Vector incrimination and effects of antimalarial drugs on malaria transmission and control in the Amazon Basin of Brazil

World ecosystems differ significantly and a multidisciplinary malaria control approach must be adjusted to meet these requirements. These include a comprehensive understanding of the malaria vectors, their behavior, seasonal distribution and abundance, susceptibility to insecticides (physiological and behavioral), methods to reduce the numbers of human gametocyte carriers through effective health care systems and antimalarial drug treatment, urban malaria transmission versus rural or forest malaria transmission, and the impact of vaccine development. Many malaria vectors are members of species complexes and individual relationship to malaria transmission, seasonal distribution, bitting behavior, etc. is poorly understood. Additionaly, malaria patients are not examined for circulating gametocytes and both falciparum and vivax malaria patients may be highly infective to mosquitoes after treatment with currently used antimalarial drugs. Studies on the physiological and behavioral effects of DDT and other insecticides are inconclusive and need to be evalusted.

Multisensory mechanisms in temporo-parietal cortex support self-location and first-person perspective.

Self-consciousness has mostly been approached by philosophical enquiry and not by empirical neuroscientific study, leading to an overabundance of diverging theories and an absence of data-driven theories. Using robotic technology, we achieved specific bodily conflicts and induced predictable changes in a fundamental aspect of self-consciousness by altering where healthy subjects experienced themselves to be (self-location). Functional magnetic resonance imaging revealed that temporo-parietal junction (TPJ) activity reflected experimental changes in self-location that also depended on the first-person perspective due to visuo-tactile and visuo-vestibular conflicts. Moreover, in a large lesion analysis study of neurological patients with a well-defined state of abnormal self-location, brain damage was also localized at TPJ, providing causal evidence that TPJ encodes self-location. Our findings reveal that multisensory integration at the TPJ reflects one of the most fundamental subjective feelings of humans: the feeling of being an entity localized at a position in space and perceiving the world from this position and perspective.

Active Learning of Very-High Resolution Optical Imagery with SVM: Entropy vs Margin Sampling

An active learning method is proposed for the semi-automatic selection of training sets in remote sensing image classification. The method adds iteratively to the current training set the unlabeled pixels for which the prediction of an ensemble of classifiers based on bagged training sets show maximum entropy. This way, the algorithm selects the pixels that are the most uncertain and that will improve the model if added in the training set. The user is asked to label such pixels at each iteration. Experiments using support vector machines (SVM) on an 8 classes QuickBird image show the excellent performances of the methods, that equals accuracies of both a model trained with ten times more pixels and a model whose training set has been built using a state-of-the-art SVM specific active learning method

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.

What do older people with high support needs want and value from life? Involving older people in shaping 'A Better Life'

JRF has recently embarked on a major new programme: 'A Better Life', the central question of which is: 'How can we ensure a better life and better choices for older people who need high levels of support?' JRF now want to commission a project to work with older people with high support needs (current and future generations) and with JRF to ensure that older people with high support needs are at the heart throughout this programme.The deadline for receipt of full proposals is 12 noon on Tuesday 24 November 2009 for decision by 18 December.

Vector bionomics and malaria transmission in the Upper Orinoco River, Southern Venezuela

A longitudinal epidemiological and entomological study was carried out in Ocamo, Upper Orinoco River, between January 1994 and February 1995 to understand the dynamics of malaria transmission in this area. Malaria transmission occurs throughout the year with a peak in June at the beginning of the rainy season. The Annual Parasite Index was 1,279 per 1,000 populations at risk. Plasmodium falciparum infections accounted for 64% of all infections, P. vivax for 28%, and P. malariae for 4%. Mixed P. falciparum/P. vivax infections were diagnosed in 15 people representing 4% of total cases. Children under 10 years accounted for 58% of the cases; the risk for malaria in this age group was 77% higher than for those in the greater than 50 years age group. Anopheles darlingi was the predominant anopheline species landing on humans indoors with a biting peak between midnight and dawn. A significant positive correlation was found between malaria monthly incidence and mean number of An. darlingi caught. There was not a significant relationship between mean number of An. darlingi and rainfall or between incidence and rainfall. A total of 7295 anophelines were assayed by ELISA for detection of Plasmodium circumsporozoite (CS) protein. Only An. darlingi (55) was positive for CS proteins of P. falciparum (0.42%), P. malariae (0.25%), and P. vivax-247 (0.1%). The overall estimated entomological inoculation rate was 129 positive bites/person/year. The present study was the first longitudinal entomological and epidemiological study conducted in this area and set up the basic ground for subsequent intervention with insecticide-treated nets.

