Showing papers by "Technical University of Madrid published in 2018"
University of Oxford1, University of Minnesota2, Commonwealth Scientific and Industrial Research Organisation3, International Food Policy Research Institute4, Potsdam Institute for Climate Impact Research5, Technical University of Madrid6, Wageningen University and Research Centre7, Chatham House8, Stockholm Resilience Centre9, Royal Swedish Academy of Sciences10, Bioversity International11, American University of Beirut12, Johns Hopkins University13, University of California, Santa Barbara14, Harvard University15
TL;DR: A global model finds that the environmental impacts of the food system could increase by 60–90% by 2050, and that dietary changes, improvements in technologies and management, and reductions in food loss and waste will all be needed to mitigate these impacts.
Abstract: The food system is a major driver of climate change, changes in land use, depletion of freshwater resources, and pollution of aquatic and terrestrial ecosystems through excessive nitrogen and phosphorus inputs. Here we show that between 2010 and 2050, as a result of expected changes in population and income levels, the environmental effects of the food system could increase by 50–90% in the absence of technological changes and dedicated mitigation measures, reaching levels that are beyond the planetary boundaries that define a safe operating space for humanity. We analyse several options for reducing the environmental effects of the food system, including dietary changes towards healthier, more plant-based diets, improvements in technologies and management, and reductions in food loss and waste. We find that no single measure is enough to keep these effects within all planetary boundaries simultaneously, and that a synergistic combination of measures will be needed to sufficiently mitigate the projected increase in environmental pressures.
1,521 citations
University of Tartu1, Uppsala University2, Swedish University of Agricultural Sciences3, Max Delbrück Center for Molecular Medicine4, Charité5, Leiden University6, University of Wisconsin-Madison7, Braunschweig University of Technology8, Technical University of Madrid9, Wageningen University and Research Centre10, University of California, Riverside11, University of Oslo12, Lund University13, ETH Zurich14, American Museum of Natural History15, University of Würzburg16
TL;DR: It is shown that bacterial, but not fungal, genetic diversity is highest in temperate habitats and that microbial gene composition varies more strongly with environmental variables than with geographic distance, and that the relative contributions of these microorganisms to global nutrient cycling varies spatially.
Abstract: Soils harbour some of the most diverse microbiomes on Earth and are essential for both nutrient cycling and carbon storage. To understand soil functioning, it is necessary to model the global distribution patterns and functional gene repertoires of soil microorganisms, as well as the biotic and environmental associations between the diversity and structure of both bacterial and fungal soil communities1–4. Here we show, by leveraging metagenomics and metabarcoding of global topsoil samples (189 sites, 7,560 subsamples), that bacterial, but not fungal, genetic diversity is highest in temperate habitats and that microbial gene composition varies more strongly with environmental variables than with geographic distance. We demonstrate that fungi and bacteria show global niche differentiation that is associated with contrasting diversity responses to precipitation and soil pH. Furthermore, we provide evidence for strong bacterial–fungal antagonism, inferred from antibiotic-resistance genes, in topsoil and ocean habitats, indicating the substantial role of biotic interactions in shaping microbial communities. Our results suggest that both competition and environmental filtering affect the abundance, composition and encoded gene functions of bacterial and fungal communities, indicating that the relative contributions of these microorganisms to global nutrient cycling varies spatially.
1,108 citations
Aix-Marseille University1, Spanish National Research Council2, University of Paris3, Sciences Po4, Technical University of Madrid5, The Cyprus Institute6, University of Salento7, Central Maine Community College8, University of Barcelona9, University of Haifa10, City University of Hong Kong11, University of Giessen12
TL;DR: In this paper, a dedicated effort to synthesize existing scientific knowledge across disciplines is underway and aims to provide a better understanding of the combined risks posed in the Mediterranean Basin, where fewer systematic observations schemes and impact models are based.
