Mapping Step Research Articles

An approach for establishing correspondence between OpenStreetMap and reference datasets for land use and land cover mapping

AbstractOpenStreetMap (OSM) provides free source data for land use and land cover (LULC) mapping of many regions globally. Earlier work has used just manual and subjective approaches to establish correspondence between paired OSM and reference datasets, an essential step for LULC mapping. This study proposes an approach to establish correspondence via three steps: (1) convert line feature(s) into polygon feature(s); (2) merge multiple polygon feature(s) into a single layer; and (3) establish correspondence and reclassify OSM and/or reference datasets. Study areas in Sheffield, London, Rome, and Paris were used for testing, and two measures (overall accuracy, OA and kappa index) were used for evaluation. Experiments were designed to verify this approach, with each pair of OSM and reference datasets initially compared after reclassification. Correspondence from one study area was then applied to another for further validation. Results show that OA was between 70 and 90% and the kappa index varied between 0.6 and 0.8. Evaluation also indicates that the correspondence obtained from one study area is applicable to another, and we illustrate the effectiveness of this approach.

Identification and Sequencing of N-Terminal Peptides in Proteins by LC-Fluorescence-MS/MS: An Approach to Replacement of the Edman Degradation.

Automated Edman degradation for sequencing is the classic tool for determining the N-terminal sequences of proteins. Although it has been complemented or largely replaced by a variety of mass spectrometric methods in recent years, there is no simple mass spectrometric method for identification and de novo determination of the N-terminal sequence of a protein. Here, we present a simple and reliable approach which identifies N-terminal peptides of proteins by selectively labeling the N-terminal α-amino groups with a fluorescent reagent followed by enzyme digestion and tandem mass spectrometric analysis using an LC-MS/MS system coupled with a fluorescence detector. This approach also includes a differential peptide mapping step for identification of pyroglutamate-blocked N-terminal peptides. In addition, the strategy can be applied to proteins in gel. With this approach, complete N-terminal sequences of proteins can be obtained precisely and efficiently.

Chaos-enhanced synchronized bat optimizer

In this paper, a chaos-enhanced bat algorithm is proposed to tackle the global optimization problems. Bat algorithm is a relatively new stochastic optimizer inspired by the echolocation behavior of bats in nature. Due to its effectiveness, it has been applied to many fields such as engineering design, feature selection, and machine learning. However, the classical approach is often prone to falling into local optima. This paper proposes an enhanced bat algorithm to alleviate this problem observed in the original algorithm. The proposed method controls the steps of chaotic mapping by a threshold and synchronizes the velocity of agents using a velocity inertia weight. These mechanisms are designed to boost the stability and convergence speed of the bat algorithm, instantly. Eighteen well-established and the state-of-the-art meta-heuristic approaches are considered to validate the effectiveness of the developed algorithm. Experimental results reveal that the proposed chaos-enhanced bat algorithm is not only superior to the well-established algorithms such as the original method but also the latest improved approaches. Also, the proposed method is successfully applied to I-beam design problems, welded beam design, and pressure vessel design. The results show that chaos-enhanced bat algorithm can deal with unconstrained and constrained feature spaces, effectively.

In-situ recommendation of alternative soil samples during field sampling based on environmental similarity

Field sampling is an essential step for digital soil mapping and various sampling strategies have been designed for achieving desirable mapping results. Some unpredictable complex circumstances in the field, however, often prevent some samples from being collected based on the pre-designed sampling strategies. Such circumstances include inaccessibility of some locations, change of land surface types, and heavily-disturbed soil at some locations, among others. This may result in the missing of some essential samples, which could impact the quality of digital soil mapping. Previous studies have attempted to design alternative samples for the selected ones beforehand to address this issue. It cannot solve the problem completely as those pre-designed alternative samples could also be inaccessible. In this paper, we propose a dynamic method to recommend alternative samples for those unavailable samples in the field. The identification of alternative samples is based on the environmental similarity between an unavailable soil sample and its alternative candidates, as well as the spatial accessibility of these candidates. For the convenience of fieldwork, the proposed method was implemented to be a mobile application on the Android platform. A simulated soil sampling study in Xuancheng county, Anhui province of China was used to evaluate its performance. From a sample set for the study are, 30 samples were assumed to be inaccessible. For each of them, an alternative soil sample from the set was recommended using the proposed method. A deviation analysis of silt and sand content at the depth of 20 ~ 40 cm between the soil samples and their alternatives shows that the deviation on silt content is less than 20% for half of the soil samples. A larger deviation on sand content might be attribute to the limited alternative candidates in this virtual experiment. In a second experiment, we randomly selected a number of existing soil samples and replaced them with their corresponding alternative soil samples. This created 1000 hybrid sample sets. Each hybrid sample set was then used for digital soil mapping with iPSM. An evaluation using 59 independent soil samples indicated that the RMSE and MAE with the hybrid sample sets were close to that with the original sample set. The proposed method proved to be able to recommend effective alternative samples for those unavailable samples in the field.

Assessment of flood hazard mapping in urban areas using entropy weighting method: a case study in Hamadan city, Iran

Flood is one of the major natural disasters which cause enormous casualties and damages particularly in urban areas. In urban areas, studies on flood hazards have been accompanied by tensions for various reasons, including complexity of urban levels, different spatial modeling indices, lack of accurate hydrological data, and precise modeling of land surface simulations. This paper used a Constrained Delaunay Triangular Irregular Network to model fine urban surfaces (based on the detailed ground sampling data), and subsequently discusses five indicators regarding the dangers of flood, namely (1) elevation, (2) slope, (3) distance to discharge channels, (4) index of development and persistence of the drainage network (IDPR), and (5) infiltration rate. In the next step for flood hazard mapping, the combination of geographical information systems and the entropy weight method as the multi-criteria decision analysis was used to combine the indicators. The proposed methodology was used for Hamadan city that is located in the central part of Hamadan Province in Iran where several floods occur annually. The flood hazard mapping indicates that approximately 15.83% of the total study area is classified as very highly hazardous, 31.72% as hazardous, 20.11% as moderate, 16.02% as minor, and 16.32% as the least hazardous. Finally, superimposition and receiver operating characteristic (ROC) curve methods were used to verify the accuracy of the obtained flood hazard map. In terms of superimposition and ROC curve, the accuracy of the model was approximately 70% and 73%, respectively.

Immunoinformatics-Aided Design and Evaluation of a Potential Multi-Epitope Vaccine against Klebsiella Pneumoniae.

Klebsiella pneumoniae is an opportunistic gram-negative bacterium that causes nosocomial infection in healthcare settings. Despite the high morbidity and mortality rate associated with these bacterial infections, no effective vaccine is available to counter the pathogen. In this study, the pangenome of a total of 222 available complete genomes of K. pneumoniae was explored to obtain the core proteome. A reverse vaccinology strategy was applied to the core proteins to identify four antigenic proteins. These proteins were then subjected to epitope mapping and prioritization steps to shortlist nine B-cell derived T-cell epitopes which were linked together using GPGPG linkers. An adjuvant (Cholera Toxin B) was also added at the N-terminal of the vaccine construct to improve its immunogenicity and a stabilized multi-epitope protein structure was obtained using molecular dynamics simulation. The designed vaccine exhibited sustainable and strong bonding interactions with Toll-like receptor 2 and Toll-like receptor 4. In silico reverse translation and codon optimization also confirmed its high expression in E. coli K12 strain. The computer-aided analyses performed in this study imply that the designed multi-epitope vaccine can elicit specific immune responses against K. pneumoniae. However, wet lab validation is necessary to further verify the effectiveness of this proposed vaccine candidate.

Targeted variant detection using unaligned RNA-Seq reads.

Mutations identified in acute myeloid leukemia patients are useful for prognosis and for selecting targeted therapies. Detection of such mutations using next-generation sequencing data requires a computationally intensive read mapping step followed by several variant calling methods. Targeted mutation identification drastically shifts the usual tradeoff between accuracy and performance by concentrating all computations over a small portion of sequence space. Here, we present km, an efficient approach leveraging k-mer decomposition of reads to identify targeted mutations. Our approach is versatile, as it can detect single-base mutations, several types of insertions and deletions, as well as fusions. We used two independent cohorts (The Cancer Genome Atlas and Leucegene) to show that mutation detection by km is fast, accurate, and mainly limited by sequencing depth. Therefore, km allows the establishment of fast diagnostics from next-generation sequencing data and could be suitable for clinical applications.

Data assimilation using adaptive, non-conservative, moving mesh models

Abstract. Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. Motivating problems include the study of fluids in a Lagrangian frame and the presence of highly localized structures such as shock waves or interfaces. In the former case, Lagrangian solvers move the nodes of the mesh with the dynamical flow; in the latter, mesh resolution is increased in the proximity of the localized structure. Mesh adaptation can include remeshing, a procedure that adds or removes mesh nodes according to specific rules reflecting constraints in the numerical solver. In this case, the number of mesh nodes will change during the integration and, as a result, the dimension of the model's state vector will not be conserved. This work presents a novel approach to the formulation of ensemble data assimilation (DA) for models with this underlying computational structure. The challenge lies in the fact that remeshing entails a different state space dimension across members of the ensemble, thus impeding the usual computation of consistent ensemble-based statistics. Our methodology adds one forward and one backward mapping step before and after the ensemble Kalman filter (EnKF) analysis, respectively. This mapping takes all the ensemble members onto a fixed, uniform reference mesh where the EnKF analysis can be performed. We consider a high-resolution (HR) and a low-resolution (LR) fixed uniform reference mesh, whose resolutions are determined by the remeshing tolerances. This way the reference meshes embed the model numerical constraints and are also upper and lower uniform meshes bounding the resolutions of the individual ensemble meshes. Numerical experiments are carried out using 1-D prototypical models: Burgers and Kuramoto–Sivashinsky equations and both Eulerian and Lagrangian synthetic observations. While the HR strategy generally outperforms that of LR, their skill difference can be reduced substantially by an optimal tuning of the data assimilation parameters. The LR case is appealing in high dimensions because of its lower computational burden. Lagrangian observations are shown to be very effective in that fewer of them are able to keep the analysis error at a level comparable to the more numerous observers for the Eulerian case. This study is motivated by the development of suitable EnKF strategies for 2-D models of the sea ice that are numerically solved on a Lagrangian mesh with remeshing.

Parallel and Distributed Pattern Mining

The treatment of large data is difficult and it looks like the arrival of the framework MapReduce is a solution of this problem. This framework can be used to analyze and process vast amounts of data. This happens by distributing the computational work across a cluster of virtual servers running in a cloud or a large set of machines. Process mining provides an important bridge between data mining and business process analysis. Its techniques allow for extracting information from event logs. Generally, there are two steps in process mining, correlation definition or discovery and the inference or composition. First of all, their work mines small patterns from log traces. Those patterns are the representation of the traces execution from a log file of a business process. In this step, the authors use existing techniques. The patterns are represented by finite state automaton or their regular expression; and the final model is the combination of only two types of different patterns whom are represented by the regular expressions (ab)* and (ab*c)*. Second, they compute these patterns in parallel, and then combine those small patterns using the Hadoop framework. They have two steps; the first is the Map Step through which they mine patterns from execution traces, and the second one is the combination of these small patterns as a reduce step. The results show that their approach is scalable, general and precise. It minimizes the execution time by the use of the Hadoop framework.

Implementation and Application of Video Distribution Technology Based on OpenGL

The panoramic video contains a wealth of information that vividly reproduces the surrounding scene. However, the larger video information puts forward higher requirements for data transmission, interactivity and real-time. All the video information directly shows that the phenomenon of stagnation and black screen is easy to occur, and the user’s traffic is greatly consumed, so that the payment cost of the terminal is large. In a traditional live video broadcast, the information displayed by the client is information with a fixed perspective. The client has no right to select the information, but only displays the obtained information. In order to solve the above problems, this paper has improved the traditional live broadcast mode. In this new live broadcast mode, the host side synthesizes the collected surrounding scene into a super wide-angle video. Then with the OpenGL technology, the host side performs the texture mapping, projection and other steps on the panoramic video. The host then distributes the video to the client. As a result, the client not only gets undistorted video, but also interacts with the server in real time. This approach greatly enhances the visual experience of the client.

GECKO is a genetic algorithm to classify and explore high throughput sequencing data

Comparative analysis of high throughput sequencing data between multiple conditions often involves mapping of sequencing reads to a reference and downstream bioinformatics analyses. Both of these steps may introduce heavy bias and potential data loss. This is especially true in studies where patient transcriptomes or genomes may vary from their references, such as in cancer. Here we describe a novel approach and associated software that makes use of advances in genetic algorithms and feature selection to comprehensively explore massive volumes of sequencing data to classify and discover new sequences of interest without a mapping step and without intensive use of specialized bioinformatics pipelines. We demonstrate that our approach called GECKO for GEnetic Classification using k-mer Optimization is effective at classifying and extracting meaningful sequences from multiple types of sequencing approaches including mRNA, microRNA, and DNA methylome data.

