Sparse Probit Linear Mixed Model

Linear Mixed Models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a continuous phenotype of interest, while simultaneously correcting for various confounding factors such as age, ethnicity and population structure. Formulated as models for linear regression, LMMs have been restricted to continuous phenotypes. We introduce the Sparse Probit Linear Mixed Model (Probit-LMM), where we generalize the LMM modeling paradigm to binary phenotypes. As a technical challenge, the model no longer possesses a closed-form likelihood function. In this paper, we present a scalable approximate inference algorithm that lets us fit the model to high-dimensional data sets. We show on three real-world examples from different domains that in the setup of binary labels, our algorithm leads to better prediction accuracies and also selects features which show less correlation with the confounding factors.Comment: Published version, 21 pages, 6 figure

Synchronized flow and wide moving jams from balanced vehicular traffic

Recently we proposed an extension to the traffic model of Aw, Rascle and Greenberg. The extended traffic model can be written as a hyperbolic system of balance laws and numerically reproduces the reverse $\lambda$ shape of the fundamental diagram of traffic flow. In the current work we analyze the steady state solutions of the new model and their stability properties. In addition to the equilibrium flow curve the trivial steady state solutions form two additional branches in the flow-density diagram. We show that the characteristic structure excludes parts of these branches resulting in the reverse $\lambda$ shape of the flow-density relation. The upper branch is metastable against the formation of synchronized flow for intermediate densities and unstable for high densities, whereas the lower branch is unstable for intermediate densities and metastable for high densities. Moreover, the model can reproduce the typical speed of the downstream front of wide moving jams. It further reproduces a constant outflow from wide moving jams, which is far below the maximum free flow. Applying the model to simulate traffic flow at a bottleneck we observe a general pattern with wide moving jams traveling through the bottleneck.Comment: 10 pages, 12 figure

Breathing 100% oxygen during water immersion improves postimmersion cardiovascular responses to orthostatic stress

Physiological compensation to postural stress is weakened after long-duration water immersion (WI), thus predisposing individuals to orthostatic intolerance. This study was conducted to compare hemodynamic responses to postural stress following exposure to WI alone (Air WI), hyperbaric oxygen alone in a hyperbaric chamber (O2HC), and WI combined with hyperbaric oxygen (O2WI), all at a depth of 1.35 ATA, and to determine whether hyperbaric oxygen is protective of orthostatic tolerance. Thirty-two healthy men underwent up to 15 min of 70° head-up tilt (HUT) testing before and after a single 6-h resting exposure to Air WI (N = 10), O2HC (N = 12), or O2WI (N = 10). Heart rate (HR), blood pressure (BP), cardiac output (Q), stroke volume (SV), forearm blood flow (FBF), and systemic and forearm vascular resistance (SVR and FVR) were measured. Although all subjects completed HUT before Air WI, three subjects reached presyncope after Air WI exposure at 10.4, 9.4, and 6.9 min. HUT time did not change after O2WI or O2HC exposures. Compared to preexposure responses, HR increased (+10 and +17%) and systolic BP (-13 and -8%), and SV (-16 and -23%) decreased during HUT after Air WI and O2WI, respectively. In contrast, HR and SV did not change, and systolic (+5%) and diastolic BP (+10%) increased after O2HC. Q decreased (-13 and -7%) and SVR increased (+12 and +20%) after O2WI and O2HC, respectively, whereas SVR decreased (-9%) after Air WI. Opposite patterns were evident following Air WI and O2HC for FBF (-26 and +52%) and FVR (+28 and -30%). Therefore, breathing hyperbaric oxygen during WI may enhance post-WI cardiovascular compensatory responses to orthostatic stress

Implications of the E-selectin S128R mutation for drug discovery

The C-type lectin E-selectin mediates the rolling of circulating leukocytes on vascular endothelial cells during the inflammatory process. In numerous studies, the S128R mutation of the E-selectin was associated with cardiovascular and autoimmune diseases. There is evidence that the S128R E-selectin mutation leads to a loss in ligand specificity, thus increasing leukocyte recruitment. Apart from the natural tetrasaccharide ligand sialyl Lewisx (sLex), it has previously been proposed that non-fucosylated carbohydrates also bind to S128R E-selectin. To evaluate the therapeutic potential of the antagonism of the E-selectin mutant, ligand specificity was reinvestigated on a molecular basis. We determined the ligand specificity of wild-type and S128R E-selectin in a target-based competitive assay, a glycan array screen and cell-based binding assays under static and flow conditions. Regarding ligand-specificity, the binding properties of S128R E-selectin were identical to those of wt E-selectin, i.e., no mutant-specific binding of 3′-sialyl-N-acetyllactosamine, heparin, fetuin and K562 cells was observed. Additionally, the binding affinities of glycomimetic E-selectin antagonists were identical for wt and S128R E-selectin. Overall, the previous reports on carbohydrate ligand promiscuity of S128R E-selectin could not be confirme

Data sharing and reanalysis of randomized controlled trials in leading biomedical journals with a full data sharing policy: survey of studies published in the BMJ and PLOS Medicine

Objectives To explore the effectiveness of data sharing by randomized controlled trials (RCTs) in journals with a full data sharing policy and to describe potential difficulties encountered in the process of performing reanalyses of the primary outcomes. Design Survey of published RCTs. Setting PubMed/Medline. Eligibility criteria RCTs that had been submitted and published by The BMJ and PLOS Medicine subsequent to the adoption of data sharing policies by these journals. Main outcome measure The primary outcome was data availability, defined as the eventual receipt of complete data with clear labelling. Primary outcomes were reanalyzed to assess to what extent studies were reproduced. Difficulties encountered were described. Results 37 RCTs (21 from The BMJ and 16 from PLOS Medicine) published between 2013 and 2016 met the eligibility criteria. 17/37 (46%, 95% confidence interval 30% to 62%) satisfied the definition of data availability and 14 of the 17 (82%, 59% to 94%) were fully reproduced on all their primary outcomes. Of the remaining RCTs, errors were identified in two but reached similar conclusions and one paper did not provide enough information in the Methods section to reproduce the analyses. Difficulties identified included problems in contacting corresponding authors and lack of resources on their behalf in preparing the datasets. In addition, there was a range of different data sharing practices across study groups. Conclusions Data availability was not optimal in two journals with a strong policy for data sharing. When investigators shared data, most reanalyses largely reproduced the original results. Data sharing practices need to become more widespread and streamlined to allow meaningful reanalyses and reuse of data

Assembling and Validating Data from Multiple Sources to Study Care for Veterans with Bladder Cancer

Despite the high prevalence of bladder cancer, research on optimal bladder cancer care is limited. One way to advance observational research on care is to use linked data from multiple sources. Such big data research can provide real-world details of care and outcomes across a large number of patients. We assembled and validated such data including (1) administrative data from the Department of Veterans Affairs (VA), (2) Medicare claims, (3) data abstracted by tumor registrars, (4) data abstracted via chart review from the national electronic health record, and (5) full text pathology reports

Effects of Resting, Consecutive, Long-Duration Water Immersions on Neuromuscular Endurance in Well-Trained Males

