A satellite system for land-mobile communications in Europe

There exists a great unsatisified demand for land mobile communications in Europe, particularly in sectors of business activity such as the road transport industry. This demand could best be satisfied by means of satellite-based private networks providing voice and data communications in a hub configuration. The potential market is estimated to encompass several hundred thousand road vehicles and the transmission capacity required would be several thousand channels. ESA is currently demonstrating the potential of satellite communications for this type of application, using a system called PRODAT. System studies are being performed with the aim of defining the architecture of a regional satellite system for Europe

Binge Eating Disorder Mediates Links between Symptoms of Depression and Anxiety and Caloric Intake in Obese Women

Despite considerable comorbidity between mood disorders, binge eating disorder (BED), and obesity, the underlying mechanisms remain unresolved. Therefore, the purpose of this study was to examine models by which internalizing behaviors of depression and anxiety influence food intake in overweight/obese women. Thirty-two women (15 BED, 17 controls) participated in a laboratory eating-episode and completed questionnaires assessing symptoms of anxiety and depression. Path analysis was used to test mediation and moderation models to determine the mechanisms by which internalizing symptoms influenced kilocalorie (kcal) intake. The BED group endorsed significantly more symptoms of depression (10.1 versus 4.8, P=0.005 ) and anxiety (8.5 versus 2.7, P=0.003). Linear regression indicated that BED diagnosis and internalizing symptoms accounted for 30% of the variance in kcal intake. Results from path analysis suggested that BED mediates the influence of internalizing symptoms on total kcal intake. The associations between internalizing symptoms and food intake are best described as operating indirectly through a BED diagnosis. This suggests that symptoms of depression and anxiety influence whether one engages in binge eating, which influences kcal intake. Greater understanding of the mechanisms underlying the associations between mood, binge eating, and food intake will facilitate the development of more effective prevention and treatment strategies for both BED and obesity

Tuning the Mechanical Properties in Model Nanocomposites: Influence of the Polymer-Filler Interfacial Interactions

This paper presents a study of the polymer-filler interfacial effects on filler dispersion and mechanical reinforcement in Polystyrene (PS) / silica nanocomposites by direct comparison of two model systems: un-grafted and PS-grafted silica dispersed in PS matrix. The structure of nanoparticles has been investigated by combining Small Angle Neutron Scattering (SANS) measurements and Transmission Electronic Microscopic (TEM) images. The mechanical properties were studied over a wide range of deformation by plate/plate rheology and uni-axial stretching. At low silica volume fraction, the particles arrange, for both systems, in small finite size non-connected aggregates and the materials exhibit a solid-like behavior independent of the local polymer/fillers interactions suggesting that reinforcement is dominated by additional long range effects. At high silica volume fraction, a continuous connected network is created leading to a fast increase of reinforcement whose amplitude is then directly dependent on the strength of the local particle/particle interactions and lower with grafting likely due to deformation of grafted polymer.Comment: Journal Polymer Science (2011

The evolution of a revolution: re-designing green revolution breeding programs in Asia and Africa to increase rates of genetic gain. [W020]

As rice feeds nearly half of the human population, rice breeding is a critical focal point for achieving the UN Sustainable Development Goal of eliminating hunger and poverty by 2030 and to providing a sufficient quantity of safe and nutritious food to vulnerable populations in the developing world. However, despite dramatic improvements in understanding the genetic basis of complex traits in rice over the last 20 years, annual rates of genetic gain for yield and other important traits in most public rice breeding programs in Asia and Africa are extremely low. Understanding and manipulating the key drivers of genetic gain will be necessary for rice breeding programs to fully meet the expectations of the 21st century. Funded by the Bill and Melinda Gates foundation and in coordination with the CGIAR Excellence in Breeding Platform, the International Rice Research Institute (IRRI) aims to transform rice breeding by aligning IRRI's international breeding efforts together with national public breeding programs (NARs programs) into collaborative regional breeding networks. These CGIAR-NARs breeding networks serve as a platform to deploy an integrated breeding model that combines modern genomic technologies with regional knowledge and testing capabilities to ensure that smallholder rice farmers have access to a steady stream of consistently improved, high yielding, locally adapted, and market-ready rice varieties

breedgenr: a non-parametric based breeding program simulator with applications on rice

Effective harnessing by crop breeding programs of genetic gains expected from the integration of genomic resources, high throughput phenotyping capabilities, and efficient statistical methods to model the complexity of empirical data requires adjustments of the breeding strategies. Tools that simulate breeding features are needed to explore the effects of those adjustments and their potential interactions. Simulations are useful because they allow rapid replicated testing of a wide range of hypotheses at low cost, for example, the initial feasibility of genomic selection or the impact of the reference population structure. Simulation of breeding strategies is well adapted to investigate long-term effects of selection, which are often infeasible using real experiments due to time and cost requirements. Different tools have been proposed to reflect the complexity of the studied population (genomes structure, genetic architecture of the traits, and relatedness among individuals). However, few of them are specifically designed to simulate breeding strategies in crop species. We have therefore developed breedgenr, a simulation tool dedicated to meet breeder's needs to rationalize the different steps of a breeding scheme. breedgenr is an R package that present two distinctive features compared to existing tools. First, breedgenr uses real genotypic and phenotypic data from breeding programs to generate the reference population. Thus, it limits the number of possible hypotheses and scenarios regarding the evolutionary history and the structure of the breeding populations. Second breedgenr is based on non-parametric approaches to calibrate genotype-phenotype relationship. Consequently, it does not rest on specific assumptions regarding the genetic model and the genetic architecture of the phenotypic traits considered. Robustness and functionality of breedgenr were evaluated using real datasets on rice and different breeding schemes. The objective was to design breeding schemes that integrate different genomic selection scenarios. The results confirmed the robustness of the calibration of genotype-phenotype relationships based on the chosen non-parametric kernel methods, and the capability of breedgenr to simulate breeding populations and different breeding schemes: pedigree breeding and recurrent selection. Further ascertaining of breedgenr robustness and adjustment-expansion of its functionalities are presented

Genomic selection in rice: lessons learned from a large set of proof-of-concept studies embedded in current breeding programs

The increase in rice production needed to meet future demand requires renewed cropping systems and rice varieties with enhanced resources use efficiency and adaptation to environmental stresses in the context of climate change. Genomic selection (GS) has the potential to accelerate the development of such varieties. We present an overview of the proof-of-concept studies conducted during the last decade with the aim of providing rice breeders with tailored GS methods and tools. These studies involved complementary breeding programs (pedigree breeding, population improvement, etc.), mobilising different compartments of rice genetic diversity (indica, tropical and temperate japonica), and targeting a wide range of traits (yield potential, nitrogen use efficiency, adaptation to alternate watering and drying, salinity tolerance, drought tolerance, exclusion of heavy metals, outcrossing and hybrid seed production abilities, etc.). The objective was to evaluate the importance of different factors known to influence the accuracy of genomic prediction: marker density, linkage disequilibrium, trait heritability and genetic architecture, characteristics of the training population, relatedness between training and candidate populations, statistical models, etc.). Our results showed notably that GS can accelerate genetic gain in both pedigree and population breeding schemes by increasing selection intensity and by shortening the selection cycle. Rice diversity panels provide accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel. Genomic prediction accounting for genotype-by-environment interactions offers an effective framework for breeding simultaneously for adaptation to an abiotic stress and for performance under normal cropping conditions. The degree of relatedness between the training and the candidate population matter more than the size of the training set per se. Whatever the genetic background of the training and the candidate populations (and the associated linkage disequilibrium), average marker density of more than one SNP every 20 kb does not improve prediction accuracy. Further simulation studies are needed to assess the impact of GS on long-term genetic gain and diversity, to adjust the GS strategy accordingly. In the light of these results, we propose a strategy for embedding international rice gene discovery and ecophysiological ideotype modeling research in a GS based rice breeding program

Genomic prediction accounting for genotype by environment interaction offers an effective framework for breeding simultaneously for adaptation to an abiotic stress and performance under normal cropping conditions in rice

Developing rice varieties adapted to alternate wetting and drying water management is crucial for the sustainability of irrigated rice cropping systems. Here we report the first study exploring the feasibility of breeding rice for adaptation to alternate wetting and drying using genomic prediction methods that account for genotype by environment interactions. Two breeding populations (a reference panel of 284 accessions and a progeny population of 97 advanced lines) were evaluated under alternate wetting and drying and continuous flooding management systems. The predictive ability of genomic prediction for response variables (index of relative performance and the slope of the joint regression) and for multi-environment genomic prediction models were compared. For the three traits considered (days to flowering, panicle weight and nitrogen-balance index), significant genotype by environment interactions were observed in both populations. In cross validation, predictive ability for the index was on average lower (0.31) than that of the slope of the joint regression (0.64) whatever the trait considered. Similar results were found for progeny validation. Both cross-validation and progeny validation experiments showed that the performance of multi-environment models predicting unobserved phenotypes of untested entrees was similar to the performance of single environment models with differences in predictive ability ranging from -6–4% depending on the trait and on the statistical model concerned. The predictive ability of multi-environment models predicting unobserved phenotypes of entrees evaluated under both water management systems outperformed single environment models by an average of 30%. Practical implications for breeding rice for adaptation to alternate wetting and drying system are discussed

Empowering global rice breeding programs using genomic selection. [W291]

Rice is the staple food for half of the global population and irrigated rice contributes 70% of total rice produced. Given the strategic importance of this market segment to global food security, the irrigated rice breeding program at IRRI is mandated to breed varietal rice and empower rice breeding programs in South Asia and Eastern and Southern Africa. As public sector breeding budgets are insufficient to adequately test all new lines across such a varied environmental landscape, advanced molecular tools such as genomic selection (GS) are critical to achieving high levels of selection intensity and selection accuracy within each region. For the past two years, the irrigated lowland breeding program at IRRI has distributed carefully selected 'estimation sets' containing a few hundred breeding lines to global partners in Africa and South Asia with the intention of using genomic prediction to evaluate several thousand selection candidates that have only been genotyped. Combining different software tools such as B4R (phenotypic data management), GOBii (genotypic data management) and ASReml-R (modeling) into an analytical workflow, we have generated initial prediction accuracies for grain yield, plant height, and flowering time which are routinely used in the program to make optimized breeding decisions for each of these disparate global environments. An overview of the results will be presented as well as a discussion about the future integration of sparse testing designs, multi-environmental models, use of weather data through machine learning, and phenotyping with unmanned aerial vehicles (UAV)

Fine scale genomic signals of admixture and alien introgression among Asian rice landraces

Modern rice cultivars are adapted to a range of environmental conditions and human preferences. At the root of this diversity is a marked genetic structure, owing to multiple foundation events. Admixture and recurrent introgression from wild sources have played upon this base to produce the myriad adaptations existing today. Genome-wide studies bring support to this idea, but understanding the history and nature of particular genetic adaptations requires the identification of specific patterns of genetic exchange. In this study, we explore the patterns of haplotype similarity along the genomes of a subset of rice cultivars available in the 3,000 Rice Genomes data set. We begin by establishing a custom method of classification based on a combination of dimensionality reduction and kernel density estimation. Through simulations, the behavior of this classifier is studied under scenarios of varying genetic divergence, admixture, and alien introgression. Finally, the method is applied to local haplotypes along the genome of a Core set of Asian Landraces. Taking the Japonica, Indica, and cAus groups as references, we find evidence of reciprocal introgressions covering 2.6% of reference genomes on average. Structured signals of introgression among reference accessions are discussed. We extend the analysis to elucidate the genetic structure of the group circum-Basmati: we delimit regions of Japonica, cAus, and Indica origin, as well as regions outlier to these groups (13% on average). Finally, the approach used highlights regions of partial to complete loss of structure that can be attributed to selective pressures during domestication

Effectiveness of China's National Forest Protection Program and nature reserves

There is profound interest in knowing the degree to which China's institutions are capable of protecting its natural forests and biodiversity in the face of economic and political change. China's 2 most important forest-protection policies are its National Forest Protection Program (NFPP) and its national-level nature reserves (NNRs). The NFPP was implemented in 2000 in response to deforestation-caused flooding. We undertook the first national, quantitative assessment of the NFPP and NNRs to examine whether the NFPP achieved its deforestation-reduction target and whether the NNRs deter deforestation altogether. We used MODIS data to estimate forest cover and loss across mainland China (2000-2010). We also assembled the first-ever polygon dataset for China's forested NNRs (n = 237, 74,030 km(2) in 2000) and used both conventional and covariate-matching approaches to compare deforestation rates inside and outside NNRs (2000-2010). In 2000, 1.765 million km(2) or 18.7% of mainland China was forested (12.3% with canopy cover of >= 70%)) or woodland (6.4% with canopy cover = 40%). By 2010, 480,203 km(2) of forest and woodland had been lost, an annual deforestation rate of 2.7%. Forest-only loss was 127,473 km(2) (1.05% annually). In the NFPP provinces, the forest-only loss rate was 0.62%, which was 3.3 times lower than in the non-NFPP provinces. Moreover, the Landsat data suggest that these loss rates are overestimates due to large MODIS pixel size. Thus, China appears to have achieved, and even exceeded, its target of reducing deforestation to 1.1% annually in the NFPP provinces. About two-thirds of China's NNRs were effective in protecting forest cover (prevented loss 4073 km(2) unmatched approach; 3148 km(2) matched approach), and within-NNR deforestation rates were higher in provinces with higher overall deforestation. Our results indicate that China's existing institutions can protect domestic forest cover