Concentration of Access to Information and Communication Technologies in the Municipalities of the Brazilian Legal Amazon

This study fills demand for data on access and use of information and communication technologies (ICT) in the Brazilian legal Amazon, a region of localities with identical economic, political, and social problems. We use the 2010 Brazilian Demographic Census to compile data on urban and rural households (i) with computers and Internet access, (ii) with mobile phones, and (iii) with fixed phones. To compare the concentration of access to ICT in the municipalities of the Brazilian Amazon with other regions of Brazil, we use a concentration index to quantify the concentration of households in the following classes: with computers and Internet access, with mobile phones, with fixed phones, and no access. These data are analyzed along with municipal indicators on income, education, electricity, and population size. The results show that for urban households, the average concentration in the municipalities of the Amazon for computers and Internet access and for fixed phones is lower than in other regions of the country; meanwhile, that for no access and mobile phones is higher than in any other region. For rural households, the average concentration in the municipalities of the Amazon for computers and Internet access, mobile phones, and fixed phones is lower than in any other region of the country; meanwhile, that for no access is higher than in any other region. In addition, the study shows that education and income are determinants of inequality in accessing ICT in Brazilian municipalities and that the existence of electricity in rural households is directly associated with the ownership of ICT resources.     

BioWAP, mobile Internet service for bioinformatics

We have developed a new Internet service, which provides mobile access to bioinformatics databases and software tools. The BioWAP service facilitates access to basic bioinformatics databases and analysis tools from everywhere without a PC or a laptop computer. Both open source bioinformatics program suites and Internet services, which are not designed for mobile Internet access, were utilized in the BioWAP service. AVAILABILITY: The BioWAP service starting page can be browsed with any WAP terminal from http://bioinf.uta.fi/wml/welcome.wml.     

The ABCD of primary biodiversity data access

Abstract

Within the context of the Global Biodiversity Information Facility (GBIF), the Biological Collections Access Service (BioCASe) has been set up to foment data provision by natural history content providers. Products include the BioCASe Protocol and the PyWrapper software, a web service allowing to access rich natural history data using complex schemas like ABCD (Access to Biological Collection Data). New developments include the possibility to produce DarwinCore-Archive files using PyWrapper, in order to facilitate the indexing of large datasets by aggregators such as GBIF. However, BioCASe continues to be committed to distributed data access and continues to provide the possibility to directly query the web service for up-to-date data directly from the provider's database. ABCD provides comprehensive coverage of natural history data, and has been extended to cover DNA collections (ABCD-DNA) and geosciences (ABCD-EFG, the extension for geosciences). BioCASe also developed web portal software that allows to access and display rich data provided by special interest networks. We posit that the XML-based networking approach using a highly standardised data definition such as ABCD continues to be a valuable approach towards mobilising natural history information. Some suggestions are made regarding further improvements of ABCD.     

Mobile Money,Smallholder Farmers,and Household Welfare in Kenya

The use of mobile phones has increased rapidly in many developing countries, including in rural areas. Besides reducing the costs of communication and improving access to information, mobile phones are an enabling technology for other innovations. One important example are mobile phone based money transfers, which could be very relevant for the rural poor, who are often underserved by the formal banking system. We analyze impacts of mobile money technology on the welfare of smallholder farm households in Kenya. Using panel survey data and regression models we show that mobile money use has a positive impact on household income. One important pathway is through remittances received from relatives and friends. Such remittances contribute to income directly, but they also help to reduce risk and liquidity constraints, thus promoting agricultural commercialization. Mobile money users apply more purchased farm inputs, market a larger proportion of their output, and have higher profits than non-users of this technology. These results suggest that mobile money can help to overcome some of the important smallholder market access constraints that obstruct rural development and poverty reduction.     

GLOBAL HEALTH IMPACT: A BASIS FOR LABELING AND LICENSING CAMPAIGNS?

Most of the world's health problems afflict poor countries and their poorest inhabitants. There are many reasons why so many people die of poverty‐related causes. One reason is that the poor cannot access many of the existing drugs and technologies they need. Another, is that little of the research and development (R&D) done on new drugs and technologies benefits the poor. There are several proposals on the table that might incentivize pharmaceutical companies to extend access to essential drugs and technologies to the global poor. 1 Still, the problem remains – the poor are suffering and dying from lack of access to essential medicines. So, it is worth considering a new alternative. This paper suggests rating pharmaceutical and biotechnology companies based on how some of their policies impact poor people's health. It argues that it might be possible to leverage a rating system to encourage companies to extend access to essential drugs and technologies to the poor.     

Exploring massive, genome scale datasets with the GenometriCorr package

We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features), for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets. AVAILABILITY AND IMPLEMENTATION: The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor.     

Approximate distance correlation for selecting highly interrelated genes across datasets

With the rapid accumulation of biological omics datasets, decoding the underlying relationships of cross-dataset genes becomes an important issue. Previous studies have attempted to identify differentially expressed genes across datasets. However, it is hard for them to detect interrelated ones. Moreover, existing correlation-based algorithms can only measure the relationship between genes within a single dataset or two multi-modal datasets from the same samples. It is still unclear how to quantify the strength of association of the same gene across two biological datasets with different samples. To this end, we propose Approximate Distance Correlation (ADC) to select interrelated genes with statistical significance across two different biological datasets. ADC first obtains the k most correlated genes for each target gene as its approximate observations, and then calculates the distance correlation (DC) for the target gene across two datasets. ADC repeats this process for all genes and then performs the Benjamini-Hochberg adjustment to control the false discovery rate. We demonstrate the effectiveness of ADC with simulation data and four real applications to select highly interrelated genes across two datasets. These four applications including 21 cancer RNA-seq datasets of different tissues; six single-cell RNA-seq (scRNA-seq) datasets of mouse hematopoietic cells across six different cell types along the hematopoietic cell lineage; five scRNA-seq datasets of pancreatic islet cells across five different technologies; coupled single-cell ATAC-seq (scATAC-seq) and scRNA-seq data of peripheral blood mononuclear cells (PBMC). Extensive results demonstrate that ADC is a powerful tool to uncover interrelated genes with strong biological implications and is scalable to large-scale datasets. Moreover, the number of such genes can serve as a metric to measure the similarity between two datasets, which could characterize the relative difference of diverse cell types and technologies.     

Snake Envenoming: A Disease of Poverty

Background

Most epidemiological and clinical reports on snake envenoming focus on a single country and describe rural communities as being at greatest risk. Reports linking snakebite vulnerability to socioeconomic status are usually limited to anecdotal statements. The few reports with a global perspective have identified the tropical regions of Asia and Africa as suffering the highest levels of snakebite-induced mortality. Our analysis examined the association between globally available data on snakebite-induced mortality and socioeconomic indicators of poverty.

Methodology/Principal Findings

We acquired data on (i) the Human Development Index, (ii) the Per Capita Government Expenditure on Health, (iii) the Percentage Labour Force in Agriculture and (iv) Gross Domestic Product Per Capita from publicly available databases on the 138 countries for which snakebite-induced mortality rates have recently been estimated. The socioeconomic datasets were then plotted against the snakebite-induced mortality estimates (where both datasets were available) and the relationship determined. Each analysis illustrated a strong association between snakebite-induced mortality and poverty.

Conclusions/Significance

This study, the first of its kind, unequivocally demonstrates that snake envenoming is a disease of the poor. The negative association between snakebite deaths and government expenditure on health confirms that the burden of mortality is highest in those countries least able to deal with the considerable financial cost of snakebite.     

Rapid acquisition and dissemination of standardized biological inventories from Russian Biosphere Reserves

We accumulate, standardize, and disseminate the results of biological inventories of 21 protected areas in the Russian Federation; each of these areas has been recognized by UNESCO's Man and the Biosphere (MAB) program as a Biosphere Reserve. These inventories, comprising 4702 species of plants and 1094 species of vertebrate animals of the breadth of Russia, represent 20% of the vascular flora and 73% of the vertebrate fauna of Russia and are made widely available for the first time. We describe an approach to the development and Internet availability of large databases of species inventories which were produced through the provision of custom biological inventory software supported by training, cost-free distribution, and a modest amount of taxonomic support. Our experiences also highlight the essential role of an active national office in a system of protected areas. Our approach overcomes many of the obstacles inherent in previous and existing attempts to assemble the results of biological inventories derived from a variety of sources. We suggest a similar application of biological inventory software, supported by national coordination, as an effective means to provide access to species inventories of a system of protected areas. Standardized, taxonomically current species inventories are essential components of regional and global conservation efforts.     

geoknife: reproducible web‐processing of large gridded datasets

Geoprocessing of large gridded data according to overlap with irregular landscape features is common to many large‐scale ecological analyses. The geoknife R package was created to facilitate reproducible analyses of gridded datasets found on the U.S. Geological Survey Geo Data Portal web application or elsewhere, using a web‐enabled workflow that eliminates the need to download and store large datasets that are reliably hosted on the Internet. The package provides access to several data subset and summarization algorithms that are available on remote web processing servers. Outputs from geoknife include spatial and temporal data subsets, spatially‐averaged time series values filtered by user‐specified areas of interest, and categorical coverage fractions for various land‐use types.     

