The future of post-reproductive health: The role of the Internet, the Web, information provision and access

The World Wide Web celebrated its 25th birthday in 2014. In those 25 years, the Web has evolved from static websites (Web 1.0) to a highly complex dynamic system (Web 3.0) with health information processing one of the primary uses. Until now, the western biomedical paradigm has been effective in delivering healthcare, but this model is not positioned to tackle the complex challenges facing healthcare today. These challenges have arisen by increasing healthcare demands across the world, exacerbated by an ageing population, increased lifespan and chronic conditions. To meet these needs, a ‘biopsychosocial’ shift from reactive to proactive health is necessary with a patient-centric emphasis (personalised, preventative, participatory and predictive) that includes ‘gender-specific medicine’. The management of the menopause, part of post-reproductive health, requires a life-course approach as it provides a framework for achieving a women’s preferred health outcome. Surveys from www.menopausematters.co.uk have consistently shown that women do not feel informed enough to make decisions regarding Hormone Replacement Therapy and alternative therapies. Health professionals must meet this challenge. The recently published National Institute for Health and Care Excellence guidance on the diagnosis and management of the menopause highlights the need for tailored information provision. The Internet underpinned by the academic disciplines of Health Web Science and Medicine 2.0 has potential to facilitate this shift to biopsychosocial medicine and tailored information within a life-course framework. The concept of Health Web Observatories and their potential benefit to a life-course approach using tools such as www.managemymenopause.co.uk is discussed

SAKE (Single-cell RNA-Seq Analysis and Klustering Evaluation) Identifies Markers of Resistance to Targeted BRAF Inhibitors in Melanoma Cell Populations

Single-cell RNA-Seq’s (scRNA-Seq) unprecedented cellular resolution at a genome wide scale enables us to address questions about cellular heterogeneity that are inaccessible using methods that average over bulk tissue extracts. However, scRNA-Seq datasets also present additional challenges such as high transcript dropout rates, stochastic transcription events, and complex population substructures. Here, we present SAKE (Single-cell RNA-Seq Analysis and Klustering Evaluation): a robust method for scRNA-Seq analysis that provides quantitative statistical metrics at each step of the scRNA-Seq analysis pipeline including metrics for: the determination of the number of clusters present, the likelihood that each cell belongs to a given cluster, and the association of each gene marker in determining cluster membership. Comparing SAKE to multiple single-cell analysis methods shows that most methods perform similarly across a wide range cellular contexts, with SAKE outperforming these methods in the case of large complex populations. We next applied the SAKE algorithms to identify drug-resistant cellular populations as human melanoma cells respond to targeted BRAF inhibitors. Single-cell RNA-Seq data from both the Fluidigm C1 and 10x Genomics platforms were analyzed with SAKE to dissect this problem at multiple scales. Data from both platforms indicate that BRAF inhibitor resistant cells can emerge from rare populations already present before drug application, with SAKE identifying both novel and known markers of resistance. In addition, we compare integrated genomic and transcriptomic markers to show that resistance can arise stochastically within multiple distinct clonal populations

Combining natural language processing and metabarcoding to reveal pathogen-environment associations.

Cryptococcus neoformans is responsible for life-threatening infections that primarily affect immunocompromised individuals and has an estimated worldwide burden of 220,000 new cases each year-with 180,000 resulting deaths-mostly in sub-Saharan Africa. Surprisingly, little is known about the ecological niches occupied by C. neoformans in nature. To expand our understanding of the distribution and ecological associations of this pathogen we implement a Natural Language Processing approach to better describe the niche of C. neoformans. We use a Latent Dirichlet Allocation model to de novo topic model sets of metagenetic research articles written about varied subjects which either explicitly mention, inadvertently find, or fail to find C. neoformans. These articles are all linked to NCBI Sequence Read Archive datasets of 18S ribosomal RNA and/or Internal Transcribed Spacer gene-regions. The number of topics was determined based on the model coherence score, and articles were assigned to the created topics via a Machine Learning approach with a Random Forest algorithm. Our analysis provides support for a previously suggested linkage between C. neoformans and soils associated with decomposing wood. Our approach, using a search of single-locus metagenetic data, gathering papers connected to the datasets, de novo determination of topics, the number of topics, and assignment of articles to the topics, illustrates how such an analysis pipeline can harness large-scale datasets that are published/available but not necessarily fully analyzed, or whose metadata is not harmonized with other studies. Our approach can be applied to a variety of systems to assert potential evidence of environmental associations

The third international hackathon for applying insights into large-scale genomic composition to use cases in a wide range of organisms

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metabarcode | metagenomic | metagenetic | microbiome enrichment toolkit

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molikd/VSV_W8_Analysis: Pre-release

Pre-alpha release of scripts required for publishing of "Vesicular stomatitis virus elicits early transcriptomic response in Culicoides sonorensis cells" Contains some of the Scripts Edward Bird Wrote for this analysis

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met analysis julia softwar

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