Unique establishment of procephalic head segments is supported by the identification of cis-regulatory elements driving segment-specific segment polarity gene expression in Drosophila

Anterior head segmentation is governed by different regulatory mechanisms than those that control trunk segmentation in Drosophila. For segment polarity genes, both initial mode of activation as well as cross-regulatory interactions among them differ from the typical genetic circuitry in the trunk and are unique for each of the procephalic segments. In order to better understand the segment-specific gene network responsible for the procephalic expression of the earliest active segment polarity genes wingless and hedgehog, we started to identify and analyze cis-regulatory DNA elements of these genes. For hedgehog, we could identify a cis-regulatory element, ic-CRE, that mediates expression specifically in the posterior part of the intercalary segment and requires promoter-specific interaction for its function. The intercalary stripe is the last part of the metameric hedgehog expression pattern that appears during embryonic development, which probably reflects the late and distinct establishment of this segment. The identification of a cis-regulatory element that is specific for one head segment supports the mutant-based observation that the expression of segment polarity genes is governed by a unique gene network in each of the procephalic segments. This provides further indication that the anterior-most head segments represent primary segments, which are set up independently, in contrast to the secondary segments of the trunk, which resemble true repetitive units

A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data

Comparing benthic biogeochemistry at a sandy and a muddy site in the Celtic Sea using a model and observations

Results from a 1D setup of the European Regional Seas Ecosystem Model (ERSEM) biogeochemical model were compared with new observations collected under the UK Shelf Seas Biogeochemistry (SSB) programme to assess model performance and clarify elements of shelf-sea benthic biogeochemistry and carbon cycling. Observations from two contrasting sites (muddy and sandy) in the Celtic Sea in otherwise comparable hydrographic conditions were considered, with the focus on the benthic system. A standard model parameterisation with site-specific light and nutrient adjustments was used, along with modifications to the within-seabed diffusivity to accommodate the modelling of permeable (sandy) sediments. Differences between modelled and observed quantities of organic carbon in the bed were interpreted to suggest that a large part (>90%) of the observed benthic organic carbon is biologically relatively inactive. Evidence on the rate at which this inactive fraction is produced will constitute important information to quantify offshore carbon sequestration. Total oxygen uptake and oxic layer depths were within the range of the measured values. Modelled depth average pore water concentrations of ammonium, phosphate and silicate were typically 5–20% of observed values at the muddy site due to an underestimate of concentrations associated with the deeper sediment layers. Model agreement for these nutrients was better at the sandy site, which had lower pore water concentrations, especially deeper in the sediment. Comparison of pore water nitrate with observations had added uncertainty, as the results from process studies at the sites indicated the dominance of the anammox pathway for nitrogen removal; a pathway that is not included in the model. Macrofaunal biomasses were overestimated, although a model run with increased macrofaunal background mortality rates decreased macrofaunal biomass and improved agreement with observations. The decrease in macrofaunal biomass was compensated by an increase in meiofaunal biomass such that total oxygen demand remained within the observed range. The permeable sediment modification reproduced some of the observed behaviour of oxygen penetration depth at the sandy site. It is suggested that future development in ERSEM benthic modelling should focus on: (1) mixing and degradation rates of benthic organic matter, (2) validation of benthic faunal biomass against large scale spatial datasets, (3) incorporation of anammox in the benthic nitrogen cycle, and (4) further developments to represent permeable sediment processes

Wavelet-Based Nonparametric Bayes Methods

In this chapter, we will provide an overview of the current status of research involving Bayesian inference in wavelet nonparametric problems. In many statistical applications, there is a need for procedures to (i) adapt to data and (ii) use prior information. The interface of wavelets and the Bayesian paradigm provide a natural terrain for both of these goals