Vegetation height products between 60° S and 60° N from ICESat GLAS data.

We present new coarse resolution (0.5� ×0.5�)vegetation height and vegetation-cover fraction data sets between 60� S and 60� N for use in climate models and ecological models. The data sets are derived from 2003–2009 measurements collected by the Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat), the only LiDAR instrument that provides close to global coverage. Initial vegetation height is calculated from GLAS data using a development of the model of Rosette et al. (2008) with further calibration on desert sites. Filters are developed to identify and eliminate spurious observations in the GLAS data, e.g. data that are affected by clouds, atmosphere and terrain and as such result in erroneous estimates of vegetation height or vegetation cover. Filtered GLAS vegetation height estimates are aggregated in histograms from 0 to 70m in 0.5m intervals for each 0.5�×0.5�. The GLAS vegetation height product is evaluated in four ways. Firstly, the Vegetation height data and data filters are evaluated using aircraft LiDAR measurements of the same for ten sites in the Americas, Europe, and Australia. Application of filters to the GLAS vegetation height estimates increases the correlation with aircraft data from r =0.33 to r =0.78, decreases the root-mean-square error by a factor 3 to about 6m (RMSE) or 4.5m (68% error distribution) and decreases the bias from 5.7m to −1.3 m. Secondly, the global aggregated GLAS vegetation height product is tested for sensitivity towards the choice of data quality filters; areas with frequent cloud cover and areas with steep terrain are the most sensitive to the choice of thresholds for the filters. The changes in height estimates by applying different filters are, for the main part, smaller than the overall uncertainty of 4.5–6m established from the site measurements. Thirdly, the GLAS global vegetation height product is compared with a global vegetation height product typically used in a climate model, a recent global tree height product, and a vegetation greenness product and is shown to produce realistic estimates of vegetation height. Finally, the GLAS bare soil cover fraction is compared globally with the MODIS bare soil fraction (r = 0.65) and with bare soil cover fraction estimates derived from AVHRR NDVI data (r =0.67); the GLAS treecover fraction is compared with the MODIS tree-cover fraction (r =0.79). The evaluation indicates that filters applied to the GLAS data are conservative and eliminate a large proportion of spurious data, while only in a minority of cases at the cost of removing reliable data as well. The new GLAS vegetation height product appears more realistic than previous data sets used in climate models and ecological models and hence should significantly improve simulations that involve the land surface

Tasks for multivariate network analysis

In Chap. 1, a multivariate network was defined as having two important characteristics. First, nodes are connected to each other via links; there is topological structure. Second, being multivariate, nodes and links have attributes associated with them, with these attributes having a value. In this chapter, we describe tasks associated with multivariate networks. We consider a task to be an activity that a user wishes to accomplish by interacting with a visual representation of a multivariate network. This implies that there is user intent [13], and that the network has been presented visually. At the highest level, this intent is usually described as the goal of obtaining insight about the data being studied [6]

Human Factors Aspects of the Transfer of Control from the Automated Highway System to the Driver

DTFH61-92-C-00100The first two experiments in a series exploring human factors issues related to the Automated Highway System (AHS) used a generic AHS configuration--the left lane reserved for automated vehicles, the center and right lanes containing unautomated vehicles, no transition lane, and no barriers between the automated and unautomated lanes--that was simulated in the Iowa Driving Simulator (IDS). The IDS has a moving base hexapod platform containing a mid-sized sedan. Imagery was projected onto a 3.35-rad (180 deg) screen in front of the driver, and onto a 1.13-rad (60 deg) screen to the rear. Thirty-six drivers between the ages of 25 and 34 years participated in the first experiment; 24 drivers who were age 65 or older took part in the second. Both experiments explored the transfer of control from the AHS to the driver when the driver's task was to leave the automated lane. The driver, who was traveling under automated control in a string of vehicles in the automated lane, had to take control, drive from the automated lane into the center lane, then leave the freeway. Results were as follows: (1) The mean time to respond to an "Exit" advisory decreased from 13.41 s to 10.16 s as the design velocity increased from 104.7 km/h (65 mi/h) to 153.0 km/h (95 mi/h). (2) After the transfer of control, the driver remained in the automated lane, decelerating until the velocity was slow enough to allow a safe transition into the slower traffic in the unautomated lanes. It took longer to decelerate (13.19 s vs. 10.26 s) and the exit velocity dropped [105.30 km/h (65.40 mi/h) vs. 99.54 km/h (61.83 mi/h)] as the unautomated traffic density decreased from 12.42 v/km/ln (20 v/mi/ln) to 6.21 v/km/ln (10 v/mi/ln). It also took longer to decelerate (15.23 s vs. 8.62 s) and the extent of the deceleration decreased [42.7 km/h (26.49 mi/h) vs. 13.18 km/h (8.16 mi/h)] as the design velocity decreased from 153.0 km/h (95 mi/h) to 104.7 km/h (65 mi/h). (3) Once in the unautomated lanes, the younger drivers were in the center lane 70% longer than the older drivers. (4) The vehicle immediately behind the driver's vehicle in the automated lane was delayed after control was transferred--the delay increased from 1.36 s to 6.70 s as the design velocity increased from 104.7 km/h (65 mi/h) to 153 km/h (95 mi/h). (5) Allowing for the delay times obtained in these experiments, it was determined that the potential capacity of an automated lane should increase from 634.6 v/h to 2087.8 v/h as the design velocity decreases from 153.0 km/h (95 mi/h) to 104.7 km/h (65 mi/h). (6) Collisions and incursions occurred at unacceptably high rates. (7) The responses to the questionnaire suggest that the drivers were receptive to the AHS concept

Tune in to your emotions: a robust personalized affective music player

The emotional power of music is exploited in a personalized affective music player (AMP) that selects music for mood enhancement. A biosignal approach is used to measure listeners’ personal emotional reactions to their own music as input for affective user models. Regression and kernel density estimation are applied to model the physiological changes the music elicits. Using these models, personalized music selections based on an affective goal state can be made. The AMP was validated in real-world trials over the course of several weeks. Results show that our models can cope with noisy situations and handle large inter-individual differences in the music domain. The AMP augments music listening where its techniques enable automated affect guidance. Our approach provides valuable insights for affective computing and user modeling, for which the AMP is a suitable carrier application

On the accretion disc properties in eclipsing dwarf nova EM Cyg

In this paper we analyzed the behavior of the unusual dwarf nova EM Cyg using the data obtained in April-October, 2007 in Vyhorlat observatory (Slovak Republic) and in September, 2006 in Crimean Astrophysical Observatory (Ukraine). During our observations EM Cyg has shown outbursts in every 15-40 days. Because on the light curves of EM Cyg the partial eclipse of an accretion disc is observed we applied the eclipse mapping technique to reconstruct the temperature distribution in eclipsed parts of the disc. Calculations of the accretion rate in the system were made for the quiescent and the outburst states of activity for different distances.Comment: 6 pages, 3 figures, accepted in Astrophysics and Space Scienc

Phenomenology of the Lense-Thirring effect in the Solar System

Recent years have seen increasing efforts to directly measure some aspects of the general relativistic gravitomagnetic interaction in several astronomical scenarios in the solar system. After briefly overviewing the concept of gravitomagnetism from a theoretical point of view, we review the performed or proposed attempts to detect the Lense-Thirring effect affecting the orbital motions of natural and artificial bodies in the gravitational fields of the Sun, Earth, Mars and Jupiter. In particular, we will focus on the evaluation of the impact of several sources of systematic uncertainties of dynamical origin to realistically elucidate the present and future perspectives in directly measuring such an elusive relativistic effect.Comment: LaTex, 51 pages, 14 figures, 22 tables. Invited review, to appear in Astrophysics and Space Science (ApSS). Some uncited references in the text now correctly quoted. One reference added. A footnote adde

Associations of autozygosity with a broad range of human phenotypes

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (F-ROH) for >1.4 million individuals, we show that F-ROH is significantly associated (p <0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: F-ROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of F-ROH are confirmed within full-sibling pairs, where the variation in F-ROH is independent of all environmental confounding.Peer reviewe

Whole-exome sequencing study identifies four novel gene loci associated with diabetic kidney disease

Diabetic kidney disease (DKD) is recognized as an important public health challenge. However, its genomic mechanisms are poorly understood. To identify rare variants for DKD, we conducted a whole-exome sequencing (WES) study leveraging large cohorts well-phenotyped for chronic kidney disease and diabetes. Our two-stage WES study included 4372 European and African ancestry participants from the Chronic Renal Insufficiency Cohort and Atherosclerosis Risk in Communities studies (stage 1) and 11 487 multi-ancestry Trans-Omics for Precision Medicine participants (stage 2). Generalized linear mixed models, which accounted for genetic relatedness and adjusted for age, sex and ancestry, were used to test associations between single variants and DKD. Gene-based aggregate rare variant analyses were conducted using an optimized sequence kernel association test implemented within our mixed model framework. We identified four novel exome-wide significant DKD-related loci through initiating diabetes. In single-variant analyses, participants carrying a rare, in-frame insertion in the DIS3L2 gene (rs141560952) exhibited a 193-fold increased odds [95% confidence interval (CI): 33.6, 1105] of DKD compared with noncarriers (P = 3.59 × 10-9). Likewise, each copy of a low-frequency KRT6B splice-site variant (rs425827) conferred a 5.31-fold higher odds (95% CI: 3.06, 9.21) of DKD (P = 2.72 × 10-9). Aggregate gene-based analyses further identified ERAP2 (P = 4.03 × 10-8) and NPEPPS (P = 1.51 × 10-7), which are both expressed in the kidney and implicated in renin-angiotensin-aldosterone system modulated immune response. In the largest WES study of DKD, we identified novel rare variant loci attaining exome-wide significance. These findings provide new insights into the molecular mechanisms underlying DKD

The Value of Rare Genetic Variation in the Prediction of Common Obesity in European Ancestry Populations

Polygenic risk scores (PRSs) aggregate the effects of genetic variants across the genome and are used to predict risk of complex diseases, such as obesity. Current PRSs only include common variants (minor allele frequency (MAF) ≥1%), whereas the contribution of rare variants in PRSs to predict disease remains unknown. Here, we examine whether augmenting the standard common variant PRS (PRScommon) with a rare variant PRS (PRSrare) improves prediction of obesity. We used genome-wide genotyped and imputed data on 451,145 European-ancestry participants of the UK Biobank, as well as whole exome sequencing (WES) data on 184,385 participants. We performed single variant analyses (for both common and rare variants) and gene-based analyses (for rare variants) for association with BMI (kg/m2), obesity (BMI ≥ 30 kg/m2), and extreme obesity (BMI ≥ 40 kg/m2). We built PRSscommon and PRSsrare using a range of methods (Clumping+Thresholding [C+T], PRS-CS, lassosum, gene-burden test). We selected the best-performing PRSs and assessed their performance in 36,757 European-ancestry unrelated participants with whole genome sequencing (WGS) data from the Trans-Omics for Precision Medicine (TOPMed) program. The best-performing PRScommon explained 10.1% of variation in BMI, and 18.3% and 22.5% of the susceptibility to obesity and extreme obesity, respectively, whereas the best-performing PRSrare explained 1.49%, and 2.97% and 3.68%, respectively. The PRSrare was associated with an increased risk of obesity and extreme obesity (ORobesity = 1.37 per SDPRS, Pobesity = 1.7x10-85; ORextremeobesity = 1.55 per SDPRS, Pextremeobesity = 3.8x10-40), which was attenuated, after adjusting for PRScommon (ORobesity = 1.08 per SDPRS, Pobesity = 9.8x10-6; ORextremeobesity= 1.09 per SDPRS, Pextremeobesity = 0.02). When PRSrare and PRScommon are combined, the increase in explained variance attributed to PRSrare was small (incremental Nagelkerke R2 = 0.24% for obesity and 0.51% for extreme obesity). Consistently, combining PRSrare to PRScommon provided little improvement to the prediction of obesity (PRSrare AUC = 0.591; PRScommon AUC = 0.708; PRScombined AUC = 0.710). In summary, while rare variants show convincing association with BMI, obesity and extreme obesity, the PRSrare provides limited improvement over PRScommon in the prediction of obesity risk, based on these large populations