Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Mapping HIV-related behavioural surveillance among injecting drug users in Europe, 2008.

The systematic collection of behavioural information is an important component of second-generation HIV surveillance. The extent of behavioural surveillance among injecting drug users (IDUs) in Europe was examined using data collected through a questionnaire sent to all 31 countries of the European Union and European Free Trade Association as part of a European-wide behavioural surveillance mapping study on HIV and other sexually transmitted infections. The questionnaire was returned by 28 countries during August to September 2008: 16 reported behavioural surveillance studies (two provided no further details). A total of 12 countries used repeated surveys for behavioural surveillance and five used their Treatment Demand Indicator system (three used both approaches). The data collected focused on drug use, injecting practices, testing for HIV and hepatitis C virus and access to healthcare. Eight countries had set national indicators: three indicators were each reported by five countries: the sharing any injecting equipment, uptake of HIV testing and uptake of hepatitis C virus testing. The recall periods used varied. Seven countries reported conducting one-off behavioural surveys (in one country without a repeated survey, these resulted an informal surveillance structure). All countries used convenience sampling, with service-based recruitment being the most common approach. Four countries had used respondent-driven sampling. Three fifths of the countries responding (18/28) reported behavioural surveillance activities among IDUs; however, harmonisation of behavioural surveillance indicators is needed.

Plasmodium falciparum merozoite surface protein 2: epitope mapping and fine specificity of human antibody response against non-polymorphic domains.

BACKGROUND: Two long synthetic peptides representing the dimorphic and constant C-terminal domains of the two allelic families of Plasmodium falciparum merozoite surface proteins 2 are considered promising malaria vaccine candidates. The aim of the current study is to characterize the immune response (epitope mapping) in naturally exposed individuals and relate immune responses to the risk of clinical malaria. METHODS: To optimize their construction, the fine specificity of human serum antibodies from donors of different age, sex and living in four distinct endemic regions was determined in ELISA by using overlapping 20 mer peptides covering the two domains. Immune purified antibodies were used in Western blot and immunofluorescence assay to recognize native parasite derivate proteins. RESULTS: Immunodominant epitopes were characterized, and their distribution was similar irrespective of geographic origin, age group and gender. Acquisition of a 3D7 family and constant region-specific immune response and antibody avidity maturation occur early in life while a longer period is needed for the corresponding FC27 family response. In addition, the antibody response to individual epitopes within the 3D7 family-specific region contributes to protection from malaria infection with different statistical weight. It is also illustrated that affinity-purified antibodies against the dimorphic or constant regions recognized homologous and heterologous parasites in immunofluorescence and homologous and heterologous MSP2 and other polypeptides in Western blot. CONCLUSION: Data from this current study may contribute to a development of MSP2 vaccine candidates based on conserved and dimorphic regions thus bypassing the complexity of vaccine development related to the polymorphism of full-length MSP2.

Healthy child, healthy future - Speech and language therapy: Factsheet for parents 5. Does my child have a voice problem?

This factsheet describes voice disorders such as 'hoarseness' in children and what parents can do to help their child with a voice problem.

Gene expression variation and expression quantitative trait mapping of human chromosome 21 genes.

Inter-individual differences in gene expression are likely to account for an important fraction of phenotypic differences, including susceptibility to common disorders. Recent studies have shown extensive variation in gene expression levels in humans and other organisms, and that a fraction of this variation is under genetic control. We investigated the patterns of gene expression variation in a 25 Mb region of human chromosome 21, which has been associated with many Down syndrome (DS) phenotypes. Taqman real-time PCR was used to measure expression variation of 41 genes in lymphoblastoid cells of 40 unrelated individuals. For 25 genes found to be differentially expressed, additional analysis was performed in 10 CEPH families to determine heritabilities and map loci harboring regulatory variation. Seventy-six percent of the differentially expressed genes had significant heritabilities, and genomewide linkage analysis led to the identification of significant eQTLs for nine genes. Most eQTLs were in trans, with the best result (P=7.46 x 10(-8)) obtained for TMEM1 on chromosome 12q24.33. A cis-eQTL identified for CCT8 was validated by performing an association study in 60 individuals from the HapMap project. SNP rs965951 located within CCT8 was found to be significantly associated with its expression levels (P=2.5 x 10(-5)) confirming cis-regulatory variation. The results of our study provide a representative view of expression variation of chromosome 21 genes, identify loci involved in their regulation and suggest that genes, for which expression differences are significantly larger than 1.5-fold in control samples, are unlikely to be involved in DS-phenotypes present in all affected individuals.