Abstract: Recent accelerated climate change has exacerbated existing environmental problems in the Mediterranean Basin that are caused by the combination of changes in land use, increasing pollution and declining biodiversity. For five broad and interconnected impact domains (water, ecosystems, food, health and security), current change and future scenarios consistently point to significant and increasing risks during the coming decades. Policies for the sustainable development of Mediterranean countries need to mitigate these risks and consider adaptation options, but currently lack adequate information — particularly for the most vulnerable southern Mediterranean societies, where fewer systematic observations schemes and impact models are based. A dedicated effort to synthesize existing scientific knowledge across disciplines is underway and aims to provide a better understanding of the combined risks posed.
699 citations
23 Jan 2018
TL;DR: The proposed DroNet is a convolutional neural network that can safely drive a drone through the streets of a city, trained from data collected by cars and bicycles, which, already integrated into the urban environment, would not endanger other vehicles and pedestrians.
Abstract: Civilian drones are soon expected to be used in a wide variety of tasks, such as aerial surveillance, delivery, or monitoring of existing architectures. Nevertheless, their deployment in urban environments has so far been limited. Indeed, in unstructured and highly dynamic scenarios, drones face numerous challenges to navigate autonomously in a feasible and safe way. In contrast to traditional “map-localize-plan” methods, this letter explores a data-driven approach to cope with the above challenges. To accomplish this, we propose DroNet: a convolutional neural network that can safely drive a drone through the streets of a city. Designed as a fast eight-layers residual network, DroNet produces two outputs for each single input image: A steering angle to keep the drone navigating while avoiding obstacles, and a collision probability to let the UAV recognize dangerous situations and promptly react to them. The challenge is however to collect enough data in an unstructured outdoor environment such as a city. Clearly, having an expert pilot providing training trajectories is not an option given the large amount of data required and, above all, the risk that it involves for other vehicles or pedestrians moving in the streets. Therefore, we propose to train a UAV from data collected by cars and bicycles, which, already integrated into the urban environment, would not endanger other vehicles and pedestrians. Although trained on city streets from the viewpoint of urban vehicles, the navigation policy learned by DroNet is highly generalizable. Indeed, it allows a UAV to successfully fly at relative high altitudes and even in indoor environments, such as parking lots and corridors. To share our findings with the robotics community, we publicly release all our datasets, code, and trained networks.
382 citations
University of Queensland1, University of Salford2, University of Western Australia3, Queensland University of Technology4, University of Lausanne5, Wildfowl & Wetlands Trust6, Canadian Parks and Wilderness Society7, University of Évora8, University of Freiburg9, University of Melbourne10, University of Plymouth11, Mississippi State University12, Australian Institute of Marine Science13, Griffith University14, Zurich University of Applied Sciences/ZHAW15, University of British Columbia16, Duke University17, Finnish Environment Institute18, University of Adelaide19, King Fahd University of Petroleum and Minerals20, IFREMER21, University of California, Davis22, Technical University of Madrid23, National Institute of Water and Atmospheric Research24, Memorial University of Newfoundland25, Royal Society for the Protection of Birds26, University of Kansas27, University of California, Merced28, University of the Sunshine Coast29, Nelson Mandela Metropolitan University30, Swedish University of Agricultural Sciences31, University of Grenoble32, Oregon State University33, Gulf of Maine Research Institute34, University of Toronto35, University of Georgia36, Newcastle University37, Parks Canada38, Humboldt University of Berlin39
TL;DR: Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.
Abstract: Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world However, limited understanding of the accuracy and precision of models transferred to novel conditions (their ‘transferability’) undermines confidence in their predictions Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions
358 citations
18 Jun 2018
TL;DR: A deep neural network approach is presented that unlocks the potential of event cameras on a challenging motion-estimation task: prediction of a vehicle's steering angle, and outperforms state-of-the-art algorithms based on standard cameras.
Abstract: Event cameras are bio-inspired vision sensors that naturally capture the dynamics of a scene, filtering out redundant information. This paper presents a deep neural network approach that unlocks the potential of event cameras on a challenging motion-estimation task: prediction of a vehicle's steering angle. To make the best out of this sensor-algorithm combination, we adapt state-of-the-art convolutional architectures to the output of event sensors and extensively evaluate the performance of our approach on a publicly available large scale event-camera dataset (~1000 km). We present qualitative and quantitative explanations of why event cameras allow robust steering prediction even in cases where traditional cameras fail, e.g. challenging illumination conditions and fast motion. Finally, we demonstrate the advantages of leveraging transfer learning from traditional to event-based vision, and show that our approach outperforms state-of-the-art algorithms based on standard cameras.
344 citations
University of Western Ontario1, University of Alberta2, Université du Québec à Trois-Rivières3, Queen's University4, Children's Hospital of Eastern Ontario5, University of Ottawa6, Technical University of Madrid7, University of Saskatchewan8, Sunnybrook Health Sciences Centre9, University of Toronto10, Camosun College11
TL;DR: These guidelines provided guidance for pregnant women and obstetric care and exercise professionals on prenatal physical activity and the majority of stakeholders and end users indicated that following these recommendations would be feasible, acceptable and equitable.
Abstract: The objective is to provide guidance for pregnant women and obstetric care and exercise professionals on prenatal physical activity. The outcomes evaluated were maternal, fetal or neonatal morbidity, or fetal mortality during and following pregnancy. Literature was retrieved through searches of MEDLINE, EMBASE, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Scopus and Web of Science Core Collection, CINAHL Plus with Full Text, Child Development & Adolescent Studies, Education Resources Information Center, SPORTDiscus, ClinicalTrials.gov and the Trip Database from inception up to 6 January 2017. Primary studies of any design were eligible, except case studies. Results were limited to English-language, Spanish-language or French-language materials. Articles related to maternal physical activity during pregnancy reporting on maternal, fetal or neonatal morbidity, or fetal mortality were eligible for inclusion. The quality of evidence was rated using the Grading of Recommendations Assessment, Development and Evaluation methodology. The Guidelines Consensus Panel solicited feedback from end users (obstetric care providers, exercise professionals, researchers, policy organisations, and pregnant and postpartum women). The development of these guidelines followed the Appraisal of Guidelines for Research and Evaluation II instrument. The benefits of prenatal physical activity are moderate and no harms were identified; therefore, the difference between desirable and undesirable consequences (net benefit) is expected to be moderate. The majority of stakeholders and end users indicated that following these recommendations would be feasible, acceptable and equitable. Following these recommendations is likely to require minimal resources from both individual and health systems perspectives.
340 citations
National Research Council1, Newcastle University2, University of Reims Champagne-Ardenne3, Technical University of Madrid4, University of Trento5, Nova Southeastern University6, Academy of Sciences of the Czech Republic7, University of Tartu8, Umeå University9, Aalborg University10, University of the Basque Country11, VU University Amsterdam12, Luleå University of Technology13, Energy Institute14, Frederick University15, University of Piraeus16, Erasmus University Rotterdam17
TL;DR: In this paper, the state of local planning for climate change by collecting and analysing information about local climate mitigation and adaptation plans across 885 urban areas of the EU-28 was reported.
334 citations
TL;DR: The identification of these CWI sensors and PRR-DAMP pairs will help to understand the immune functions of the wall monitoring system, and might allow the breeding of crop varieties and the design of agricultural strategies that would enhance crop disease resistance.
Abstract: Plants have evolved a repertoire of monitoring systems to sense plant morphogenesis and to face environmental changes and threats caused by different attackers. These systems integrate different signals into overreaching triggering pathways which coordinate developmental and defence-associated responses. The plant cell wall, a dynamic and complex structure surrounding every plant cell, has emerged recently as an essential component of plant monitoring systems, thus expanding its function as a passive defensive barrier. Plants have a dedicated mechanism for maintaining cell wall integrity (CWI) which comprises a diverse set of plasma membrane-resident sensors and pattern recognition receptors (PRRs). The PRRs perceive plant-derived ligands, such as peptides or wall glycans, known as damage-associated molecular patterns (DAMPs). These DAMPs function as 'danger' alert signals activating DAMP-triggered immunity (DTI), which shares signalling components and responses with the immune pathways triggered by non-self microbe-associated molecular patterns that mediate disease resistance. Alteration of CWI by impairment of the expression or activity of proteins involved in cell wall biosynthesis and/or remodelling, as occurs in some plant cell wall mutants, or by wall damage due to colonization by pathogens/pests, activates specific defensive and growth responses. Our current understanding of how these alterations of CWI are perceived by the wall monitoring systems is scarce and few plant sensors/PRRs and DAMPs have been characterized. The identification of these CWI sensors and PRR-DAMP pairs will help us to understand the immune functions of the wall monitoring system, and might allow the breeding of crop varieties and the design of agricultural strategies that would enhance crop disease resistance.
298 citations
TL;DR: In conclusion, exercise-only interventions were effective at lowering the odds of developing GDM, GH and PE.
Abstract: Objective Gestational diabetes mellitus (GDM), gestational hypertension (GH) and pre-eclampsia (PE) are associated with short and long-term health issues for mother and child; prevention of these complications is critically important. This study aimed to perform a systematic review and meta-analysis of the relationships between prenatal exercise and GDM, GH and PE. Design Systematic review with random effects meta-analysis and meta-regression. Data sources Online databases were searched up to 6 January 2017. Study eligibility criteria Studies of all designs were included (except case studies) if published in English, Spanish or French, and contained information on the Population (pregnant women without contraindication to exercise), Intervention (subjective or objective measures of frequency, intensity, duration, volume or type of exercise, alone [“exercise-only”] or in combination with other intervention components [e.g., dietary; “exercise + co-intervention”]), Comparator (no exercise or different frequency, intensity, duration, volume and type of exercise) and Outcomes (GDM, GH, PE). Results A total of 106 studies (n=273 182) were included. ‘Moderate’ to ‘high’-quality evidence from randomised controlled trials revealed that exercise-only interventions, but not exercise+cointerventions, reduced odds of GDM (n=6934; OR 0.62, 95% CI 0.52 to 0.75), GH (n=5316; OR 0.61, 95% CI 0.43 to 0.85) and PE (n=3322; OR 0.59, 95% CI 0.37 to 0.9) compared with no exercise. To achieve at least a 25% reduction in the odds of developing GDM, PE and GH, pregnant women need to accumulate at least 600 MET-min/week of moderate-intensity exercise (eg, 140 min of brisk walking, water aerobics, stationary cycling or resistance training). Summary/conclusions In conclusion, exercise-only interventions were effective at lowering the odds of developing GDM, GH and PE.
285 citations
TL;DR: In this article, the authors explore the possibility that a broad spectrum of primordial black holes is formed in models of Critical Higgs Inflation (CHI), where the near-inflection point is related to the critical value of the RGE running of both the Higgs self-coupling and its non-minimal coupling to gravity.
TL;DR: In this article, the effect of different operating parameters (i.e. solvent concentration, temperature, time and solid/liquid ratio) were further evaluated to determine the optimal extraction conditions for polyphenols recovery in terms of total phenolic content (TPC) and antioxidant activity based on DPPH radical scavenging method.
TL;DR: In this article, the authors reviewed the current knowledge about some particular kinds of coherent structures in the logarithmic and outer layers of wall-bounded turbulent flows and argued that a concerned effort is required to quantitatively identify which one (or ones) of the plausible available dynamical models is a better representation of the observed behaviour.
Abstract: The current knowledge about some particular kinds of coherent structures in the logarithmic and outer layers of wall-bounded turbulent flows is briefly reviewed. It is shown that a lot has been learned about their geometry, flow properties and temporal behaviour. It is also shown that, although the wall-attached structures carry the largest fraction of most flow properties, they are only extreme cases of smaller wall-detached eddies, and that the latter connect with the more classical behaviour of homogeneous turbulence away from walls. Nevertheless, it is argued that little is known about the dynamical origin of these structures, and that a concerned effort is required to quantitatively identify which one (or ones) of the plausible available dynamical models is a better representation of the observed behaviour.
University of Bonn1, Aarhus University2, Potsdam Institute for Climate Impact Research3, Finnish Environment Institute4, Goddard Institute for Space Studies5, Montana State University6, University of Montpellier7, Groupe Limagrain8, University of Queensland9, University of Florence10, Agricultural & Applied Economics Association11, Hungarian Academy of Sciences12, University of Nottingham13, Technical University of Madrid14, University of Castilla–La Mancha15, Rothamsted Research16, University of Göttingen17, Institut national de la recherche agronomique18
TL;DR: It is reported that drought stress will remain a key driver of yield losses in wheat and maize across Europe, and benefits from CO2 will be limited in low-yielding years.
