Modeling large scale species abundance with latent spatial processes

Modeling species abundance patterns using local environmental features is an important, current problem in ecology. The Cape Floristic Region (CFR) in South Africa is a global hot spot of diversity and endemism, and provides a rich class of species abundance data for such modeling. Here, we propose a multi-stage Bayesian hierarchical model for explaining species abundance over this region. Our model is specified at areal level, where the CFR is divided into roughly $37{,}000$ one minute grid cells; species abundance is observed at some locations within some cells. The abundance values are ordinally categorized. Environmental and soil-type factors, likely to influence the abundance pattern, are included in the model. We formulate the empirical abundance pattern as a degraded version of the potential pattern, with the degradation effect accomplished in two stages. First, we adjust for land use transformation and then we adjust for measurement error, hence misclassification error, to yield the observed abundance classifications. An important point in this analysis is that only $28$ of the grid cells have been sampled and that, for sampled grid cells, the number of sampled locations ranges from one to more than one hundred. Still, we are able to develop potential and transformed abundance surfaces over the entire region. In the hierarchical framework, categorical abundance classifications are induced by continuous latent surfaces. The degradation model above is built on the latent scale. On this scale, an areal level spatial regression model was used for modeling the dependence of species abundance on the environmental factors.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS335 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

CIRCULAR COMPARISON OF CONVENTIONAL PRESSURE STANDARDS USING A TRANSPORTABLE OPTICAL REFRACTOMETER: PREPARATION AND TRANSPORTATION

Using a transportable Fabry-Pérot cavity refractometer, a circular comparison of existing primary standards at several national metrology institutes is currently underway. This paper provides information about the refractometer, the preparation for the comparison, and the transportation procedur

A Genome-Wide Analysis of Promoter-Mediated Phenotypic Noise in Escherichia coli

Gene expression is subject to random perturbations that lead to fluctuations in the rate of protein production. As a consequence, for any given protein, genetically identical organisms living in a constant environment will contain different amounts of that particular protein, resulting in different phenotypes. This phenomenon is known as “phenotypic noise.” In bacterial systems, previous studies have shown that, for specific genes, both transcriptional and translational processes affect phenotypic noise. Here, we focus on how the promoter regions of genes affect noise and ask whether levels of promoter-mediated noise are correlated with genes' functional attributes, using data for over 60% of all promoters in Escherichia coli. We find that essential genes and genes with a high degree of evolutionary conservation have promoters that confer low levels of noise. We also find that the level of noise cannot be attributed to the evolutionary time that different genes have spent in the genome of E. coli. In contrast to previous results in eukaryotes, we find no association between promoter-mediated noise and gene expression plasticity. These results are consistent with the hypothesis that, in bacteria, natural selection can act to reduce gene expression noise and that some of this noise is controlled through the sequence of the promoter region alon

Update of the Minimum Information About BIobank Data Sharing (MIABIS) Core Terminology to the 3^rd Version

Introduction: The Minimum Information About BIobank Data Sharing (MIABIS) is a biobank-specific terminology enabling the sharing of biobank-related data for different purposes across a wide range of database implementations. After 4 years in use and with the first version of the individual-level MIABIS component Sample, Sample donor, and Event, it was necessary to revise the terminology, especially to include biobanks that work more in the data domain than with samples.Materials & Methods: Nine use-cases representing different types of biobanks, studies, and networks participated in the development work. They represent types of data, specific sample types, or levels of organization that were not included earlier in MIABIS. To support our revision of the Biobank entity, we conducted a survey of European biobanks to chart the services they provide. An important stakeholder group for biobanks include researchers as the main users of biobanks. To be able to render MIABIS more researcher-friendly, we collected different sample/data requests to analyze the terminology adjustment needs in detail. During the update process, the Core terminology was iteratively reviewed by a large group of experts until a consensus was reached.Results: With this update, MIABIS was adjusted to encompass data-driven biobanks and to include data collections, while also describing the services and capabilities biobanks offer to their users, besides the retrospective samples. The terminology was also extended to accommodate sample and data collections of nonhuman origin. Additionally, a set of organizational attributes was compiled to describe networks.Discussion: The usability of MIABIS Core v3 was increased by extending it to cover more topics of the biobanking domain. Additionally, the focus was on a more general terminology and harmonization of attributes with the individual-level entities Sample, Sample donor, and Event to keep the overall terminology minimal. With this work, the internal semantics of the MIABIS terminology was improved

