No abstract

To evaluate the diagnostic value of genome sequencing in children with epilepsy, and to provide genome sequencing-based insights into the molecular genetic mechanisms of epilepsy to help establish accurate diagnoses, design appropriate treatments, and assist in genetic counseling. We performed genome sequencing on 320 Chinese children with epilepsy, and interpreted single nucleotide variants and copy number variants of all samples. The complete pedigree and clinical data of the probands were established and followed up. The clinical phenotypes, treatments, prognoses, and genotypes of the patients were analyzed. Age at seizure onset ranged from 1 day to 17 years, with a median of 4.3 years. Pathogenic/likely pathogenic variants were found in 117 of the 320 children (36.6%), of whom 93 (29.1%) had single nucleotide variants, 22 (6.9%) had copy number variants, and 2 had both single nucleotide variants and copy number variants. Single nucleotide variants were most frequently found in SCN1A (10/95, 10.5%), which is associated with Dravet syndrome, followed by PRRT2 (8/95, 8.4%), which is associated with benign familial infantile epilepsy, and TSC2 (7/95, 7.4%), which is associated with tuberous sclerosis. Among the copy number variants, there were 3 with a length < 25Kb. The most common recurrent copy number variants were 17p13.3 deletions (5/24, 20.8%), 16p11.2 deletions (4/24, 16.7%), and 7q11.23 duplications (2/24, 8.3%), which are associated with epilepsy, developmental retardation, and congenital abnormalities. Four particular 16p11.2 deletions and two 15q11.2 deletions were considered to be susceptibility factors contributing to neurodevelopmental disorders associated with epilepsy. The diagnostic yield was 75.0% in patients with seizure onset during the first postnatal month, and gradually decreased in patients with seizure onset at a later age. Forty-two patients (13.1%) were found to be specifically treatable for the underlying genetic cause identified by genome sequencing. Three of them received corresponding targeted therapies and demonstrated favorable prognoses. Genome sequencing provides complete genetic diagnosis, thus enabling individualized treatment and genetic counseling for the parents of the patients. Genome sequencing is expected to become the first choice of methods for genetic testing of patients with epilepsy.

Background During pregnancy, mother–child interactions trigger a variety of subtle changes in the maternal body, which may be reflected in the status of peripheral blood mononuclear cells (PBMCs). Although these cells are easy to access and monitor, a PBMC atlas for pregnant women has not yet been constructed. Methods We applied single‐cell RNA sequencing (scRNA‐seq) to profile 198,356 PBMCs derived from 136 pregnant women (gestation weeks 6 to 40) and a control cohort. We also used scRNA‐seq data to establish a transcriptomic clock and thereby predicted the gestational age of normal pregnancy. Results We identified reconfiguration of the peripheral immune cell phenotype during pregnancy, including interferon‐stimulated gene upregulation, activation of RNA splicing‐related pathways and immune activity of cell subpopulations. We also developed a cell‐type‐specific model to predict gestational age of normal pregnancy. Conclusions We constructed a single‐cell atlas of PBMCs in pregnant women spanning the entire gestation period, which should help improve our understanding of PBMC composition turnover in pregnant women.

In this paper, an intensive study on online social networks is studied. Through the presented mothod, the relationships between entities can be analyzed to be positive or negative. The positive relationship indicates trust or friendship and the negative relationship represents opposition or antagonism. We investigate some basic characteristics of signed networks and make certain extensions to particular features. A modified version of the PageRank algorithm is proposed, which is applicable to signed networks. Based on the creative features, an edge sign predictor using supervised machine learning algorithms is also established. The experimental results show that our model can significantly improve the prediction accuracy and decrease the false positive rate. 

It is well recognized that batch effect in single-cell RNA sequencing (scRNA-seq) data remains a big challenge when integrating different datasets. Here, we proposed deepMNN, a novel deep learning-based method to correct batch effect in scRNA-seq data. We first searched mutual nearest neighbor (MNN) pairs across different batches in a principal component analysis (PCA) subspace. Subsequently, a batch correction network was constructed by stacking two residual blocks and further applied for the removal of batch effects. The loss function of deepMNN was defined as the sum of a batch loss and a weighted regularization loss. The batch loss was used to compute the distance between cells in MNN pairs in the PCA subspace, while the regularization loss was to make the output of the network similar to the input. The experiment results showed that deepMNN can successfully remove batch effects across datasets with identical cell types, datasets with non-identical cell types, datasets with multiple batches, and large-scale datasets as well. We compared the performance of deepMNN with state-of-the-art batch correction methods, including the widely used methods of Harmony, Scanorama, and Seurat V4 as well as the recently developed deep learning-based methods of MMD-ResNet and scGen. The results demonstrated that deepMNN achieved a better or comparable performance in terms of both qualitative analysis using uniform manifold approximation and projection (UMAP) plots and quantitative metrics such as batch and cell entropies, ARI F1 score, and ASW F1 score under various scenarios. Additionally, deepMNN allowed for integrating scRNA-seq datasets with multiple batches in one step. Furthermore, deepMNN ran much faster than the other methods for large-scale datasets. These characteristics of deepMNN made it have the potential to be a new choice for large-scale single-cell gene expression data analysis.