Estimating Grid-Induced Errors in CFD by Discrete-Error-Transport Equations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77113/1/AIAA-2004-656-838.pd

Identification of Complex Rumen Microbiome Interaction Within Diverse Functional Niches as Mechanisms Affecting the Variation of Methane Emissions in Bovine

A network analysis including relative abundances of all ruminal microbial genera (archaea, bacteria, fungi, and protists) and their genes was performed to improve our understanding of how the interactions within the ruminal microbiome affects methane emissions (CH 4). Metagenomics and CH 4 data were available from 63 bovines of a two-breed rotational cross, offered two basal diets. Co-abundance network analysis revealed 10 clusters of functional niches. The most abundant hydrogenotrophic Methanobacteriales with key microbial genes involved in methanogenesis occupied a different functional niche (i.e., "methanogenesis" cluster) than methylotrophic Methanomassiliicoccales (Candidatus Methanomethylophylus) and acetogens ( Blautia). Fungi and protists clustered together and other plant fiber degraders like Fibrobacter occupied a seperate cluster. A Partial Least Squares analysis approach to predict CH 4 variation in each cluster showed the methanogenesis cluster had the best prediction ability (57.3%). However, the most important explanatory variables in this cluster were genes involved in complex carbohydrate degradation, metabolism of sugars and amino acids and Candidatus Azobacteroides carrying nitrogen fixation genes, but not methanogenic archaea and their genes. The cluster containing Fibrobacter, isolated from other microorganisms, was positively associated with CH 4 and explained 49.8% of its variability, showing fermentative advantages compared to other bacteria and fungi in providing substrates (e.g., formate) for methanogenesis. In other clusters, genes with enhancing effect on CH 4 were related to lactate and butyrate ( Butyrivibrio and Pseudobutyrivibrio) production and simple amino acids metabolism. In comparison, ruminal genes negatively related to CH 4 were involved in carbohydrate degradation via lactate and succinate and synthesis of more complex amino acids by γ-Proteobacteria. When analyzing low- and high-methane emitters data in separate networks, competition between methanogens in the methanogenesis cluster was uncovered by a broader diversity of methanogens involved in the three methanogenesis pathways and larger interactions within and between communities in low compared to high emitters. Generally, our results suggest that differences in CH 4 are mainly explained by other microbial communities and their activities rather than being only methanogens-driven. Our study provides insight into the interactions of the rumen microbial communities and their genes by uncovering functional niches affecting CH 4, which will benefit the development of efficient CH 4 mitigation strategies

Visualization and analysis of RNA-Seq assembly graphs.

RNA-Seq is a powerful transcriptome profiling technology enabling transcript discovery and quantification. Whilst most commonly used for gene-level quantification, the data can be used for the analysis of transcript isoforms. However, when the underlying transcript assemblies are complex, current visualization approaches can be limiting, with splicing events a challenge to interpret. Here, we report on the development of a graph-based visualization method as a complementary approach to understanding transcript diversity from short-read RNA-Seq data. Following the mapping of reads to a reference genome, a read-to-read comparison is performed on all reads mapping to a given gene, producing a weighted similarity matrix between reads. This is used to produce an RNA assembly graph, where nodes represent reads and edges similarity scores between them. The resulting graphs are visualized in 3D space to better appreciate their sometimes large and complex topology, with other information being overlaid on to nodes, e.g. transcript models. Here we demonstrate the utility of this approach, including the unusual structure of these graphs and how they can be used to identify issues in assembly, repetitive sequences within transcripts and splice variants. We believe this approach has the potential to significantly improve our understanding of transcript complexity

Stem cell-derived porcine macrophages as a new platform for studying host-pathogen interactions

BACKGROUND: Infectious diseases of farmed and wild animals pose a recurrent threat to food security and human health. The macrophage, a key component of the innate immune system, is the first line of defence against many infectious agents and plays a major role in shaping the adaptive immune response. However, this phagocyte is a target and host for many pathogens. Understanding the molecular basis of interactions between macrophages and pathogens is therefore crucial for the development of effective strategies to combat important infectious diseases. RESULTS: We explored how porcine pluripotent stem cells (PSCs) can provide a limitless in vitro supply of genetically and experimentally tractable macrophages. Porcine PSC-derived macrophages (PSCdMs) exhibited molecular and functional characteristics of ex vivo primary macrophages and were productively infected by pig pathogens, including porcine reproductive and respiratory syndrome virus (PRRSV) and African swine fever virus (ASFV), two of the most economically important and devastating viruses in pig farming. Moreover, porcine PSCdMs were readily amenable to genetic modification by CRISPR/Cas9 gene editing applied either in parental stem cells or directly in the macrophages by lentiviral vector transduction. CONCLUSIONS: We show that porcine PSCdMs exhibit key macrophage characteristics, including infection by a range of commercially relevant pig pathogens. In addition, genetic engineering of PSCs and PSCdMs affords new opportunities for functional analysis of macrophage biology in an important livestock species. PSCs and differentiated derivatives should therefore represent a useful and ethical experimental platform to investigate the genetic and molecular basis of host-pathogen interactions in pigs, and also have wider applications in livestock. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12915-021-01217-8

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

A feasibility study for NOn-Traditional providers to support the management of Elderly People with Anxiety and Depression: the NOTEPAD study Protocol

BACKGROUND: Anxiety and depression are common among older people, with up to 20% reporting such symptoms, and the prevalence increases with co-morbid chronic physical health problems. Access to treatment for anxiety and depression in this population is poor due to a combination of factors at the level of patient, practitioner and healthcare system. There is evidence to suggest that older people with anxiety and/or depression may benefit both from one-to-one interventions and group social or educational activities, which reduce loneliness, are participatory and offer some activity. Non-traditional providers (support workers) working within third-sector (voluntary) organisations are a valuable source of expertise within the community but are under-utilised by primary care practitioners. Such a resource could increase access to care, and be less stigmatising and more acceptable for older people. METHODS: The study is in three phases and this paper describes the protocol for phase III, which will evaluate the feasibility of recruiting general practices and patients into the study, and determine whether support workers can deliver the intervention to older people with sufficient fidelity and whether this approach is acceptable to patients, general practitioners and the third-sector providers. Phase III of the NOTEPAD study is a randomised controlled trial (RCT) that is individually randomised. It recruited participants from approximately six general practices in the UK. In total, 100 participants aged 65 years and over who score 10 or more on PHQ9 or GAD7 for anxiety or depression will be recruited and randomised to the intervention or usual general practice care. A mixed methods approach will be used and follow-up will be conducted 12 weeks post-randomisation. DISCUSSION: This study will inform the design and methods of a future full-scale RCT. TRIAL REGISTRATION: ISRCTN, ID: ISRCTN16318986 . Registered 10 November 2016. The ISRCTN registration is in line with the World Health Organization Trial Registration Data Set. The present paper represents the original version of the protocol. Any changes to the protocol will be communicated to ISRCTN

Deficient Induction Response in a Xenopus Nucleocytoplasmic Hybrid

Defects in induction signaling and response underlie the nucleocytoplasmic incompatibility between two evolutionarily distant frog species, while specific treatments partially restore this response in explants and whole embryos