High resolution population maps for low income nations: combining land cover and census in East Africa

Background

Between 2005 and 2050, the human population is forecast to grow by 2.7 billion, with the vast majority of this growth occurring in low income countries. This growth is likely to have significant social, economic and environmental impacts, and make the achievement of international development goals more difficult. The measurement, monitoring and potential mitigation of these impacts require high resolution, contemporary data on human population distributions. In low income countries, however, where the changes will be concentrated, the least information on the distribution of population exists. In this paper we investigate whether satellite imagery in combination with land cover information and census data can be used to create inexpensive, high resolution and easily-updatable settlement and population distribution maps over large areas.

Methodology/Principal Findings

We examine various approaches for the production of maps of the East African region (Kenya, Uganda, Burundi, Rwanda and Tanzania) and where fine resolution census data exists, test the accuracies of map production approaches and existing population distribution products. The results show that combining high resolution census, settlement and land cover information is important in producing accurate population distribution maps.

Conclusions

We find that this semi-automated population distribution mapping at unprecedented spatial resolution produces more accurate results than existing products and can be undertaken for as little as $0.01 per km². The resulting population maps are a product of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk) and are freely available.     

The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts

Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species’ threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project – and avert – future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups – including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems – http://www.predicts.org.uk). We make site-level summary data available alongside this article. The full database will be publicly available in 2015.     

Host genetic architecture and the landscape of microbiome composition: humans weigh in

Comparative analyses of the control of mammalian microbiomes by host genetic architecture reveal striking conserved features that have implications for the evolution of host–microbiome interactions.See related Research article: http://www.genomebiology.com/2015/16/1/191     

PROcEED: Probabilistic reverse dosimetry approaches for estimating exposure distributions

As increasing amounts of biomonitoring survey data become available, a new discipline focused on converting such data into estimates of chemical exposures has developed. Reverse dosimetry uses a pharmacokinetic model along with measured biomarker concentrations to determine the plausible exposure concentrations-- a critical step to incorporate ground-truthing experimental data into a distribution of probable exposures that reduces model uncertainty and variability. At the population level, probabilistic reverse dosimetry can utilize a distribution of measured biomarker concentrations to identify the most likely exposure concentrations (or intake doses) experienced by the study participants. PROcEED is software that provides access to probabilistic reverse dosimetry approaches for estimating exposure distributions via a simple user interface.

Availability

PROcEED along with installation instructions is freely available for download from http://www.epa.gov/heasd/products/proceed/proceed.html     

Microbial Bebop: Creating Music from Complex Dynamics in Microbial Ecology

In order for society to make effective policy decisions on complex and far-reaching subjects, such as appropriate responses to global climate change, scientists must effectively communicate complex results to the non-scientifically specialized public. However, there are few ways however to transform highly complicated scientific data into formats that are engaging to the general community. Taking inspiration from patterns observed in nature and from some of the principles of jazz bebop improvisation, we have generated Microbial Bebop, a method by which microbial environmental data are transformed into music. Microbial Bebop uses meter, pitch, duration, and harmony to highlight the relationships between multiple data types in complex biological datasets. We use a comprehensive microbial ecology, time course dataset collected at the L4 marine monitoring station in the Western English Channel as an example of microbial ecological data that can be transformed into music. Four compositions were generated (www.bio.anl.gov/MicrobialBebop.htm.) from L4 Station data using Microbial Bebop. Each composition, though deriving from the same dataset, is created to highlight different relationships between environmental conditions and microbial community structure. The approach presented here can be applied to a wide variety of complex biological datasets.     

The Mps1 Kinase Modulates the Recruitment and Activity of Cnn1CENP-T at Saccharomyces cerevisiae Kinetochores

Kinetochores are conserved protein complexes that bind the replicated chromosomes to the mitotic spindle and then direct their segregation. To better comprehend Saccharomyces cerevisiae kinetochore function, we dissected the phospho-regulated dynamic interaction between conserved kinetochore protein Cnn1^CENP-T, the centromere region, and the Ndc80 complex through the cell cycle. Cnn1 localizes to kinetochores at basal levels from G1 through metaphase but accumulates abruptly at anaphase onset. How Cnn1 is recruited and which activities regulate its dynamic localization are unclear. We show that Cnn1 harbors two kinetochore-localization activities: a C-terminal histone-fold domain (HFD) that associates with the centromere region and a N-terminal Spc24/Spc25 interaction sequence that mediates linkage to the microtubule-binding Ndc80 complex. We demonstrate that the established Ndc80 binding site in the N terminus of Cnn1, Cnn1^60–84, should be extended with flanking residues, Cnn1^25–91, to allow near maximal binding affinity to Ndc80. Cnn1 localization was proposed to depend on Mps1 kinase activity at Cnn1–S74, based on in vitro experiments demonstrating the Cnn1–Ndc80 complex interaction. We demonstrate that from G1 through metaphase, Cnn1 localizes via both its HFD and N-terminal Spc24/Spc25 interaction sequence, and deletion or mutation of either region results in anomalous Cnn1 kinetochore levels. At anaphase onset (when Mps1 activity decreases) Cnn1 becomes enriched mainly via the N-terminal Spc24/Spc25 interaction sequence. In sum, we provide the first in vivo evidence of Cnn1 preanaphase linkages with the kinetochore and enrichment of the linkages during anaphase.     

Translational Control of the Oogenic Program by Components of OMA Ribonucleoprotein Particles in Caenorhabditis elegans

The oocytes of most sexually reproducing animals arrest in meiotic prophase I. Oocyte growth, which occurs during this period of arrest, enables oocytes to acquire the cytoplasmic components needed to produce healthy progeny and to gain competence to complete meiosis. In the nematode Caenorhabditis elegans, the major sperm protein hormone promotes meiotic resumption (also called meiotic maturation) and the cytoplasmic flows that drive oocyte growth. Prior work established that two related TIS11 zinc-finger RNA-binding proteins, OMA-1 and OMA-2, are redundantly required for normal oocyte growth and meiotic maturation. We affinity purified OMA-1 and identified associated mRNAs and proteins using genome-wide expression data and mass spectrometry, respectively. As a class, mRNAs enriched in OMA-1 ribonucleoprotein particles (OMA RNPs) have reproductive functions. Several of these mRNAs were tested and found to be targets of OMA-1/2-mediated translational repression, dependent on sequences in their 3′-untranslated regions (3′-UTRs). Consistent with a major role for OMA-1 and OMA-2 in regulating translation, OMA-1-associated proteins include translational repressors and activators, and some of these proteins bind directly to OMA-1 in yeast two-hybrid assays, including OMA-2. We show that the highly conserved TRIM-NHL protein LIN-41 is an OMA-1-associated protein, which also represses the translation of several OMA-1/2 target mRNAs. In the accompanying article in this issue, we show that LIN-41 prevents meiotic maturation and promotes oocyte growth in opposition to OMA-1/2. Taken together, these data support a model in which the conserved regulators of mRNA translation LIN-41 and OMA-1/2 coordinately control oocyte growth and the proper spatial and temporal execution of the meiotic maturation decision.     

The Abundant Histone Chaperones Spt6 and FACT Collaborate to Assemble,Inspect, and Maintain Chromatin Structure in Saccharomyces cerevisiae

Saccharomyces cerevisiae Spt6 protein is a conserved chromatin factor with several distinct functional domains, including a natively unstructured 30-residue N-terminal region that binds competitively with Spn1 or nucleosomes. To uncover physiological roles of these interactions, we isolated histone mutations that suppress defects caused by weakening Spt6:Spn1 binding with the spt6-F249K mutation. The strongest suppressor was H2A-N39K, which perturbs the point of contact between the two H2A-H2B dimers in an assembled nucleosome. Substantial suppression also was observed when the H2A-H2B interface with H3-H4 was altered, and many members of this class of mutations also suppressed a defect in another essential histone chaperone, FACT. Spt6 is best known as an H3-H4 chaperone, but we found that it binds with similar affinity to H2A-H2B or H3-H4. Like FACT, Spt6 is therefore capable of binding each of the individual components of a nucleosome, but unlike FACT, Spt6 did not produce endonuclease-sensitive reorganized nucleosomes and did not displace H2A-H2B dimers from nucleosomes. Spt6 and FACT therefore have distinct activities, but defects can be suppressed by overlapping histone mutations. We also found that Spt6 and FACT together are nearly as abundant as nucleosomes, with ∼24,000 Spt6 molecules, ∼42,000 FACT molecules, and ∼75,000 nucleosomes per cell. Histone mutations that destabilize interfaces within nucleosomes therefore reveal multiple spatial regions that have both common and distinct roles in the functions of these two essential and abundant histone chaperones. We discuss these observations in terms of different potential roles for chaperones in both promoting the assembly of nucleosomes and monitoring their quality.     

A Novel Method for Identification and Quantification of Consistently Differentially Methylated Regions

Advances in biotechnology have resulted in large-scale studies of DNA methylation. A differentially methylated region (DMR) is a genomic region with multiple adjacent CpG sites that exhibit different methylation statuses among multiple samples. Many so-called “supervised” methods have been established to identify DMRs between two or more comparison groups. Methods for the identification of DMRs without reference to phenotypic information are, however, less well studied. An alternative “unsupervised” approach was proposed, in which DMRs in studied samples were identified with consideration of nature dependence structure of methylation measurements between neighboring probes from tiling arrays. Through simulation study, we investigated effects of dependencies between neighboring probes on determining DMRs where a lot of spurious signals would be produced if the methylation data were analyzed independently of the probe. In contrast, our newly proposed method could successfully correct for this effect with a well-controlled false positive rate and a comparable sensitivity. By applying to two real datasets, we demonstrated that our method could provide a global picture of methylation variation in studied samples. R source codes to implement the proposed method were freely available at http://www.csjfann.ibms.sinica.edu.tw/eag/programlist/ICDMR/ICDMR.html.     

Prediction of Disease Causing Non-Synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP

We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further investigation. NetDiseaseSNP is publicly available as an online tool as well as a web service: http://www.cbs.dtu.dk/services/NetDiseaseSNP     

SDMdata: A Web-Based Software Tool for Collecting Species Occurrence Records

It is important to easily and efficiently obtain high quality species distribution data for predicting the potential distribution of species using species distribution models (SDMs). There is a need for a powerful software tool to automatically or semi-automatically assist in identifying and correcting errors. Here, we use Python to develop a web-based software tool (SDMdata) to easily collect occurrence data from the Global Biodiversity Information Facility (GBIF) and check species names and the accuracy of coordinates (latitude and longitude). It is an open source software (GNU Affero General Public License/AGPL licensed) allowing anyone to access and manipulate the source code. SDMdata is available online free of charge from <http://www.sdmserialsoftware.org/sdmdata/>.     

Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies

The primary goal of genome-wide association studies (GWAS) is to discover variants that could lead, in isolation or in combination, to a particular trait or disease. Standard approaches to GWAS, however, are usually based on univariate hypothesis tests and therefore can account neither for correlations due to linkage disequilibrium nor for combinations of several markers. To discover and leverage such potential multivariate interactions, we propose in this work an extension of the Random Forest algorithm tailored for structured GWAS data. In terms of risk prediction, we show empirically on several GWAS datasets that the proposed T-Trees method significantly outperforms both the original Random Forest algorithm and standard linear models, thereby suggesting the actual existence of multivariate non-linear effects due to the combinations of several SNPs. We also demonstrate that variable importances as derived from our method can help identify relevant loci. Finally, we highlight the strong impact that quality control procedures may have, both in terms of predictive power and loci identification. Variable importance results and T-Trees source code are all available at www.montefiore.ulg.ac.be/~botta/ttrees/ and github.com/0asa/TTree-source respectively.     

