Maternal educational level and risk of gestational hypertension: the Generation R Study.

We examined whether maternal educational level as an indicator of socioeconomic status is associated with gestational hypertension. We also examined the extent to which the effect of education is mediated by maternal substance use (that is smoking, alcohol consumption and illegal drug use), pre-existing diabetes, anthropometrics (that is height and body mass index (BMI)) and blood pressure at enrolment. This was studied in 3262 Dutch pregnant women participating in the Generation R Study, a population-based cohort study. Level of maternal education was established by questionnaire at enrolment, and categorized into high, mid-high, mid-low and low. Diagnosis of gestational hypertension was retrieved from medical records using standard criteria. Odds ratios (OR) of gestational hypertension for educational levels were calculated, adjusted for potential confounders and additionally adjusted for potential mediators. Adjusted for age and gravidity, women with mid-low (OR: 1.52; 95% CI: 1.02, 2.27) and low education (OR: 1.30; 95% CI: 0.80, 2.12) had a higher risk of gestational hypertension than women with high education. Additional adjustment for substance use, pre-existing diabetes, anthropometrics and blood pressure at enrolment attenuated these ORs to 1.09 (95% CI: 0.70, 1.69) and 0.89 (95% CI: 0.50, 1.58), respectively. These attenuations were largely due to the effects of BMI and blood pressure at enrolment. Women with relatively low educational levels have a higher risk of gestational hypertension, which is largely due to higher BMI and blood pressure levels from early pregnancy. The higher risk of gestational hypertension in these women is probably caused by pre-existing hypertensive tendencies that manifested themselves during pregnancy

Study of hadronic event-shape variables in multijet final states in pp collisions at √s=7 TeV

Peer reviewe

Search for new physics in the multijet and missing transverse momentum final state in proton-proton collisions at √s=8 Tev

Peer reviewe

Measurement of Higgs boson production and properties in the WW decay channel with leptonic final states

Peer reviewe

Constraints on parton distribution functions and extraction of the strong coupling constant from the inclusive jet cross section in pp collisions at √s=7 TeV

Peer reviewe

Trapping in irradiated p-on-n silicon sensors at fluences anticipated at the HL-LHC outer tracker

The degradation of signal in silicon sensors is studied under conditions expected at the CERN High-Luminosity LHC. 200 $\mu$m thick n-type silicon sensors are irradiated with protons of different energies to fluences of up to $3 \cdot 10^{15}$ neq/cm$^2$. Pulsed red laser light with a wavelength of 672 nm is used to generate electron-hole pairs in the sensors. The induced signals are used to determine the charge collection efficiencies separately for electrons and holes drifting through the sensor. The effective trapping rates are extracted by comparing the results to simulation. The electric field is simulated using Synopsys device simulation assuming two effective defects. The generation and drift of charge carriers are simulated in an independent simulation based on PixelAV. The effective trapping rates are determined from the measured charge collection efficiencies and the simulated and measured time-resolved current pulses are compared. The effective trapping rates determined for both electrons and holes are about 50% smaller than those obtained using standard extrapolations of studies at low fluences and suggests an improved tracker performance over initial expectations

Study of double parton scattering using W+2-jet events in proton-proton collisions at √s=7 TeV

Peer reviewe

Measurements of the tt¯ charge asymmetry using the dilepton decay channel in pp collisions at √s=7 TeV

Peer reviewe

XSTREAM: A practical algorithm for identification and architecture modeling of tandem repeats in protein sequences

<p>Abstract</p> <p>Background</p> <p>Biological sequence repeats arranged in tandem patterns are widespread in DNA and proteins. While many software tools have been designed to detect DNA tandem repeats (TRs), useful algorithms for identifying protein TRs with varied levels of degeneracy are still needed.</p> <p>Results</p> <p>To address limitations of current repeat identification methods, and to provide an efficient and flexible algorithm for the detection and analysis of TRs in protein sequences, we designed and implemented a new computational method called XSTREAM. Running time tests confirm the practicality of XSTREAM for analyses of multi-genome datasets. Each of the key capabilities of XSTREAM (e.g., merging, nesting, long-period detection, and TR architecture modeling) are demonstrated using anecdotal examples, and the utility of XSTREAM for identifying TR proteins was validated using data from a recently published paper.</p> <p>Conclusion</p> <p>We show that XSTREAM is a practical and valuable tool for TR detection in protein and nucleotide sequences at the multi-genome scale, and an effective tool for modeling TR domains with diverse architectures and varied levels of degeneracy. Because of these useful features, XSTREAM has significant potential for the discovery of naturally-evolved modular proteins with applications for engineering novel biostructural and biomimetic materials, and identifying new vaccine and diagnostic targets.</p