Comparing city image and brand identity in polycentric regions using network analysis

In a globalising world, cities find themselves competing for visitors, residents, investors, and companies. They use place branding strategies to become more visible. However, conceptual and analytical confusion exists on the subjects of place image and brand identity, and current studies are limited to single cities, neglecting inter-city relationships at the regional level. In this paper, we examine how place image and brand identity of cities in polycentric regions can be compared with each other. Inspired by Zenker and Beckmann’s network analysis approach for studying place branding (J Place Manag Dev 6(1): 6–17, 2013), a method is introduced to compare image and identity networks for polycentric regions. We use this to complement traditional steps of concept mapping (i.e. elicitation, mapping, and aggregation), and apply it to analyse the illustrative case of four cities in the MRDH region within the Netherlands. Results of the comparative analysis between the image network and the identity network provide both visual and quantitative insights revealing structural differences. The network analysis research approach can be useful to both policy-makers and researchers in analysing city image and brand identity, and to develop place branding strategies accordingly, even at the regional level.

Implementation Mapping: Using Intervention Mapping to Develop Implementation Strategies.

Background: The ultimate impact of a health innovation depends not only on its effectiveness but also on its reach in the population and the extent to which it is implemented with high levels of completeness and fidelity. Implementation science has emerged as the potential solution to the failure to translate evidence from research into effective practice and policy evident in many fields. Implementation scientists have developed many frameworks, theories and models, which describe implementation determinants, processes, or outcomes; yet, there is little guidance about how these can inform the development or selection of implementation strategies (methods or techniques used to improve adoption, implementation, sustainment, and scale-up of interventions) (1, 2). To move the implementation science field forward and to provide a practical tool to apply the knowledge in this field, we describe a systematic process for planning or selecting implementation strategies: Implementation Mapping.Methods: Implementation Mapping is based on Intervention Mapping (a six-step protocol that guides the design of multi-level health promotion interventions and implementation strategies) and expands on Intervention Mapping step 5. It includes insights from both the implementation science field and Intervention Mapping. Implementation Mapping involves five tasks: (1) conduct an implementation needs assessment and identify program adopters and implementers; (2) state adoption and implementation outcomes and performance objectives, identify determinants, and create matrices of change objectives; (3) choose theoretical methods (mechanisms of change) and select or design implementation strategies; (4) produce implementation protocols and materials; and (5) evaluate implementation outcomes. The tasks are iterative with the planner circling back to previous steps throughout this process to ensure all adopters and implementers, outcomes, determinants, and objectives are addressed.Discussion: Implementation Mapping provides a systematic process for developing strategies to improve the adoption, implementation, and maintenance of evidence-based interventions in real-world settings.

CLOUD-BASED SOLUTION FOR NATIONWIDE POWER LINE MAPPING

Abstract. Automatic tools for power line mapping and monitoring are increasingly required by modern societies. Since traditional methods, like ground-based onsite inspections, are very labour- and time-intensive, the use of Geomatics techniques is becoming the most promising solution. However, there is a need for an all-in-one solution that allows the entire 3D mapping pipeline in a nationwide data context. The aim of this paper is to introduce a novel cloud-based solution for nationwide power line mapping. The innovative aspects of the system are threefold. First, to exploit image-based 3D reconstruction algorithms to derive dense point clouds over power line corridors, thus demonstrating the potential of photogrammetry as a promising alternative to costly LiDAR surveys. Second, to supply an all-in-one web-based pipeline that automatically manages all steps of power line mapping, from 3D data generation to clearance anomaly detection. Finally, to exploit cloud-computing technology, to handle massive input data. First tests show promising results for (i) 3D image-based reconstruction, (ii) point cloud classification and (iii) anomaly detection.

Path optimization for robots in a constrained workspace

In this work, the classical A* algorithm serves as path planner to generate the optimum path that would avoid collisions and take the start, collisions, and goal as an input and give the optimal path as an output. The work was done in a static environment, so the coordinates of the obstacles are predefined for the planner. The obtained path is just a sequence of points in space, and this path may be considered later the task space and the first step for another sequential operation like mapping from Cartesian space to joint space, topology optimization, dimensional synthesis, etc. The case study was Lab-Volt 5150 manipulator; it is an accurate educational five degree of freedom 5DOF stationary robot driven by five stepper motors.

Healing the Past by Nurturing the Future—co-designing perinatal strategies for Aboriginal and Torres Strait Islander parents experiencing complex trauma: framework and protocol for a community-based participatory action research study

IntroductionChild maltreatment and other traumatic events can have serious long-term physical, social and emotional effects, including a cluster of distress symptoms recognised as ‘complex trauma’. Aboriginal and Torres Strait Islander...

