Best practices to evaluate the impact of biomedical research software-metric collection beyond citations.

Motivation: Software is vital for the advancement of biology and medicine. Impact evaluations of scientific software have primarily emphasized traditional citation metrics of associated papers, despite these metrics inadequately capturing the dynamic picture of impact and despite challenges with improper citation.

Results: To understand how software developers evaluate their tools, we conducted a survey of participants in the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI).

Sharing Data from the Human Tumor Atlas Network through Standards, Infrastructure, and Community Engagement.

The Data Coordinating Center (DCC) of the Human Tumor Atlas Network (HTAN) has played a crucial role in enabling the broad sharing and effective utilization of HTAN data within the scientific community. Data from the first phase of HTAN are now available publicly. We describe the diverse datasets and modalities shared, multiple access routes to HTAN assay data and metadata, data standards, technical infrastructure and governance approaches, as well as our approach to sustained community engagement.

The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma.

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date.

The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms.

Evaluation of software impact designed for biomedical research: Are we measuring what's meaningful?

Software is vital for the advancement of biology and medicine. Through analysis of usage and impact metrics of software, developers can help determine user and community engagement. These metrics can be used to justify additional funding, encourage additional use, and identify unanticipated use cases.

The Brain Tumor Segmentation (BraTS) Challenge 2023: .

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations.

GA4GH: International policies and standards for data sharing across genomic research and healthcare.

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution.

Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions.

The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer's Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes).

Meta-Analysis of the Alzheimer's Disease Human Brain Transcriptome and Functional Dissection in Mouse Models.

We present a consensus atlas of the human brain transcriptome in Alzheimer's disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples. We discover 30 brain coexpression modules from seven regions as the major source of AD transcriptional perturbations. We next examine overlap with 251 brain differentially expressed gene sets from mouse models of AD and other neurodegenerative disorders.

Preliminary Evidence for the Validity of the Family Caregiver Identity Scale.

The purpose of this study was to develop and evaluate the Family Caregiver Identity Scale (FCIS), an instrument designed to measure the extent to which an individual identifies with the family caregiver role. The process of instrument development outlined in the was combined with Dillman's four stages of pretesting. This was a multistage, iterative process, including several revisions based on feedback from experts, interviews, and pilot testing.

Reproducible biomedical benchmarking in the cloud: lessons from crowd-sourced data challenges.

Challenges are achieving broad acceptance for addressing many biomedical questions and enabling tool assessment. But ensuring that the methods evaluated are reproducible and reusable is complicated by the diversity of software architectures, input and output file formats, and computing environments. To mitigate these problems, some challenges have leveraged new virtualization and compute methods, requiring participants to submit cloud-ready software packages.

The Immune Landscape of Cancer.

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers.

Network reconstruction and systems analysis of plant cell wall deconstruction by .

Background: Plant biomass degradation by fungal-derived enzymes is rapidly expanding in economic importance as a clean and efficient source for biofuels. The ability to rationally engineer filamentous fungi would facilitate biotechnological applications for degradation of plant cell wall polysaccharides. However, incomplete knowledge of biomolecular networks responsible for plant cell wall deconstruction impedes experimental efforts in this direction.

Partial exhaustion of CD8 T cells and clinical response to teplizumab in new-onset type 1 diabetes.

Biologic treatment of T1D typically results in transient stabilization of C-peptide levels (a surrogate for endogenous insulin secretion) in some patients, followed by progression at the same rate as in untreated control groups. Here, we used integrated systems biology and flow cytometry approaches with clinical trial blood samples to elucidate pathways associated with C-peptide stabilization in T1D subjects treated with the anti-CD3 monoclonal antibody teplizumab. We identified a population of CD8 T cells that accumulated in subjects with the best response to treatment (responders) and showed that these cells phenotypically resembled exhausted T cells by expressing high levels of the transcription factor EOMES, effector molecules, and multiple inhibitory receptors (IRs), including TIGIT and KLRG1.

Single-Cell RNA Sequencing Reveals Expanded Clones of Islet Antigen-Reactive CD4 T Cells in Peripheral Blood of Subjects with Type 1 Diabetes.

The significance of islet Ag-reactive T cells found in peripheral blood of type 1 diabetes (T1D) subjects is unclear, partly because similar cells are also found in healthy control (HC) subjects. We hypothesized that key disease-associated cells would show evidence of prior Ag exposure, inferred from expanded TCR clonotypes, and essential phenotypic properties in their transcriptomes. To test this, we developed single-cell RNA sequencing procedures for identifying TCR clonotypes and transcript phenotypes in individual T cells.

Human whole genome genotype and transcriptome data for Alzheimer's and other neurodegenerative diseases.

Previous genome-wide association studies (GWAS), conducted by our group and others, have identified loci that harbor risk variants for neurodegenerative diseases, including Alzheimer's disease (AD). Human disease variants are enriched for polymorphisms that affect gene expression, including some that are known to associate with expression changes in the brain. Postulating that many variants confer risk to neurodegenerative disease via transcriptional regulatory mechanisms, we have analyzed gene expression levels in the brain tissue of subjects with AD and related diseases.

Fostering synergy between cell biology and systems biology.

In the shared pursuit of elucidating detailed mechanisms of cell function, systems biology presents a natural complement to ongoing efforts in cell biology. Systems biology aims to characterize biological systems through integrated and quantitative modeling of cellular information. The process of model building and analysis provides value through synthesizing and cataloging information about cells and molecules, predicting mechanisms and identifying generalizable themes, generating hypotheses and guiding experimental design, and highlighting knowledge gaps and refining understanding.

IL-10 alters immunoproteostasis in APP mice, increasing plaque burden and worsening cognitive behavior.

Anti-inflammatory strategies are proposed to have beneficial effects in Alzheimer's disease. To explore how anti-inflammatory cytokine signaling affects Aβ pathology, we investigated the effects of adeno-associated virus (AAV2/1)-mediated expression of Interleukin (IL)-10 in the brains of APP transgenic mouse models. IL-10 expression resulted in increased Aβ accumulation and impaired memory in APP mice.