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Background Next-generation sequencing platforms allow us to sequence millions of small fragments of DNA simultaneously, revolutionizing cancer research. Sequence analysis has revealed that cancer driver genes operate across multiple intricate pathways and networks with mutations often occurring in a mutually exclusive pattern. Currently, low-frequency mutations are understudied as cancer-relevant genes, especially in the context of networks. Results Here we describe a tool, gcMECM, that enables us to visualize the functionality of mutually exclusive genes in the subnetworks derived from mutation associations, gene–gene interactions, and graph clustering. These subnetworks have revealed crucial biological components in the canonical pathway, especially those mutated at low frequency. Examining the subnetwork, and not just the impact of a single gene, significantly increases the statistical power of clinical analysis and enables us to build models to better predict how and why cancer develops. Conclusions gcMECM uses a computationally efficient and scalable algorithm to identify subnetworks in a canonical pathway with mutually exclusive mutation patterns and distinct biological functions.

Social inclusion in climate governance is related to social justice and inclusive climate justice explicitly aims to open up climate policy and politics to a broader range of actors and voices, especially those most exposed to climate‐related injustice. This article employs qualitative research methods to comprehensively examine the issue of social inclusion in the context of local government‐led climate governance in J County, Zhejiang province, China. The study finds that the climate governance in J County demonstrates a certain degree of social inclusion in terms of participation by local farmers and benefit distribution. However, this social inclusion has a hidden fragility: It is limited and unstable. The limited social inclusion is manifested in the fact that, throughout the entire process, bamboo farmers were unable to participate due to their lack of a comprehensive understanding of the climate governance action plan, and the distribution of climate governance benefits is characterised by a lack of transparency in the design process and uncertainty regarding potential benefits. The unstable social inclusion is manifested in the great differences in the  environmental governance actions of J County in different periods, especially regarding public participation and benefit distribution. Fundamentally, this is mainly due to the significant influence of China’s unique top‐down performance evaluation system on local government‐led climate governance actions in J County. Social inclusion in local government‐led environmental governance may again be marginalised if the top‐down performance evaluation indicators faced by local governments change in the future.

The Cancer Immune Monitoring and Analysis Centers - Cancer Immunologic Data Center (CIMAC-CIDC) network aims to improve cancer immunotherapy by providing harmonized molecular assays and standardized bioinformatics analysis. In response to evolving bioinformatics standards and the migration of the CIDC to the National Cancer Institute (NCI), we undertook the enhancement of the CIDC's extant whole exome sequencing (WES) and RNA sequencing (RNA-Seq) pipelines. Leveraging open-source tools and cloud-based technologies, we implemented modular workflows using Snakemake and Docker for efficient deployment on the Google Cloud Platform (GCP). Benchmarking analyses demonstrate improved reproducibility, precision, and recall across validated truth sets for variant calling, transcript quantification, and fusion detection. This work establishes a scalable framework for harmonized multi-omic analyses, ensuring the continuity and reliability of bioinformatics workflows in multi-site clinical research aimed at advancing cancer biomarker discovery and personalized medicine.

Evapotranspiration (ET) is one of the most important factors in urban water and energy regimes. Because of the extremely high spatial heterogeneity of urban area, accurately measuring ET using conventional methods remains a challenge due to their fetch requirements and low spatial resolution. The goals of this study were to investigate the characteristics of urban ET and its main influencing factors and subsequently to improve a fetch-free, high spatial resolution method for urban ET estimation. The Bowen ratio and the ‘three-temperature model (3T model) + infrared remote sensing (RS)’ methods were used for these purposes. The results of this study are listed in the following lines. (1) Urban ET is mainly affected by solar radiation and the effects of air humidity, wind velocity, and air temperature are very weak; (2) The average daily, monthly, and annual ETs of the urban lawn are 2.70, 60–100, and 990 mm, respectively, which are obvious compared with other landscapes; (3) The ratio of ET to precipitation is 0.65 in the wet season and 2.6 in the dry season, indicating that most of the precipitation is evaporated; (4) The fetch-free approach of ‘3T model + infrared RS’ is verified to be an accurate method for measuring urban ET and it agrees well with the Bowen ratio method (R2 is over 0.93 and the root mean square error is less than 0.04 mm h−1); (5) The spatial heterogeneity of urban ET can also be accurately estimated by the proposed approach. These results are helpful for improving the accuracy of ET estimation in urban areas and are useful for urban water and environmental planning and management.

Background Copy number variation (CNV) is a key genetic characteristic for cancer diagnostics and can be used as a biomarker for the selection of therapeutic treatments. Using data sets established in our previous study, we benchmark the performance of cancer CNV calling by six most recent and commonly used software tools on their detection accuracy, sensitivity, and reproducibility. In comparison to other orthogonal methods, such as microarray and Bionano, we also explore the consistency of CNV calling across different technologies on a challenging genome. Results While consistent results are observed for copy gain, loss, and loss of heterozygosity (LOH) calls across sequencing centers, CNV callers, and different technologies, variation of CNV calls are mostly affected by the determination of genome ploidy. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we establish a high confident CNV call set for the reference cancer cell line (HCC1395). Conclusions NGS technologies and current bioinformatics tools can offer reliable results for detection of copy gain, loss, and LOH. However, when working with a hyper-diploid genome, some software tools can call excessive copy gain or loss due to inaccurate assessment of genome ploidy. With performance matrices on various experimental conditions, this study raises awareness within the cancer research community for the selection of sequencing platforms, sample preparation, sequencing coverage, and the choice of CNV detection tools.

Cancer cells experience endoplasmic reticulum (ER) stress due to activated oncogenes and conditions of nutrient deprivation and hypoxia. The ensuing unfolded protein response (UPR) is executed by ATF6, IRE1 and PERK pathways. Adaptation to mild ER stress promotes tumor cell survival and aggressiveness. Unmitigated ER stress, however, will result in cell death and is a potential avenue for cancer therapies. Because of this yin-yang nature of ER stress, it is imperative that we fully understand the mechanisms and dynamics of the UPR and its contribution to the complexity of tumor biology. The PERK pathway inhibits global protein synthesis while allowing translation of specific mRNAs, such as the ATF4 transcription factor. Using thapsigargin and tunicamycin to induce acute ER stress, we identified the transcription factor C/EBPδ ( CEBPD ) as a mediator of PERK signaling to secretion of tumor promoting chemokines. In melanoma and breast cancer cell lines, PERK mediated early induction of C/EBPδ through ATF4-independent pathways that involved at least in part Janus kinases and the STAT3 transcription factor. Transcriptional profiling revealed that C/EBPδ contributed to 20% of thapsigargin response genes including chaperones, components of ER-associated degradation, and apoptosis inhibitors. In addition, C/EBPδ supported the expression of the chemokines CXCL8 (IL-8) and CCL20, which are known for their tumor promoting and immunosuppressive properties. With a paradigm of short-term exposure to thapsigargin, which was sufficient to trigger prolonged activation of the UPR in cancer cells, we found that conditioned media from such cells induced cytokine expression in myeloid cells. In addition, activation of the CXCL8 receptor CXCR1 during thapsigargin exposure supported subsequent sphere formation by cancer cells. Taken together, these investigations elucidated a novel mechanism of ER stress-induced transmissible signals in tumor cells that may be particularly relevant in the context of pharmacological interventions.

Neural stem cells are self-renewing, multipotent and undifferentiated precursors that retain the capacity for differentiation into both glial (astrocytes and oligodendrocytes) and neuronal lineages. Neural stem cells offer cell-based therapies for neurological disorders such as Alzheimer's disease, Parkinson's disease, Huntington's disease and spinal cord injuries. However, their cellular behavior is poorly understood. MicroRNAs (miRNAs) are a class of small noncoding RNAs involved in cell development, proliferation and differentiation through regulating gene expression at post-transcriptional level. The role of miR-381 in the development of neural stem cells remains unknown. In this study, we showed that overexpression of miR-381 promoted neural stem cells proliferation. It induced the neural stem cells differentiation to neurons and inhibited their differentiation to astrocytes. Furthermore, we identified HES1 as a direct target of miR-381 in neural stem cells. Moreover, re-expression of HES1 impaired miR-381-induced promotion of neural stem cells proliferation and induce neural stem cells differentiation to neurons. In conclusion, miR-381 played important role in neural stem cells proliferation and differentiation.

Planar micro-supercapacitors show great potential as the energy storage unit in miniaturized electronic devices. Asymmetric structures have been widely inves- tigated in micro-supercapacitors, and carbon-based materials are commonly applied in the electrodes. To integrate different metal oxides in both electrodes in micro-supercapacitors, the critical challenge is the pairing of different faradic metal oxides. Herein, we propose a strategy of matching the voltage and capadtance of two faradic materials that are fully integrated into one high-performance asymmetric micro-supercapacitor by a facile and controllable fabrication process. The fabricated micro-supercapacitors employ MnO2 as the positive active material and Fe203 as the negative active material, respectively. The planar asymmetric micro-supercapacitors possess a high capacitance of 60 F-cm-3, a high energy density of 12 mW.h.cm-3, and a broad operation voltage range up to 1.2 V.