Applying computer‐aided photo‐identification to messy datasets: a case study of Thornicroft's giraffe (Giraffa camelopardalis thornicrofti)

Digital photography enables researchers to rapidly compile large quantities of data from individually identifiable animals, and computer software improves the management of such large datasets while aiding the identification process. Wild‐ID software has performed well with uniform datasets controlling for angle and portion of the animal photographed; however, few datasets are collected under such controlled conditions. We examined the effectiveness of Wild‐ID in identifying individual Thornicroft's giraffe from a dataset of photographs (n = 552) collected opportunistically in the Luangwa Valley, Zambia from March to October 2009. We assessed the programme's accuracy in correctly identifying individuals and the effect of five image quality factors on identification success: blurriness, background type and complexity, amount of sky and the presence of other giraffe. The programme correctly identified individuals in 71.6% of photographs. Background complexity was the only significant variable affecting identification success and removing background imagery reduced identification error by 52.8% (from 28.4 to 13.4%). Our results indicate higher levels of error than previously reported for Wild‐ID. However, they also suggest the programme is an effective tool for quickly identifying individuals in large field datasets, especially if photograph backgrounds are removed beforehand and postanalysis visual verification is performed.     

NFU-Enabled FASTA: moving bioinformatics applications onto wide area networks

Background  

Advances in Internet technologies have allowed life science researchers to reach beyond the lab-centric research paradigm to create distributed collaborations. Of the existing technologies that support distributed collaborations, there are currently none that simultaneously support data storage and computation as a shared network resource, enabling computational burden to be wholly removed from participating clients. Software using computation-enable logistical networking components of the Internet Backplane Protocol provides a suitable means to accomplish these tasks. Here, we demonstrate software that enables this approach by distributing both the FASTA algorithm and appropriate data sets within the framework of a wide area network.     

NMRShiftDB -- compound identification and structure elucidation support through a free community-built web database

Compound identification and support for computer-assisted structure elucidation via a free community-built web database for organic structures and their NMR data is described. The new database NMRShiftDB is available on . As the first NMR database, NMRShiftDB allows not only open access to the database but also open and peer reviewed submission of datasets, enabling the natural products community to build its first free repository of assigned 1H and 13C NMR spectra. In addition to the open access, the underlying database software is built solely from free software and is available under an open source license. This allows collaborating laboratories to fully replicate the database and to create a highly available network of NMRShiftDB mirrors. The database contains about 10,000 structures and assigned spectra, with new datasets constantly added. Its functionality includes (sub-) spectra and (sub-) structure searches as well as shift prediction of 13C spectra based on the current database material.     

Biomarker discovery across annotated and unannotated microarray datasets using semi-supervised learning

The growing body of DNA microarray data has the potential to advance our understanding of the molecular basis of disease. However annotating microarray datasets with clinically useful information is not always possible, as this often requires access to detailed patient records. In this study we introduce GLAD, a new Semi-Supervised Learning (SSL) method for combining independent annotated datasets and unannotated datasets with the aim of identifying more robust sample classifiers. In our method, independent models are developed using subsets of genes for the annotated and unannotated datasets. These models are evaluated according to a scoring function that incorporates terms for classification accuracy on annotated data, and relative cluster separation in unannotated data. Improved models are iteratively generated using a genetic algorithm feature selection technique. Our results show that the addition of unannotated data into training, significantly improves classifier robustness.     

The BioImage Database Project: organizing multidimensional biological images in an object-relational database

The BioImage Database Project collects and structures multidimensional data sets recorded by various microscopic techniques relevant to modern life sciences. It provides, as precisely as possible, the circumstances in which the sample was prepared and the data were recorded. It grants access to the actual data and maintains links between related data sets. In order to promote the interdisciplinary approach of modern science, it offers a large set of key words, which covers essentially all aspects of microscopy. Nonspecialists can, therefore, access and retrieve significant information recorded and submitted by specialists in other areas. A key issue of the undertaking is to exploit the available technology and to provide a well-defined yet flexible structure for dealing with data. Its pivotal element is, therefore, a modern object relational database that structures the metadata and ameliorates the provision of a complete service. The BioImage database can be accessed through the Internet.     

Global guidance principles for life cycle assessment databases: development of training material and other implementation activities on the publication

Purpose

The paper introduces the publication on “Global Guidance Principles for Life Cycle Assessment Databases”; it focuses on the development of training material and other implementation activities on the publication.

Methods

The document is the output of the “Shonan Guidance Principles” workshop. The publication provides guidance principles for life cycle assessment (LCA) databases; this includes how to collect raw data, how to develop datasets, and how to manage databases. The publication also addresses questions concerning data documentation and review, coordination among databases, capacity building, and future scenarios. As a next step, the publication is used to prepare training material and other implementation activities.

Results

The publication was launched at the LCM 2011 Conference. Since then outreach activities have been organized in particular in emerging economies. Further developments with regard to the guidance principles are foreseen as part of a flagship project within phase 3 of the Life Cycle Initiative. Training material is being developed that will include how to set up databases and develop datasets. The topic has been taken up by United Nations Environment Programme (UNEP) in its Rio?+?20 Voluntary Commitments: UNEP and Society of Environmental Toxicology and Chemistry (SETAC) through the UNEP/SETAC Life Cycle Initiative commit to facilitate improved access to good quality life cycle data and databases as well as expanded use of key environmental indicators that allows the measurement and monitoring of progress towards the environmental sustainability of selected product chains.

Conclusions

The adoption of the “Global Guidance Principles” publication as a de facto global standard is expected to facilitate the work of database teams, especially, in developing countries, and the collaboration in regional networks. These efforts are supported by the development of training material and other implementation activities.     

Evaluating temporal aggregation for predicting the sea surface temperature of the Atlantic Ocean

Extreme environmental events such as droughts affect millions of people all around the world. Although it is not possible to prevent this type of event, its prediction under different time horizons enables the mitigation of eventual damages caused by its occurrence. An important variable for identifying occurrences of droughts is the sea surface temperature (SST). In the tropical Atlantic Ocean, SST data are collected and provided by the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) project, which is an observation network composed of sensor buoys arranged in this region. Sensors of this type, and more generally Internet of Things (IoT) sensors, commonly lead to data losses that influence the quality of datasets collected for adjusting prediction models. In this paper, we explore the influence of temporal aggregation in predicting step-ahead SST considering different prediction horizons and different sizes for training datasets. We have conducted several experiments using data collected by PIRATA project. Our results point out scenarios for training datasets and prediction horizons indicating whether or not temporal aggregated SST time series may be beneficial for prediction.     

Need for and Access to Health Care and Medicines: Are There Gender Inequities?

Objective

Differences between women and men in political and economic empowerment, education, and health risks are well-documented. Similar gender inequities in access to care and medicines have been hypothesized but evidence is lacking.

Methods

We analyzed 2002 World Health Survey data for 257,922 adult respondents and 80,932 children less than 5 years old from 53 mostly low and middle-income countries. We constructed indicators of need for, access to, and perceptions of care, and we described the number of countries with equal and statistically different proportions of women and men for each indicator. Using multivariate logistic regression models, we estimated effects of gender on our study outcomes, overall and by household poverty.

Findings

Women reported significantly more need for care for three of six chronic conditions surveyed, and they were more likely to have at least one of the conditions (OR 1.41 [95% CI 1.38, 1.44]). Among those with reported need for care, there were no consistent differences in access to care between women and men overall (e.g., treatment for all reported chronic conditions, OR 1.00 [0.96, 1.04]) or by household poverty. Of concern, access to care for chronic conditions was distressingly low among both men and women in many countries, as was access to preventive services among boys and girls less than 5 years old.

Conclusions

These cross-country results do not suggest a systematic disadvantage of women in access to curative care and medicines for treating selected chronic conditions or acute symptoms, or to preventive services among boys and girls.     

Food Security Monitoring via Mobile Data Collection and Remote Sensing: Results from the Central African Republic

The Central African Republic is one of the world’s most vulnerable countries, suffering from chronic poverty, violent conflicts and weak disaster resilience. In collaboration with Doctors without Borders/Médecins Sans Frontières (MSF), this study presents a novel approach to collect information about socio-economic vulnerabilities related to malnutrition, access to resources and coping capacities. The first technical test was carried out in the North of the country (sub-prefecture Kabo) in May 2015. All activities were aimed at the investigation of technical feasibility, not at operational data collection, which requires a random sampling strategy. At the core of the study is an open-source Android application named SATIDA COLLECT that facilitates rapid and simple data collection. All assessments were carried out by local MSF staff after they had been trained for one day. Once a mobile network is available, all assessments can easily be uploaded to a database for further processing and trend analysis via MSF in-house software. On one hand, regularly updated food security assessments can complement traditional large-scale surveys, whose completion can take up to eight months. Ideally, this leads to a gain in time for disaster logistics. On the other hand, recording the location of every assessment via the smart phones’ GPS receiver helps to analyze and display the coupling between drought risk and impacts over many years. Although the current situation in the Central African Republic is mostly related to violent conflict it is necessary to consider information about drought risk, because climatic shocks can further disrupt the already vulnerable system. SATIDA COLLECT can easily be adapted to local conditions or other applications, such as the evaluation of vaccination campaigns. Most importantly, it facilitates the standardized collection of information without pen and paper, as well as straightforward sharing of collected data with the MSF headquarters or other aid organizations.