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Viruses threatening stable production of cereal crops

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889196128 Year: Pages: 117 DOI: 10.3389/978-2-88919-612-8 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Microbiology
Added to DOAB on : 2016-08-16 10:34:25
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Cereal crops such as maize, wheat, and rice account for a majority of biomass produced globally in agriculture. Continuous economic and population growth especially in developing countries accompanied more intensive production of cereal crops to meet increasing demands for them as main staple foods and livestock feeds. However, imbalance between production and consumption of cereal crops, which is inevitably reflected as their higher market prices, is becoming palpable in recent years. Stable production of cereal crops has been threatened by various abiotic and biotic stresses. One of the most threatening constraints is virus diseases. Especially, intensification of cereal crop production is often achieved by monoculture of a popular crop variety in a wide area. Such agroecosystems with low biodiversity is usually more conducive to biotic stresses, and may result in the outbreaks of existing and emerging cereal viruses. Numerous reports on incidences of various virus diseases of cereal crops attested that viruses have been a long-standing obstacle eroding yields of cereal crops worldwide. Despite of the evident economic losses incurred by virus disease of cereal crops, the progress in basic research on virus species causing major diseases of cereal crops lagged behind compared to that carried out for viruses that can infect dicotyledonous plants. This was partially due to the lack of ideal experimental systems to investigate the interaction between viruses and monocotyledonous crops. For example, inoculation of many viruses to cereal plants still requires tedious manipulation of vector organisms, and reverse genetic systems are not available for many cereal viruses. However, application of latest molecular biology technologies has led to significant advance in cereal virology recently; transient gene expression systems through particle bombardment and agroinfiltration have been exploited to examine the functions of cereal virus proteins. Cell culture systems of vector insects enabled to investigate the molecular interactions between cereal viruses and insect vectors. Furthermore, RNAi technologies for vector insects and monocotyledonous plants facilitated identification of specific host and viral factors involved in viral replication and transmission cycles. Also, accumulating information on the genome sequences of cereal crop species has been simplifying the roadmap to pinpoint resistance genes against cereal viruses. The objective of this research topic is to provide and share the information which can contribute to advances in cereal virology by covering recent progresses in areas such as: 1) characterization of emerging viruses, 2) analyses of genetic and biological diversities within particular viruses, 3) development of experimental systems applicable to cereal viruses, 4) elucidation of the molecular interactions among viruses, vector organisms, and host plants, 5) identification of traits and genes linked to virus resistance in cereal crops, 6) development of novel genetic approaches for virus resistance, and 7) assessment of epidemiological factors affecting the incidences of cereal virus diseases. Synergistic integration of ideas from such areas under this research topic should help to formulate practical alternatives to the current management options for virus diseases in cereal crops.

Emerging Zoonoses: Eco-Epidemiology, Involved Mechanisms and Public Health Implications

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889196180 Year: Pages: 248 DOI: 10.3389/978-2-88919-618-0 Language: English
Publisher: Frontiers Media SA
Subject: Medicine (General) --- Public Health
Added to DOAB on : 2015-11-02 10:49:06
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Zoonoses are currently considered as one of the most important threats for public health worldwide. Zoonoses can be defined as any disease or infection that is naturally transmissible from vertebrate or invertebrate animals to humans and vice-versa. Approximately 75% of recently emerging infectious diseases affecting humans are diseases of animal origin; approximately 60% of all human pathogens are zoonotic. All types of potential pathogenic agents, including viruses, parasites, bacteria and fungi, can cause these zoonotic infections. From the wide range of potential vectors of zoonoses, insects are probably those of major significance due to their abundance, high plasticity and adaptability to different kinds of pathogens, high degrees of synanthropism in several groups and difficulties to apply effective programs of population control. Although ticks, flies, cockroaches, bugs and fleas are excellent insects capable to transmit viruses, parasites and bacteria, undoubtedly mosquitoes are the most important disease vectors. Mosquito borne diseases like malaria, dengue, equine encephalitis, West Nile, Mayaro or Chikungunya are zoonoses with increasing incidence in last years in tropical and temperate countries. Vertebrates can also transmit serious zoonoses, highlighting the role of some carnivorous animals in rabies dissemination or the spread of rodent borne diseases in several rural and urban areas. Moreover, the significance of other food borne zoonoses such as taeniasis, trichinellosis or toxoplasmosis may not been underestimated. According to WHO, FAO and OIE guidelines an emerging zoonotic disease can be defined as a zoonosis that is newly recognized or newly evolved, or that has occurred previously but shows an increase of incidence or expansion in geographical, host or vector range. There are many factors that can provoke or accelerate the emergence of zoonoses, such as environmental changes, habitat modifications, variations of human and animal demography, pathogens and vectors anomalous mobilization related with human practices and globalization, deterioration of the strategies of vector control or changes in pathogen genetics. To reduce public health risks from zoonoses is absolutely necessary to acquire an integrative perspective that includes the study of the complexity of interactions among humans, animals and environment in order to be able to fight against these issues of primary interest for human health. In any case, although zoonoses represent significant public health threats, many of them still remain as neglected diseases and consequently are not prioritized by some health international organisms.

Tick-Host-Pathogen Interactions

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889455423 Year: Pages: 543 DOI: 10.3389/978-2-88945-542-3 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Internal medicine
Added to DOAB on : 2019-01-23 14:53:42
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Besides causing direct damage associated with blood feeding and in some cases through the excretion of toxins with their saliva, the main relevance of ticks lies in the wide variety of pathogens that they can transmit, including viruses, bacteria, protozoa and helminths. Owing to socioeconomic and environmental changes, tick distribution is changing with incursions of ticks and tick-borne diseases occurring in different regions of the world when the widespread deployment of chemical acaricides and repellents has led to the selection of resistance in multiple populations of ticks. New approaches that are environmentally sustainable and that provide broad protection against current and future tick-borne pathogen (TBP) are thus urgently needed. Such development, however, requires improved understanding of factors resulting in vector competence and tick-host-pathogen interactions. This Research Topic provides an overview of known molecular tick-host-pathogen interactions for a number of TBPs and highlights how this knowledge can contribute to novel control and prevention strategies for tick-borne diseases.

Hybrid Advanced Techniques for Forecasting in Energy Sector

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ISBN: 9783038972907 9783038972914 Year: Pages: 250 DOI: 10.3390/books978-3-03897-291-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science --- General and Civil Engineering
Added to DOAB on : 2018-10-19 10:39:42
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Accurate forecasting performance in the energy sector is a primary factor in the modern restructured power market, accomplished by any novel advanced hybrid techniques. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated by factors such as seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. To comprehensively address this issue, it is insufficient to concentrate only on simply hybridizing evolutionary algorithms with each other, or on hybridizing evolutionary algorithms with chaotic mapping, quantum computing, recurrent and seasonal mechanisms, and fuzzy inference theory in order to determine suitable parameters for an existing model. It is necessary to also consider hybridizing or combining two or more existing models (e.g., neuro-fuzzy model, BPNN-fuzzy model, seasonal support vector regression–chaotic quantum particle swarm optimization (SSVR-CQPSO), etc.). These advanced novel hybrid techniques can provide more satisfactory energy forecasting performances.This book aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards recent developments, i.e., hybridizing or combining any advanced techniques in energy forecasting, with the superior capabilities over the traditional forecasting approaches, with the ability to overcome some embedded drawbacks, and with the very superiority to achieve significant improved forecasting accuracy.

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

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ISBN: 9783038972860 9783038972877 Year: Pages: 250 DOI: 10.3390/books978-3-03897-287-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2018-10-19 11:45:03
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More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers.This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy.

Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting

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ISBN: 9783038972921 9783038972938 Year: Pages: 186 DOI: 10.3390/books978-3-03897-293-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science --- General and Civil Engineering
Added to DOAB on : 2018-10-22 10:01:53
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The development of kernel methods and hybrid evolutionary algorithms (HEAs) to support experts in energy forecasting is of great importance to improving the accuracy of the actions derived from an energy decision maker, and it is crucial that they are theoretically sound. In addition, more accurate or more precise energy demand forecasts are required when decisions are made in a competitive environment. Therefore, this is of special relevance in the Big Data era. These forecasts are usually based on a complex function combination. These models have resulted in over-reliance on the use of informal judgment and higher expense if lacking the ability to catch the data patterns. The novel applications of kernel methods and hybrid evolutionary algorithms can provide more satisfactory parameters in forecasting models. We aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards the development of HEAs with kernel methods or with other novel methods (e.g., chaotic mapping mechanism, fuzzy theory, and quantum computing mechanism), which, with superior capabilities over the traditional optimization approaches, aim to overcome some embedded drawbacks and then apply these new HEAs to be hybridized with original forecasting models to significantly improve forecasting accuracy.

Short-Term Load Forecasting by Artificial Intelligent Technologies

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ISBN: 9783038975823 / 9783038975830 Year: Pages: 444 DOI: 10.3390/books978-3-03897-583-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-01-29 10:55:39
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In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency analysis, load flow analysis, planning, and maintenance of power systems. There are lots of forecasting models proposed for STLF, including traditional statistical models (such as ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, and so on) and artificial-intelligence-based models (such as artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, and so on). Recently, due to the great development of evolutionary algorithms (EA) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, and cloud mapping process, and so on), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve satisfactory forecasting accuracy levels. In addition, combining some superior mechanisms with an existing model could empower that model to solve problems it could not deal with before; for example, the seasonal mechanism from the ARIMA model is a good component to be combined with any forecasting models to help them to deal with seasonal problems.

Clinical Text Mining: Secondary Use of Electronic Patient Records

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ISBN: 9783319785028 9783319785035 Year: Pages: 181 DOI: https://doi.org/10.1007/978-3-319-78503-5 Language: English
Publisher: Springer Grant: Riksbankens Jubileumsfond; Stockholm University
Subject: Medical technology
Added to DOAB on : 2018-06-22 15:52:54
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This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records.It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters.The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields.

Molecular Biology of Bamboo mosaic Virus - A Type Member of the Potexvirus Genus

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889454396 Year: Pages: 109 DOI: 10.3389/978-2-88945-439-6 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Microbiology --- Botany
Added to DOAB on : 2018-11-16 17:17:57
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The flexible filamentous plant viruses are responsible for more than half of all agricultural loss worldwide. Potexvirus is one of the two most important flexible filamentous plant viruses. Bamboo mosaic virus (BaMV), a single-stranded positive-sense RNA virus, is a member of the Potexvirus genus of Alphaflexiviridae. It can infect at least 12 species of bamboo, causing a huge economic impact on the bamboo industry in Taiwan. The study of BaMV did not start extensively until the completion of the full-length sequencing of genomic RNA of BaMV and generation of the BaMV infectious cDNA clone in the early 1990s. Since then, BaMV has been extensively studied at the molecular, cellular and ecological level, covering both basic and applied researches, by a group of researchers in Taiwan. In this eBook, the content comprises 6 reviews and 4 articles. Seven of them are involved in the infection of BaMV covering viral RNA replication, viral RNA trafficking, and the host factors. Two of them are related to the vector transmission and the ecology of BaMV. The last one is the application of using BaMV as a viral vector to produce vaccines in plants.

Language Technologies for the Challenges of the Digital Age: 27th International Conference, GSCL 2017, Berlin, Germany, September 13-14, 2017, Proceedings

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Book Series: Lecture Notes in Artificial Intelligence ISSN: 0302-9743 ISBN: 9783319737058 9783319737065 Year: Volume: 10713 Pages: 310 DOI: https://doi.org/10.1007/978-3-319-73706-5 Language: English
Publisher: Springer Grant: Bundesministerium für Bildung und Forschung (BMBF)
Subject: Linguistics --- Education --- Technology (General)
Added to DOAB on : 2018-06-29 12:25:05
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This open access volume constitutes the refereed proceedings of the 27th biennial conference of the German Society for Computational Linguistics and Language Technology, GSCL 2017, held in Berlin, Germany, in September 2017, which focused on language technologies for the digital age. The 16 full papers and 10 short papers included in the proceedings were carefully selected from 36 submissions. Topics covered include text processing of the German language, online media and online content, semantics and reasoning, sentiment analysis, and semantic web description languages.

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