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We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

In the version of this article initially published, some reference citations were incorrect. The three references to Jupyter Notebooks should have cited Kluyver et al. instead of Gonzalez et al. The reference to Qiita should have cited Gonzalez et al. instead of Schloss et al. The reference to mothur should have cited Schloss et al. instead of McMurdie & Holmes. The reference to phyloseq should have cited McMurdie & Holmes instead of Huber et al. The reference to Bioconductor should have cited Huber et al. instead of Franzosa et al. And the reference to the biobakery suite should have cited Franzosa et al. instead of Kluyver et al. The errors have been corrected in the HTML and PDF versions of the article.

We present QIIME 2, an opensource microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27295v2 | CC BY 4.0 Open Access | rec:

We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

The One Health concept stresses the ecological relationships between human, animal, and environmental health. Much of the One Health literature to date has examined the transfer of pathogens from animals (e.g., emerging zoonoses) and the environment to humans. The recent rapid development of technology to perform high throughput DNA sequencing has expanded this view to include the study of entire microbial communities. Applying the One Health approach to the microbiome allows for consideration of both pathogenic and non-pathogenic microbial transfer between humans, animals, and the environment. We review recent research studies of such transmission, the molecular and statistical methods being used, and the implications of such microbiome relationships for human health. Our review identified evidence that the environmental microbiome as well as the microbiome of animals in close contact can affect both the human microbiome and human health outcomes. Such microbiome transfer can take place in the household as well as the workplace setting. Urbanization of built environments leads to changes in the environmental microbiome which could be a factor in human health. While affected by environmental exposures, the human microbiome also can modulate the response to environmental factors through effects on metabolic and immune function. Better understanding of these microbiome interactions between humans, animals, and the shared environment will require continued development of improved statistical and ecological modeling approaches. Such enhanced understanding could lead to innovative interventions to prevent and manage a variety of human health and disease states.

Background Stroke is one of the leading complications during continuous flow-left ventricular assist device (CF-LVAD) support. Risk factors have been well described, though less is known regarding treatment and outcomes. We present a large single center experience on stroke outcome and transplant eligibility by stroke subtype and severity in CF-LVAD patients. Methods 301 patients underwent CF-LVAD (266 HeartMate II (HM II) and 35 HeartWare (HVAD)) between 1/1/2008 and 4/1/2015. Stroke was defined as a focal neurological deficit with abnormal neuroimaging. Intracerebral hemorrhage (ICH) definition excluded subdural hematoma and hemorrhagic conversion of an ischemic stroke (IS). Treatment in IS included intra-arterial embolectomy (IAE) when appropriate; treatment in ICH included reversal of coagulopathy. Stroke severity was measured using the National Institutes of Health Stroke Scale (NIHSS). Outcomes were in-hospital mortality and transplant status. Results 40 patients suffered a stroke: 8 ICH (4 HM II, 4 HVAD) and 32 IS (26 HM II, 6 HVAD). Among 8 ICH there were 4 deaths (50%) (NIHSS 18.8±13.7 vs 1.8±1.7 in survivors, p=0.049). Among 32 IS, 12 had hemorrhagic conversion and 5 were treated with IAE. There were 9 deaths (28%) (NIHSS 16.2±10.8 vs 7.0±7.6 in survivors, p=0.011). Among the 32 IS patients, 12 underwent transplant and 1 is awaiting transplant; no ICH patients were transplanted. Conclusions In-hospital mortality after stroke is significantly affected by the initial neurological impairment. Patients with IS appear to benefit the most from in-hospital treatment and often make sufficient recovery to be able to progress to transplant.

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