Quantifying dissipative beach and vegetated dune morphology with multiple aerial photography systems

Sand dunes provide coastal communities critical protection from flooding and erosion, as well as a habitat for a range of species- some threatened or endangered. As such, it is of importance to develop a quantitative understanding of the processes through which these systems evolve at a variety of temporal and spatial scales. During summer 2016, a large field campaign in southwest Washington called the Sandbar-aEolian Dune EXchange EXperiment (SEDEX2) focused on developing a suite of data sets fundamental to improving our understanding of the ways in which beaches and dunes grow during fair weather conditions. As part of this experiment, daily to bi-weekly measurements of upper-beach and vegetated dune morphology were collected by post-processing images acquired using a consumer grade kite-based aerial photography system with low-cost Agisoft Photoscan Structure from Motion (SfM) software. Under the appropriate environmental conditions (e.g., sufficient wind, no precipitation, and minimal fog), kite-based SfM techniques minimize survey effort and time as compared to traditional coastal surveying methods such as RTK DGPS and Terrestrial Laser Scanning (TLS), making this approach ideally suited for frequent surveys of small (~< .0025 km2) coastal areas. However, while the dominant grass in the area, A. breviligulta (American Beachgrass), is critical for perturbing the wind field, impacting sediment transport processes, and partially dictating dune morphology, it grows densely and therefore complicates the development of aerial photography derived bare-earth digital elevation models. Here we document daily-to-weekly-scale upper beach and dune evolution using kite-based SfM techniques, focusing particularly on improving the efficacy of this technology both in vegetated areas and over short timescales. Our kite-based SfM approach, validated by concurrent RTK DGPS surveys, TLS scans, and dune vegetation surveys, is allowing us to develop quantitative estimates of the sediment exchange between the back-beach and dunes. Other data collected during SEDEX2 are being utilized to determine the primary drivers of these observed changes

WHAT GOOD IS WEALTH WITHOUT HEALTH? THE EFFECT OF HEALTH ON THE MARGINAL UTILITY OF CONSUMPTION

We estimate how the marginal utility of consumption varies with health. To do so, we develop a simple model in which the impact of health on the marginal utility of consumption can be estimated from data on permanent income, health, and utility proxies. We estimate the model using the Health and Retirement Study's panel data on the elderly and near-elderly, and proxy for utility with measures of subjective well-being. Across a wide range of alternative specifications and assumptions, we find that the marginal utility of consumption declines as health deteriorates, and we are able to clearly reject the null of no state dependence. Our point estimates indicate that a one-standard-deviation increase in the number of chronic diseases is associated with a 10%–25% decline in the marginal utility of consumption relative to this marginal utility when the individual has no chronic diseases. We present some simple, illustrative calibration results that suggest that state dependence of the magnitude we estimate can have a substantial effect on important economic problems such as the optimal level of health insurance benefits and the optimal level of life-cycle savings.United States. Social Security Administration (National Bureau of Economic Research Grant 10-P-98363-1-05)National Institute on Aging (Grant T32-AG000186

Performance of ACMG-AMP Variant-Interpretation Guidelines among Nine Laboratories in the Clinical Sequencing Exploratory Research Consortium

Evaluating the pathogenicity of a variant is challenging given the plethora of types of genetic evidence that laboratories consider. Deciding how to weigh each type of evidence is difficult, and standards have been needed. In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published guidelines for the assessment of variants in genes associated with Mendelian diseases. Nine molecular diagnostic laboratories involved in the Clinical Sequencing Exploratory Research (CSER) consortium piloted these guidelines on 99 variants spanning all categories (pathogenic, likely pathogenic, uncertain significance, likely benign, and benign). Nine variants were distributed to all laboratories, and the remaining 90 were evaluated by three laboratories. The laboratories classified each variant by using both the laboratory's own method and the ACMG-AMP criteria. The agreement between the two methods used within laboratories was high (K-alpha = 0.91) with 79% concordance. However, there was only 34% concordance for either classification system across laboratories. After consensus discussions and detailed review of the ACMG-AMP criteria, concordance increased to 71%. Causes of initial discordance in ACMG-AMP classifications were identified, and recommendations on clarification and increased specification of the ACMG-AMP criteria were made. In summary, although an initial pilot of the ACMG-AMP guidelines did not lead to increased concordance in variant interpretation, comparing variant interpretations to identify differences and having a common framework to facilitate resolution of those differences were beneficial for improving agreement, allowing iterative movement toward increased reporting consistency for variants in genes associated with monogenic disease

Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Goldstein, E. B., Buscombe, D., Lazarus, E. D., Mohanty, S. D., Rafique, S. N., Anarde, K. A., Ashton, A. D., Beuzen, T., Castagno, K. A., Cohn, N., Conlin, M. P., Ellenson, A., Gillen, M., Hovenga, P. A., Over, J.-S. R., Palermo, R., Ratliff, K. M., Reeves, I. R. B., Sanborn, L. H., Straub, J. A., Taylor, L. A., Wallace E. J., Warrick, J., Wernette, P., Williams, H. E. Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement. Earth and Space Science, 8(9), (2021): e2021EA001896, https://doi.org/10.1029/2021EA001896.Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.The authors gratefully acknowledge support from the U.S. Geological Survey (G20AC00403 to EBG and SDM), NSF (1953412 to EBG and SDM; 1939954 to EBG), Microsoft AI for Earth (to EBG and SDM), The Leverhulme Trust (RPG-2018-282 to EDL and EBG), and an Early Career Research Fellowship from the Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine (to EBG). U.S. Geological Survey researchers (DB, J-SRO, JW, and PW) were supported by the U.S. Geological Survey Coastal and Marine Hazards and Resources Program as part of the response and recovery efforts under congressional appropriations through the Additional Supplemental Appropriations for Disaster Relief Act, 2019 (Public Law 116-20; 133 Stat. 871)

Abemaciclib in Combination With Endocrine Therapy for Patients With Hormone Receptor-Positive, HER2-Negative Metastatic Breast Cancer: A Phase 1b Study

Background Cyclin-dependent kinases (CDK) 4 and 6 regulate G1 to S cell cycle progression and are often altered in cancers. Abemaciclib is a selective inhibitor of CDK4 and CDK6 approved for administration on a continuous dosing schedule as monotherapy or as combination therapy with an aromatase inhibitor or fulvestrant in patients with advanced or metastatic breast cancer. This Phase 1b study evaluated the safety and tolerability, pharmacokinetics, and antitumor activity of abemaciclib in combination with endocrine therapy for metastatic breast cancer (MBC), including aromatase inhibitors (letrozole, anastrozole, or exemestane) or tamoxifen. Patients and Methods Women ≥18 years old with hormone receptor positive (HR+), human epidermal growth factor receptor 2 negative (HER2-) MBC were eligible for enrollment. Eligibility included measurable disease or non-measurable but evaluable bone disease by Response Evaluation Criteria in Solid Tumours (RECIST) v1.1, Eastern Cooperative Oncology Group performance status 0–1, and no prior chemotherapy for metastatic disease. Adverse events were graded by the National Cancer Institute Common Terminology Criteria for Adverse Events v4.0 and tumor response were assessed by RECIST v1.1. Results Sixty-seven patients were enrolled and received abemaciclib 200 mg every 12 hours in combination with letrozole (Part A, n=20), anastrozole (Part B, n=16), tamoxifen (Part C, n=16), or exemestane (Part D, n=15). The most common treatment-emergent adverse events (TEAE) were diarrhea, fatigue, nausea, and abdominal pain. Grade 4 TEAEs were reported in five patients (one each with hyperglycemia, hypertension, neutropenia, procedural hemorrhage, and sepsis). There was no effect of abemaciclib or endocrine therapy on the pharmacokinetics of any combination study drug. Across all treated patients, the median progression-free survival was 25.4 months (95% confidence interval: 18.0, 35.8). The objective response rate was 38.9% in 36 patients with measurable disease. Conclusions Abemaciclib in combination with multiple endocrine therapy options exhibited manageable safety and promising antitumor activity in patients with HR+, HER2- MBC. Clinical Trial Registration https://clinicaltrials.gov/, identifier NCT0205713

The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species.

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven\u27t been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics

Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources.

The Human Phenotype Ontology (HPO)-a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases-is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO\u27s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes

Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine

Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo

Meeting Abstracts: Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo Clearwater Beach, FL, USA. 9-11 June 201