We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

The incidence and mortality of early-onset colorectal cancer (EOCRC) are rising; outcomes appear to differ by race and ethnicity. We aimed to assess differences in mutational landscape and gene expression of EOCRC by racial and ethnic groups (Non-Hispanic Asian, Non-Hispanic Black, Non-Hispanic White, White Hispanic) using data from AACR Project GENIE (10.2) and University of Texas Southwestern, the latter enriched in Hispanic patients. All statistical tests were 2-sided. Of 1,752 EOCRC patients, Non-Hispanic Black patients had higher rates of KRAS mutations (60.9%, p = .001, q = 0.015) and Non-Hispanic White and Non-Hispanic Black patients had higher rates of APC mutations (77.1% and 76.6% among Non-Hispanic White and Non-Hispanic Black patients, respectively; p = .001, q = 0.015) via the Fisher exact test with Benjamini-Hochberg correction. Using R packages DESeq2 and clusterProfiler, we found that White Hispanic patients had increased expression of genes involved in oxidative phosphorylation (p < .001, q = 0.025). Genomic profiling has the potential to identify novel diagnostics and influence individualized treatment options to address the currently limited prognosis of EOCRC.

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Heavy metal ethnography and historiography has been extensively explored from a qualitative perspective. However, quantitative methods of analysis have not been developed. We conduct a Bayesian geographical analysis of heavy metal subgenres, investigating the relative prevalence of each subgenre in nations (for 86 countries in northern Europe and the West), and the overall popularity, according to the selected countries. Data from two different websites, MetalStorm and Encyclopaedia Metallum, were harvested via web ‘scraping’ and used for analysis. Results for Norway and Sweden in particular clearly agree with the qualitative historical documentation, while Germany surprisingly favoured black metal above Gothic.

Queer in AI is an organization that aims to combat the harms faced by queer researchers within AI. Several inclusion initiatives are outlined, including those centered on policy and financial aid.