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PubMed Central reuse of GEO datasets deposited in 2007This is the raw data behind the analysis. It contains one row for every mention of a 2007 GEO dataset in PubMed Central. Each row identifies the mentioned GEO dataset, the PubMed Central article that mentions the dataset's accession number, whether the authors of the dataset and the attributing article overlap, and whether this is considered an instance of third-party data reuse.PMC_reuse_of_2007_GEO_datasets.csvAggregate Table DataAggregate table data behind the figures and results in the README associated with the main dataset. Includes Baseline metrics used for extrapolating PubMed Central (PMC) results to PubMed, Number of mentions of a 2007 GEO dataset by authors who submitted the dataset, and Number of mentions of a dataset by authors who DID NOT submit the dataset across 2007-2010.tables.csv Funding agencies are reluctant to support data archiving, even though large research funders such as the National Science Foundation (NSF) and the National Institutes of Health acknowledge its importance for scientific progress. Our quantitative estimates of data reuse indicate that ongoing financial investment in data-archiving infrastructure provides a high scientific return.

We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from standard file formats such as European Data Format, in addition to a range of commercial data formats. Most importantly, Sleep is built on top of the VisPy library, which provides GPU-based fast and high-level visualization. As a result, it is capable of efficiently handling and displaying large sleep datasets. Sleep is freely available (http://visbrain.org/sleep) and comes with sample datasets and an extensive documentation. Novel functionalities will continue to be added and open-science community efforts are expected to enhance the capacities of this module.

Phenotypic integration patterns in the mammalian skull have long been a focus of intense interest as a result of their suspected influence on the trajectory of hominid evolution. Here we test the hypothesis that perturbation of cartilage growth, which directly affects only the chondrocranium during development, will produce coordinated shape changes in the adult calvarium and face regardless of mechanism. Using two murine models of cartilage undergrowth that target two very different mechanisms, we show that strong reduction in cartilage growth produces a short, wide, and more flexed cranial base. This in turn produces a short, wide face in both models. Cranial base and face are already correlated early in ontogeny, and the relationship between these modules gains structure through postnatal growth and development. These results provide further evidence that there exist physical interactions between developing parts of the phenotype that produce variation at a distance from the actual locus upon which a particular selective pressure is acting. Phenotypic changes observed over the course of evolution may not all require adaptationist explanations; rather, it is likely that a substantial portion of observed phenotypic variation over the history of a clade is not directly adaptive but rather a secondary consequence of some local response to selection. Adult Skull Landmark DataThis file contains raw landmark coordinates for adult mouse skulls.Adult.csvNeonatal Skull Landmark DataThis file contains raw landmark coordinates for neonatal mouse skulls.Neonatal.csv

Bluegill sunfish (Lepomis macrochirus) are one of the classic systems for studying male alternative reproductive tactics (ARTs) in teleost fishes. In this species, there are two distinct life histories: parental and cuckolder, encompassing three reproductive tactics, parental, satellite, and sneaker. The parental life history is fixed, whereas individuals who enter the cuckolder life history transition from sneaker to satellite tactic as they grow. For this study, we used RNAseq to characterize the brain transcriptome of the three male tactics and females during spawning to identify gene ontology (GO) categories and potential candidate genes associated with each tactic. We found that sneaker males had higher levels of gene expression differentiation compared to the other two male tactics. Sneaker males also had higher expression in ionotropic glutamate receptor genes, specifically AMPA receptors, compared to other males, which may be important for increased spatial working memory while attempting to cuckold parental males at their nests. Larger differences in gene expression also occurred among male tactics than between males and females. We found significant expression differences in several candidate genes that were previously identified in other species with ARTs and suggest a previously undescribed role for cAMP-responsive element modulator (crem) in influencing parental male behaviors during spawning. Fish sampling informationA text file detailing sampling information for fish used in this study. It includes the sex, tactic, total and standard length, sampling colony, sampling location, and RNA integrity numbers.Fish_sampling_info.txtTranscript CountsRaw transcripts counts produced from eXpress for all samplesMerged.results.xprsR scriptContains the R scripts used with edgeR for differential gene expression analysisRscript_DE_Analysis.txtAssembled transcriptomeAssembled bluegill transcriptome. Assembled de novo using Trinity.bluegill.final.fa

Magnetic resonance imaging (MRI) is a non-destructive technique that is capable of localizing pathologies and assessing other anatomical features (e.g., tissue volume, microstructure, and white matter connectivity) in postmortem, ex vivo human brains. However, when brains are removed from the skull and cerebrospinal fluid (i.e., their normal in vivo magnetic environment), air bubbles and air–tissue interfaces typically cause magnetic susceptibility artifacts that severely degrade the quality of ex vivo MRI data. In this report, we describe a relatively simple and cost-effective experimental setup for acquiring artifact-free ex vivo brain images using a clinical MRI system with standard hardware. In particular, we outline the necessary steps, from collecting an ex vivo human brain to the MRI scanner setup, and have also described changing the formalin (as might be necessary in longitudinal postmortem studies). Finally, we share some representative ex vivo MRI images that have been acquired using the proposed setup in order to demonstrate the efficacy of this approach. We hope that this protocol will provide both clinicians and researchers with a straight-forward and cost-effective solution for acquiring ex vivo MRI data from whole postmortem human brains.

Super-resolution and single-molecule microscopy are increasingly applied to complex biological systems. A major challenge of this approach is that fluorescent puncta must be detected in the low signal, high noise, heterogeneous background environments of cells and tissue. We present RASP, Radiality Analysis of Single Puncta, a bioimaging-segmentation method that solves this problem. RASP removes false positive puncta that other analysis methods detect, and detects features over a broad range of spatial scales: from single proteins to complex cell phenotypes. RASP outperforms the state-of-the-art in precision and speed, using image gradients to separate Gaussian-shaped objects from background. We demonstrate RASP's power by showing it can extract spatial correlations between microglia, neurons, and alpha-synuclein oligomers in the human brain. This sensitive, computationally efficient approach enables fluorescent puncta and cellular features to be distinguished in cellular and tissue environments with a sensitivity down to the level of the single protein.