bluuebunny/crossref_metadata_2025_split huggingface dataset:
Economic impacts of climate change on agriculture to 2030.
Authors: Lobell D. , Burke M. 
Published: January 2011

Abstract

The global food economy is vast and technologically advanced, but none the less prone to substantial negative outcomes in poor weather years. The economic gains or losses associated with climate change will depend on the pace of climate change, the response of cropping systems - including the vast number of growers, researchers, extension agents and others who determine how croplands are managed - to these changes, and the response of global markets to resulting changes in crop yields. We outline here the processes involved in each of these steps, and provide some estimates of likely and possible extreme outcomes by 2030. Though adaptation holds great promise for reducing negative outcomes, it is by no means guaranteed, and its realization will be likely to require significant advances in crop research as well as in the ability of farmers to recognize climate trends and adopt appropriate technologies.
Preventing potential disease and pest epidemics under a changing climate.
Authors: Legrève A. , Duveiller E. 
Published: January 2011

Abstract

For a disease or pest to cause yield losses, the host and pathogen or pest must coincide within a favourable environment. With changing weather patterns and cropping systems, abiotic and biotic components influencing potential epidemics are modified and new interactions occur. Since they affect plant phenology and the survival and multiplication rates of microorganisms and insects, temperature and humidity are key factors of epidemics. The incidence of pathogens and pests has noticeably evolved in recent years; globalization, in particular, has increased threats from new transboundary pests and diseases. Factors driving new outbreaks include extraordinary climatic events and trends in temperature selecting pathogens and their natural enemies towards new critical thresholds for inoculum survival. Disease cycle components such as survival, infection, colonization processes and latency period, in addition to production and dispersal of inoculum, are all affected. Climate is most likely a strong driver of evolutionary change in plant and pathogen populations by interfering with host-pathogen interactions, gene expression and population dynamics. Disease monitoring and identifying the parameters affecting pest outbreaks improve epidemic risk assessment and knowledge of the enemy. Strategies to prevent the negative effects of pests and diseases include stringent quarantine regulations, adopting cropping systems that favour biocontrol or avoidance and, most importantly, resistance breeding, cultural practices and sound phytosanitary measures. This review highlights recent changes in microbial communities and the evolution of selected pathosystems encompassing small grains, tubers and agroforestry. The value and effectiveness of integrated crop management and sustainable approaches for controlling potential new disease and pest epidemics, in the context of climate change, are emphasized.
Breeding for adaptation to heat and drought stress.
Authors: Reynolds M. P. , Hays D. , Chapman S. 
Published: January 2011

Abstract

Crops respond similarly to drought and heat stress: life cycle is accelerated reducing photosynthetic capacity via restricted leaf area and duration. Metabolism is inhibited at temperature and water potential ranges outside those optimal for growth. Reproductive processes are impaired when stress occurs at critical developmental stages reducing seed set. Both stresses can be exacerbated by nutrient deficiencies and biotic factors while elevated CO2 levels may partially ameliorate stress in C3 species. Although stress adaptive traits - and consistent quantitative trait loci associated with them - are used to design new cultivars, the physiological and genetic bases of adaptation are only partially understood. Therefore, plant selection requires empirical approaches such as multi-location testing across representative environments, while detailed characterization of target sites permits genotype × environment interaction to be dissected, providing feedback into breeding and research. Precision phenotyping approaches assist by dissecting yield into its physiological components and have application in breeding and gene discovery. Examples of stress-adaptive traits which have been selected for in several species include deeper roots enabling plants to remain hydrated under drought and permitting canopy cooling under heat stress, transpiration efficiency, delayed senescence in sorghum, and synchronous flowering in maize. New traits and genes must be identified - perhaps among crop wild relatives or in model species - that permit cultivars to be buffered against temporal variation in water supply, adapt to higher temperatures without loss of water-use efficiency, and tolerate sudden extreme climatic events or combinations of stress factors. Examples of past successes and promising new approaches are discussed.
Genetic approaches to reduce greenhouse gas emissions: increasing carbon capture and decreasing environmental impact.
Authors: Parry M. A. J. , Hawkesford M. J. 
Published: January 2011

Abstract

Genetic improvements aimed at increasing crop performance and decreasing the environmental costs of production are essential to mitigate the impact of climate change. This can be achieved by increasing production efficiency and decreasing losses during storage and processing thus decreasing the emissions of greenhouse gases needed to support production. Progress by conventional approaches may be too small to achieve the necessary progress quickly enough. Biotechnology has the potential to make larger changes more quickly by both identifying and introducing novel variation in key agronomic traits. In this chapter we focus on some specific examples of key targets that could be modified by such directed approaches.
Greenhouse gas mitigation in the main cereal systems: rice, wheat and maize.
Authors: Ortiz-Monasterio I. , Wassmann R. , Govaerts B. , Hosen Y. , Katayanagi N. , Verhulst N. 
Published: January 2011