The problem of Antimicrobial Resistance in the food chain

Antimicrobial resistance (AMR) associated with the food chain is currently a subject of major interest to many food chain stakeholders. In response safefood commissioned this report to update our knowledge of this area and to raise awareness of the issue. Its primary focus is on the food chain where it impacts consumer health. This review will inform and underpin any future action to be taken by safefood with regard to AMR.

COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography.

Tractography is a class of algorithms aiming at in vivo mapping the major neuronal pathways in the white matter from diffusion magnetic resonance imaging (MRI) data. These techniques offer a powerful tool to noninvasively investigate at the macroscopic scale the architecture of the neuronal connections of the brain. However, unfortunately, the reconstructions recovered with existing tractography algorithms are not really quantitative even though diffusion MRI is a quantitative modality by nature. As a matter of fact, several techniques have been proposed in recent years to estimate, at the voxel level, intrinsic microstructural features of the tissue, such as axonal density and diameter, by using multicompartment models. In this paper, we present a novel framework to reestablish the link between tractography and tissue microstructure. Starting from an input set of candidate fiber-tracts, which are estimated from the data using standard fiber-tracking techniques, we model the diffusion MRI signal in each voxel of the image as a linear combination of the restricted and hindered contributions generated in every location of the brain by these candidate tracts. Then, we seek for the global weight of each of them, i.e., the effective contribution or volume, such that they globally fit the measured signal at best. We demonstrate that these weights can be easily recovered by solving a global convex optimization problem and using efficient algorithms. The effectiveness of our approach has been evaluated both on a realistic phantom with known ground-truth and in vivo brain data. Results clearly demonstrate the benefits of the proposed formulation, opening new perspectives for a more quantitative and biologically plausible assessment of the structural connectivity of the brain.

Multi-ethnic fine-mapping of 14 central adiposity loci.

The Genetic Investigation of Anthropometric Traits (GIANT) consortium identified 14 loci in European Ancestry (EA) individuals associated with waist-to-hip ratio (WHR) adjusted for body mass index. These loci are wide and narrowing the signals remains necessary. Twelve of 14 loci identified in GIANT EA samples retained strong associations with WHR in our joint EA/individuals of African Ancestry (AA) analysis (log-Bayes factor >6.1). Trans-ethnic analyses at five loci (TBX15-WARS2, LYPLAL1, ADAMTS9, LY86 and ITPR2-SSPN) substantially narrowed the signals to smaller sets of variants, some of which are in regions that have evidence of regulatory activity. By leveraging varying linkage disequilibrium structures across different populations, single-nucleotide polymorphisms (SNPs) with strong signals and narrower credible sets from trans-ethnic meta-analysis of central obesity provide more precise localizations of potential functional variants and suggest a possible regulatory role. Meta-analysis results for WHR were obtained from 77 167 EA participants from GIANT and 23 564 AA participants from the African Ancestry Anthropometry Genetics Consortium. For fine mapping we interrogated SNPs within ± 250 kb flanking regions of 14 previously reported index SNPs from loci discovered in EA populations by performing trans-ethnic meta-analysis of results from the EA and AA meta-analyses. We applied a Bayesian approach that leverages allelic heterogeneity across populations to combine meta-analysis results and aids in fine-mapping shared variants at these locations. We annotated variants using information from the ENCODE Consortium and Roadmap Epigenomics Project to prioritize variants for possible functionality.

The Road to Recovery: A New Approach to Tackling Scotland's Drug Problem

The Scottish National Drugs Strategy requires the 22 regional Drug Action Teams to prepare and submit to the Scottish Executive annual action plans for tackling drug misuse in their areas. These plans should address national and local priorities, including their contribution to the achievement of national targets. These comprise three parts: Part A provides an overview of the DAT structures and working; Part B provides detailed information on current local services and Part C reports plans for 2003/04.This resource was contributed by The National Documentation Centre on Drug Use.

A Rapid Mapping Study of Smoking Projects and Services Targeted at People Living on Low Income and/or Minority Ethnic Groups

This is a study concerned with community based services aimed at smokers living on a low income and/or black and minority ethnic groups.

Mapping health inequalities across London

Analysis of infant mortality and life expectancy across London by various dimensions of inequality- socioeconomic; gender; geographical, and trends

The Size of the Problem: NAO Report - Tackling Obesity in England

Powerpoint presentation by Rob Pridequx from the National Audit office