scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles.

Simultaneous measurements of transcriptomic and epigenomic profiles in the same individual cells provide an unprecedented opportunity to understand cell fates. However, effective approaches for the integrative analysis of such data are lacking. Here, we present a single-cell aggregation and integration (scAI) method to deconvolute cellular heterogeneity from parallel transcriptomic and epigenomic profiles. Through iterative learning, scAI aggregates sparse epigenomic signals in similar cells learned in an unsupervised manner, allowing coherent fusion with transcriptomic measurements. Simulation studies and applications to three real datasets demonstrate its capability of dissecting cellular heterogeneity within both transcriptomic and epigenomic layers and understanding transcriptional regulatory mechanisms

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

scMC learns biological variation through the alignment of multiple single-cell genomics datasets.

Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical variation inferred in an unsupervised manner. Application of scMC to both simulated and real datasets from single-cell RNA-seq and ATAC-seq experiments demonstrates its capability of detecting context-shared and context-specific biological signals via accurate alignment

scMC learns biological variation through the alignment of multiple single-cell genomics datasets.

Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical variation inferred in an unsupervised manner. Application of scMC to both simulated and real datasets from single-cell RNA-seq and ATAC-seq experiments demonstrates its capability of detecting context-shared and context-specific biological signals via accurate alignment

DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data.

The emergence of single-cell multiomics data provides unprecedented opportunities to scrutinize the transcriptional regulatory mechanisms controlling cell identity. However, how to use those datasets to dissect the cis-regulatory element (CRE)-to-gene relationships at a single-cell level remains a major challenge. Here, we present DIRECT-NET, a machine-learning method based on gradient boosting, to identify genome-wide CREs and their relationship to target genes, either from parallel single-cell gene expression and chromatin accessibility data or from single-cell chromatin accessibility data alone. By extensively evaluating and characterizing DIRECT-NET's predicted CREs using independent functional genomics data, we find that DIRECT-NET substantially improves the accuracy of inferring CRE-to-gene relationships in comparison to existing methods. DIRECT-NET is also capable of revealing cell subpopulation-specific and dynamic regulatory linkages. Overall, DIRECT-NET provides an efficient tool for predicting transcriptional regulation codes from single-cell multiomics data

Synergistic extraction of copper(II) and zinc(II) with 1-phenyl-3-methyl-4-benzoyl-5-pyrazolone and tri-<i>n</i>-butyl phosphate by two-phase <i>p</i>H titration

108-110The extraction of Cu(II) and Zn(II) with 1-phenyl-3- methyl-4-benzoyl-5-pyrazolone (PMBP) and tri-n-butyl phosphate(TPB) in benzene has been studied by two-phase pH titration. The formation constants of the extracted complexes have been computed with computer program SCTPT. The effects of pH and nature of extractants on the extraction have been investigated

Understanding the Environmentally Sustainable Behavior of Chinese University Students as Tourists: An Integrative Framework

The purpose of this study is to develop a theoretical framework by integrating the value-belief-norm (VBN) theory with environmental awareness in measuring Chinese university students’ environmentally sustainable behavior toward tourism destinations. University students tend to engage in sustainability efforts since their values and beliefs are still being formed. The participants were 301 university students from a university in eastern China. The empirical findings demonstrate that: (1) environmental awareness has positive influences on biospheric value, altruistic value and egoistic value; (2) biospheric value positively predicts the new ecological paradigm (NEP), whereas altruistic and egoistic values do not; (3) the NEP, awareness of consequence and personal norms play an important mediating role. Results indicate that extended VBN can explain students’ environmentally sustainable behavior. This research supports the growth of sustainable tourism and has a number of practical implications for universities and the relevant environmental departments to promote university students’ involvement in sustainable tourism

Fungal Virus, FgHV1-Encoded p20 Suppresses RNA Silencing through Single-Strand Small RNA Binding

Fungal viruses are widespread in fungi infecting plants, insects and animals. High-throughput sequencing has rapidly led to the discovery of fungal viruses. However, the interactive exploration between fungi and viruses is relatively limited. RNA silencing is the fundamental antivirus pathway in fungi. Fusarium graminearum small RNA (sRNA) pattern was regulated by Fusarium graminearum hypovirus 1 (FgHV1) infection, indicating the activation of RNA silencing in virus defense. In this study, we focused on the function of an uncharacterized protein sized at 20 kD (p20) encoded by FgHV1. In the agro-infiltration assay, p20 was identified as a novel fungal RNA silencing suppressor. p20 can block systemic RNA silencing signals besides local RNA silencing suppression. We further elucidated the RNA silencing suppression mechanism of p20. The single-strand sRNA, instead of double-strand sRNA, can be incorporated by p20 in electrophoretic mobility shift assay. p20 binds sRNA originating from virus and non-virus sources in a non-sequence-specific manner. In addition, The F. graminearum 22 and 23-nt sRNA abundance and pathways related to RNA processing and redox regulation were regulated by p20. Our study revealed the first fungal virus-encoded RNA silencing suppressor with sRNA binding capability