Search

The MethaneSAT satellite instrument and its aircraft precursor, MethaneAIR, are imaging spectrometers designed to measure methane concentrations with wide spatial coverage, fine spatial resolution, and high precision compared to currently deployed remote sensing instruments. At 12 960 m cruise altitude above ground (13 850 m above sea level), MethaneAIR datasets have a 4.5 km swath gridded to 10 m × 10 m pixels with 17-20 ppb standard deviation on a flat scene. MethaneAIR was deployed in the summer of 2021 in the Permian Basin to test the accuracy of the retrieved methane concentrations and emission rates using the algorithms developed for MethaneSAT. We report here point source emissions obtained during a single-blind volume-controlled release experiment, using two methods. (1) The modified integrated mass enhancement (mIME) method estimates emission rates using the total mass enhancement of methane in an observed plume combined with winds obtained from Weather Research Forecast driven by High-Resolution Rapid Refresh meteorological data in Large Eddy Simulations mode (WRF-LES-HRRR). WRF-LES-HRRR simulates winds in stochastic eddy-scale (100-1000 m) variability, which is particularly important for low-wind conditions and informing the error budget. The mIME can estimate emission rates of plumes of any size that are detectable by MethaneAIR. (2) The divergence integral (DI) method applies Gauss's theorem to estimate the flux divergence fields through a series of closed surfaces enclosing the sources. The set of boxes grows from the upwind side of the plume through the core of each plume and downwind. No selection of inflow concentration, as used in the mIME, is required. The DI approach can efï¬ ciently determine ï¬ uxes from large sources and clusters of sources but cannot resolve small point emissions. These methods account for the effects of eddy-scale variation in different ways: The DI averages across many eddies, whereas the mIME re-samples many eddies from the LES simulation. The DI directly uses HRRR winds, while mIME uses WRF-LES-HRRR wind products. Emissions estimates from both the mIME and DI methods agreed closely with the single-blind volume-controlled experiments (N Combining double low line 21). The York regression between the estimated emissions and the released emissions has a slope of 0.96 [0.84, 1.08], R Combining double low line 0.83 and N Combining double low line 21, with 30 % mean percentage error for the whole dataset, which indicates that MethaneAIR can quantify point sources emitting more than 200 kg h-1 for the mIME and 500 kg h-1 for the DI method. The two methods also agreed on methane emission estimates from various uncontrolled sources in the Permian Basin. The experiment thus demonstrates the powerful potential of the MethaneAIR instrument and suggests that the quantification method should be transferable to MethaneSAT if it meets the design specifications.

Predicting elemental cycles and maintaining water quality under increasing anthropogenic influence requires understanding the spatial drivers of river microbiomes. However, the unifying microbial processes governing river biogeochemistry are hindered by a lack of genome-resolved functional insights and sampling across multiple rivers. Here we employed a community science effort to accelerate the sampling, sequencing, and genome-resolved analyses of river microbiomes to create the Genome Resolved Open Watersheds database (GROWdb). This resource profiled the identity, distribution, function, and expression of thousands of microbial genomes across rivers covering 90% of United States watersheds. Specifically, GROWdb encompasses 1,469 microbial species from 27 phyla, including novel lineages from 10 families and 128 genera, and defines the core river microbiome for the first time at genome level. GROWdb analyses coupled to extensive geospatial information revealed local and regional drivers of microbial community structuring, while also presenting a myriad of foundational hypotheses about ecosystem function. Building upon the previously conceived River Continuum Concept 1 , we layer on microbial functional trait expression, which suggests the structure and function of river microbiomes is predictable. We make GROWdb available through various collaborative cyberinfrastructures 2, 3 so that it can be widely accessed across disciplines for watershed predictive modeling and microbiome-based management practices.

High-grade serous ovarian cancer (HGSC) is the most lethal histotype of ovarian cancer and the majority of cases present with metastasis and late-stage disease. Over the last few decades, the overall survival for patients has not significantly improved, and there are limited targeted treatment options. We aimed to better characterize the distinctions between primary and metastatic tumors based on short- or long-term survival. We characterized 39 matched primary and metastatic tumors by whole exome and RNA sequencing. Of these, 23 were short-term (ST) survivors (overall survival (OS)  5 years). We compared somatic mutations, copy number alterations, mutational burden, differential gene expression, immune cell infiltration, and gene fusion predictions between the primary and metastatic tumors and between ST and LT survivor cohorts. There were few differences in RNA expression between paired primary and metastatic tumors, but significant differences between the transcriptomes of LT and ST survivors in both their primary and metastatic tumors. These findings will improve the understanding of the genetic variation in HGSC that exist between patients with different prognoses and better inform treatments by identifying new targets for drug development.

Previous studies have shown Relational Coordination improves team functioning in healthcare settings. The aim of this study was to examine the relational factors needed to support team functioning in outpatient mental health care teams with low staffing ratios. We interviewed interdisciplinary mental health teams that had achieved high team functioning despite low staffing ratios in U.S. Department of Veterans Affairs medical centers. We conducted qualitative interviews with 21 interdisciplinary team members across three teams within two medical centers. We used directed content analysis to code the transcripts with a priori codes based on the Relational Coordination dimensions, while also being attentive to emergent themes. We found that all seven dimensions of Relational Coordination were relevant to improved team functioning: frequent communication, timely communication, accurate communication, problem-solving communication, shared goals, shared knowledge, and mutual respect. Participants also described these dimensions as reciprocal processes that influenced each other. In conclusion, relational Coordination dimensions can play pivotal roles in improving team functioning both individually and in combination. Communication dimensions were a catalyst for developing relationship dimensions; once relationships were developed, there was a mutually reinforcing cycle between communication and relationship dimensions. Our results suggest that establishing high-functioning mental health care teams, even in low-staffed settings, requires encouraging frequent communication within teams. Moreover, attention should be given to ensuring appropriate representation of disciplines among leadership and defining roles of team members when teams are formed.

Clinical evidence of vascular dysfunction and hypercoagulability as well as pulmonary vascular damage and microthrombosis are frequently reported in severe cases of human coronavirus disease 2019 (COVID-19). Syrian golden hamsters recapitulate histopathologic pulmonary vascular lesions reported in patients with COVID-19. Herein, special staining techniques and transmission electron microscopy further define vascular pathologies in a Syrian golden hamster model of human COVID-19. The results show that regions of active pulmonary inflammation in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are characterized by ultrastructural evidence of endothelial damage with platelet marginalization and both perivascular and subendothelial macrophage infiltration. SARS-CoV-2 antigen/RNA was not detectable within affected blood vessels. Taken together, these findings suggest that the prominent microscopic vascular lesions in SARS-CoV-2-inoculated hamsters likely occur due to endothelial damage followed by platelet and macrophage infiltration.

Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. Because most somatic mutations are single-nucleotide variants, changes between wild-type and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient's specific MHC molecules. Whereas a subset of peptide positions are presented to the T cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T cell responses. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6 to 38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline, and improve the identification process for relevant clinical studies.

Microbial genome annotation is the process of identifying structural and functional elements in DNA sequences and subsequently attaching biological information to those elements. DRAM is a tool developed to annotate bacterial, archaeal, and viral genomes derived from pure cultures or metagenomes. DRAM goes beyond traditional annotation tools by distilling multiple gene annotations to genome level summaries of functional potential. Despite these benefits, a downside of DRAM is the requirement of large computational resources, which limits its accessibility. Further, it did not integrate with downstream metabolic modeling tools that require genome annotation. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated with the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point-and-click interface, as well as generate genome-scale metabolic models from DRAM annotations.Availability and implementationFor kb_DRAM users, the kb_DRAM apps on KBase can be found in the catalog at https://narrative.kbase.us/#catalog/modules/kb_DRAM. For kb_DRAM users, a tutorial workflow with all documentation is available at https://narrative.kbase.us/narrative/129480. For kb_DRAM developers, software is available at https://github.com/shafferm/kb_DRAM.

Granulocyte-macrophage colony-stimulating factor (GM-CSF) is known to play a key role in enhancing multiple immune functions that affect response to infectious pathogens including antigen presentation, complement- and antibody-mediated phagocytosis, microbicidal activity, and neutrophil chemotaxis. Reduced GM-CSF activity and immune response provides a mechanism for increased infection risk associated with autoimmune pulmonary alveolar proteinosis (aPAP) and other disorders involving the presence of GM-CSF autoantibodies. We present a case series of five patients with persistent or unusual pulmonary and central nervous system opportunistic infections (Cryptococcus gattii, Flavobacterium, Nocardia) and elevated GM-CSF autoantibody levels, as well as 27 cases identified on systematic review of the literature.

Access to high-quality health care, including mental health care, remains a high priority for the Department of Veterans Affairs and civilian health care systems. Increased access to mental health care is associated with improved outcomes, including decreased suicidal behavior. Multiple policy changes and interventions are being developed and implemented to improve access to mental health care. The Perceived Access Inventory (PAI) is a patient-centered questionnaire developed to understand the veteran perspective about access to mental health services. The PAI is a self-report measure that includes 43 items across 5 domains: Logistics (6 items), Culture (4 items), Digital (9 items), Systems of Care (13 items), and Experiences of Care (11 items). This article is a preliminary examination of the concurrent and convergent validity of the PAI with respect to the Hoge Perceived Barriers to Seeking Mental Health Services scale (concurrent) and the Client Satisfaction Questionnaire (CSQ; convergent). Telephone interviews were conducted with veterans from 3 geographic regions. Eligibility criteria included screening positive for posttraumatic stress disorder, alcohol use disorder, or depression in the past 12 months. Data from 92 veterans were analyzed using correlation matrices. PAI scores were significantly correlated with the Hoge total score (concurrent validity) and CSQ scores (convergent validity). The PAI items with the strongest correlation with CSQ were in the Systems of Care domain and the weakest were in the Logistics domain. Future efforts will evaluate validity using larger data sets and utilize the PAI to develop and test interventions to improve access to care. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

BackgroundMany previous studies of health care teamwork have taken place in clinical teams with high staffing ratios (i.e., high ratios of staff to patients).PurposeThe aim of this study was to identify clinicians' viewpoints of foundational resources necessary to support good team functioning in the context of low staffing ratios.MethodologyWe used administrative data, validated with local mental health chiefs, to identify mental health teams that had achieved high team functioning despite low staffing ratios in U.S. Department of Veterans Affairs medical centers. Guided by a recently developed model of team effectiveness, the Team Effectiveness Pyramid, we conducted qualitative interviews with 21 team members across three teams within two medical centers. Interview questions focused on the resources needed to support good team functioning despite low staffing ratios. We used directed content analysis to analyze results.ResultsWe found there were several domains of relevant resources: material, staffing, temporal, organizational, and psychological. These represent an expansion of the domains originally included in the Team Effectiveness Pyramid.ConclusionsWithin the five domains, we identified key tensions to be addressed when forming teams, including the balances between providing care for new versus established patients, emphasizing shared caseloads within the team versus matching patients to clinicians based on individual expertise, and establishing reporting structures by clinical discipline versus team membership.Practice implicationsEstablishing high-functioning health care teams in the context of low staffing ratios requires attention to key resource domains and fundamental trade-offs in how teams are structured.

As novel SARS-CoV-2 variants continue to emerge, it is critical that their potential to cause severe disease and evade vaccine-induced immunity is rapidly assessed in humans and studied in animal models. In early January 2021, a novel SARS-CoV-2 variant designated B.1.429 comprising 2 lineages, B.1.427 and B.1.429, was originally detected in California (CA) and it was shown to have enhanced infectivity in vitro and decreased antibody neutralization by plasma from convalescent patients and vaccine recipients. Here we examine the virulence, transmissibility, and susceptibility to pre-existing immunity for B 1.427 and B 1.429 in the Syrian hamster model. We find that both variants exhibit enhanced virulence as measured by increased body weight loss compared to hamsters infected with ancestral B.1 (614G), with B.1.429 causing the most marked body weight loss among the 3 variants. Faster dissemination from airways to parenchyma and more severe lung pathology at both early and late stages were also observed with B.1.429 infections relative to B.1. (614G) and B.1.427 infections. In addition, subgenomic viral RNA (sgRNA) levels were highest in oral swabs of hamsters infected with B.1.429, however sgRNA levels in lungs were similar in all three variants. This demonstrates that B.1.429 replicates to higher levels than ancestral B.1 (614G) or B.1.427 in the oropharynx but not in the lungs. In multi-virus in-vivo competition experiments, we found that B.1. (614G), epsilon (B.1.427/B.1.429) and gamma (P.1) dramatically outcompete alpha (B.1.1.7), beta (B.1.351) and zeta (P.2) in the lungs. In the nasal cavity, B.1. (614G), gamma, and epsilon dominate, but the highly infectious alpha variant also maintains a moderate size niche. We did not observe significant differences in airborne transmission efficiency among the B.1.427, B.1.429 and ancestral B.1 (614G) and WA-1 variants in hamsters. These results demonstrate enhanced virulence and high relative oropharyngeal replication of the epsilon (B.1.427/B.1.429) variant in Syrian hamsters compared to an ancestral B.1 (614G) variant.

Human clinical studies investigating use of convalescent plasma (CP) for treatment of coronavirus disease 2019 (COVID-19) have produced conflicting results. Outcomes in these studies may vary at least partly due to different timing of CP administration relative to symptom onset. The mechanisms of action of CP include neutralizing antibodies but may extend beyond virus neutralization to include normalization of blood clotting and dampening of inflammation. Unresolved questions include the minimum therapeutic titer in the CP units or CP recipient as well as the optimal timing of administration. Here, we show that treatment of macaques with CP within 24 h of infection does not reduce viral shedding in nasal or lung secretions compared to controls and does not detectably improve any clinical endpoint. We also demonstrate that CP administration does not impact viral sequence diversity in vivo, although the selection of a viral sequence variant in both macaques receiving normal human plasma was suggestive of immune pressure. Our results suggest that CP, administered to medium titers, has limited efficacy, even when given very early after infection. Our findings also contribute information important for the continued development of the nonhuman primate model of COVID-19. These results should inform interpretation of clinical studies of CP in addition to providing insights useful for developing other passive immunotherapies and vaccine strategies. IMPORTANCE Antiviral treatment options for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain very limited. One treatment that was explored beginning early in the pandemic (and that is likely to be tested early in future pandemics) is plasma collected from people who have recovered from coronavirus disease 2019 (COVID-19), known as convalescent plasma (CP). We tested if CP reduces viral shedding or disease in a nonhuman primate model. Our results demonstrate that administration of CP 1 day after SARS-CoV-2 infection had no significant impact on viral loads, clinical disease, or sequence diversity, although treatment with normal human plasma resulted in selection of a specific viral variant. Our results demonstrate that passive immunization with CP, even during early infection, provided no significant benefit in a nonhuman primate model of SARS-CoV-2 infection.

Shortly after its discovery, General Relativity (GR) was applied to predict the behavior of our Universe on the largest scales, and later became the foundation of modern cosmology. Its validity has been verified on a range of scales and environments from the Solar system to merging black holes. However, experimental confirmations of GR on cosmological scales have so far lacked the accuracy one would hope for-its applications on those scales being largely based on extrapolation and its validity there sometimes questioned in the shadow of the discovery of the unexpected cosmic acceleration. Future astronomical instruments surveying the distribution and evolution of galaxies over substantial portions of the observable Universe, such as the Dark Energy Spectroscopic Instrument (DESI), will be able to measure the fingerprints of gravity and their statistical power will allow strong constraints on alternatives to GR. In this paper, based on a set of N-body simulations and mock galaxy catalogs, we study the predictions of a number of traditional and novel summary statistics beyond linear redshift distortions in two well-studied modified gravity models-chameleon f(R) gravity and a braneworld model-and the potential of testing these deviations from GR using DESI. These summary statistics employ a wide array of statistical properties of the galaxy and the underlying dark matter field, including two-point and higher-order statistics, environmental dependence, redshift space distortions and weak lensing. We find that they hold promising power for testing GR to unprecedented precision. The major future challenge is to make realistic, simulation-based mock galaxy catalogs for both GR and alternative models to fully exploit the statistic power of the DESI survey (by matching the volumes and galaxy number densities of the mocks to those in the real survey) and to better understand the impact of key systematic effects. Using these, we identify future simulation and analysis needs for gravity tests using DESI.