Open access, open data, open source and other open scholarship practices are growing in popularity and necessity. However, widespread adoption of these practices has not yet been achieved. One reason is that researchers are uncertain about how sharing their work will affect their careers. We review literature demonstrating that open research is associated with increases in citations, media attention, potential collaborators, job opportunities and funding opportunities. These findings are evidence that open research practices bring significant benefits to researchers relative to more traditional closed practices.DOI: http://dx.doi.org/10.7554/eLife.16800.001

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.

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.

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Conventional spectrometers are limited by trade-offs set by size, cost, signal-to-noise ratio (SNR), and spectral resolution. Here, we demonstrate a deep learning-based spectral reconstruction framework, using a compact and low-cost on-chip sensing scheme that is not constrained by the design trade-offs inherent to grating-based spectroscopy. The system employs a plasmonic spectral encoder chip containing 252 different tiles of nanohole arrays fabricated using a scalable and low-cost imprint lithography method, where each tile has a unique geometry and, thus, a unique optical transmission spectrum. The illumination spectrum of interest directly impinges upon the plasmonic encoder, and a CMOS image sensor captures the transmitted light, without any lenses, gratings, or other optical components in between, making the entire hardware highly compact, light-weight and field-portable. A trained neural network then reconstructs the unknown spectrum using the transmitted intensity information from the spectral encoder in a feed-forward and non-iterative manner. Benefiting from the parallelization of neural networks, the average inference time per spectrum is ~28 µs, which is orders of magnitude faster compared to other computational spectroscopy approaches. When blindly tested on unseen new spectra (N = 14,648) with varying complexity, our deep-learning based system identified 96.86% of the spectral peaks with an average peak localization error, bandwidth error, and height error of 0.19 nm, 0.18 nm, and 7.60%, respectively. This system is also highly tolerant to fabrication defects that may arise during the imprint lithography process, which further makes it ideal for applications that demand cost-effective, field-portable and sensitive high-resolution spectroscopy tools.

Self-renewal and differentiation are essential for intestinal epithelium absorptive functioning and adaptation to pathological states such as short gut syndrome, ulcers, and inflammatory bowel disease. The rodent Slfn3 and its human analog Slfn12 are critical in regulating intestinal epithelial differentiation. We sought to characterize intestinal function in Slfn3 knockout (KO) mice. Male and female pair-fed Slfn3KO mice gained less weight with decreased food efficiency than wild type (WT) mice, with more pronounced effects in females. RNA sequencing performed on intestinal mucosa of Slfn3KO and WT mice showed gene ontology decreases in cell adhesion molecule signaling, tumor necrosis factor receptor binding, and adaptive immune cell proliferation/functioning genes in Slfn3KO mice, with greater effects in females. qPCR analysis of fatty acid metabolism genes, Pla2g4c, Pla2g2f, and Cyp3c55 revealed an increase in Pla2g4c, and a decrease in Pla2g2f in Slfn3KO females. Additionally, adipogenesis genes, Fabp4 and Lpl were decreased and ketogenesis gene Hmgcs2 was increased in female Slfn3KO mice. Sequencing did not reveal significant changes in differentiation markers, so qPCR was utilized. Slfn3KO tended to have decreased expression of intestinal differentiation markers sucrase isomaltase, dipeptidyl peptidase 4, villin 1, and glucose transporter 1 (Glut1) vs. WT males, although these trends did not achieve statistical significance unless data from several markers was pooled. Differentiation markers, Glut2 and sodium-glucose transporter 1 (SGLT1), did show statistically significant sex-dependent differences. Glut2 mRNA was reduced in Slfn3KO females, while SGLT1 increased in Slfn3KO males. Notch2 and Cdx2 were only increased in female Slfn3KO mice. Although Slfn3KO mice gain less weight and decreased food efficiency, their biochemical phenotype is more subtle and suggests a complex interplay between gender effects, Slfn3, and another regulatory pathway yet to be identified that compensates for the chronic loss of Slfn3.

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