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Probabilistic prediction models exist to reduce surprise about future events. This paper explores the evaluation of such forecasts when the event of interest is rare. We review how the family of Brier-type scores may be ill-suited to evaluate predictions of rare events, and we offer an alternative to information-theoretical scores such as Ignorance. The reduction in surprise provided by a set of forecasts is represented as information gain, a frequent loss function in machine learning training, meaning the reduction in ignorance over a baseline having received a new forecast. We evaluate predictions of a synthetic dataset of rare events and demonstrate the differences in interpretation of the same datasets depending on whether the Brier or Ignorance score is used. While the two types of scores are broadly similar, there are substantial differences in interpretation at extreme probabilities. Information gain is measured in units of bits, an irreducible unit of information, that allows forecasts of different variables to be comparatively evaluated fairly. Further insight from information-based scores is gained via a similar reliability–discrimination decomposition as found in Brier-type scores. We conclude by crystallising multiple concepts to better equip forecast-system developers and decision-makers with tools to navigate complex trade-offs and uncertainties that characterise meteorological forecasting. To this end, we also provide computer code to reproduce data and figures herein.

Purpose Vertebral compression fractures (VCFs) are common and associated with high morbidity including severe, debilitating pain. Percutaneous vertebroplasty/kyphoplasty is a demonstrated effective treatment for VCF. Sarcopenia has been implicated as a risk factor for VCF and refracture following cement augmentation, and as a risk factor for procedural complications in some populations; however, the effect of sarcopenia on VCF patients undergoing these procedures is unknown. This study aims to improve outcomes and patient selection by investigating the effects of highly common VCF comorbidities.

Breast cancer survival has increased significantly over the last few decades due to more effective strategies for prevention and risk modification, advancements in imaging detection, screening, and multimodal treatment algorithms. However, many have observed disparities in benefits derived from such improvements across populations and demographic groups. This review summarizes published works that contextualize modern disparities in breast cancer prevention, diagnosis, and treatment and presents potential strategies for reducing disparities. We conducted searches for studies that directly investigated and/or reported disparities in breast cancer prevention, detection, or treatment. Demographic factors, social determinants of health, and inequitable healthcare delivery may impede the ability of individuals and communities to employ risk-mitigating behaviors and prevention strategies. The disparate access to quality screening and timely diagnosis experienced by various groups poses significant hurdles to optimal care and survival. Finally, barriers to access and inequitable healthcare delivery patterns reinforce inequitable application of standards of care. Cumulatively, these disparities underlie notable differences in the incidence, severity, and survival of breast cancers. Efforts toward mitigation will require collaborative approaches and partnerships between communities, governments, and healthcare organizations, which must be considered equal stakeholders in the fight for equity in breast cancer care and outcomes.

Barley (Hordeum vulgare ssp. vulgare) is cultivated from the equator to the Arctic Circle. The wild progenitor species, Hordeum vulgare ssp. spontaneum, occupies a relatively narrow latitudinal range (∼30 - 40° N) primarily at low elevation (< 1,500 m). Adaptation to the range of cultivation has occurred over ∼8,000 years. The genetic basis of adaptation is amenable to study through environmental association. An advantage of environmental association in a well-characterized crop is that many loci that contribute to climatic adaptation and abiotic stress tolerance have already been identified. This provides the opportunity to determine if environmental association approaches effectively identify these loci of large effect. Using published genotyping from 7,864 SNPs in 803 barley landraces, we examined allele frequency differentiation across multiple partitions of the data and mixed model associations relative to bioclimatic variables. Using newly generated resequencing data from a subset of these landraces, we tested for linkage disequilibrium (LD) between SNPs queried in genotyping and SNPs in neighboring loci. Six loci previously reported to contribute to adaptive differences in flowering time and abiotic stress in barley and six loci previously identified in other plant species were identified in our analyses. In many cases, patterns of LD are consistent with the causative variant occurring in the immediate vicinity of the queried SNP. The identification of barley orthologs to well-characterized genes may provide a new understanding of the nature of adaptive variation and could permit a more targeted use of potentially adaptive variants in barley breeding and germplasm improvement.

Germplasm collections hold valuable allelic diversity for crop improvement and genetic mapping of complex traits. To gain access to the genetic diversity within the USDA National Small Grain Collection (NSGC), we developed the Barley Recombinant Inbred Diverse Germplasm Population (BRIDG6), a six-row spring barley multiparent population (MPP) with 88 cultivated accessions crossed to a common parent (Rasmusson). The parents were randomly selected from a core subset of the NSGC that represents the genetic diversity of landrace and breeding accessions. In total, we generated 6160 F5 recombinant inbred lines (RILs), with an average of 69 and a range of 37-168 RILs per family, that were genotyped with 7773 SNPs, with an average of 3889 SNPs segregating per family. We detected 23 quantitative trait loci (QTL) associated with flowering time with five QTL found coincident with previously described flowering time genes. A major QTL was detected near the flowering time gene, HvPpd-H1 which affects photoperiod. Haplotype-based analysis of HvPpd-H1 identified private alleles to families of Asian origin conferring both positive and negative effects, providing the first observation of flowering time-related alleles private to Asian accessions. We evaluated several subsampling strategies to determine the effect of sample size on the power of QTL detection, and found that, for flowering time in barley, a sample size >50 families or 3000 individuals results in the highest power for QTL detection. This MPP will be useful for uncovering large and small effect QTL for traits of interest, and identifying and utilizing valuable alleles from the NSGC for barley improvement.

IntroductionThe field of machine learning and its subfield of deep learning have grown rapidly in recent years. With the speed of advancement, it is nearly impossible for data scientists to maintain expert knowledge of cutting-edge techniques. This study applies human factors methods to the field of machine learning to address these difficulties.MethodsUsing semi-structured interviews with data scientists at a National Laboratory, we sought to understand the process used when working with machine learning models, the challenges encountered, and the ways that human factors might contribute to addressing those challenges.ResultsResults of the interviews were analyzed to create a generalization of the process of working with machine learning models. Issues encountered during each process step are described.DiscussionRecommendations and areas for collaboration between data scientists and human factors experts are provided, with the goal of creating better tools, knowledge, and guidance for machine learning scientists.

Rationale and Objectives: Powered bone biopsy technique is popular due to its ease of use. However, there is conflicting evidence regarding the diagnostic quality of the samples. The purpose of this study is to evaluate the diagnostic adequacy of different bone biopsy devices and techniques as it relates to the frequency of sample artifacts. Materials and Methods: Bone biopsy was performed on same-day processed lamb femora using the following techniques: manual, pulsed powered and full powered. Ten samples were collected using each method by a single musculoskeletal-trained radiologist and were reviewed by 3 blinded pathologists. Samples were compared across multiple categories: length, bone dust, thermal/crush artifact, cellular morphology, fragmentation, and diagnostic acceptability. Bayesian Multilevel Nonlinear Regression models were performed assessing the association between the techniques across the categories. Results: Statistical analysis revealed that the manual technique outperformed any powered technique across all categories: decreased thermal/crush artifact (P = 0.014), decreased bone dust (p

Probabilistic prediction models exist to reduce surprise about future events. This paper explores the evaluation of such forecasts when the event of interest is rare. We review how the family of Brier-type scores may be ill-suited to evaluate predictions of rare events, and we offer an alternative to information-theoretical scores such as Ignorance. The reduction in surprise provided by a set of forecasts is represented as information gain, a frequent loss function in machine learning training, meaning the reduction in ignorance over a baseline having received a new forecast. We evaluate predictions of a synthetic dataset of rare events and demonstrate the differences in interpretation of the same datasets depending on whether the Brier or Ignorance score is used. While the two types of scores are broadly similar, there are substantial differences in interpretation at extreme probabilities. Information gain is measured in units of bits, an irreducible unit of information, that allows forecasts of different variables to be comparatively evaluated fairly. Further insight from information-based scores is gained via a similar reliability–discrimination decomposition as found in Brier-type scores. We conclude by crystallising multiple concepts to better equip forecast-system developers and decision-makers with tools to navigate complex trade-offs and uncertainties that characterise meteorological forecasting. To this end, we also provide computer code to reproduce data and figures herein. [ABSTRACT FROM AUTHOR]