Explaining species distribution patterns through hierarchical modeling

Understanding spatial patterns of species diversity and the distri- butions of individual species is a consuming problem in biogeography and con- servation. The Cape Floristic Region (CFR) of South Africa is a global hotspot of diversity and endemism, and the Protea Atlas Project, with some 60,000 site records across the region, provides an extraordinarily rich data set to analyze bio- diversity patterns. Analysis for the region is developed at the spatial scale of one minute grid-cells ( 37; 000 cells total for the region). We report on results for 40 species of a owering plant family Proteaceae (of about 330 in the CFR) for a de ned subregion. Using a Bayesian framework, we develop a two stage, spatially explicit, hierar- chical logistic regression. Stage one models the suitability or potential presence for each species at each cell, given species attributes along with grid cell (site-level) climate, precipitation, topography and geology data using species-level coe cients, and a spatial random e ect. The second level of the hierarchy models, for each species, observed presence=absence at a sampling site through a conditional speci- cation of the probability of presence at an arbitrary location in the grid cell given that the location is suitable. Because the atlas data are not evenly distributed across the landscape, grid cells contain variable numbers of sampling localities. Indeed, some grid cells are entirely unsampled; others have been transformed by human intervention (agriculture, urbanization) such that none of the species are there though some may have the potential to be present in the absence of distur- bance. Thus the modeling takes the sampling intensity at each site into account by assuming that the total number of times that a particular species was observed within a site follows a binomial distribution.In fact, a range of models can be examined incorporating di erent rst and second stage speci cations. This necessitates model comparison in a misaligned multilevel setting. All models are tted using MCMC methods. A best" model is selected. Parameter summaries o er considerable insight. In addition, results are mapped as the model-estimated potential presence for each species across the domain. This probability surface provides an alternative to customary empiri- cal \range of occupancy" displays. Summing yields the predicted species richness over the region. Summaries of the posterior for each environmental coe cient show which variables are most important in explaining species presence. Other biodi- versity measures emerge as model unknowns. A considerable range of inference is available. We illustrate with only a portion of the analyses we have conducted, noting that these initial results describe biogeographical patterns over the modeled region remarkably well

Quantum-based realizations of the pascal: status and progress of the EMPIR-project: quantumpascal

The QuantumPascal (QP) project combines the capabilities of 12 European institutions to enable traceable pressure measurements utilizing quantum-based methods that evaluate the number density instead of force per area to target the wide pressure range between 1 Pa and 3 MPa. This article summarizes the goals and results since the project start in June 201

Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution.

To identify genetic loci influencing central obesity and fat distribution, we performed a meta-analysis of 16 genome-wide association studies (GWAS, N = 38,580) informative for adult waist circumference (WC) and waist-hip ratio (WHR). We selected 26 SNPs for follow-up, for which the evidence of association with measures of central adiposity (WC and/or WHR) was strong and disproportionate to that for overall adiposity or height. Follow-up studies in a maximum of 70,689 individuals identified two loci strongly associated with measures of central adiposity; these map near TFAP2B (WC, P = 1.9x10(-11)) and MSRA (WC, P = 8.9x10(-9)). A third locus, near LYPLAL1, was associated with WHR in women only (P = 2.6x10(-8)). The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass, whereas LYPLAL1 displays a strong female-only association with fat distribution. By focusing on anthropometric measures of central obesity and fat distribution, we have identified three loci implicated in the regulation of human adiposity

A Simple Screen to Identify Promoters Conferring High Levels of Phenotypic Noise

Genetically identical populations of unicellular organisms often show marked variation in some phenotypic traits. To investigate the molecular causes and possible biological functions of this phenotypic noise, it would be useful to have a method to identify genes whose expression varies stochastically on a certain time scale. Here, we developed such a method and used it for identifying genes with high levels of phenotypic noise in Salmonella enterica ssp. I serovar Typhimurium (S. Typhimurium). We created a genomic plasmid library fused to a green fluorescent protein (GFP) reporter and subjected replicate populations harboring this library to fluctuating selection for GFP expression using fluorescent-activated cell sorting (FACS). After seven rounds of fluctuating selection, the populations were strongly enriched for promoters that showed a high amount of noise in gene expression. Our results indicate that the activity of some promoters of S. Typhimurium varies on such a short time scale that these promoters can absorb rapid fluctuations in the direction of selection, as imposed during our experiment. The genomic fragments that conferred the highest levels of phenotypic variation were promoters controlling the synthesis of flagella, which are associated with virulence and host–pathogen interactions. This confirms earlier reports that phenotypic noise may play a role in pathogenesis and indicates that these promoters have among the highest levels of noise in the S. Typhimurium genome. This approach can be applied to many other bacterial and eukaryotic systems as a simple method for identifying genes with noisy expression

TRIB1 constitutes a molecular link between regulation of sleep and lipid metabolism in humans

Epidemiological studies show association between sleep duration and lipid metabolism. In addition, inactivation of circadian genes induces insulin resistance and hyperlipidemia. We hypothesized that sleep length and lipid metabolism are partially controlled by the same genes. We studied the association of total sleep time (TST) with 60 genetic variants that had previously been associated with lipids. The analyses were performed in a Finnish population-based sample (N = 6334) and replicated in 2189 twins. Finally, RNA expression from mononuclear leucocytes was measured in 10 healthy volunteers before and after sleep restriction. The genetic analysis identified two variants near TRIB1 gene that independently contributed to both blood lipid levels and to TST (rs17321515, P = 8.92(*)10(-5), Bonferroni corrected P = 0.0053, β = 0.081 h per allele; rs2954029, P = 0.00025, corrected P = 0.015, β = 0.076; P<0.001 for both variants after adjusting for blood lipid levels or body mass index). The finding was replicated in the twin sample (rs17321515, P = 0.022, β = 0.063; meta-analysis of both samples P = 8.1(*)10(-6), β = 0.073). After the experimentally induced sleep restriction period TRIB1 expression increased 1.6-fold and decreased in recovery phase (P = 0.006). In addition, a negative correlation between TRIB1 expression and slow wave sleep was observed in recovery from sleep restriction. These results show that allelic variants of TRIB1 are independently involved in regulation of lipid metabolism and sleep. The findings give evidence for the pleiotropic nature of TRIB1 and may reflect the shared roots of sleep and metabolism. The shared genetic background may at least partially explain the mechanism behind the well-established connection between diseases with disrupted metabolism and sleep.Peer reviewe

New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes