Trabecular microstructure is influenced by race and sex in Black and White young adults.

Lower fracture rates in Black men and women compared to their White counterparts are incompletely understood. High-resolution imaging specific to trabecular bone may provide insight. Black participants have enhanced trabecular morphology. These differences may contribute to the lower fracture risk in Black versus White individuals. INTRODUCTION: Lower fracture rates in Black men and women compared to their White counterparts may be explained by favorable bone microstructure in Black individuals. Individual trabecular segmentation (ITS) analysis, which characterizes the alignment and plate- and rod-like nature of trabecular bone using high-resolution peripheral quantitative computed tomography (HR-pQCT), may provide insight into trabecular differences by race/ethnic origin. PURPOSE: We determined differences in trabecular bone microarchitecture, connectivity, and alignment according to race/ethnic origin and sex in young adults. METHODS: We analyzed HR-pQCT scans of 184 adult (24.2 ± 3.4 years) women (n = 51 Black, n = 50 White) and men (n = 34 Black, n = 49 White). We used ANCOVA to compare bone outcomes, and adjusted for age, height, and weight. RESULTS: Overall, the effect of race on bone outcomes did not differ by sex, and the effect of sex on bone outcomes did not differ by race. After adjusting for covariates, Black participants and men of both races had greater trabecular plate volume fraction, plate thickness, plate number density, plate surface area, and greater axial alignment of trabeculae, leading to higher trabecular bone stiffness compared to White participants and women, respectively (p < 0.05 for all). CONCLUSION: These findings demonstrate that more favorable bone microarchitecture in Black individuals compared to White individuals and in men compared to women is not unique to the cortical bone compartment. Enhanced plate-like morphology and greater trabecular axial alignment, established in young adulthood, may contribute to the improved bone strength and lower fracture risk in Black versus White individuals and in men compared to women.

Assessment of the Effectiveness of Combat Eyewear Protection Against Blast Overpressure.

It is unclear whether combat eyewear used by U. S. Service members is protective against blast overpressures (BOPs) caused by explosive devices. Here, we investigated the mechanisms by which BOP bypasses eyewear and increases eye surface pressure. We performed experiments and developed three-dimensional (3D) finite element (FE) models of a head form (HF) equipped with an advanced combat helmet (ACH) and with no eyewear, spectacles, or goggles in a shock tube at three BOPs and five head orientations relative to the blast wave. Overall, we observed good agreement between experimental and computational results, with average discrepancies in impulse and peak-pressure values of less than 15% over 90 comparisons. In the absence of eyewear and depending on the head orientation, we identified three mechanisms that contributed to pressure loading on the eyes. Eyewear was most effective at 0 deg orientation, with pressure attenuation ranging from 50 (spectacles) to 80% (goggles) of the peak pressures observed in the no-eyewear configuration. Spectacles and goggles were considerably less effective when we rotated the HF in the counter-clockwise direction around the superior-inferior axis of the head. Surprisingly, at certain orientations, spectacles yielded higher maximum pressures (80%) and goggles yielded larger impulses (150%) than those observed without eyewear. The findings from this study will aid in the design of eyewear that provides better protection against BOP.

Large-scale interaction effects reveal missing heritability in schizophrenia, bipolar disorder and posttraumatic stress disorder.

Genetic susceptibility factors behind psychiatric disorders typically contribute small effects individually. A possible explanation for the missing heritability is that the effects of common variants are not only polygenic but also non-additive, appearing only when interactions within large groups are taken into account. Here, we tested this hypothesis for schizophrenia (SZ) and bipolar disorder (BP) disease risks, and identified genetic factors shared with posttraumatic stress disorder (PTSD). When considered independently, few single-nucleotide polymorphisms (SNPs) reached genome-wide significance. In contrast, when SNPs were selected in groups (containing up to thousands each) and the collective effects of all interactions were estimated, the association strength for SZ/BP rose dramatically with a combined sample size of 7187 cases and 8309 controls. We identified a large number of genes and pathways whose association was significant only when interaction effects were included. The gene with highest association was CSMD1, which encodes a negative regulator of complement activation. Pathways for glycosaminoglycan (GAG) synthesis exhibited strong association in multiple contexts. Taken together, highly associated pathways suggested a pathogenesis mechanism where maternal immune activation causes disruption of neurogenesis (compounded by impaired cell cycle, DNA repair and neuronal migration) and deficits in cortical interneurons, leading to symptoms triggered by synaptic pruning. Increased risks arise from GAG deficiencies causing complement activation and excessive microglial action. Analysis of PTSD data sets suggested an etiology common to SZ/BP: interneuron deficiency can also lead to impaired control of fear responses triggered by trauma. We additionally found PTSD risk factors affecting synaptic plasticity and fatty acid signaling, consistent with the fear extinction model. Our results suggest that much of the missing heritability of psychiatric disorders resides in non-additive interaction effects.

Development and validation of a portable platform for deploying decision-support algorithms in prehospital settings.

BACKGROUND: Advanced decision-support capabilities for prehospital trauma care may prove effective at improving patient care. Such functionality would be possible if an analysis platform were connected to a transport vital-signs monitor. In practice, there are technical challenges to implementing such a system. Not only must each individual component be reliable, but, in addition, the connectivity between components must be reliable. OBJECTIVE: We describe the development, validation, and deployment of the Automated Processing of Physiologic Registry for Assessment of Injury Severity (APPRAISE) platform, intended to serve as a test bed to help evaluate the performance of decision-support algorithms in a prehospital environment. METHODS: We describe the hardware selected and the software implemented, and the procedures used for laboratory and field testing. RESULTS: The APPRAISE platform met performance goals in both laboratory testing (using a vital-sign data simulator) and initial field testing. After its field testing, the platform has been in use on Boston MedFlight air ambulances since February of 2010. CONCLUSION: These experiences may prove informative to other technology developers and to healthcare stakeholders seeking to invest in connected electronic systems for prehospital as well as in-hospital use. Our experiences illustrate two sets of important questions: are the individual components reliable (e.g., physical integrity, power, core functionality, and end-user interaction) and is the connectivity between components reliable (e.g., communication protocols and the metadata necessary for data interpretation)? While all potential operational issues cannot be fully anticipated and eliminated during development, thoughtful design and phased testing steps can reduce, if not eliminate, technical surprises.

A fuzzy logic algorithm to assign confidence levels to heart and respiratory rate time series.

We have developed a fuzzy logic-based algorithm to qualify the reliability of heart rate (HR) and respiratory rate (RR) vital-sign time-series data by assigning a confidence level to the data points while they are measured as a continuous data stream. The algorithm's membership functions are derived from physiology-based performance limits and mass-assignment-based data-driven characteristics of the signals. The assigned confidence levels are based on the reliability of each HR and RR measurement as well as the relationship between them. The algorithm was tested on HR and RR data collected from subjects undertaking a range of physical activities, and it showed acceptable performance in detecting four types of faults that result in low-confidence data points (receiver operating characteristic areas under the curve ranged from 0.67 (SD 0.04) to 0.83 (SD 0.03), mean and standard deviation (SD) over all faults). The algorithm is sensitive to noise in the raw HR and RR data and will flag many data points as low confidence if the data are noisy; prior processing of the data to reduce noise allows identification of only the most substantial faults. Depending on how HR and RR data are processed, the algorithm can be applied as a tool to evaluate sensor performance or to qualify HR and RR time-series data in terms of their reliability before use in automated decision-assist systems.

An intelligent emissions controller for fuel lean gas reburn in coal-fired power plants.

The application of artificial intelligence techniques for performance optimization of the fuel lean gas reburn (FLGR) system is investigated. A multilayer, feedforward artificial neural network is applied to model static nonlinear relationships between the distribution of injected natural gas into the upper region of the furnace of a coal-fired boiler and the corresponding oxides of nitrogen (NOx) emissions exiting the furnace. Based on this model, optimal distributions of injected gas are determined such that the largest NOx reduction is achieved for each value of total injected gas. This optimization is accomplished through the development of a new optimization method based on neural networks. This new optimal control algorithm, which can be used as an alternative generic tool for solving multidimensional nonlinear constrained optimization problems, is described and its results are successfully validated against an off-the-shelf tool for solving mathematical programming problems. Encouraging results obtained using plant data from one of Commonwealth Edison's coal-fired electric power plants demonstrate the feasibility of the overall approach. Preliminary results show that the use of this intelligent controller will also enable the determination of the most cost-effective operating conditions of the FLGR system by considering, along with the optimal distribution of the injected gas, the cost differential between natural gas and coal and the open-market price of NOx emission credits. Further study, however, is necessary, including the construction of a more comprehensive database, needed to develop high-fidelity process models and to add carbon monoxide (CO) emissions to the model of the gas reburn system.