Electrocardiographic repolarization-related variables as predictors of coronary heart disease death in the women's health initiative study.

BackgroundWe evaluated 25 repolarization-related ECG variables for the risk of coronary heart disease (CHD) death in 52 994 postmenopausal women from the Women's Health Initiative study.Methods and resultsHazard ratios from Cox regression were computed for subgroups of women with and without cardiovascular disease (CVD). During the average follow-up of 16.9 years, 941 CHD deaths occurred. Based on electrophysiological considerations, 2 sets of ECG variables with low correlations were considered as candidates for independent predictors of CHD death: Set 1, Ѳ(Tp|Tref), the spatial angle between T peak (Tp) and normal T reference (Tref) vectors; Ѳ(Tinit|Tterm), the angle between the initial and terminal T vectors; STJ depression in V6 and rate-adjusted QTp interval (QTpa); and Set 2, TaVR and TV1 amplitudes, heart rate, and QRS duration. Strong independent predictors with over 2-fold increased risk for CHD death in women with and without CVD were Ѳ(Tp|Tref) >42° from Set 1 and TaVR amplitude >-100 μV from Set 2. The risk for these CHD death predictors remained significant after multivariable adjustment for demographic/clinical factors. Other significant predictors for CHD death in fully adjusted risk models were Ѳ(Tinit|Tterm) >30°, TV1 >175 μV, and QRS duration >100 ms.ConclusionsѲ(Tp|Tref) angle and TaVR amplitude are associated with CHD mortality in postmenopausal women. The use of these measures to identify high-risk women for further diagnostic evaluation or more intense preventive intervention warrants further study.Clinical trial registration urlhttp://www.clinicaltrials.gov. Unique identifier: NCT00000611

Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence.

Recent developments allowed generating multiple high-quality \u27omics\u27 data that could increase the predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here, we have assessed the performance of parametric and nonparametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using the best linear unbiased prediction, while nonparametric models were implemented using the gradient boosting machine algorithm. We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While gradient boosting machine models captured more phenotypic variation, their predictive performance did not exceed the best linear unbiased prediction models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the gradient boosting machine model was able to outperform best linear unbiased prediction for most traits except body weight, but the same pattern was not observed when using both single nucleotide polymorphism genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed the highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values

Prediction performance of linear models and gradient boosting machine on complex phenotypes in outbred mice.

We compared the performance of linear (GBLUP, BayesB, and elastic net) methods to a nonparametric tree-based ensemble (gradient boosting machine) method for genomic prediction of complex traits in mice. The dataset used contained genotypes for 50,112 SNP markers and phenotypes for 835 animals from 6 generations. Traits analyzed were bone mineral density, body weight at 10, 15, and 20 weeks, fat percentage, circulating cholesterol, glucose, insulin, triglycerides, and urine creatinine. The youngest generation was used as a validation subset, and predictions were based on all older generations. Model performance was evaluated by comparing predictions for animals in the validation subset against their adjusted phenotypes. Linear models outperformed gradient boosting machine for 7 out of 10 traits. For bone mineral density, cholesterol, and glucose, the gradient boosting machine model showed better prediction accuracy and lower relative root mean squared error than the linear models. Interestingly, for these 3 traits, there is evidence of a relevant portion of phenotypic variance being explained by epistatic effects. Using a subset of top markers selected from a gradient boosting machine model helped for some of the traits to improve the accuracy of prediction when these were fitted into linear and gradient boosting machine models. Our results indicate that gradient boosting machine is more strongly affected by data size and decreased connectedness between reference and validation sets than the linear models. Although the linear models outperformed gradient boosting machine for the polygenic traits, our results suggest that gradient boosting machine is a competitive method to predict complex traits with assumed epistatic effects

Detection of a Corrugated Velocity Pattern in the Spiral Galaxy NGC 5427

Here we report the detection, in Halpha emission, of a radial corrugation in the velocity field of the spiral galaxy NGC 5427. The central velocity of the Halpha line displays coherent, wavy-like variations in the vicinity of the spiral arms. The spectra along three different arm segments show that the maximum amplitude of the sinusoidal line variations are displaced some 500 pc from the central part of the spiral arms. The peak blueshifted velocities appear some 500 pc upstream the arm, whereas the peak redshifted velocities are located some 500 pc downstream the arm. This kinematical behavior is similar to the one expected in a galactic bore generated by the interaction of a spiral density wave with a thick gaseous disk, as recently modeled by Martos & Cox (1998).Comment: Accepted for publication in Ap

Towards an understanding of the rapid decline of the cosmic star formation rate

We present a first analysis of deep 24 micron observations with the Spitzer Space Telescope of a sample of nearly 1500 galaxies in a thin redshift slice, 0.65<z<0.75. We combine the infrared data with redshifts, rest-frame luminosities, and colors from COMBO-17, and with morphologies from Hubble Space Telescope images collected by the GEMS and GOODS projects. To characterize the decline in star-formation rate (SFR) since z~0.7, we estimate the total thermal infrared (IR) luminosities, SFRs, and stellar masses for the galaxies in this sample. At z~0.7, nearly 40% of intermediate and high-mass galaxies (with stellar masses >2x10^10 solar masses) are undergoing a period of intense star formation above their past-averaged SFR. In contrast, less than 1% of equally-massive galaxies in the local universe have similarly intense star formation activity. Morphologically-undisturbed galaxies dominate the total infrared luminosity density and SFR density: at z~0.7, more than half of the intensely star-forming galaxies have spiral morphologies, whereas less than \~30% are strongly interacting. Thus, a decline in major-merger rate is not the underlying cause of the rapid decline in cosmic SFR since z~0.7. Physical properties that do not strongly affect galaxy morphology - for example, gas consumption and weak interactions with small satellite galaxies - appear to be responsible.Comment: To appear in the Astrophysical Journal 1 June 2005. 14 pages with 8 embedded figure

Tyrosine Phosphorylation Regulates the Endocytosis and Surface Expression of GluN3A-Containing NMDA Receptors

Selective control of receptor trafficking provides a mechanism for remodeling the receptor composition of excitatory synapses, and thus supports synaptic transmission, plasticity, and development. GluN3A (formerly NR3A) is a nonconventional member of the NMDA receptor (NMDAR) subunit family, which endows NMDAR channels with low calcium permeability and reduced magnesium sensitivity compared with NMDARs comprising only GluN1 and GluN2 subunits. Because of these special properties, GluN3A subunits act as a molecular brake to limit the plasticity and maturation of excitatory synapses, pointing toward GluN3A removal as a critical step in the development of neuronal circuitry. However, the molecular signals mediating GluN3A endocytic removal remain unclear. Here we define a novel endocytic motif (YWL), which is located within the cytoplasmic C-terminal tail of GluN3A and mediates its binding to the clathrin adaptor AP2. Alanine mutations within the GluN3A endocytic motif inhibited clathrin-dependent internalization and led to accumulation of GluN3A-containing NMDARs at the cell surface, whereas mimicking phosphorylation of the tyrosine residue promoted internalization and reduced cell-surface expression as shown by immunocytochemical and electrophysiological approaches in recombinant systems and rat neurons in primary culture. We further demonstrate that the tyrosine residue is phosphorylated by Src family kinases, and that Src-activation limits surface GluN3A expression in neurons. Together, our results identify a new molecular signal for GluN3A internalization that couples the functional surface expression of GluN3A-containing receptors to the phosphorylation state of GluN3A subunits, and provides a molecular framework for the regulation of NMDAR subunit composition with implications for synaptic plasticity and neurodevelopment

Comparative efficacy between atorvastatin and rosuvastatin in the prevention of cardiovascular disease recurrence

Background: There is no randomized clinical trials with recurrence of atherosclerotic cardiovascular disease (ASCVD) as a major outcome with rosuvastatin. In order to analyze potential differences in the clinical response to atorvastatin and rosuvastatin in secondary ASCVD prevention, we have analyzed the clinical evolution of those subjects of the Dyslipemia Registry of the Spanish Society of Arteriosclerosis (SEA) who at the time of inclusion in the Registry had already suffered an ASCVD. Methods: This observational, retrospective, multicenter, national study was designed to determine potential differences between the use of atorvastatin and rosuvastatin in the ASCVD recurrence. Three different follow-up start-times were performed: time of inclusion in the registry; time of first event if this occurred after 2005, and time of first event without date restriction. Results: Baseline characteristics were similar between treatment groups. Among atorvastatin or rosuvastatin users, 89 recurrences of ASCVD were recorded (21.9%), of which 85.4% were coronary. At the inclusion of the subject in the registry, 345 participants had not suffered a recurrence yet. These 345 subjects accumulated 1050 person-years in a mean follow-up of 3 years. Event rates were 2.73 (95% CI: 1.63, 4.25) cases/100 person-years and 2.34 (95% CI: 1.17, 4.10) cases/100 person-years in the atorvastatin and rosuvastatin groups, respectively. There were no statistically significant differences between the two groups independently of the follow-up start-time. Conclusions: This study does not find differences between high doses of rosuvastatin and atorvastatin in the recurrence of ASCVD, and supports their use as clinically equivalent in secondary prevention of ASCVD

Accretion, ejection and reprocessing in supermassive black holes

This is a White Paper in support of the mission concept of the Large Observatory for X-ray Timing (LOFT), proposed as a medium-sized ESA mission. We discuss the potential of LOFT for the study of active galactic nuclei. For a summary, we refer to the paper.Comment: White Paper in Support of the Mission Concept of the Large Observatory for X-ray Timin