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Mesozoic crustal shortening in the North American Cordillera’s hinterland was related to the construction of the Nevadaplano orogenic plateau. Petrologic and geochemical proxies in Cordilleran core complexes suggest substantial Late Cretaceous crustal thickening during plateau construction. In eastern Nevada, geobarometry from the Snake Range and Ruby Mountains-East Humboldt Range-Wood Hills-Pequop Mountains (REWP) core complexes suggests that the ~10–12 km thick Neoproterozoic-Triassic passive-margin sequence was buried to great depths (>30 km) during Mesozoic shortening and was later exhumed to the surface via high-magnitude Cenozoic extension. Deep regional burial is commonly reconciled with structural models involving cryptic thrust sheets, such as the hypothesized Windermere thrust in the REWP. We test the viability of deep thrust burial by examining the least-deformed part of the REWP in the Pequop Mountains. Observations include a compilation of new and published peak temperature estimates (n=60) spanning the Neoproterozoic-Triassic strata, documentation of critical field relationships that constrain deformation style and timing, and new 40Ar/39Ar ages. This evidence refutes models of deep regional thrust burial, including (1) recognition that most contractional structures in the Pequop Mountains formed in the Jurassic, not Cretaceous, and (2) peak temperature constraints and field relationships are inconsistent with deep burial. Jurassic deformation recorded here correlates with coeval structures spanning western Nevada to central Utah, which highlights that Middle-Late Jurassic shortening was significant in the Cordilleran hinterland. These observations challenge commonly held views for the Mesozoic-early Cenozoic evolution of the REWP and Cordilleran hinterland, including the timing of contractional strain, temporal evolution of plateau growth, and initial conditions for high-magnitude Cenozoic extension. The long-standing differences between peak-pressure estimates and field relationships in Nevadan core complexes may reflect tectonic overpressure.

Health-seeking behaviors change during pandemics and may increase with regard to illnesses with symptoms similar to the pandemic. The global reaction to COVID-19 may drive interest in vaccines for other diseases. Our study investigated the correlation between global online interest in COVID-19 and interest in CDC-recommended routine vaccines. This infodemiology study used Google Trends data to quantify worldwide interest in COVID-19 and CDC-recommended vaccines using the unit search volume index (SVI), which estimates volume of online search activity relative to highest volume of searches within a specified period. SVIs from December 30, 2019 to March 30, 2020 were collected for “coronavirus (Virus)” and compared with SVIs of search terms related to CDC-recommended adult vaccines. To account for seasonal variation, we compared SVIs from December 30, 2019 to March 30, 2020 with SVIs from the same months in 2015 to 2019. We performed country-level analyses in ten COVID-19 hotspots and ten countries with low disease burden. There were significant positive correlations between SVIs for “coronavirus (Virus)” and search terms for pneumococcal (R = 0.89, p < 0.0001) and influenza vaccines (R = 0.93, p < 0.0001) in 2020, which were greater than SVIs for the same terms in 2015–2019 (p = 0.005, p < 0.0001, respectively). Eight in ten COVID-19 hotspots demonstrated significant positive correlations between SVIs for coronavirus and search terms for pneumococcal and influenza vaccines. SVIs estimate relative changes in online interest and do not represent the interest of people with no Internet access. A peak in worldwide interest in pneumococcal and influenza vaccines coincided with the COVID-19 pandemic in February and March 2020. Trends are likely not seasonal in origin and may be driven by COVID-19 hotspots. Global events may change public perception about the importance of vaccines. Our findings may herald higher demand for pneumonia and influenza vaccines in the upcoming season.

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals 1 . Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch–seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations. An examination of motor cortex in humans, marmosets and mice reveals a generally conserved cellular makeup that is likely to extend to many mammalian species, but also differences in gene expression, DNA methylation and chromatin state that lead to species-dependent specializations.

While artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively homogeneous datasets, and populations poorly-representative of underlying diversity, limits generalisability and risks biased AI-based decisions. Here, we describe the landscape of AI in clinical medicine to delineate population and data-source disparities. We performed a scoping review of clinical papers published in PubMed in 2019 using AI techniques. We assessed differences in dataset country source, clinical specialty, and author nationality, sex, and expertise. A manually tagged subsample of PubMed articles was used to train a model, leveraging transfer-learning techniques (building upon an existing BioBERT model) to predict eligibility for inclusion (original, human, clinical AI literature). Of all eligible articles, database country source and clinical specialty were manually labelled. A BioBERT-based model predicted first/last author expertise. Author nationality was determined using corresponding affiliated institution information using Entrez Direct. And first/last author sex was evaluated using the Gendarize.io API. Our search yielded 30,576 articles, of which 7,314 (23.9%) were eligible for further analysis. Most databases came from the US (40.8%) and China (13.7%). Radiology was the most represented clinical specialty (40.4%), followed by pathology (9.1%). Authors were primarily from either China (24.0%) or the US (18.4%). First and last authors were predominately data experts (i.e., statisticians) (59.6% and 53.9% respectively) rather than clinicians. And the majority of first/last authors were male (74.1%). U.S. and Chinese datasets and authors were disproportionately overrepresented in clinical AI, and almost all of the top 10 databases and author nationalities were from high income countries (HICs). AI techniques were most commonly employed for image-rich specialties, and authors were predominantly male, with non-clinical backgrounds. Development of technological infrastructure in data-poor regions, and diligence in external validation and model re-calibration prior to clinical implementation in the short-term, are crucial in ensuring clinical AI is meaningful for broader populations, and to avoid perpetuating global health inequity.

