Mapping the common antigenic determinants in avidin and streptavidin

An epitope scan analysis of the whole sequence of avidin and core streptavidin using polyclonal antibodies to these two antigens reveal the presence of multiple common epitopes in both the proteins. These antigenic determinants consist mostly of either identical or similar residues. The antibody recognition sites in both antigens are shown to be localized to homologous regions.

Numerical integration over arbitrary polygonal domains based on Schwarz-Christoffel conformal mapping

This paper presents a new numerical integration technique oil arbitrary polygonal domains. The polygonal domain is mapped conformally to the unit disk using Schwarz-Christoffel mapping and a midpoint quadrature rule defined oil this unit disk is used. This method eliminates the need for a two-level isoparametric mapping Usually required. Moreover, the positivity of the Jacobian is guaranteed. Numerical results presented for a few benchmark problems in the context of polygonal finite elements show that the proposed method yields accurate results.

Mapping of fuelwood trees using geoinformatics

Rural population of India constitutes about 70% of the total population and traditional fuels account for 75% of the rural energy needs. Depletion of woodlands coupled with the persistent dependency on fuel wood has posed a serious problem for household energy provision in many parts. This study highlights that the traditional fuels still meet 85-95% of fuel needs in rural areas of Kolar district: people prefer fuel wood for cooking and agriculture residues for water heating and other purposes. However, rapid changes in land cover and land use in recent times have affected these traditional fuels availability necessitating inventorying, mapping and monitoring of bioresources for sustainable management of bioresources. Remote sensing data (Multispectal and Panchromatic), Geographic Information System (GIS), field surveys and non-destructive sampling were used to assess spatially the availability and demand of energy. Field surveys indicate that rural household depends on species such as Prosopis juliflora, Acacia nilotica, Acacia auriculiformis to meet fuel wood requirement for domestic activities. Hence, to take stock of fuel wood availability, mapping was done at species level (with 88% accuracy) considering villages as sampling units using fused multispectral and panchromatic data. (C) 2009 Elsevier Ltd. All rights reserved.

Mapping the risk of soil-transmitted helminthic infections in the Philippines

Background In order to increase the efficient allocation of soil-transmitted helminth (STH) disease control resources in the Philippines, we aimed to describe for the first time the spatial variation in the prevalence of A. lumbricoides, T. trichiura and hookworm across the country, quantify the association between the physical environment and spatial variation of STH infection and develop predictive risk maps for each infection. Methodology/Principal Findings Data on STH infection from 35,573 individuals across the country were geolocated at the barangay level and included in the analysis. The analysis was stratified geographically in two major regions: 1) Luzon and the Visayas and 2) Mindanao. Bayesian geostatistical models of STH prevalence were developed, including age and sex of individuals and environmental variables (rainfall, land surface temperature and distance to inland water bodies) as predictors, and diagnostic uncertainty was incorporated. The role of environmental variables was different between regions of the Philippines. This analysis revealed that while A. lumbricoides and T. trichiura infections were widespread and highly endemic, hookworm infections were more circumscribed to smaller foci in the Visayas and Mindanao. Conclusions/Significance This analysis revealed significant spatial variation in STH infection prevalence within provinces of the Philippines. This suggests that a spatially targeted approach to STH interventions, including mass drug administration, is warranted. When financially possible, additional STH surveys should be prioritized to high-risk areas identified by our study in Luzon.

Investigating limitations of SURF approach for thermal imaging analysis and mapping

Wildlife conservation involves an understanding of a specific animal, its environment and the interaction within a local ecosystem. Unmanned Aerial Vehicles (UAVs) present cost effective, non-intrusive solution for detecting animals over large areas and the use thermal imaging cameras offer the ability detect animals that would otherwise be concealed to visible light cameras. This report examines some of limitations on using SURF for the development of large maps using multiple stills images extracted from the thermal imaging video camera which contain wildlife (eg. Koala in them).

Decision fusion for reliable flood mapping using remote sensing images

Flood extent mapping is a basic tool for flood damage assessment, which can be done by digital classification techniques using satellite imageries, including the data recorded by radar and optical sensors. However, converting the data into the information we need is not a straightforward task. One of the great challenges involved in the data interpretation is to separate the permanent water bodies and flooding regions, including both the fully inundated areas and the wet areas where trees and houses are partly covered with water. This paper adopts the decision fusion technique to combine the mapping results from radar data and the NDVI data derived from optical data. An improved capacity in terms of identifying the permanent or semi-permanent water bodies from flood inundated areas has been achieved. Computer software tools Multispec and Matlab were used.

Urban satellite image classification using biologically inspired techniques

This paper focuses on optimisation algorithms inspired by swarm intelligence for satellite image classification from high resolution satellite multi- spectral images. Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to satisfactorily classify all the basic land cover classes of an urban region. In both supervised and unsupervised classification methods, the evolutionary algorithms are not exploited to their full potential. This work tackles the land map covering by Ant Colony Optimisation (ACO) and Particle Swarm Optimisation (PSO) which are arguably the most popular algorithms in this category. We present the results of classification techniques using swarm intelligence for the problem of land cover mapping for an urban region. The high resolution Quick-bird data has been used for the experiments.

Place categorization and semantic mapping on a mobile robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot with-out environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot’s behaviour during navigation tasks. The system is made available to the community as a ROS module.

Mapping sociocultural controversies across digital media platforms: One week of #gamergate on Twitter, YouTube, and Tumblr

Social media play a prominent role in mediating issues of public concern, not only providing the stage on which public debates play out but also shaping their topics and dynamics. Building on and extending existing approaches to both issue mapping and social media analysis, this article explores ways of accounting for popular media practices and the special case of ‘born digital’ sociocultural controversies. We present a case study of the GamerGate controversy with a particular focus on a spike in activity associated with a 2015 Law and Order: SVU episode about gender-based violence and harassment in games culture that was widely interpreted as being based on events associated with GamerGate. The case highlights the importance and challenges of accounting for the cultural dynamics of digital media within and across platforms.

Mixed pixel analysis for flood mapping using extended support vector machine

This paper addresses the challenges of flood mapping using multispectral images. Quantitative flood mapping is critical for flood damage assessment and management. Remote sensing images obtained from various satellite or airborne sensors provide valuable data for this application, from which the information on the extent of flood can be extracted. However the great challenge involved in the data interpretation is to achieve more reliable flood extent mapping including both the fully inundated areas and the 'wet' areas where trees and houses are partly covered by water. This is a typical combined pure pixel and mixed pixel problem. In this paper, an extended Support Vector Machines method for spectral unmixing developed recently has been applied to generate an integrated map showing both pure pixels (fully inundated areas) and mixed pixels (trees and houses partly covered by water). The outputs were compared with the conventional mean based linear spectral mixture model, and better performance was demonstrated with a subset of Landsat ETM+ data recorded at the Daly River Basin, NT, Australia, on 3rd March, 2008, after a flood event.