Adverse Childhood Experiences (ACEs) and Their Impact on Substance Misuse & Overall Health

Adverse childhood experiences (ACEs) encompass a wide variety of distressing events, including emotional, physical, or sexual abuse; witnessing maternal domestic violence; or living with a household member who has a substance use disorder, is mentally ill or suicidal, or is currently or was ever incarcerated during the first 18 years of a child’s life. According to most recent estimates, nearly half of Indiana’s youth have experienced at least one ACE in their life. ACEs are linked to many risk behaviors, including substance use, which can adversely affect health outcomes

Substance Abuse Trends in Indiana: A 10-Year Perspective

Substance use is a significant public health problem in the United States. Excessive use of alcohol and drugs has been linked to increased morbidity and mortality from cardiovascular conditions; injuries and motor vehicle crashes; sexually transmitted and blood-borne illnesses, including HIV/AIDS and hepatitis B and C, resulting from risky sexual behaviors and/or injection drug use; pregnancy complications and neonatal abstinence syndrome (NAS); and drug overdoses [5, 6]

The Impact of Parental Incarceration on Children’s Health & Development

The incarceration boom in the United States has resulted in high rates of parents serving time. According to recent estimates, one in ten Hoosier children has a parent who is or has been in prison or jail. Though incarceration is often treated as a discrete event, it is important to note that the time period extends both prior to and beyond the incarcerated phase (pre- and post-incarceration). Evidence on the relationship between parental incarceration and various children’s outcomes is inconsistent across the literature and often disappears when controlling for demographic and family characteristics. However, whether the relationship between parental incarceration and children’s health and development is causal or simply correlational, this population is at high risk for adverse outcomes and should be the target of interventions

Aeroheating Measurements of BOLT Aerodynamic Fairings and Transition Module

The Air Force Office of Scientific Research (AFOSR) has sponsored the Boundary Layer Transition (BOLT) Experiments to investigate hypersonic boundary layer transition on a low-curvature, concave surface with swept leading edges. This paper presents aeroheating measurements on a subscale model of the BOLT Flight Geometry, aerodynamic fairings, and Transition Module (TSM) in the NASA Langley 20-Inch Mach 6 Air Tunnel. The purpose of the test was to investigate and identify any areas of localized heating on the TSM for inclusion in the BOLT Critical Design Review (CDR). Surface heating distributions were measured using global phosphor thermography, and data were obtained for a range of model attitudes and free stream Reynolds numbers. Measurements showed low heating on the fairings and TSM. Additional analysis was completed after the CDR to compare heating on the TSM for the nominal BOLT vehicle reentry angle-of-attack with heating on the TSM for possible reentry angle-of-attack excursions. The results of this analysis were used in conjunction with thermal analyses from Johns Hopkins Applied Physics Lab (JHU/APL) and the Air Force Research Laboratory (AFRL) to assess the need for thermal protection on the flight vehicle TSM

Cross-Platform Normalization of Microarray and Rna-Seq Data for Machine Learning Applications

Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning models built from legacy data can be applied to RNA-seq data, larger, more diverse training datasets can be created and validation can be performed on newly generated data. We developed Training Distribution Matching (TDM), which transforms RNA-seq data for use with models constructed from legacy platforms. We evaluated TDM, as well as quantile normalization, nonparanormal transformation, and a simple log 2 transformation, on both simulated and biological datasets of gene expression. Our evaluation included both supervised and unsupervised machine learning approaches. We found that TDM exhibited consistently strong performance across settings and that quantile normalization also performed well in many circumstances. We also provide a TDM package for the R programming language

Review of Downscaling Methodologies for Africa Climate Applications

Downscaling is the term used to describe the various methods used to translate the climate projections from coarse resolution GCMs to finer resolutions deemed more useful for assessing impacts. Projections of future climate are produced using complex, coupled atmosphere-ocean models (GCMs). The GCMs are most reliable at the continental scale. Due to the inherent uncertainty of the climate system and the inevitable existence of model errors, multi-model ensembling is the recommended approach for characterizing expected climate changes. As downscaling is dependent on the ability of GCMs to successfully project the climate change signal, it is limited to where that signal is clear. Assessments of climate change in Africa indicate some consensus of reduced winter rainfall in southern Africa, increased annual rainfall in east Africa and uncertainty for the rest of Africa. Selection of GCMs that "do better" over Africa, or any region, is difficult and probably not warranted, given the general parity in model skill and the difficulty in identifying which models are more skillful. Ensemble means or medians offer the highest level of projection accuracy. Downscaling approaches are generally categorized as dynamical, using regional climate models, and statistical, using empirical relationships. However, dynamical downscaling often includes statistical modeling in the form of "bias correction." Dynamical downscaling is useful for incorporating topographic features, such as strong orography, and land use and vegetation. It is recommended where those features play a significant role in regional climate. However, computational time and the uncertainties that accompany complex models outweigh the benefits of dynamical downscaling where these features are not significant. The spatial resolution that can be achieved is on the order of tens of kilometers. Statistical downscaling is simpler and more efficient than dynamical downscaling. It is preferred where estimates of specific variables, especially at point locations, are sought for input to sector models (e.g., hydrologic models) or decision making. However, statistical modeling can mask a true understanding of regional climate dynamics and estimates may be overconfident. In summary, downscaling is best understood as an attempt to increase the understanding of climate change influences at the regional scale. In that context, a variety of methodologies should be explored, using all tools possible to increase that understanding. A set of "Best Practices" is recommended for pursuing this effort

Transforming Researchers into Educators: Some Reflections on the University College Dublin School of Law Syllabus Design Workshop 2010

The priority given to the development of research skills during doctrinal legal education often neglects the importance of equipping PhD students with the pedagogical skills necessary to fulfill their important educational role as academics. Thus, in many instances there is a significant gap in the requisite skill base that PhD students acquire when they complete their doctrinal education. This paper outlines a first step that has been taken to address this deficiency in postgraduate legal education in Ireland. The PhD community of the University College Dublin (UCD) School of Law convened an internal Syllabus Design Workshop in April 2010 in order to provide doctrinal students with an opportunity to design a university module and to explore the issues which arise in undertaking such an exercise. The first part of this paper outlines how the workshop was conceived and convened, and provides an account of the considerations that each student had to take into account in the design of a syllabus. From here, we address the content of the workshop and reflect upon some of the important issues.