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain 1 – 3 . With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas—containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities—is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions 4 . We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis. The authors describe an integrated atlas of the diverse cell types in the mouse primary motor cortex.

Purpose There has been little research on the healthcare cost-related coping mechanisms of families of patients with cancer. Therefore, we assessed the association between a cancer diagnosis and the healthcare cost-related coping mechanisms of participant family members through their decision to forego or delay seeking medical care, one of the manifestations of financial toxicity. Methods Using data from the National Health Interview Survey (NHIS) between 2000 and 2018, sample weight-adjusted prevalence was calculated and multivariable logistic regressions defined adjusted odds ratios (aORs) for participant family members who needed but did not get medical care or who delayed seeking medical care due to cost in the past 12 months, adjusting for relevant sociodemographic covariates, including participant history of cancer (yes vs. no) and participant age (18–45 vs. 46–64 years old). The analysis of family members foregoing or delaying medical care was repeated using a cancer diagnosis * age interaction term. Results Participants with cancer were more likely than those without a history of cancer to report family members delaying (19.63% vs. 16.31%, P  < 0.001) or foregoing (14.53% vs. 12.35%, P  = 0.001) medical care. Participants with cancer in the 18 to 45 years old age range were more likely to report family members delaying ( p interaction  = 0.028) or foregoing ( p interaction  < 0.001) medical care. Other factors associated with cost-related coping mechanisms undertaken by the participants’ family members included female sex, non-married status, poorer health status, lack of health insurance coverage, and lower household income. Conclusion A cancer diagnosis may be associated with familial healthcare cost-related coping mechanisms, one of the manifestations of financial toxicity. This is seen through delayed/omitted medical care of family members of people with a history of cancer, an association that may be stronger among young adult cancer survivors. These findings underscore the need to further explore how financial toxicity associated with a cancer diagnosis can affect patients’ family members and to design interventions to mitigate healthcare cost-related coping mechanisms.

Neuroanatomically precise, genome-wide maps of transcript distributions are critical resources to complement genomic sequence data and to correlate functional and genetic brain architecture. Here we describe the generation and analysis of a transcriptional atlas of the adult human brain, comprising extensive histological analysis and comprehensive microarray profiling of ∼900 neuroanatomically precise subdivisions in two individuals. Transcriptional regulation varies enormously by anatomical location, with different regions and their constituent cell types displaying robust molecular signatures that are highly conserved between individuals. Analysis of differential gene expression and gene co-expression relationships demonstrates that brain-wide variation strongly reflects the distributions of major cell classes such as neurons, oligodendrocytes, astrocytes and microglia. Local neighbourhood relationships between fine anatomical subdivisions are associated with discrete neuronal subtypes and genes involved with synaptic transmission. The neocortex displays a relatively homogeneous transcriptional pattern, but with distinct features associated selectively with primary sensorimotor cortices and with enriched frontal lobe expression. Notably, the spatial topography of the neocortex is strongly reflected in its molecular topography—the closer two cortical regions, the more similar their transcriptomes. This freely accessible online data resource forms a high-resolution transcriptional baseline for neurogenetic studies of normal and abnormal human brain function. Laser microdissection and microarrays are used to assess 900 precise subdivisions of the brains from three healthy men with 60,000 gene expression probes; the resulting atlas allows comparisons between humans and other animals, and will facilitate studies of human neurological and psychiatric diseases. Atlas of the brain High-resolution maps of genome-wide gene expression have been available for mice for a few years, but only relatively coarse equivalents have been published for the human brain because of the challenges presented by the 1,000-fold increase in size and the limited availability and quality of postmortem tissue. Now Michael Hawrylycz and colleagues at the Allen Institute for Brain Science in Seattle, Washington, have used laser microdissection and microarrays to assess 900 precise subdivisions in brains from two healthy men with 60,000 gene-expression probes. The resulting atlas, freely available at www.brain-map.org, allows comparisons between humans and other animals, and will facilitate studies of human neurological and psychiatric diseases. One early observation from the data is a human-specific pattern — compared with the mouse and rhesus monkey — for the calcium-binding protein CALB1 in the hippocampus.

Objectives. To provide adjusted rates of self-reported receipt of the influenza vaccine in the 2018–2019 flu season among adults in large metropolitan, medium and small metropolitan, and nonmetropolitan areas of the United States by age group, gender, and race. Methods. We queried the 2019 National Health Interview Survey for respondents aged 18 years and older. To provide national estimates of influenza vaccination coverage, we performed sample-weighted multivariable logistic regressions and predicted marginal modeling while adjusting for age, gender, race/ethnicity, and urban–rural household designation. Results. After weighting, 48.1%, 46.2%, and 43.6% of adults from large metropolitan, small and medium metropolitan, and nonmetropolitan areas, respectively, received the influenza vaccine. Additionally, there was a trend toward declining influenza vaccination status from large metropolitan to rural areas in all age groups, both genders, and multiple racial/ethnic groups. Conclusions. Self-reported influenza vaccination rates were lower in rural than in urban areas among adults of all age groups and both genders. Using community leaders for health promotion, augmentation of the community health care workforce, and provision of incentives for providers to integrate influenza vaccination in regular visits may expand influenza vaccine coverage. (Am J Public Health. 2022;112(2):304–307. https://doi.org/10.2105/AJPH.2021.306575 )