Epidemiological Trends of Dengue Disease in Thailand (2000–2011): A Systematic Literature Review

A literature survey and analysis was conducted to describe the epidemiology of dengue disease in Thailand reported between 2000 and 2011. The literature search identified 610 relevant sources, 40 of which fulfilled the inclusion criteria defined in the review protocol. Peaks in the number of cases occurred during the review period in 2001, 2002, 2008 and 2010. A shift in age group predominance towards older ages continued through the review period. Disease incidence and deaths remained highest in children aged ≤15 years and case fatality rates were highest in young children. Heterogeneous geographical patterns were observed with higher incidence rates reported in the Southern region and serotype distribution varied in time and place. Gaps identified in epidemiological knowledge regarding dengue disease in Thailand provide several avenues for future research, in particular studies of seroprevalence.

Protocol registration

PROSPERO CRD42012002170     

TRIPATH: A Biological Genetic and Genomic Database of Three Economically Important Fungal Pathogen of Wheat – Rust: Smut: Bunt

Wheat, the major source of vegetable protein in human diet, provides staple food globally for a large proportion of the human population. With higher protein content than other major cereals, wheat has great socio- economic importance. Nonetheless for wheat, three important fungal pathogens i.e. rust, smut and bunt are major cause of significant yield losses throughout the world. Researchers are putting up a strong fight against devastating wheat pathogens, and have made progress in tracking and controlling disease outbreaks from East Africa to South Asia. The aim of the present work hence was to develop a fungal pathogens database dedicated to wheat, gathering information about different pathogen species and linking them to their biological classification, distribution and control. Towards this end, we developed an open access database Tripath: A biological, genetic and genomic database of economically important wheat fungal pathogens – rust: smut: bunt. Data collected from peer-reviewed publications and fungal pathogens were added to the customizable database through an extended relational design. The strength of this resource is in providing rapid retrieval of information from large volumes of text at a high degree of accuracy. Database TRIPATH is freely accessible.

Availability

http://www.gbpuat-cbsh.ac.in/departments/bi/database/tripath/     

Synapse-Assembly Proteins Maintain Synaptic Vesicle Cluster Stability and Regulate Synaptic Vesicle Transport in Caenorhabditis elegans

The functional integrity of neurons requires the bidirectional active transport of synaptic vesicles (SVs) in axons. The kinesin motor KIF1A transports SVs from somas to stable SV clusters at synapses, while dynein moves them in the opposite direction. However, it is unclear how SV transport is regulated and how SVs at clusters interact with motor proteins. We addressed these questions by isolating a rare temperature-sensitive allele of Caenorhabditis elegans unc-104 (KIF1A) that allowed us to manipulate SV levels in axons and dendrites. Growth at 20° and 14° resulted in locomotion rates that were ∼3 and 50% of wild type, respectively, with similar effects on axonal SV levels. Corresponding with the loss of SVs from axons, mutants grown at 14° and 20° showed a 10- and 24-fold dynein-dependent accumulation of SVs in their dendrites. Mutants grown at 14° and switched to 25° showed an abrupt irreversible 50% decrease in locomotion and a 50% loss of SVs from the synaptic region 12-hr post-shift, with no further decreases at later time points, suggesting that the remaining clustered SVs are stable and resistant to retrograde removal by dynein. The data further showed that the synapse-assembly proteins SYD-1, SYD-2, and SAD-1 protected SV clusters from degradation by motor proteins. In syd-1, syd-2, and sad-1 mutants, SVs accumulate in an UNC-104-dependent manner in the distal axon region that normally lacks SVs. In addition to their roles in SV cluster stability, all three proteins also regulate SV transport.