Abstract: Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984-2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.
TL;DR: Using students as participants remains a valid simplification of reality needed in laboratory contexts but, like any other aspect of study settings, should be carefully considered during the design, execution, interpretation, and reporting of an experiment.
Abstract: [Context] Controlled experiments are an important empirical method to generate and validate theories. Many software engineering experiments are conducted with students. It is often claimed that the ...
Australian National University1, University of California, Santa Barbara2, Commonwealth Scientific and Industrial Research Organisation3, Centre national de la recherche scientifique4, Technical University of Berlin5, Harvard University6, Technical University of Madrid7, University of Wollongong8, Leipzig University9
TL;DR: An overview of the SOSA/SSN ontologies is given and the rationale behind key design decisions are discussed, reporting on the differences between the new SSN ontology presented here and its predecessor [9] developed by the W3C Semantic Sensor Network Incubator group (the SSN-XG).
Abstract: The joint W3C (World Wide Web Consortium) and OGC (Open Geospatial Consortium) Spatial Data on the Web (SDW) Working Group developed a set of ontologies to describe sensors, actuators, samplers as well as their observations, actuation, and sampling activities. The ontologies have been published both as a W3C recommendation and as an OGC implementation standard. The set includes a lightweight core module called SOSA (Sensor, Observation, Sampler, and Actuator) available at: http://www.w3.org/ns/sosa/, and a more expressive extension module called SSN (Semantic Sensor Network) available at: http://www.w3.org/ns/ssn/. Together they describe systems of sensors and actuators, observations, the used procedures, the subjects and their properties being observed or acted upon, samples and the process of sampling, and so forth. The set of ontologies adopts a modular architecture with SOSA as a self-contained core that is extended by SSN and other modules to add expressivity and breadth. The SOSA/SSN ontologies are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Internet of Things. In this paper we give an overview of the ontologies and discuss the rationale behind key design decisions, reporting on the differences between the new SSN ontology presented here and its predecessor [9] developed by the W3C Semantic Sensor Network Incubator group (the SSN-XG). We present usage examples and describe alignment modules that foster interoperability with other ontologies.
TL;DR: A comprehensive and state-of-the art survey on common surrogate modeling techniques and surrogate-based optimization methods is given, with an emphasis on models selection and validation, dimensionality reduction, sensitivity analyses, constraints handling or infill and stopping criteria.
TL;DR: Characterization of biochar and hydrochar samples showed that pyrolysis led to chars with more aromatic structures and high thermal stability while HTC process originated char with more aliphatic structures, suggesting that HTC could be an interesting method to obtain soil growing media or green roof materials with adequate hydrophysical properties.
TL;DR: In this paper, the authors introduce the concept of food security with an overview of its scale and depth in the context of the global food industry and highlight the key factors affecting food security and highlights the many national and international measures adopted to tackle the problem at both policy and technological level.
Abstract: Background Food security is both a complex and challenging issue to resolve as it cannot be characterized or limited by geography nor defined by a single grouping, i.e., demography, education, geographic location or income. Currently, approximately one billion people (16% of global population) suffer from chronic hunger in a time when there is more than enough food to feed everyone on the planet. Therein lies the Food security challenge to implement an ability to deal with increasing food shortages, caused by a combination of waste and an ever expanding world population. At current levels prediction state that we must increase global food production by 70% on already over exploited finite infrastructures before 2050. Scope and approach This review paper firstly introduces the concept of Food Security with an overview of its scale and depth in the context of the global food industry. It then highlights the main sources. The readership is then introduced to the key factors affecting food security and highlights the many national and international measures adopted to tackle the problem at both policy and technological level. Key findings and conclusions Food experts indicate that no one single solution will provide a sustainable food security solution into the future. Collective stakeholder engagement will prove essential in bringing about the policy changes and investment reforms required to achieve a solution. Achieving truly sustainable global food security will require a holistic systems-based approach, built on a combination of policy and technological reform, which will utilize existing systems combined with state-of-the-art technologies, techniques and best practices some of which are outlined herein.
