The coordination of collective and individual solutions in risk-resistant scenarios.

Abstract: Human societies are constantly coping with global risks. In the face of these risks, people typically have two options, that is, to respond together as a whole (collective solution) or to respond independently (individual solution). Based on these two solutions, individuals have a variety of behavioral strategies. On the other hand, various regulatory bodies supported by the population limit people's choices and punish individuals who do not contribute to collective solutions. So with different risks, how do the two solutions, the various individual strategies, and the constraints from regulators affect the group's response to risk? This paper proposes an extended public goods game model involving opportunists and the regulator to explore the effectiveness of collective and individual solutions against risks. The results show that requiring individuals to invest more in the collective solution reduces the group' s success in resisting risk. To improve the group's ability to resist risk, investment in individual solution should be at least no less than that in collective solution. The establishment fund and punishment intensity of the regulatory agency have no significant effect on the success of collective and individual solutions. This inspires us to contemplate the role and measures of various types of authorities in coping with global risks.

Authority or Autonomy? Exploring Interactions between Central and Peer Punishments in Risk-Resistant Scenarios.

Game theory provides a powerful means to study human cooperation and better understand cooperation-facilitating mechanisms in general. In classical game-theoretic models, an increase in group cooperation constantly increases people's gains, implying that individual gains are a continuously varying function of the cooperation rate. However, this is inconsistent with the increasing number of risk-resistant scenarios in reality. A risk-resistant scenario means once a group does not successfully resist the risk, all individuals lose their resources, such as a community coping with COVID-19 and a village resisting a flood. In other words, individuals' gains are segmented about the collaboration rate. This paper builds a risk-resistant model to explore whether punishment still promotes collaboration when people resist risk. The results show that central and peer punishments can both encourage collaboration but with different characteristics under different risk-resistant scenarios. Specifically, central punishment constrains the collaboration motivated by peer punishment regardless of risk, while peer punishment limits the collaboration induced by central punishment only when the risk is high. Our findings provide insights into the balance between peer punishment from public autonomy and central punishment from central governance, and the proposed model paves the way for the development of richer risk-resistant models.

Genomic architecture of fetal central nervous system anomalies using whole-genome sequencing.

Structural anomalies of the central nervous system (CNS) are one of the most common fetal anomalies found during prenatal imaging. However, the genomic architecture of prenatal imaging phenotypes has not yet been systematically studied in a large cohort. Patients diagnosed with fetal CNS anomalies were identified from medical records and images. Fetal samples were subjected to low-pass and deep whole-genome sequencing (WGS) for aneuploid, copy number variation (CNV), single-nucleotide variant (SNV, including insertions/deletions (indels)), and small CNV identification. The clinical significance of variants was interpreted based on a candidate gene list constructed from ultrasound phenotypes. In total, 162 fetuses with 11 common CNS anomalies were enrolled in this study. Primary diagnosis was achieved in 62 cases, with an overall diagnostic rate of 38.3%. Causative variants included 18 aneuploids, 17 CNVs, three small CNVs, and 24 SNVs. Among the 24 SNVs, 15 were novel mutations not reported previously. Furthermore, 29 key genes of diagnostic variants and critical genes of pathogenic CNVs were identified, including five recurrent genes: i.e., TUBA1A, KAT6B, CC2D2A, PDHA1, and NF1. Diagnostic variants were present in 34 (70.8%) out of 48 fetuses with both CNS and non-CNS malformations, and in 28 (24.6%) out of 114 fetuses with CNS anomalies only. Hypoplasia of the cerebellum (including the cerebellar vermis) and holoprosencephaly had the highest primary diagnosis yields (>70%), while only four (11.8%) out of 34 neural tube defects achieved genetic diagnosis. Compared with the control group, rare singleton loss-of-function variants (SLoFVs) were significantly accumulated in the patient cohort.

Single-cell atlas of peripheral blood mononuclear cells from pregnant women.

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.

Helicobacter pylori infection affects the human gastric microbiome, as revealed by metagenomic sequencing.

Helicobacter pylori infection is a prevalent infectious disease, associated with many gastric diseases, including gastritis, gastric ulcer, and gastric cancer. To reveal the characteristics of the gastric microbiome in patients infected with H. pylori, we performed metagenomic shotgun sequencing of stomach swab samples from 96 patients and then conducted metagenomic association analyses between alterations in the gastric microbiome and H. pylori infection status. The overall composition of the gastric microbiota in H. pylori-infected individuals was distinctly different from the negative controls; H. pylori became the dominant species after colonizing the human stomach and significantly decreased the α-diversity of the gastric community (P < 0.05, Wilcoxon rank-sum test). We also identified 6 HPI-associated microbial species (FDR < 0.05, Wilcoxon rank-sum test): Stenotrophomonas maltophilia, Stenotrophomonas unclassified, Chryseobacterium unclassified, Pedobacter unclassified, Variovorax unclassified, and Pseudomonas stutzeri. Furthermore, 55 gastric microbial pathways were enriched in the H. pylori-positive group, whereas only 2 pathways were more abundant in the H. pylori-negative group: dTDP-L-rhamnose biosynthesis and tetrapyrrole biosynthesis (FDR < 0.05, Wilcoxon rank-sum test). Gastritis was not associated with non-H. pylori species in the stomach (P > 0.05, Wilcoxon rank-sum test). This study revealed alterations in gastric microbial taxa and function associated with HPI in the Chinese population, which provides an insight into gastric microbial interactions and their potential role in the pathological process of gastric diseases.

Whole-Genome-Based Helicobacter pylori Geographic Surveillance: A Visualized and Expandable Webtool.

Helicobacter pylori exhibit specific geographic distributions that are related to clinical outcomes. Despite the high infection rate of H. pylori throughout the world, the genetic epidemiology surveillance of H. pylori still needs to be improved. This study used the single nucleotide polymorphisms (SNPs) profiling approach based on whole genome sequencing (WGS) to facilitate genomic population analyses of H. pylori and encourage the dissemination of microbial genotyping strategies worldwide. A total number of 1,211 public H. pylori genomes were downloaded and used to construct the typing tool, named HpTT (H. pylori Typing Tool). Combined with the metadata, we developed two levels of genomic typing, including a continent-scale and a country scale that nested in the continent scale. Results showed that Asia was the largest isolate source in our dataset, while isolates from Europe and Oceania were comparatively more widespread. More specifically, Switzerland and Australia are the main sources of widespread isolates in their corresponding continents. To integrate all the typing information and enable researchers to compare their dataset against the existing global database easily and rapidly, a user-friendly website (https://db.cngb.org/HPTT/) was developed with both genomic typing tools and visualization tools. To further confirm the validity of the website, ten newly assembled genomes were downloaded and tested precisely located on the branch as we expected. In summary, the H. pylori typing tool (HpTT) is a novel genomic epidemiological tool that can achieve high-resolution analysis of genomic typing and visualizing simultaneously, providing insights into the genetic population structure, evolution analysis, and epidemiological surveillance of H. pylori.

deepMNN: Deep Learning-Based Single-Cell RNA Sequencing Data Batch Correction Using Mutual Nearest Neighbors.

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.

Genome sequencing of 320 Chinese children with epilepsy: a clinical and molecular study.

The aim of this study is 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 counselling. 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 analysed. 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 two 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 three with a length <25 kilobases. 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 favourable prognoses. Genome sequencing provides complete genetic diagnosis, thus enabling individualized treatment and genetic counselling for the parents of the patients. Genome sequencing is expected to become the first choice of methods for genetic testing of patients with epilepsy.

Genotype-phenotype analysis in Mowat-Wilson syndrome associated with two novel and two recurrent ZEB2 variants.

The current study aimed to analyze the genotype-phenotype relationship in patients with variants of zinc finger E box-binding homeobox 2 (ZEB2), which is a gene encoding a homeobox transcription factor known to be mutated in Mowat Wilson syndrome (MWS). Whole genome sequencing (WGS) was performed in 530 children, of whom 333 had epilepsy with or without developmental delay and 197 developmental delay alone. Pathogenic variants were identified and verified using Sanger sequencing, and the disease phenotypes of the corresponding patients were analyzed for features of MWS. WGS was performed in 333 children with epilepsy, with or without developmental delays or intellectual disability and 197 children with developmental delay alone. A total of 4 unrelated patients were indicated to be heterozygous for truncating mutations in ZEB2. A total of three of these were nonsense mutations (novel Gln1072X and recurrent Trp97X and Arg921X), and one was a frameshift mutation (novel Val357Aspfs*15). The mutations have occurred de novo as confirmed by Sanger sequence comparisons in patients and their parents. All 4 patients exhibited signs of MWS, whereby the severity increased the closer a mutation was located to the amino terminus of the protein. The results suggest that the clinical outcome in MWS depends on the relative position of the truncation in the ZEB2 gene. A number of interpretations of this genotype/phenotype association are discussed in the present study.

Novel compound heterozygous variants in the STIL gene identified in a Chinese family with presentation of foetal microcephaly.

Primary microcephaly 7 (MCPH7) is an autosomal recessive human neurodevelopmental disorder characterized by microcephaly, sloping forehead, and prominent midface. The STIL gene encodes a protein that regulates the mitotic spindle checkpoint. STIL is the pathogenic gene of MCPH7. Although more than 25 genes have been reported to cause MCPH, many patients lack a molecular diagnosis. The clinical manifestations and genetic factors of MCPH7 remain to be revealed. This research reported two consecutive microcephalic foetuses from unaffected parents. Prenatal ultrasound examination and pre- and postnatal MRI studies were performed. Whole-genome sequencing (WGS) was performed using blood derived from the umbilical cord, and variants were confirmed by Sanger sequencing on the parents. Ultrasound examination showed that the two foetuses suffered primary microcephaly. Using the WGS approach, novel compound heterozygous variants in STIL (c.2344_2347delTTGC, p. Leu782Thrfs*2 in exon 13; c.3838C > T, p. Arg1280Cys in exon 17) were identified in two foetuses with MCPH7. The MRI results of the two siblings were quite similar. Postnatal MRI confirmed the ultrasound and prenatal examinations. The two foetuses had typical microcephaly. Ultrasound and MRI showed that the two foetuses had a thick skull plate, significantly reduced bilateral frontal lobe, upward rotated cerebellum vermis, and dilated fourth ventricle. Our findings have important implications for prenatal diagnosis and genetic counselling for any patients with MCPH7. We extend both the mutational spectrum in the STIL gene and the clinical spectrum of MCPH7.

Genetic diagnosis of acute aortic dissection in South China Han population using next-generation sequencing.

Acute aortic dissection (AAD) is a clinically "silent," but emergent and life-threatening cardiovascular disease, and hereditary factors play an important etiologic role in the development of AAD. The purposes of this study are to definitize the diagnostic yield of 59 AAD patients, investigate the molecular pathological spectrum of AAD by NGS, and explore the future preclinical prospects of genetic diagnosis on AAD high-risk groups. We performed next-generation sequencing (NGS) based on screening of the 69 currently aortic dissections/aneurysms-associated genes on 59 sporadic AAD samples from South China. A Kaplan-Meier survival curve was constructed to compare the event-free survival depending on variant number. Overall, 67 variants were detected in 39 patients, among which 4 patients were identified with pathogenic variants and 13 patients were diagnosed with likely pathogenic variants. Seventeen genotype positive patients were identified in aggregate, and the diagnostic yield of our study is 28.8%. All genotype-positive variants were distributed in 11 genes, FBN1 variants were in the largest number among genotype-positive variants, which were detected for 4 times, ACTA2 for 3 times, ABCC6 and TGFBR1 twice, and NOS3, MYLK, XYLT1, TIMP4, TGFBR2, CNTN3, and PON1 once. Individuals with three or more variants showed shorter mean event-free survival than patients with fewer variants. Our observations broaden the genetic pathological spectrum of AAD. Furthermore, our research uncovered two susceptibility genes FBN1 and ACTA2 for Stanford type A AAD patients. Finally, our study concluded that the number of variants an individual harbored was an important consideration in risk stratification for individualized prediction and disease diagnosis.

RNA­sequence analysis of samples from patients with idiopathic adhesive capsulitis.

The present study aimed to investigate idiopathic adhesive capsulitis (frozen shoulder), to gain insights on its pathogenesis, diagnosis and therapeutic targets. Using RNA­sequencing (seq), the present study investigated differentially expressed genes (DEGs) in five samples from five idiopathic adhesive capsulitis patients and two samples from two acromioclavicular dislocation patients, without idiopathic adhesive capsulitis. The DEGs were analyzed using the following tools: Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathways analysis and protein­protein interaction analysis. A total of 188 DEGs were identified and it was observed that 150 of these were upregulated and 38 were downregulated. It was hypothesized that various nutrient associated proteins may be associated with idiopathic adhesive capsulitis. The Matrix metalloproteinase family of proteins (MMPs), may exhibit a key role in the formation of abnormal collagen cross­links. Overall, the comprehensive and detailed information collected in the present study, regarding idiopathic adhesive capsulitis, may provide a foundation on which in­depth follow­up experiments may be based, aimed at identifying novel strategies for treatment of this disease.

Syntool: A Novel Region-Based Intolerance Score to Single Nucleotide Substitution for Synonymous Mutations Predictions Based on 123,136 Individuals.

BACKGROUND: Synonymous mutation is the single nucleotide change that does not cause an amino acid change but can affect the rate and efficiency of translation. So recent increase in our knowledge has revealed a substantial contribution of synonymous mutations to human disease risk and other complex traits. Nevertheless, there are still rarely synonymous mutation prediction methods. METHODS: Nonsynonymous and synonymous coding SNPs show similar likelihood and effect size of human disease association. Here we defined synonymous and missense variation as single nucleotide substitution variation. And then we evaluated the intolerance of genic transcripts to single nucleotide substitution variation based on gnomAD 123136 individuals. After regressing all variations on common variations, we defined residuals of regression model as every genomics region intolerance scores. RESULTS: We constructed a total of 24799 nonoverlapped region-based intolerance score by their intolerance to single nucleotide substitution variation (Syntool). The results show that Syntool score can discriminate synonymous disease causing mutations in Human Gene Mutation Database (HGMD Professional) and ClinVar database much better than others. Taken together, this study provides a novel prediction system for synonymous mutations, called Syntool, which could be helpful in identifying candidate synonymous disease causing mutations.

Localized Ambient Solidity Separation Algorithm Based Computer User Segmentation.

Most of popular clustering methods typically have some strong assumptions of the dataset. For example, the k-means implicitly assumes that all clusters come from spherical Gaussian distributions which have different means but the same covariance. However, when dealing with datasets that have diverse distribution shapes or high dimensionality, these assumptions might not be valid anymore. In order to overcome this weakness, we proposed a new clustering algorithm named localized ambient solidity separation (LASS) algorithm, using a new isolation criterion called centroid distance. Compared with other density based isolation criteria, our proposed centroid distance isolation criterion addresses the problem caused by high dimensionality and varying density. The experiment on a designed two-dimensional benchmark dataset shows that our proposed LASS algorithm not only inherits the advantage of the original dissimilarity increments clustering method to separate naturally isolated clusters but also can identify the clusters which are adjacent, overlapping, and under background noise. Finally, we compared our LASS algorithm with the dissimilarity increments clustering method on a massive computer user dataset with over two million records that contains demographic and behaviors information. The results show that LASS algorithm works extremely well on this computer user dataset and can gain more knowledge from it.