Program Design and Student Outcomes in Graduate Education

Doctoral programs in the humanities and related social sciences are characterized by high attrition and long time-to-degree. In response to these long-standing problems, the Andrew W. Mellon Foundation launched the Graduate Education Initiative (GEI) to improve the structure and organization of PhD programs, and in turn reduce attrition and shorten time-to-degree. Over a 10-year period starting in 1991, the Foundation provided a total of $80 million to 51 departments at 10 major research universities. This paper estimates the impact of the GEI on attrition rates and time-to-degree. Our analysis is based on a competing-risk duration model and student-level data spanning the start of the GEI, including data on students at a set of control departments. We estimate that, on average, the GEI had modest impacts on student outcomes in the expected directions: reducing attrition rates, reducing time-to-degree, and increasing completion rates. The overall impacts of the GEI appear to have been driven in part by reductions in cohort size, increases in financial aid, and increases in student quality

Intranasal Application of S. epidermidis Prevents Colonization by Methicillin-Resistant Staphylococcus aureus in Mice

Methicillin-resistant S. aureus emerged in recent decades to become a leading cause of infection worldwide. Colonization with MRSA predisposes to infection and facilitates transmission of the pathogen; however, available regimens are ineffective at preventing MRSA colonization. Studies of human nasal flora suggest that resident bacteria play a critical role in limiting S. aureus growth, and prompted us to query whether application of commensal resident bacteria could prevent nasal colonization with MRSA. We established a murine model system to study this question, and showed that mice nasally pre-colonized with S. epidermidis became more resistant to colonization with MRSA. Our study suggests that application of commensal bacteria with antibiotics could represent a more effective strategy to prevent MRSA colonization

Effect of water vapor on the spallation of thermal barrier coating systems during laboratory cyclic oxidation testing.

The effect of water and water vapor on the lifetime of Ni-based superalloy samples coated with a typical thermal barrier coating system—b-(Ni,Pt)Al bond coat and yttria stabilized zirconia (YSZ) top coat deposited by electron beam physical vapor deposition (EB-PVD) was studied. Samples were thermally cycled to 1,150 C and subjected to a water-drop test in order to elucidate the effect of water vapor on thermal barrier coating (TBC) spallation. It was shown that the addition of water promotes spallation of TBC samples after a given number of cycles at 1,150 C. This threshold was found to be equal to 170 cycles for the present system. Systems based on b-NiAl bond coat or on Pt-rich c/c0 bond coat were also sensitive to the water-drop test. Moreover, it was shown that water vapor in ambient air after minutes or hours at room temperature, promotes also TBC spallation once the critical number of cycles has been reached. This desktop spalling (DTS) can be prevented by locking up the cycled samples in a dry atmosphere box. These results for TBC systems confirm and document Smialek’s theory about DTS and moisture induced delayed spalling (MIDS) being the same phenomenon. Finally, the mechanisms implying hydrogen embrittlement or surface tension modifications are discussed

Gentrepid V2.0: a web server for candidate disease gene prediction

Contains fulltext : 124935.pdf (publisher's version ) (Open Access)BACKGROUND: Candidate disease gene prediction is a rapidly developing area of bioinformatics research with the potential to deliver great benefits to human health. As experimental studies detecting associations between genetic intervals and disease proliferate, better bioinformatic techniques that can expand and exploit the data are required. DESCRIPTION: Gentrepid is a web resource which predicts and prioritizes candidate disease genes for both Mendelian and complex diseases. The system can take input from linkage analysis of single genetic intervals or multiple marker loci from genome-wide association studies. The underlying database of the Gentrepid tool sources data from numerous gene and protein resources, taking advantage of the wealth of biological information available. Using known disease gene information from OMIM, the system predicts and prioritizes disease gene candidates that participate in the same protein pathways or share similar protein domains. Alternatively, using an ab initio approach, the system can detect enrichment of these protein annotations without prior knowledge of the phenotype. CONCLUSIONS: The system aims to integrate the wealth of protein information currently available with known and novel phenotype/genotype information to acquire knowledge of biological mechanisms underpinning disease. We have updated the system to facilitate analysis of GWAS data and the study of complex diseases. Application of the system to GWAS data on hypertension using the ICBP data is provided as an example. An interesting prediction is a ZIP transporter additional to the one found by the ICBP analysis. The webserver URL is https://www.gentrepid.org/

Hounsfield unit change in root and alveolar bone during canine retraction

INTRODUCTION: The objective of this study was to determine the Hounsfield unit (HU) changes in the alveolar bone and root surfaces during controlled canine retractions. METHODS: Eighteen maxillary canine retraction patients were selected for this split-mouth design clinical trial. The canines in each patient were randomly assigned to receive either translation or controlled tipping treatment. Pretreatment and posttreatment cone-beam computed tomography scans of each patient were used to determine tooth movement direction and HU changes. The alveolar bone and root surface were divided into 108 divisions, respectively. The HUs in each division were measured. Mixed-model analysis of variance was applied to test the HU change distribution at the P <0.05 significance level. RESULTS: The HU changes varied with the directions relative to the canine movement. The HU reductions occurred at the root surfaces. Larger reductions occurred in the divisions that were perpendicular to the moving direction. However, HUs decreased in the alveolar bone in the moving direction. The highest HU reduction was at the coronal level. CONCLUSIONS: HU reduction occurs on the root surface in the direction perpendicular to tooth movement and in the alveolar bone in the direction of tooth movement when a canine is retracted

Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms

Myocardial infarction is the leading cause of death worldwide. In this paper, we design domain-inspired neural network models to detect myocardial infarction. First, we study the contribution of various leads. This systematic analysis, first of its kind in the literature, indicates that out of 15 ECG leads, data from the v6, vz, and ii leads are critical to correctly identify myocardial infarction. Second, we use this finding and adapt the ConvNetQuake neural network model--originally designed to identify earthquakes--to attain state-of-the-art classification results for myocardial infarction, achieving $99.43\%$ classification accuracy on a record-wise split, and $97.83\%$ classification accuracy on a patient-wise split. These two results represent cardiologist-level performance level for myocardial infarction detection after feeding only 10 seconds of raw ECG data into our model. Third, we show that our multi-ECG-channel neural network achieves cardiologist-level performance without the need of any kind of manual feature extraction or data pre-processing.Comment: Accepted to the European Medical and Biological Engineering Conference (EMBEC) 202