Abstract

Population and economic growth are expected to be the main drivers of increased food demand through 2050. This increase in food production will come primarily from intensively managed agricultural systems. Currently these systems are already important contributors of greenhouse gas (GHG) emissions. If production practices are not changed in these systems the emission of GHGs is expected to increase. Therefore, it is important to devise sustainable management practices that, in the short and long term, will help to reduce the emission of GHGs. This chapter analyses the three main cereal crops, rice, wheat and maize, and the management strategies that can help reduce GHG emissions. Rice has the unique characteristic among these cereals of being grown under flooded conditions, which result in CH4 becoming a particularly important GHG in these systems. Although there remains large uncertainty in N2O emissions from paddy fields, mid-season drainage has the potential to be an effective option to mitigate the net global warming potential (GWP) from rice fields when rice residue is returned to the fields. In the case of wheat and maize cropping systems the adoption of currently available best management practices for N management should be a good guideline for practices that reduce N2O emissions. In addition, through the adoption of conservation agriculture it is possible to reduce GHG emissions by reducing the number of tillage operations and possibly by sequestering C. Mitigation policies that encourage efficient use of fertilizers, maintain soil C and sustain agricultural production are likely to have the greatest synergy with sustainable development.
How conservation agriculture can contribute to buffering climate change.
Authors: Hobbs P. R. , Govaerts B. 
Published: January 2011

Abstract

Agriculture contributes significantly to greenhouse gas (GHG) emissions: CO2, CH4 and N2O. Promoting agricultural practices that mitigate climate change by reducing GHG emissions is important; but those same practices also have to improve farmer production and income and buffer the production system against changes in climate. New agricultural practices also need to prevent further soil degradation and improve system resilience. Conservation agriculture (CA), based on minimal soil disturbance, permanent ground cover and crop rotations is a management system that achieves these goals; it results in improved soil physical and biological health, and better nutrient cycling and crop growth. CA also increases water infiltration and soil penetration by roots, which allows crops to better adapt to lower rainfall and make better use of irrigation water. Water and wind erosion are also reduced by CA since the soil surface is protected and water runoff is lowered as more water enters the soil profile. CA can also help to mitigate climate change. Growing rice with less water and adopting CA practices results in less CH4 emission. However, care has to be taken with fertilizer management to minimize N2O emissions that can increase under resulting aerobic conditions. CA can also substantially reduce CO2 emissions through reduced diesel use and increased sequestration of C in the soil. This chapter recommends that integrated research and participatory extension are needed to fine-tune CA to specific locations to convince farmers to adopt this technology.
Statistical models for studying and understanding genotype × environment interaction in an era of climate change and increased genetic information.
Authors: Crossa J. , Burgueño J. , Vargas M. 
Published: January 2011

Abstract

Annual crop production will be greatly affected by increases in mean temperature and climate change, which will be likely to reduce agricultural production and decrease food availability. Plant breeding will play an important role in developing more sustainable lines and varieties for less favourable environments that will be subjected to extreme changes in biotic and abiotic stresses. Breeding cultivars with enhanced tolerance to heat, moisture stress and salinity is essential for long-term adaptation response to climate change. Multi-environment trials (METs) play a paramount role in breeding cultivars for general and specific adaptation and yield stability, studying genotype × environment (GE) interaction, and predicting the performance of new cultivars in future years and new locations. METs produce a vast amount of useful data, including not only phenotypic measurements of cultivars evaluated in different environments but also climatic and soil data as well as molecular markers representing genetic data. Appropriate statistical models and analyses used to study response patterns of genotypes and their molecular marker attributes across different environments undergoing varying climatic changes will be of paramount importance for developing sustainable and stable cultivars that are resistant/tolerant to diverse biotic and abiotic stresses. In this chapter, we explain the theoretical basis of several statistical models and their application for explaining the climatic and genetic causes of GE interaction.
Biomedical and environmental applications of magnetic nanoparticles
Authors: Tran Dai Lam , Le Van Hong , Pham Hoai Linh , Hoang Thi My Nhung , Nguyen Thi Quy , Luong Thien Tai , Ha Phuong Thu , Nguyen Xuan Phuc 
Published: January 2011

Abstract
This paper presents an overview of syntheses and applications of magnetic nanoparticles (MNPs) at the Institute of Materials Science, Vietnam Academy of Science and Technology. Three families of oxide MNPs, magnetite, manganite and spinel ferrite materials, were prepared in various ways: coprecipitation, sol–gel and high energy mechanical milling. Basic properties of MNPs were characterized by Vibrating Sample Magnetometer (VSM) and Physical Properties Measurement Systems (PPMS). As for biomedical application, the aim was to design a novel multifunctional, nanosized magnetofluorescent water-dispersible Fe
3
O
4-curcumin conjugate, and its ability to label, target and treat tumor cells was described. The conjugate possesses a magnetic nano Fe
3
O
4 core, chitosan (CS) or Oleic acid (OL) as an outer shell and entrapped curcumin (Cur), serving the dual function of naturally autofluorescent dye as well as antitumor model drug. Fe
3
O
4-Cur conjugate exhibited a high loading cellular uptake with the help of a macrophage, which was clearly visualized dually by Fluorescence Microscope and Laser Scanning Confocal Microscope (LSCM), as well as by magnetization measurement (PPMS). A preliminary magnetic resonance imaging (MRI) study also showed a clear contrast enhancement by using the conjugate. As for the environmental aspect, the use of magnetite MNPs for the removal of heavy toxic metals, such as Arsenic (As) and Lead (Pb), from contaminated water was studied.
An efficient hierarchical generalized linear mixed model for pathway analysis of genome-wide association studies
Authors: Wang Lily , Jia Peilin , Wolfinger Russell D. , Chen Xi , Grayson Britney L. , Aune Thomas M. , Zhao Zhongming 
Published: January 2011

AbstractMotivation: In genome-wide association studies (GWAS) of complex diseases, genetic variants having real but weak associations often fail to be detected at the stringent genome-wide significance level. Pathway analysis, which tests disease association with combined association signals from a group of variants in the same pathway, has become increasingly popular. However, because of the complexities in genetic data and the large sample sizes in typical GWAS, pathway analysis remains to be challenging. We propose a new statistical model for pathway analysis of GWAS. This model includes a fixed effects component that models mean disease association for a group of genes, and a random effects component that models how each gene's association with disease varies about the gene group mean, thus belongs to the class of mixed effects models.Results: The proposed model is computationally efficient and uses only summary statistics. In addition, it corrects for the presence of overlapping genes and linkage disequilibrium (LD). Via simulated and real GWAS data, we showed our model improved power over currently available pathway analysis methods while preserving type I error rate. Furthermore, using the WTCCC Type 1 Diabetes (T1D) dataset, we demonstrated mixed model analysis identified meaningful biological processes that agreed well with previous reports on T1D. Therefore, the proposed methodology provides an efficient statistical modeling framework for systems analysis of GWAS.Availability: The software code for mixed models analysis is freely available at http://biostat.mc.vanderbilt.edu/LilyWang.Contact: lily.wang@vanderbilt.edu; zhongming.zhao@vanderbilt.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Classifying short gene expression time-courses with Bayesian estimation of piecewise constant functions
Authors: Hafemeister Christoph , Costa Ivan G. , Schönhuth Alexander , Schliep Alexander 
Published: January 2011

Abstract
Motivation: Analyzing short time-courses is a frequent and relevant problem in molecular biology, as, for example, 90% of gene expression time-course experiments span at most nine time-points. The biological or clinical questions addressed are elucidating gene regulation by identification of co-expressed genes, predicting response to treatment in clinical, trial-like settings or classifying novel toxic compounds based on similarity of gene expression time-courses to those of known toxic compounds. The latter problem is characterized by irregular and infrequent sample times and a total lack of prior assumptions about the incoming query, which comes in stark contrast to clinical settings and requires to implicitly perform a local, gapped alignment of time series. The current state-of-the-art method (SCOW) uses a variant of dynamic time warping and models time series as higher order polynomials (splines).
Results: We suggest to model time-courses monitoring response to toxins by piecewise constant functions, which are modeled as left–right Hidden Markov Models. A Bayesian approach to parameter estimation and inference helps to cope with the short, but highly multivariate time-courses. We improve prediction accuracy by 7% and 4%, respectively, when classifying toxicology and stress response data. We also reduce running times by at least a factor of 140; note that reasonable running times are crucial when classifying response to toxins. In conclusion, we have demonstrated that appropriate reduction of model complexity can result in substantial improvements both in classification performance and running time.
Availability: A Python package implementing the methods described is freely available under the GPL from http://bioinformatics.rutgers.edu/Software/MVQueries/.
Contact: hafemeis@molgen.mpg.de; igcf@cin.ufpe.br; schliep@cs.rutgers.edu;
Supplementary information: Supplementary data are available at Bioinformatics online.
Seventy Million Years of Concerted Evolution of a Homoeologous Chromosome Pair, in Parallel, in Major Poaceae Lineages
Authors: Wang Xiyin , Tang Haibao , Paterson Andrew H. 
Published: January 2011

Abstract
Whole genome duplication ~70 million years ago provided raw material for Poaceae (grass) diversification. Comparison of rice (Oryza sativa), sorghum (Sorghum bicolor), maize (Zea mays), and Brachypodium distachyon genomes revealed that one paleo-duplicated chromosome pair has experienced very different evolution than all the others. For tens of millions of years, the two chromosomes have experienced illegitimate recombination that has been temporally restricted in a stepwise manner, producing structural stratification in the chromosomes. These strata formed independently in different grass lineages, with their similarities (low sequence divergence between paleo-duplicated genes) preserved in parallel for millions of years since the divergence of these lineages. The pericentromeric region of this homeologous chromosome pair accounts for two-thirds of the gene content differences between the modern chromosomes. Both intriguing and perplexing is a distal chromosomal region with the greatest DNA similarity between surviving duplicated genes but also with the highest concentration of lineage-specific gene pairs found anywhere in these genomes and with a significantly elevated gene evolutionary rate. Intragenomic similarity near this chromosomal terminus may be important in hom(e)ologous chromosome pairing. Chromosome structural stratification, together with enrichment of autoimmune response–related (nucleotide binding site–leucine-rich repeat) genes and accelerated DNA rearrangement and gene loss, confer a striking resemblance of this grass chromosome pair to the sex chromosomes of other taxa.
SNP‐based association mapping of Arachnomelia in Fleckvieh cattle
Authors: Seichter D. , Russ I. , Förster M. , Medugorac I. 
Published: January 2011

SummaryBovine arachnomelia is an inherited congenital disorder with malformation mainly of the limbs, the vertebral column and the skull, following a monogenic autosomal recessive heredity. Despite almost identical pathological findings, arachnomelia has previously been mapped to bovine chromosome 23 and 5 in Fleckvieh and Braunvieh respectively. Therefore, this disorder may be an example of locus heterogeneity in cattle. This study aimed to refine the candidate region to allow positional cloning and sequence analyses of candidate genes in Fleckvieh cattle. For that purpose, a case‐control association mapping design was set up with a case group of 16 pre‐selected affected individuals and a control group of 50 unrelated animals. The subset of affected animals was selected from a total of 129 pathologically confirmed cases due to the occurrence of recombination(s) within a 14.5 cM candidate interval previously mapped to chromosome 23. Six linked microsatellites currently used for indirect gene testing in Fleckvieh were analysed for this purpose. In all selected cases, a genome‐wide scan using 44 473 informative SNPs revealed shared segments of homozygosity at 15 adjacent SNPs on chromosome 23. Additional haplotype analysis of 37 carrier bulls confirmed the localization of the arachnomelia locus to a region of 927 kb (13.622–14.549 Mb) containing molybdenum cofactor biosynthesis protein 1 gene, the most likely candidate gene for arachnomelia in Fleckvieh. The number of recombinant haplotypes observed in cases was more than doubled compared with the number of expected recombinations. This remarkably increased mapping resolution and thus illustrates the benefit of pre‐selection in association studies.
Local geographic range predicts freshwater fish extinctions in Singapore
Authors: Giam Xingli , Ng Ting Hui , Lok Alvin F. S. L. , Ng Heok Hee 
Published: January 2011

Summary 1. Identifying the ecological and life‐history correlates of local extinction may elucidate mechanisms by which species traits and the environment interact to result in extinctions, and will help to predict and target extinction‐prone species for inclusion in conservation programmes. Freshwater habitats are known to be highly threatened in Southeast Asia but the correlates of extinction among tropical freshwater fish remains unclear. 2. To bridge this knowledge gap, we examined extinction correlates of the freshwater fish of Singapore using machine learning methods: conditional inference trees and forests. Singapore is an ideal study site as it has experienced a high degree of habitat loss and has a well‐studied ichthyofauna compared with other countries in the region. 3. The local range of a species was the only significant predictor of extinctions: range‐restricted species were more likely to go extinct. 4. Other traits found to be important predictors of extinction risk in temperate regions or hypothesized to predict extinctions based on theory included regional geographic distribution, vertical position, feeding guild, body size, number of congeners, air‐breathing capability and habitat preference. These factors did not appear to drive extinctions of freshwater fish in Singapore, although forest‐dependent species are more likely to have a restricted local range. 5. Synthesis and applications. Local extinctions of freshwater fish in Singapore are random with respect to ecological and life‐history traits because habitat loss is responsible for the removal of entire populations. The fish fauna of Southeast Asia is so poorly known that intensive field surveys are required to identify hotspots of freshwater fish endemism which may be vulnerable to future extinction. These hotspots should then be incorporated into national conservation plans. Where complete habitat protection is not possible, for example, in existing logging concessions and plantations, local authorities should establish partnerships with management companies to ameliorate impacts on fish fauna. Within Singapore, the Nee Soon Swamp Forest is one such hotspot of fish endemism and must be conserved to protect the last populations of five fish species endemic to this location on the island.