Gender, guns, and legislating: An analysis of state legislative policy preferences

Author's manuscript made available in accordance with the publisher's policy.Extant research suggests that gender affects the policy preferences of state legislators, particularly on women's issues. Fewer studies, however, have examined whether gender affects state legislators' policy preferences on other issues such as gun control. The current study uses 2000 National Political Awareness Test (NPAT) data to examine whether gender affects the preferences of state legislators regarding gun control policies. Results suggest that net of individual and constituency characteristics, female legislators are more likely to indicate support for gun control policies than their male counterparts

The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure

Federated Benchmarking of Medical Artificial Intelligence With MedPerf

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform

Political communication in state legislative races: An analysis of media impacts in Illinois.

Political communication in state legislative races: An analysis of media impacts in Illinois

Understanding cultural factors contributing to obesity in Head Start Hispanic preschoolers: Perceptions from one county Head Start

Obesity rates among low-income Hispanic preschoolers are higher than those of whites, highlighting the need for understanding the cultural factors that may contribute to obesity. A survey was distributed to Hispanic Head Start families; preschooler body mass index (BMI) was calculated. Two focus groups examined caregiver perceptions about obesity and meal practices. The rate of overweight/obesity in the preschoolers was 44%, whereas, 79.4% of caregivers reported child weight as “normal.†Caregivers perceived “thinness†as a disadvantage, favored home-cooked meals, and expressed a desire for children to assimilate to mainstream foods. Obesity prevention within Head Start must account for caregiver perceptions of healthy weight and incongruities between cultural values/ practices and guidelines. Head Start practitioners must understand the influence that school foods/meal styles have on cultural meal practices at home and the influence of social networks on home health behaviors. An opportunity exists to educate families within their cultural social networks

Stacking Machine Learning Algorithms for Biomarker-Based Preoperative Diagnosis of a Pelvic Mass

Objective: To identify the most predictive parameters of ovarian malignancy and develop a machine learning (ML) based algorithm to preoperatively distinguish between a benign and malignant pelvic mass. Methods: Retrospective study of 70 predictive parameters collected from 140 women with a pelvic mass. The women were split into a 3:1 “training” to “testing” dataset. Feature selection was performed using Gini impurity through an embedded random forest model and principal component analysis. Nine unique ML classifiers were assessed across a variety of model-specific hyperparameters using 25 bootstrap resamples of the training data. Model predictions were then combined into an ensemble stack by LASSO regression. The final ensemble stack and individual classifiers were then applied to the testing dataset to assess model performance. Results: Feature selection identified HE4, CA125, and transferrin as three predictive parameters of malignancy. Assessment of the ensemble stack on the testing dataset outperformed all individual ML classifiers in predicting malignancy. The ensemble stack demonstrated an accuracy of 97.1%, a receiver operating characteristic (ROC) area under the curve (AUC) of 0.951, and a sensitivity of 93.3% with a specificity of 100%. Conclusions: Combining the measurement of three distinct biomarkers with the stacking of multiple ML classifiers into an ensemble can provide valuable preoperative diagnostic predictions for patients with a pelvic mass

Multiple Biomarker Algorithms to Predict Epithelial Ovarian Cancer in Women with a Pelvic Mass: Can Additional Makers Improve Performance?

Introduction Management of a woman with a pelvic mass is complicated by difficulty in discriminating malignant from benign disease. Many serum biomarkers have been examined to determine their sensitivity for detecting malignancy. This study was designed to evaluate if the addition of biomarkers to HE4 and CA125, as used in the Risk of Malignancy Algorithm (ROMA), can improve the detection of EOC. Methods This was an IRB approved, prospective clinical trial examining serum obtained from women diagnosed with a pelvic mass who subsequently underwent surgery. Serum biomarker levels for CA125, HE4, YKL-40, transthyretin, ApoA1, Beta-2-microglobulin, transferrin, and LPA were measured. Logistic regression analysis was performed for various marker combinations, ROC curves were generated, and the area under the curves (AUCs) were determined. Results A total of 184 patients met inclusion criteria with a median age of 56 years (Range 20–91). Final pathology revealed there were 103 (56.0%) benign tumors, 4 (2.2%) LMP tumors, 61 EOC (33.1%), 2 (1.1%) non-EOC ovarian cancers, 6 (3.3%) gynecologic cancers with metastasis to the ovary and 8 (4.3%) non-gynecologic cancers with metastasis to the ovary. The combination of HE4 and CA125 (i.e. ROMA) achieved an AUC of 91.2% (95% CI: 86.0–96.4) for the detection of EOC vs benign disease. The combination of CA125, HE4, YKL-40, transthyretin, ApoA1, Beta 2 microglobulin, transferrin, LPA and menopausal status achieved the highest AUC of 94.6% (95% CI: 90.1–99.2) but this combination was not significantly better than the HE4 and CA125 combination alone (p = 0.078). Conclusions The addition of select further serum biomarkers to HE4 and CA125 does not add to the performance of the dual marker combination for the detection of ovarian cancer