Purpose: This study examined the effects of repeated long-duration water immersions (WI)s at 1.35 atmospheres absolute (ATA) on neuromuscular endurance performance. We hypothesized that, following 5 days of consecutive, resting, long-duration WIs, neuromuscular endurance performance would decrease.Methods: Fifteen well-trained, male subjects completed five consecutive 6-h resting WIs with 18-h surface intervals during the dive week while breathing compressed air at 1.35 ATA. Skeletal muscle endurance performance was assessed before and after each WI, and 24 and 72 h after the final WI. Muscular endurance assessments included 40% maximum handgrip endurance (MHE) and 50-repetition maximal isokinetic knee extensions. Near infrared spectroscopy was used to measure muscle oxidative capacity of the vastus lateralis and localized muscle tissue oxygenation of the vastus lateralis and flexor carpi radialis. Simultaneously, brachioradialis neuromuscular activation was measured by surface electromyography.Results: A 24.9% increase (p = 0.04) in the muscle oxidative capacity rate constant (k) occurred on WI 4 compared to baseline. No changes occurred in 40% MHE time to exhaustion or rate of fatigue or total work performed for the 50-repetition maximal isokinetic knee extension. The first quartile of deoxygenated hemoglobin concentration showed a 6 and 35% increase on WIs 3 and 5 (p = 0.026) with second quartile increases of 9 and 32% on WIs 3 and 5 (p = 0.049) during the 40% MHE testing when compared to WI 1.Conclusion: Our specific WI protocol resulted in no change to muscular endurance and oxygen kinetics in load bearing and non-load bearing muscles

The XXL Survey V: Detection of the Sunyaev-Zel'dovich effect of the Redshift 1.9 Galaxy Cluster XLSSU J021744.1-034536 with CARMA

We report the detection of the Sunyaev-Zel'dovich (SZ) effect of galaxy cluster XLSSU J021744.1-034536, using 30 GHz CARMA data. This cluster was discovered via its extended X-ray emission in the XMM-Newton Large Scale Structure survey, the precursor to the XXL survey. It has a photometrically determined redshift $z=1.91^{+0.19}_{-0.21}$, making it among the most distant clusters known, and nominally the most distant for which the SZ effect has been measured. The spherically integrated Comptonization is $Y_{500}=(3.0\pm0.4)\times 10^{-12}$, a measurement which is relatively insensitive to assumptions regarding the size and redshift of the cluster, as well as the background cosmology. Using a variety of locally calibrated cluster scaling relations extrapolated to z~2, we estimate a mass $M_{500} \sim (1$-$2)\times 10^{14}M_{sun}$ from the X-ray flux and SZ signal. The measured properties of this cluster are in good agreement with the extrapolation of an X-ray luminosity-SZ effect scaling relation calibrated from clusters discovered by the South Pole Telescope at higher masses and lower redshifts. The full XXL-CARMA sample will provide a more complete, multi-wavelength census of distant clusters in order to robustly extend the calibration of cluster scaling relations to these high redshifts.Comment: ApJ, in press. 9 pages, 4 figures, 4 table

Thermally activated reorientation of di-interstitial defects in silicon

We propose a di-interstitial model for the P6 center commonly observed in ion implanted silicon. The di-interstitial structure and transition paths between different defect orientations can explain the thermally activated transition of the P6 center from low-temperature C1h to room-temperature D2d symmetry. The activation energy for the defect reorientation determined by ab initio calculations is 0.5 eV in agreement with the experiment. Our di-interstitial model establishes a link between point defects and extended defects, di-interstitials providing the nuclei for the growth.Comment: 12 pages, REVTeX, Four figures, submitted to Phys. Rev. Let

Classification of Two-Dimensional Gas Chromatography Data

Gas chromatography (GC) is a popular tool for chemical analysis. Some samples are so complex that a single column does not have enough power to separate all of the analytes. In this instance a higher resolution GC method, known as comprehensive two-dimensional gas chromatography (GCxGC), is used. DSTL want to be able to use data from GCxGC to attribute samples to a particular region or cultivar. However, the nature of the data means that several difficulties must be overcome before being able to do this: noise from sample, peak mis-alignment, and low quantity of samples. In this report, we investigate several methods to overcome such difficulties, and then classify the data. We are very successful in telling apart blanks from seeds, but obtain limited success when trying to classify between seeds. The method that shows the most promise is k-Nearest Neighbours classification by Wasserstein distance. However, this is still quite sensitive to the noise created by the solvent in the sample. Thus, we suggest that more blank runs be obtained, so that the ‘ground truth’ behaviour of the solvent is better understood, allowing us to remove the effect of the solvent from seed data. We also hope that the methods explored here will be more successful on the full raw data than they were on the limited ‘peaks’ data available to us for the purpose of this study