Focus+context map labeling with optimized clutter reduction

ABSTRACTZooming is a basic operation that many digital maps support for exploring them interactively. Especially for maps on small screen devices, this is a helpful operation to uncover the user's region of interest possibly hidden by labels, e.g. points of interest represented by icons. However, by scaling the map larger the user loses the context. As a consequent the user might need to repeatedly zoom in and out to explore the map step by step.We present an approach that reduces the necessity of zooming by providing the user with the possibility of displacing the labels of a circular focus region. To that end, we utilize techniques from focus+context maps implementing the displacement of the labels by fish-eye projections. The visual association between labels and their point features is established by connecting lines aggregated to bundles. Our approach particularly guarantees that labels move smoothly when the user continuously shifts the focus region, which reduces distracting flickering effects while exploring the map by panning the map view. Further, when the user stops moving the focus region, mathematical programming is applied to optimize positions of the displaced labels. In an evaluation on real-world data and synthetically generated data, we show that our approach substantially increases the legibility of both the focus region and the displaced labels.

ALFA: annotation landscape for aligned reads

BackgroundThe last 10 years have seen the rise of countless functional genomics studies based on Next-Generation Sequencing (NGS). In the vast majority of cases, whatever the species, whatever the experiment, the two first steps of data analysis consist of a quality control of the raw reads followed by a mapping of those reads to a reference genome/transcriptome. Subsequent steps then depend on the type of study that is being made. While some tools have been proposed for investigating data quality after the mapping step, there is no commonly adopted framework that would be easy to use and broadly applicable to any NGS data type.ResultsWe present ALFA, a simple but universal tool that can be used after the mapping step on any kind of NGS experiment data for any organism with available genomic annotations. In a single command line, ALFA can compute and display distribution of reads by categories (exon, intron, UTR, etc.) and biotypes (protein coding, miRNA, etc.) for a given aligned dataset with nucleotide precision. We present applications of ALFA to Ribo-Seq and RNA-Seq on Homo sapiens, CLIP-Seq on Mus musculus, RNA-Seq on Saccharomyces cerevisiae, Bisulfite sequencing on Arabidopsis thaliana and ChIP-Seq on Caenorhabditis elegans.ConclusionsWe show that ALFA provides a powerful and broadly applicable approach for post mapping quality control and to produce a global overview using common or dedicated annotations. It is made available to the community as an easy to install command line tool and from the Galaxy Tool Shed.

Evidence mapping based on systematic reviews of therapeutic interventions for soft tissue sarcomas

PurposeSoft tissue sarcomas are a heterogeneous group of rare tumours of mesenchymal origin. Evidence mapping is one of the most didactic and friendly approaches to organise and summarise the range of research activity in broad topic fields. The objective of this evidence mapping is to identify, describe and organise the current available evidence about therapeutic interventions on soft tissues sarcomas.MethodsWe followed the methodology of global evidence mapping. We performed a search of the PubMed, EMBASE, The Cochrane Library and Epistemonikos to identify systematic reviews (SRs) with or without meta-analyses published between 1990 and March 2016. Two independent literature reviewers assessed eligibility and extracted data. Methodological quality of the included systematic reviews was assessed using AMSTAR. We organised the results according to identified PICO questions and used tables and a bubble plot to display the results.ResultsThe map is based on 24 SRs that met eligibility criteria and included 66 individual studies. Three-quarters were either observational or uncontrolled clinical trials. The quality of the included SRs was in general moderate or high. We identified 64 PICO questions from them. The corresponding results mostly favoured the intervention arm.ConclusionsThis evidence mapping was built on the basis of SRs, which mostly included non-experimental studies and were qualified by the AMSTAR tool as of moderate quality. The evidence mapping created from PICO questions is a useful approach to describe complex and huge clinical topics through graphical media and orientate further research to fulfil the existing gaps. However, it is important to delimitate the steps of the evidence mapping in a pre-established protocol.

Mantissa-Exponent-Based Tone Mapping for Wide Dynamic Range Image Sensors

The dynamic range of a scene is defined as the ratio between the maximum and minimum luminance in it. Wide dynamic range (WDR) means this ratio is so large that it exceeds the dynamic range of a traditional image sensor. Nowadays, WDR image sensors enable the capture of WDR scenes. However, the captured WDR image requires an additional tone mapping step to compress the high bit pixel of WDR image to low rate pixel so that it can be displayed on the screen. The tone mapping algorithm is mostly done in an image signal processor or with a specific software application. This brief proposes a tone mapping technique that is suitable for direct processing of the output of a WDR image sensor bitstream. The algorithm acquires statistics on the mantissa and exponent parts of the pixel value and then generates a refined histogram for tone mapping. Experiments that evaluate the image quality and hardware efficiency are carried out. The results indicate that the proposed mantissa exponent-based algorithm provides visually pleasing results and preserves details of the original WDR image better than other similar algorithms. The hardware resources’ efficiency of the algorithm makes the system on chip implementation possible.