Effects of prescreening for likelihood ratio approaches in the forensic identification of source problems.

Source prescreening is a methodology where forensic examiners select samples that are similar to given trace evidence to represent the background population. This background evidence helps assign a value of evidence using a likelihood ratio or Bayes factor. A potential benefit of prescreening is a mitigation of effects from subpopulation structures within the alternative source population by isolating the relevant subpopulation.

A machine learning (ML) approach to understanding participation in government nutrition programs.

Machine Learning (ML) affords researchers tools to advance beyond research methods commonly employed in psychology, business, and public policy studies of federal nutrition programs and participant food decision-making. It is a sub domain of AI that is applied for feature extraction - a crucial step in decision making. These features are used in context-specific automated decisions resulting in predictive AI models.

Relative Genetic Homogeneity within a Phenotypically Diverse group: The Case of Lake Tana Labeobarbus (Cyprinidae) Species Flock, Ethiopia.

The Lake Tana Labeobarbus species flock represents one of the world's most famous examples of lacustrine species radiations. Previous studies of this group have resulted in the description of at least 15 species based on their differences in functional morphology and definition of two clades (lacustrine and riverine spawning clades) based on life history traits. A total of 166 fish representing 14 Labeobarbus species were genotyped using 10 lineage-specific hexaploid microsatellite loci.

Spatial Analysis of Breast Cancer Mortality Rates in a Rural State.

Introduction: Breast cancer affects 1 in 8 women in the US and is the most frequently diagnosed cancer in women. In South Dakota, 102 women die from breast cancer each year. We assessed which sociodemographic factors contributed to mortality rates in South Dakota and used spatial analysis to investigate how counties' observed age-adjusted mortality rates compared with expected rates.

Using Electronic Medical Records and Health Claim Data to Develop a Patient Engagement Score for Patients With Multiple Chronic Conditions: An Exploratory Study.

The study objective was to (1) develop a statistical model that creates a novel patient engagement score (PES) from electronic medical records (EMR) and health claim data, and (2) validate this developed score using health-related outcomes and charges of patients with multiple chronic conditions (MCCs). This study used 2014-16 EMR and health claim data of patients with MCCs from Sanford Health. Patient engagement score was created based on selected patients' engagement behaviors using Gaussian finite mixture model.

Forecasting Participants in the All Women Count! Mammography Program.

Introduction: The All Women Count! (AWC!) program is a no-cost breast and cervical cancer screening program for qualifying women in South Dakota. Our study aimed to identify counties with similar socioeconomic characteristics and to estimate the number of women who will use the program for the next 5 years.

Methods: We used AWC! data and sociodemographic predictor variables (eg, poverty level [percentage of the population with an annual income at or below 200% of the Federal Poverty Level], median income) and a mixture of Gaussian regression time series models to perform clustering and forecasting simultaneously.