced phase inversion and interfacial polymerization reduced the time required to fabricate thin-film composite membranes from 3 – 4 days to 1 day and 40 mins. Our approach did not sacrifice membrane separation performances during desalination of a mixture comprising 2000 ppm of NaCl in water at 4 bar and room temperature. At these conditions, compared to traditional thin film composite membranes, the water permeance of our spray coated membranes was higher by 35.7 %, reaching 2.32 L m-2 h-1 bar-1, while achieving a NaCl rejection rate of 94.7 %. This demonstrated the feasibility of fabricating thin film composites via spray coating in a single process, potentially reducing fabrication time during scale-up production

SCALE method for single-cell ATAC-seq analysis via latent feature extraction.

Single-cell ATAC-seq (scATAC-seq) profiles the chromatin accessibility landscape at single cell level, thus revealing cell-to-cell variability in gene regulation. However, the high dimensionality and sparsity of scATAC-seq data often complicate the analysis. Here, we introduce a method for analyzing scATAC-seq data, called Single-Cell ATAC-seq analysis via Latent feature Extraction (SCALE). SCALE combines a deep generative framework and a probabilistic Gaussian Mixture Model to learn latent features that accurately characterize scATAC-seq data. We validate SCALE on datasets generated on different platforms with different protocols, and having different overall data qualities. SCALE substantially outperforms the other tools in all aspects of scATAC-seq data analysis, including visualization, clustering, and denoising and imputation. Importantly, SCALE also generates interpretable features that directly link to cell populations, and can potentially reveal batch effects in scATAC-seq experiments

Improve Chinese Semantic Dependency Parsing via Syntactic Dependency Parsing

Abstract-We address the problem of Chinese semantic dependency parsing. Dependency parsing is traditionally oriented to syntax analysis, which we denote by syntactic dependency parsing to distinguish it from semantic dependency parsing. In this paper, firstly we compare Chinese semantic dependency parsing and syntactic dependency parsing systematically, showing that syntactic dependency parsing can potentially improve the performance of semantic dependency parsing. Thus then we suggest an approach based on quasi-synchronous grammar to incorporate the auto-parsed syntactic dependency tree into semantic dependency parsing. We conduct experiments on the Chinese semantic dependency parsing corpus of SemEval-2012. Finally we achieve 65.25% LAS on test corpus, gaining increases of 2.45% compared to the top result of 62.80% in SemEval-2012

A pathogenic UFSP2 variant in an autosomal recessive form of pediatric neurodevelopmental anomalies and epilepsy

Purpose: Neurodevelopmental disabilities are common and genetically heterogeneous. We identified a homozygous variant in the gene encoding UFM1-specific peptidase 2 (UFSP2), which participates in the UFMylation pathway of protein modification. UFSP2 variants are implicated in autosomal dominant skeletal dysplasias, but not neurodevelopmental disorders. Homozygosity for the variant occurred in eight children from four South Asian families with neurodevelopmental delay and epilepsy. We describe the clinical consequences of this variant and its effect on UFMylation.Methods: Exome sequencing was used to detect potentially pathogenic variants and identify shared regions of homozygosity. Immunoblotting assessed protein expression and post-translational modifications in patient-derived fibroblasts.Results: The variant (c.344T\u3eA; p.V115E) is rare and alters a conserved residue in UFSP2. Immunoblotting in patient-derived fibroblasts revealed reduced UFSP2 abundance and increased abundance of UFMylated targets, indicating the variant may impair de-UFMylation rather than UFMylation. Reconstituting patient-derived fibroblasts with wild-type UFSP2 reduced UFMylation marks. Analysis of UFSP2\u27s structure indicated that variants observed in skeletal disorders localize to the catalytic domain, whereas V115 resides in an N-terminal domain possibly involved in substrate binding.Conclusion: Different UFSP2 variants cause markedly different diseases, with homozygosity for V115E causing a severe syndrome of neurodevelopmental disability and epilepsy

Maternal exposure to ambient air pollution and congenital heart defects in China

Background: Evidence of maternal exposure to ambient air pollution on congenital heart defects (CHD) has been mixed and are still relatively limited in developing countries. We aimed to investigate the association between maternal exposure to air pollution and CHD in China.Method: This longitudinal, population-based, case-control study consecutively recruited fetuses with CHD and healthy volunteers from 21 cities, Southern China, between January 2006 and December 2016. Residential address at delivery was linked to random forests models to estimate maternal exposure to particulate matter with an aerodynamic diameter of ≤1 µm (PM1), ≤2.5 µm, and ≤10 µm as well as nitrogen dioxides, in three trimesters. The CHD cases were evaluated by obstetrician, pediatrician, or cardiologist, and confirmed by cardia ultrasound. The CHD subtypes were coded using the International Classification Diseases. Adjusted logistic regression models were used to assess the associations between air pollutants and CHD and its subtypes.Results: A total of 7055 isolated CHD and 6423 controls were included in the current analysis. Maternal air pollution exposures were consistently higher among cases than those among controls. Logistic regression analyses showed that maternal exposure to all air pollutants during the first trimester was associated with an increased odds of CHD (e.g., an interquartile range [13.3 µg/m3] increase in PM1 was associated with 1.09-fold ([95% confidence interval, 1.01-1.18]) greater odds of CHD). No significant associations were observed for maternal air pollution exposures during the second trimester and the third trimester. The pattern of the associations between air pollutants and different CHD subtypes was mixed.Conclusions: Maternal exposure to greater levels of air pollutants during the pregnancy, especially the first trimester, is associated with higher odds of CHD in offspring. Further longitudinal well-designed studies are warranted to confirm our findings

Exact Inference for Meta-Analysis of Rare Events and Its Application in Human Genetics

Meta-analysis is a statistical approach that integrates data from multiple studies. By aggregating information, it enhances the power to detect the effects of interest and provides an estimate of the effect size with both accuracy and precision. Both fixed-effect and random-effect models are developed and widely used in biomedical research including clinical trials and genomic studies. In the case of rare events data, conventional meta-analysis methods that rely on large sample approximation may not be able to make reliable inferences. There have been various approaches proposed to deal with this situation, in particular, rare binary adverse events in clinical studies. Genome-wide association studies (GWAS) is the most popular study design of human genetic mapping. Large consortia are organized to increase the power of association detection, and therefore meta-analysis becomes a necessity in GWAS. Advances in sequencing technology enable a complete survey of both common and rare variants. Although numerous statistical methods to analyze rare variants are developed for a single study, there is no meta-analysis approach developed to specifically deal with rare variants. In this dissertation we aim to develop methods to make exact inferences in meta-analysis of rare variants association. The exact methods are based on exact distribution not approximate distribution so it is derived from all known parameters. We first adapt and implement a fixed-effect exact meta-analysis approach that is based on the concept of p-value function with the specific aim of performing rare variants genetic association studies. It can conduct robust inference on risk difference (RD) and construct a reliable confidence interval (CI) without ignoring studies with zero event, adding arbitrary continuity corrections, or using large sample approximation. We compare the exact method with the commonly used Mantel-Haenszel method in terms of CI coverage probability, CI length, type I error rate, statistical power, and absolute bias in various scenarios of balanced and unbalanced study sample sizes. Simulation results show that the exact methods are more stable when the event rates are extremely rare and sample sizes are unbalanced between case and control groups. We then extend the exact meta-analysis approach to a random-effect model, which, compared with the fixed-effect model, can handle between-study variances. The proposed method enables an unbiased estimate of odds ratio (OR) and makes exact inferences of CI. We propose a method to shrink the parameter search region, which can substantially reduce the computational cost. Simulation studies are conducted to investigate the performance of the proposed method in terms of CI coverage probability and CI length. The proposed method maintains stable coverage probabilities under various settings of heterogeneity, number of studies, and magnitude of ORs. We further consider a special study design that there are multiple case groups but there is only one common control group. This may happen when researchers only recruit patients but not controls, and use some large survey database from the general population as the common control group. We propose an exact method to construct CI for the event rate in the case groups and then make inferences on the pooled effect size measures, e.g. RD and OR, compared with the common controls. The proposed exact method shows stable performance regardless of the parameter settings. We particularly recommend applying it when the number of studies is small and the event rate is rare