Native American Juvenile Detainees in New Mexico: A Descriptive Study of Gender Differences, Mental and Behavioral Health Conditions, and Social Risk Factors

Risk factors for juvenile detention vary by gender but, in general, include low household income, individual and family histories of mental and behavioral disorders, sexual and physical abuse, low academic achievement/academic difficulty, and ethnic/racial minority status. In New Mexico, a number of these risk factors characterize the lives of Native American youth. However, the prevalence of and relationships among risk factors for detained Native American juveniles is unknown. Moreover the lack of data has impeded community-based mental and behavioral health treatment and prevention efforts meant to decrease destructive behavior and prevent initial or subsequent detention or incarceration

Corn Monoculture: No Friend of Biodiversity

Federal mandates for corn ethanol, which encourage farmers to plant more corn, may threaten the biodiversity of grasslands

A History and Informal Assessment of the Slacker Astronomy Podcast

Slacker Astronomy is a weekly podcast that covers a recent astronomical news event or discovery. The show has a unique style consisting of irreverent, over-the-top humor combined with a healthy dose of hard science. According to our demographic analysis, the combination of this style and the unique podcasting distribution mechanism allows the show to reach audiences younger and busier than those reached via traditional channels. We report on the successes and challenges of the first year of the show, and provide an informal assessment of its role as a source for astronomical news and concepts for its approximately 15,500 weekly listeners.Comment: 14 page

Clinical vignette: Acute esophageal necrosis

Acute Esophageal Necrosis (AEN), also known as Black Esophagus\u27 is a condition which occurs after a period of ischemia of the esophagus. On initial evaluation one must identify risk factors, diagnosis, and pathogenesis of the acute esophageal necrosis. Risk factors include age, male sex, heart disease, hemodynamic instability, alcohol ingestion, diabetes, renal insufficiency, and hypercoagulable state. The physiologic insult is often multi-factorial, ultimately leading up to ischemic compromise. Discovery of acute esophageal necrosis should be viewed as a poor prognostic factor, and may suggest eminent mortality from the underlying disease process.\u2

What is it that is being referred to as ecosystem management?

The wide range of definitions of ecosystem management depend upon the values of the persons defining it. While many of the speakers did not provide an explicit definition, four themes common to their presentations were ecological, social, political, and economical. We distilled from these presentations the following: ecosystem management is the manipulation of an ecosystem with all its species and functions to achieve specified social goals, and policed by the political system for some specified, sustainable economic return. A major source of contention is whose values should prevail. Since societal values change over time, EM must be flexible. Ecologically clear-cut boundaries do not necessarily provide socially and politically optimum results

Assessment of embedded conjugated polymer sensor arrays for potential load transmission measurement in orthopaedic implants

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Load transfer through orthopaedic joint implants is poorly understood. The longer-term outcomes of these implants are just starting to be studied, making it imperative to monitor contact loads across the entire joint implant interface to elucidate the force transmission and distribution mechanisms exhibited by these implants in service. This study proposes and demonstrates the design, implementation, and characterization of a 3D-printed smart polymer sensor array using conductive polyaniline (PANI) structures embedded within a polymeric parent phase. The piezoresistive characteristics of PANI were investigated to characterize the sensing behaviour inherent to these embedded pressure sensor arrays, including the experimental determination of the stable response of PANI to continuous loading, stability throughout the course of loading and unloading cycles, and finally sensor repeatability and linearity in response to incremental loading cycles. This specially developed multi-material additive manufacturing process for PANI is shown be an attractive approach for the fabrication of implant components having embedded smart-polymer sensors, which could ultimately be employed for the measurement and analysis of joint loads in orthopaedic implants for in vitro testing

Effects of Training Set Size on Supervised Machine-Learning Land-Cover Classification of Large-Area High-Resolution Remotely Sensed Data

The size of the training data set is a major determinant of classification accuracy. Neverthe- less, the collection of a large training data set for supervised classifiers can be a challenge, especially for studies covering a large area, which may be typical of many real-world applied projects. This work investigates how variations in training set size, ranging from a large sample size (n = 10,000) to a very small sample size (n = 40), affect the performance of six supervised machine-learning algo- rithms applied to classify large-area high-spatial-resolution (HR) (1–5 m) remotely sensed data within the context of a geographic object-based image analysis (GEOBIA) approach. GEOBIA, in which adjacent similar pixels are grouped into image-objects that form the unit of the classification, offers the potential benefit of allowing multiple additional variables, such as measures of object geometry and texture, thus increasing the dimensionality of the classification input data. The six supervised machine-learning algorithms are support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), single-layer perceptron neural networks (NEU), learning vector quantization (LVQ), and gradient-boosted trees (GBM). RF, the algorithm with the highest overall accuracy, was notable for its negligible decrease in overall accuracy, 1.0%, when training sample size decreased from 10,000 to 315 samples. GBM provided similar overall accuracy to RF; however, the algorithm was very expensive in terms of training time and computational resources, especially with large training sets. In contrast to RF and GBM, NEU, and SVM were particularly sensitive to decreasing sample size, with NEU classifications generally producing overall accuracies that were on average slightly higher than SVM classifications for larger sample sizes, but lower than SVM for the smallest sample sizes. NEU however required a longer processing time. The k-NN classifier saw less of a drop in overall accuracy than NEU and SVM as training set size decreased; however, the overall accuracies of k-NN were typically less than RF, NEU, and SVM classifiers. LVQ generally had the lowest overall accuracy of all six methods, but was relatively insensitive to sample size, down to the smallest sample sizes. Overall, due to its relatively high accuracy with small training sample sets, and minimal variations in overall accuracy between very large and small sample sets, as well as relatively short processing time, RF was a good classifier for large-area land-cover classifications of HR remotely sensed data, especially when training data are scarce. However, as performance of different supervised classifiers varies in response to training set size, investigating multiple classification algorithms is recommended to achieve optimal accuracy for a project