TL;DR: Prenatal exercise is safe and beneficial for the fetus and was associated with reduced odds of macrosomia (abnormally large babies) and was not associated with neonatal complications or adverse childhood outcomes.
Abstract: Objective We aimed to identify the relationship between maternal prenatal exercise and birth complications, and neonatal and childhood morphometric, metabolic and developmental outcomes. Design Systematic review with random-effects meta-analysis and meta-regression. Data sources Online databases were searched up to 6 January 2017. Study eligibility criteria Studies of all designs were eligible (except case studies and reviews) if published in English, Spanish or French, and contained information on the relevant population (pregnant women without contraindication to exercise), intervention (subjective/objective measures of frequency, intensity, duration, volume or type of exercise, alone (‘exercise-only’) or in combination with other intervention components (eg, dietary; ‘exercise+cointervention’)), comparator (no exercise or different frequency, intensity, duration, volume, type or trimester of exercise) and outcomes (preterm birth, gestational age at delivery, birth weight, low birth weight ( 4000 g), small for gestational age, large for gestational age, intrauterine growth restriction, neonatal hypoglycaemia, metabolic acidosis (cord blood pH, base excess), hyperbilirubinaemia, Apgar scores, neonatal intensive care unit admittance, shoulder dystocia, brachial plexus injury, neonatal body composition (per cent body fat, body weight, body mass index (BMI), ponderal index), childhood obesity (per cent body fat, body weight, BMI) and developmental milestones (including cognitive, psychosocial, motor skills)). Results A total of 135 studies (n=166 094) were included. There was ‘high’ quality evidence from exercise-only randomised controlled trials (RCTs) showing a 39% reduction in the odds of having a baby >4000 g (macrosomia: 15 RCTs, n=3670; OR 0.61, 95% CI 0.41 to 0.92) in women who exercised compared with women who did not exercise, without affecting the odds of growth-restricted, preterm or low birth weight babies. Prenatal exercise was not associated with the other neonatal or infant outcomes that were examined. Conclusions Prenatal exercise is safe and beneficial for the fetus. Maternal exercise was associated with reduced odds of macrosomia (abnormally large babies) and was not associated with neonatal complications or adverse childhood outcomes.
TL;DR: In this paper, the same short-season (FAO 300) maize hybrid PR37N01 was grown over three years of field experiments on three contrasting Spanish locations in terms of temperature regime.
TL;DR: Prenatal exercise reduced the odds and severity of prenatal depression and did not alter the odds of postpartum depression or the severity of depressive symptoms, nor anxiety or anxiety symptoms during or following pregnancy.
Abstract: Objective To examine the influence of prenatal exercise on depression and anxiety during pregnancy and the postpartum period. Design Systematic review with random effects meta-analysis and meta-regression. Data sources Online databases were searched up to 6 January 2017. Study eligibility criteria Studies of all designs were included (except case studies) if they were published in English, Spanish or French and contained information on the Population (pregnant women without contraindication to exercise), Intervention (subjective or objective measures of frequency, intensity, duration, volume or type of exercise), Comparator (no exercise or different frequency, intensity, duration, volume and type of exercise) and Outcome (prenatal or postnatal depression or anxiety). Results A total of 52 studies (n=131 406) were included. ‘Moderate’ quality evidence from randomised controlled trials (RCTs) revealed that exercise-only interventions, but not exercise+cointerventions, reduced the severity of prenatal depressive symptoms (13 RCTs, n=1076; standardised mean difference: −0.38, 95% CI −0.51 to –0.25, I2=10%) and the odds of prenatal depression by 67% (5 RCTs, n=683; OR: 0.33, 95% CI 0.21 to 0.53, I2=0%) compared with no exercise. Prenatal exercise did not alter the odds of postpartum depression or the severity of depressive symptoms, nor anxiety or anxiety symptoms during or following pregnancy. To achieve at least a moderate effect size in the reduction of the severity of prenatal depressive symptoms, pregnant women needed to accumulate at least 644 MET-min/week of exercise (eg, 150 min of moderate intensity exercise, such as brisk walking, water aerobics, stationary cycling, resistance training). Summary/Conclusions Prenatal exercise reduced the odds and severity of prenatal depression.
TL;DR: A triple-ensemble probabilistic assessment is developed using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources to provide more comprehensive information for quantifying uncertainties in climate change impact assessments.
Abstract: Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.
TL;DR: It is found that the purpose of applying gamification in the SE field is mostly directly related to improving student engagement and, to a lesser extent, to improvingStudent knowledge, although other targets are the application of SE best practices and socialization.
TL;DR: This study presents a review of methods for the incorporation of non-additive genetic effects into genomic selection procedures and their potential applications in the prediction of future performance, mate allocation, crossbreeding, and purebred selection.
Abstract: In the last decade, genomic selection has become a standard in the genetic evaluation of livestock populations. However, most procedures for the implementation of genomic selection only consider the additive effects associated with SNP (Single Nucleotide Polymorphism) markers used to calculate the prediction of the breeding values of candidates for selection. Nevertheless, the availability of estimates of non-additive effects is of interest because: (i) they contribute to an increase in the accuracy of the prediction of breeding values and the genetic response; (ii) they allow the definition of mate allocation procedures between candidates for selection; and (iii) they can be used to enhance non-additive genetic variation through the definition of appropriate crossbreeding or purebred breeding schemes. This study presents a review of methods for the incorporation of non-additive genetic effects into genomic selection procedures and their potential applications in the prediction of future performance, mate allocation, crossbreeding, and purebred selection. The work concludes with a brief outline of some ideas for future lines of that may help the standard inclusion of non-additive effects in genomic selection.
TL;DR: The importance of a technological Internet-of-Things approach is emphasized, given the fact it is key in facilitating the information exchange between items in the cold chain, RF technologies and WSN can interconnect the data and those items, and make them accessible in databases associating hardware and software.
TL;DR: A critical review on options for the management of biosolids with a focus on pyrolysis and the application of the solid fraction of pyrofullysis (biochar) into soil is performed.
Abstract: Ever increasing volumes of biosolids (treated sewage sludge) are being produced by municipal wastewater facilities. This is a consequence of the continued expansion of urban areas, which in turn require the commissioning of new treatment plants or upgrades to existing facilities. Biosolids contain nutrients and energy which can be used in agriculture or waste-to-energy processes. Biosolids have been disposed of in landfills, but there is an increasing pressure from regulators to phase out landfilling. This article performs a critical review on options for the management of biosolids with a focus on pyrolysis and the application of the solid fraction of pyrolysis (biochar) into soil.
Goethe University Frankfurt1, Spanish National Research Council2, University of Natural Resources and Life Sciences, Vienna3, Delft University of Technology4, Tel Aviv University5, Aleksandras Stulginskis University6, Technical University of Madrid7, University of Copenhagen8, Agricultural & Applied Economics Association9
TL;DR: In this paper, the authors explore the connections between farm modernisation, rural development and the resilience of agricultural and rural systems, and conclude that systemic change requires more critical reflection of conventional wisdom and approaches, and openness to ideas and practices that are outside the mainstream.
Wageningen University and Research Centre1, Aarhus University2, National Academy of Sciences of Belarus3, Flemish Institute for Technological Research4, Mendel University5, Academy of Sciences of the Czech Republic6, University of Montpellier7, University of Thessaly8, Iowa State University9, Hungarian Academy of Sciences10, Teagasc11, University of Sassari12, University of Agronomic Sciences and Veterinary Medicine of Bucharest13, Technical University of Madrid14, Swedish University of Agricultural Sciences15, ADAS16, Scotland's Rural College17
TL;DR: In this paper, the authors used a country-by-country, bottom-up approach to establish statistical estimates of actual grain yield, and compare these to modelled estimates of potential yields for either irrigated or rainfed conditions.
TL;DR: This study shows that a careful choice of feedstock is of utmost importance for successful containment of heavy metals in a multi-contaminated mining area soil and an appropriate choice ofFeedstock was more determinant with respect to the mobility of pollutants than altering pyrolysis temperature or modifying surface properties through magnetization.