Mapping Meaning : Critical Cartographies for Participatory Water Management in Taita Hills, Kenya

Participation of local people is often neglected in natural resource management, which leads to failure to understand the social aspects and historical construction of environmental problems. Participatory mapping can enhance the communication of local spatial knowledge for management processes and challenge the official maps and other spatial representations produced by state authorities and scientists. In this study, we analyze what kind of social meanings can be revealed through a multimethod participatory mapping process focusing on water resources in Taita Hills, Kenya. The participatory mapping clearly complicates the simplified image of the physical science mappings, typically depicting natural water supply, by addressing the impacts of contamination, inadequate infrastructure, poverty, distance to the sources, and restrictions in their uses on people's access to water. Moreover, this shared exercise is able to trigger discussion on issues that cannot always be localized but still contribute to place making. Local historical accounts reveal the social and political drivers of the current water-related problems, making explicit the political ecology dynamics in the area.Peer reviewe

A lightweight, flow-based toolkit for parallel and distributed bioinformatics pipelines

<p>Abstract</p> <p>Background</p> <p>Bioinformatic analyses typically proceed as chains of data-processing tasks. A pipeline, or 'workflow', is a well-defined protocol, with a specific structure defined by the topology of data-flow interdependencies, and a particular functionality arising from the data transformations applied at each step. In computer science, the dataflow programming (DFP) paradigm defines software systems constructed in this manner, as networks of message-passing components. Thus, bioinformatic workflows can be naturally mapped onto DFP concepts.</p> <p>Results</p> <p>To enable the flexible creation and execution of bioinformatics dataflows, we have written a modular framework for parallel pipelines in Python ('PaPy'). A PaPy workflow is created from re-usable components connected by data-pipes into a directed acyclic graph, which together define nested higher-order map functions. The successive functional transformations of input data are evaluated on flexibly pooled compute resources, either local or remote. Input items are processed in batches of adjustable size, all flowing one to tune the trade-off between parallelism and lazy-evaluation (memory consumption). An add-on module ('NuBio') facilitates the creation of bioinformatics workflows by providing domain specific data-containers (<it>e.g</it>., for biomolecular sequences, alignments, structures) and functionality (<it>e.g</it>., to parse/write standard file formats).</p> <p>Conclusions</p> <p>PaPy offers a modular framework for the creation and deployment of parallel and distributed data-processing workflows. Pipelines derive their functionality from user-written, data-coupled components, so PaPy also can be viewed as a lightweight toolkit for extensible, flow-based bioinformatics data-processing. The simplicity and flexibility of distributed PaPy pipelines may help users bridge the gap between traditional desktop/workstation and grid computing. PaPy is freely distributed as open-source Python code at <url>http://muralab.org/PaPy</url>, and includes extensive documentation and annotated usage examples.</p

Ergatis: a web interface and scalable software system for bioinformatics workflows

Motivation: The growth of sequence data has been accompanied by an increasing need to analyze data on distributed computer clusters. The use of these systems for routine analysis requires scalable and robust software for data management of large datasets. Software is also needed to simplify data management and make large-scale bioinformatics analysis accessible and reproducible to a wide class of target users

CloVR: A virtual machine for automated and portable sequence analysis from the desktop using cloud computing

Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software. We describe the Cloud Virtual Resource, CloVR, a new desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. In a case study, we demonstrate the use of CloVR to automatically process next-generation sequencing data on multiple cloud computing platforms. The CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing.https://doi.org/10.1186/1471-2105-12-35

Assessing the Potential Impacts to Riparian Ecosystems Resulting from Hemlock Mortality in Great Smoky Mountains National Park

Hemlock Woolly Adelgid (Adelges tsugae) is spreading across forests in eastern North America, causing mortality of eastern hemlock (Tsuga canadensis [L.] Carr.) and Carolina hemlock (Tsuga caroliniana Engelm.). The loss of hemlock from riparian forests in Great Smoky Mountains National Park (GSMNP) may result in significant physical, chemical, and biological alterations to stream environments. To assess the influence of riparian hemlock stands on stream conditions and estimate possible impacts from hemlock loss in GSMNP, we paired hardwood- and hemlock-dominated streams to examine differences in water temperature, nitrate concentrations, pH, discharge, and available photosynthetic light. We used a Geographic Information System (GIS) to identify stream pairs that were similar in topography, geology, land use, and disturbance history in order to isolate forest type as a variable. Differences between hemlock- and hardwood-dominated streams could not be explained by dominant forest type alone as forest type yields no consistent signal on measured conditions of headwater streams in GSMNP. The variability in the results indicate that other landscape variables, such as the influence of understory Rhododendron species, may exert more control on stream conditions than canopy composition. The results of this study suggest that the replacement of hemlock overstory with hardwood species will have minimal impact on long-term stream conditions, however disturbance during the transition is likely to have significant impacts. Management of riparian forests undergoing hemlock decline should, therefore, focus on facilitating a faster transition to hardwood-dominated stands to minimize long-term effects on water quality

Armadillo 1.1: An Original Workflow Platform for Designing and Conducting Phylogenetic Analysis and Simulations

In this paper we introduce Armadillo v1.1, a novel workflow platform dedicated to designing and conducting phylogenetic studies, including comprehensive simulations. A number of important phylogenetic and general bioinformatics tools have been included in the first software release. As Armadillo is an open-source project, it allows scientists to develop their own modules as well as to integrate existing computer applications. Using our workflow platform, different complex phylogenetic tasks can be modeled and presented in a single workflow without any prior knowledge of programming techniques. The first version of Armadillo was successfully used by professors of bioinformatics at Université du Quebec à Montreal during graduate computational biology courses taught in 2010–11. The program and its source code are freely available at: <http://www.bioinfo.uqam.ca/armadillo>

Reservists and veterans: Viewed from within and without

This chapter describes two important groups relative to military service – reservists and veterans. Definitions are provided regarding who is a member of each group. A summary of past and current research findings for each group is provided. The summary is organized by investigative topics or themes, which provide the current scope of the field for reservists and for veterans. Finally, approaches to the study of reservists and veterans are described, along with challenges – both substantively and methodologically – for future research studies. These serve as fertile areas for improvements and investigations in future research studies