Gestational diabetes mellitus (GDM) is a common complication of pregnancy, which has significant adverse effects on both the mother and fetus. The incidence of GDM is increasing globally, and early diagnosis is critical for timely treatment and reducing the risk of poor pregnancy outcomes. GDM is usually diagnosed and detected after 24 weeks of gestation, while complications due to GDM can occur much earlier. Copy number variations (CNVs) can be a possible biomarker for GDM diagnosis and screening in the early gestation stage. In this study, we proposed a machine-learning method to screen GDM in the early stage of gestation using cell-free DNA (cfDNA) sequencing data from maternal plasma. Five thousand and eighty-five patients from north regions of Mainland China, including 1942 GDM, were recruited. A non-overlapping sliding window method was applied for CNV coverage screening on low-coverage (~0.2×) sequencing data. The CNV coverage was fed to a convolutional neural network with attention architecture for the binary classification. The model achieved a classification accuracy of 88.14%, precision of 84.07%, recall of 93.04%, F1-score of 88.33% and AUC of 96.49%. The model identified 2190 genes associated with GDM, including DEFA1, DEFA3 and DEFB1. The enriched gene ontology (GO) terms and KEGG pathways showed that many identified genes are associated with diabetes-related pathways. Our study demonstrates the feasibility of using cfDNA sequencing data and machine-learning methods for early diagnosis of GDM, which may aid in early intervention and prevention of adverse pregnancy outcomes.

WebAssembly is the newest language to arrive on the web. It features a compact binary format, making it fast to be loaded and decoded. While WebAssembly is generally expected to be faster than JavaScript, there have been mixed results in proving which code is faster. Little research has been done to comprehend WebAssembly's performance benefit. In this paper, we conduct a systematic study to understand the performance of WebAssembly applications and compare it with JavaScript. Our measurements were performed on three sets of subject programs with diverse settings. Among others, our findings include: (1) WebAssembly compilers are commonly built atop LLVM, where their optimizations are not tailored for WebAssembly. We show that these optimizations often become ineffective for WebAssembly, leading to counter-intuitive results. (2) JIT optimization has a significant impact on JavaScript performance. However, no substantial performance increase was observed for WebAssembly with JIT. (3) The performance of WebAssembly and JavaScript varies substantially depending on the execution environment. (4) WebAssembly uses significantly more memory than its JavaScript counterparts. We hope that our findings can help WebAssembly tooling developers identify optimization opportunities. We also report the challenges encountered when compiling C benchmarks to WebAssembly and discuss our solutions.