Background Gene expression is regulated by transcription factors, cofactors, and epigenetic mechanisms. Coexpressed genes indicate similar functional categories and gene networks. Detecting gene-gene coexpression is important for understanding the underlying mechanisms of cellular function and human diseases. A common practice of identifying coexpressed genes is to test the correlation of expression in a set of genes. In single-cell RNA-seq data, an important challenge is the abundance of zero values, so-called “dropout”, which results in biased estimation of gene-gene correlations for downstream analyses. In recent years, efforts have been made to recover coexpressed genes in scRNA-seq data. Here, our goal is to detect coexpressed gene pairs to reduce the “dropout” effect in scRNA-seq data using a novel graph-based k-partitioning method by merging transcriptomically similar cells. Results We observed that the number of zero values was reduced among the merged transcriptomically similar cell clusters. Motivated by this observation, we leveraged a graph-based algorithm and develop an R package, scCorr, to recover the missing gene-gene correlation in scRNA-seq data that enables the reliable acquisition of cluster-based gene-gene correlations in three independent scRNA-seq datasets. The graphically partitioned cell clusters did not change the local cell community. For example, in scRNA-seq data from peripheral blood mononuclear cells (PBMCs), the gene-gene correlation estimated by scCorr outperformed the correlation estimated by the nonclustering method. Among 85 correlated gene pairs in a set of 100 clusters, scCorr detected 71 gene pairs, while the nonclustering method detected only 4 pairs of a dataset from PBMCs. The performance of scCorr was comparable to those of three previously published methods. As an example of downstream analysis using scCorr, we show that scCorr accurately identified a known cell type (i.e., CD4+ T cells) in PBMCs with a receiver operating characteristic area under the curve of 0.96. Conclusions Our results demonstrate that scCorr is a robust and reliable graph-based method for identifying correlated gene pairs, which is fundamental to network construction, gene-gene interaction, and cellular omic analyses. scCorr can be quickly and easily implemented to minimize zero values in scRNA-seq analysis and is freely available at https://github.com/CBIIT-CGBB/scCorr .

Epigenome-wide association studies (EWAS) have identified CpG sites associated with HIV infection in blood cells in bulk, which offer limited knowledge of cell-type specific methylation patterns associated with HIV infection. In this study, we aim to identify differentially methylated CpG sites for HIV infection in immune cell types: CD4+ T-cells, CD8+ T-cells, B cells, Natural Killer (NK) cells, and monocytes. Applying a computational deconvolution method, we performed a cell-type based EWAS for HIV infection in three independent cohorts (Ntotal = 1,382). DNA methylation in blood or in peripheral blood mononuclear cells (PBMCs) was profiled by an array-based method and then deconvoluted by Tensor Composition Analysis (TCA). The TCA-computed CpG methylation in each cell type was first benchmarked by bisulfite DNA methylation capture sequencing in a subset of the samples. Cell-type EWAS of HIV infection was performed in each cohort separately and a meta-EWAS was conducted followed by gene set enrichment analysis. The meta-analysis unveiled a total of 2,021 cell-type unique significant CpG sites for five inferred cell types. Among these inferred cell-type unique CpG sites, the concordance rate in the three cohorts ranged from 96% to 100% in each cell type. Cell-type level meta-EWAS unveiled distinct patterns of HIV-associated differential CpG methylation, where 74% of CpG sites were unique to individual cell types (false discovery rate, FDR <0.05). CD4+ T-cells had the largest number of unique HIV-associated CpG sites (N = 1,624) compared to any other cell type. Genes harboring significant CpG sites are involved in immunity and HIV pathogenesis (e.g. CD4+ T-cells: NLRC5, CX3CR1, B cells: IFI44L, NK cells: IL12R, monocytes: IRF7), and in oncogenesis (e.g. CD4+ T-cells: BCL family, PRDM16, monocytes: PRDM16, PDCD1LG2). HIV-associated CpG sites were enriched among genes involved in HIV pathogenesis and oncogenesis that were enriched among interferon-α and -γ, TNF-α, inflammatory response, and apoptotic pathways. Our findings uncovered computationally inferred cell-type specific modifications in the host epigenome for people with HIV that contribute to the growing body of evidence regarding HIV pathogenesis.