Comparison of Roadside Crash Injury Metrics using Event Data Recorders

The occupant impact velocity (OIV) and acceleration severity index (ASI) are competing measures of crash severity used to assess occupant injury risk in full-scale crash tests involving roadside safety hardware, e.g. guardrail. Delta-V, or the maximum change in vehicle velocity, is the traditional metric of crash severity for real world crashes. This study compares the ability of the OIV, ASI, and delta-V to discriminate between serious and non-serious occupant injury in real world frontal collisions. Vehicle kinematics data from event data recorders (EDRs) were matched with detailed occupant injury information for 180 real world crashes. Cumulative probability of injury risk curves were generated using binary logistic regression for belted and unbelted data subsets. By comparing the available fit statistics and performing a separate ROC curve analysis, the more computationally intensive OIV and ASI were found to offer no significant predictive advantage over the simpler delta-V.

Utilization of Probabilistic Models in Short Read Assembly from Second-Generation Sequencing

With the advent of cheaper and faster DNA sequencing technologies, assembly methods have greatly changed. Instead of outputting reads that are thousands of base pairs long, new sequencers parallelize the task by producing read lengths between 35 and 400 base pairs. Reconstructing an organism’s genome from these millions of reads is a computationally expensive task. Our algorithm solves this problem by organizing and indexing the reads using n-grams, which are short, fixed-length DNA sequences of length n. These n-grams are used to efficiently locate putative read joins, thereby eliminating the need to perform an exhaustive search over all possible read pairs. Our goal was develop a novel n-gram method for the assembly of genomes from next-generation sequencers. Specifically, a probabilistic, iterative approach was utilized to determine the most likely reads to join through development of a new metric that models the probability of any two arbitrary reads being joined together. Tests were run using simulated short read data based on randomly created genomes ranging in lengths from 10,000 to 100,000 nucleotides with 16 to 20x coverage. We were able to successfully re-assemble entire genomes up to 100,000 nucleotides in length.

Exploration Of Survival Traits In The White-Nose Syndrome-Affected Big Brown Bat (Eptesicus Fuscus)

The widespread mortality of hibernating bats is associated with the emerging infectious disease white-nose syndrome (WNS), and has provoked a strong interest in understanding which bats will survive, and why? The ability of infected bats to resist WNS may depend upon variation in the expression of different characteristics. In a captive colony of big brown bats, I sought to characterize the phenotypic variability, repeatability, and survivability for several key ¿survival¿ traits, including: torpor patterns, microclimate preferences, and wound healing capacity. Torpor patterns were profiled using temperature sensitive dataloggers throughout the hibernation season, while microclimate preferences were quantified by using temperature-graded boxes and thermal imaging. In order to assess wound healing capacity, small wing biopsies were obtained from each bat and healing progress was tracked for one month. Individuals exhibited a wide range of phenotypes that were significantly influenced by sex and body condition. Repeatability estimates suggest that there is not a strong genetic basis for the observed variation in torpor patterns or microclimate preferences. Certain phenotypes (e.g., BMI) were associated with an increased probability of overwinter survivorship, which suggests a basis for intra-species differences in WNS susceptibility. The results from this project provide novel insight into what we know about ¿who will survive,¿ and will influence the direction and implementation of future conservation and mitigation strategies.

Determinants of the Use of Specialist Mental Health Services by Nursing Home Residents

This study examines the effects of resident and facility characteristics on the probability of nursing home residents receiving treatment by mental health professionals.