A Method and Tool to Support the Analysis and Enhance the Understanding of Peer-to-Peer Learning Experiences

In this paper we look at how a web-based social software can be used to make qualitative data analysis of online peer-to-peer learning experiences. Specifically, we propose to use Cohere, a web-based social sense-making tool, to observe, track, annotate and visualize discussion group activities in online courses. We define a specific methodology for data observation and structuring, and present results of the analysis of peer interactions conducted in discussion forum in a real case study of a P2PU course. Finally we discuss how network visualization and analysis can be used to gather a better understanding of the peer-to-peer learning experience. To do so, we provide preliminary insights on the social, dialogical and conceptual connections that have been generated within one online discussion group.

Vector density and the control of kala-azar in Bihar, India

Bihar, India has been in the grip of kala-azar for many years. Its rampant and severe spread has made life miserable in most parts of the state. Such conditions require a comprehensive understanding of this affliction. The numbers coming out of the districts prone to the disease in the north and south Ganges have provided us with several startling revelations, as there are striking uniformities on both sides, including similar vegetation, water storage facilities, house construction and little change in risk factors. The northern areas have been regularly sprayed with DDT since 1977, but eradication of the disease appears to be a distant dream. In 2007 alone, there were as many as 37,738 cases in that region. In contrast, the southern districts of Patna and Nalanda have never had the disease in its epidemic form and endemic disease has been present in only some pockets of the two districts. In those cases, two rounds of spraying with DDT had very positive results, with successful control and no new established foci. In addition, an eleven-year longitudinal study of the man hour density and house index for the vector Phlebotomus argentipes demonstrated that they were quite high in Patna and Nalanda and quite low in north Bihar. Given these facts, an attempt has been made to unravel the role of P. argentipes saliva (salivary gland) in the epidemiology of kala-azar. It was determined that patchy DDT spraying should be avoided for effective control of kala-azar.

Habitat suitability of Anopheles vector species and association with human malaria in the Atlantic Forest in south-eastern Brazil

Every year, autochthonous cases of Plasmodium vivax malaria occur in low-endemicity areas of Vale do Ribeira in the south-eastern part of the Atlantic Forest, state of São Paulo, where Anopheles cruzii and Anopheles bellator are considered the primary vectors. However, other species in the subgenus Nyssorhynchus of Anopheles (e.g., Anopheles marajoara) are abundant and may participate in the dynamics of malarial transmission in that region. The objectives of the present study were to assess the spatial distribution of An. cruzii, An. bellator and An. marajoara and to associate the presence of these species with malaria cases in the municipalities of the Vale do Ribeira. Potential habitat suitability modelling was applied to determine both the spatial distribution of An. cruzii, An. bellator and An. marajoara and to establish the density of each species. Poisson regression was utilized to associate malaria cases with estimated vector densities. As a result, An. cruzii was correlated with the forested slopes of the Serra do Mar, An. bellator with the coastal plain and An. marajoara with the deforested areas. Moreover, both An. marajoara and An. cruzii were positively associated with malaria cases. Considering that An. marajoara was demonstrated to be a primary vector of human Plasmodium in the rural areas of the state of Amapá, more attention should be given to the species in the deforested areas of the Atlantic Forest, where it might be a secondary vector.

Decision fusion for the classification of hyperspectral data: Outcome of the 2008 GRS-S data fusion contest

The 2008 Data Fusion Contest organized by the IEEE Geoscience and Remote Sensing Data Fusion Technical Committee deals with the classification of high-resolution hyperspectral data from an urban area. Unlike in the previous issues of the contest, the goal was not only to identify the best algorithm but also to provide a collaborative effort: The decision fusion of the best individual algorithms was aiming at further improving the classification performances, and the best algorithms were ranked according to their relative contribution to the decision fusion. This paper presents the five awarded algorithms and the conclusions of the contest, stressing the importance of decision fusion, dimension reduction, and supervised classification methods, such as neural networks and support vector machines.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

Automatic classification of MR scans in Alzheimer's disease.

To be diagnostically useful, structural MRI must reliably distinguish Alzheimer's disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice.