Design of a Two-level Adaptive Multi-Agent System for Malaria Vectors driven by an ontology

<p>Abstract</p> <p>Background</p> <p>The understanding of heterogeneities in disease transmission dynamics as far as malaria vectors are concerned is a big challenge. Many studies while tackling this problem don't find exact models to explain the malaria vectors propagation.</p> <p>Methods</p> <p>To solve the problem we define an Adaptive Multi-Agent System (AMAS) which has the property to be elastic and is a two-level system as well. This AMAS is a dynamic system where the two levels are linked by an Ontology which allows it to function as a reduced system and as an extended system. In a primary level, the AMAS comprises organization agents and in a secondary level, it is constituted of analysis agents. Its entry point, a User Interface Agent, can reproduce itself because it is given a minimum of background knowledge and it learns appropriate "behavior" from the user in the presence of ambiguous queries and from other agents of the AMAS in other situations.</p> <p>Results</p> <p>Some of the outputs of our system present a series of tables, diagrams showing some factors like Entomological parameters of malaria transmission, Percentages of malaria transmission per malaria vectors, Entomological inoculation rate. Many others parameters can be produced by the system depending on the inputted data.</p> <p>Conclusion</p> <p>Our approach is an intelligent one which differs from statistical approaches that are sometimes used in the field. This intelligent approach aligns itself with the distributed artificial intelligence. In terms of fight against malaria disease our system offers opportunities of reducing efforts of human resources who are not obliged to cover the entire territory while conducting surveys. Secondly the AMAS can determine the presence or the absence of malaria vectors even when specific data have not been collected in the geographical area. In the difference of a statistical technique, in our case the projection of the results in the field can sometimes appeared to be more general.</p

Monthly entomological inoculation rate data for studying the seasoanality of malaria transmission in Africa

A comprehensive literature review was conducted to create a new database of 197 field surveys of monthly malaria Entomological Inoculation Rates (EIR), a metric of malaria transmission intensity. All field studies provide data at a monthly temporal resolution and have a duration of at least one year in order to study the seasonality of the disease. For inclusion, data collection methodologies adhered to a specific standard and the location and timing of the measurements were documented. Auxiliary information on the population and hydrological setting were also included. The database includes measurements that cover West and Central Africa and the period from 1945 to 2011, and hence facilitates analysis of interannual transmission variability over broad regions

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

31th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

Genomics and spatial surveillance of Chagas disease and American visceral leishmaniasis

The Trypanosomatidae are a family of parasitic protozoa that infect various animals and plants. Several species within the Trypanosoma and Leishmania genera also pose a major threat to human health. Among these are Trypanosoma cruzi and Leishmania infantum, aetiological agents of the highly debilitating and often deadly vector-borne zoonoses Chagas disease and American visceral leishmaniasis. Current treatment options are far from safe, only partially effective and rarely available in the impoverished regions of Latin America where these ‘neglected tropical diseases’ prevail. Wider-reaching, sustainable protection against T. cruzi and L. infantum might best be achieved by intercepting key routes of zoonotic transmission, but this prophylactic approach requires a better understanding of how these parasites disperse and evolve at various spatiotemporal scales. This dissertation addresses key questions around trypanosomatid parasite biology and spatial epidemiology based on high-resolution, geo-referenced DNA sequence datasets constructed from disease foci throughout Latin America: Which forms of genetic exchange occur in T. cruzi, and are exchange events frequent enough to significantly alter the distribution of important epidemiological traits? How do demographic histories, for example, the recent invasive expansion of L. infantum into the Americas, impact parasite population structure, and do structural changes pose a threat to public health? Can environmental variables predict parasite dispersal patterns at the landscape scale? Following the first chapter’s review of population genetic and genomic approaches in the study of trypanosomatid diseases in Latin America, Chapter 2 describes how reproductive polymorphism segregates T. cruzi populations in southern Ecuador. The study is the first to clearly demonstrate meiotic sex in this species, for decades thought to exchange genetic material only very rarely, and only by non-Mendelian means. T. cruzi subpopulations from the Ecuadorian study site exhibit all major hallmarks of sexual reproduction, including genome-wide Hardy-Weinberg allele frequencies, rapid decay of linkage disequilibrium with map distance and genealogies that fluctuate among chromosomes. The presence of sex promotes the transfer and transformation of genotypes underlying important epidemiological traits, posing great challenges to disease surveillance and the development of diagnostics and drugs. Chapter 3 demonstrates that mating events are also pivotal to L. infantum population structure in Brazil, where introduction bottlenecks have led to striking genetic discontinuities between sympatric strains. Genetic hybridization occurs genome-wide, including at a recently identified ‘miltefosine sensitivity locus’ that appears to be deleted from the majority of Brazilian L. infantum genomes. The study combines an array of genomic and phenotypic analyses to determine whether rapid population expansion or strong purifying selection has driven this prominent > 12 kb deletion to high abundance across Brazil. Results expose deletion size differences that covary with phylogenetic structure and suggest that deletion-carrying strains do not form a private monophyletic clade. These observations are inconsistent with the hypothesis that the deletion genotype rose to high prevalence simply as the result of a founder effect. Enzymatic assays show that loss of ecto-3’-nucleotidase gene function within the deleted locus is coupled to increased ecto-ATPase activity, raising the possibility that alternative metabolic strategies enhance L. infantum fitness in its introduced range. The study also uses demographic simulation modelling to determine whether L. infantum populations in the Americas have expanded from just one or multiple introduction events. Comparison of observed vs. simulated summary statistics using random forests suggests a single introduction from the Old World, but better spatial sampling coverage is required to rule out other demographic scenarios in a pattern-process modelling approach. Further sampling is also necessary to substantiate signs of convergent selection introduced above. Chapter 4 therefore develops a ‘genome-wide locus sequence typing’ (GLST) tool to summarize parasite genetic polymorphism at a fraction of genomic sequencing cost. Applied directly to the infection source (e.g., vector or host tissue), the method also avoids bias from cell purification and culturing steps typically involved prior to sequencing of trypanosomatid and other obligate parasite genomes. GLST scans genomic pilot data for hundreds of polymorphic sequence fragments whose thermodynamic properties permit simultaneous PCR amplification in a single reaction tube. For proof of principle, GLST is applied to metagenomic DNA extracts from various Chagas disease vector species collected in Colombia, Venezuela, and Ecuador. Epimastigote DNA from several T. cruzi reference clones is also analyzed. The method distinguishes 387 single-nucleotide polymorphisms (SNPs) in T. cruzi sub-lineage TcI and an additional 393 SNPs in non-TcI clones. Genetic distances calculated from these SNPs correlate with geographic distances among samples but also distinguish parasites from triatomines collected at common collection sites. The method thereby appears suitable for agent-based spatio-genetic (simulation) analyses left wanted by Chapter 3 – and further formulated in Chapter 5. The potential to survey parasite genetic diversity abundantly across landscapes compels deeper, more systematic exploration of how environmental variables influence the spread of disease. As environmental context is only marginally considered in the population genetic analyses of Chapters 2 – 4, Chapter 5 proposes a new, spatially explicit modelling framework to predict vector-borne parasite gene flow through heterogeneous environment. In this framework, remotely sensed environmental raster values are re-coded and merged into a composite ‘resistance surface’ that summarizes hypothesized effects of landscape features on parasite transmission among vectors and hosts. Parasite population genetic differentiation is then simulated on this surface and fitted to observed diversity patterns in order to evaluate original hypotheses on how environmental variables modulate parasite gene flow. The chapter thereby makes a maiden step from standard population genetic to ‘landscape genomic’ approaches in understanding the ecology and evolution of vector-borne disease. In summary, this dissertation first demonstrates the power of population genetics and genomics to understand fundamental biological properties of important protist parasites, then identifies areas where analytical tools are missing and creates new technical and conceptual frameworks to help fill these gaps. The general discussion (Chapter 6) also outlines several follow-up projects on the key finding of meiotic genetic signatures in T. cruzi. Exploiting recently developed T. cruzi genome-editing systems for the detection of meiotic gene expression and heterozygosis will help understand why and in which life cycle stage some parasite populations use sex and others do not. Long-read sequencing of parental and recombinant genomes will help understand the extent to which sex is diversifying T. cruzi phenotypes, especially virulence and drug resistance properties conferred by surface molecules with repetitive genetic bases intractable to short-read analysis. Chapter 6 also provides follow-up plans for all other research chapters. Emphasis is placed on advancing the complementarity, transferability and public health benefit of the many different methods and concepts employed in this work

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Emerging Vaccine Informatics

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning