Bayesian multilevel modelling of compositional data for simultaneous inferences on Allele compositions and inbreeding coefficients

Sample data from a number of sub-populations are often investigated in order to integrate the findings of different research studies on a particular area. In case of compositional samples, like the allele frequencies collected at a single locus in different surveys, the data are independent multinomial vectors. Each multinomial distribution depends on a specific probability vector, that is, the unknown relative composition of the sub-population. A Bayesian hierarchy approach is proposed here to model the variability of the sub-composition vectors around a common mean with possibly different scales. The common mean can be seen as the relative composition of the aggregated population. Scale parameters are well known in Biology as the Wright's inbreeding coefficients. The method presented here extends some previous work by assuming less prior knowledge on the subject and constraints on the model. A relatively simple Monte Carlo algorithm is described to perform joint inferences on general and local compositions and inbreeding coefficients. The method is applied on two case studies. The first one is based on DNA samples from ten Italian regions at the loci TH01 and FES, obtained from a database currently used for forensic identification, in which inbreeding assessments can be crucial. The second application is based on a set of colour-blind sample rates in North-East Indian populations collected by Choudhury (1994). The Author found some controversial results from the classical test for comparing proportions. A clearer picture, instead, is obtained by the current Bayesian approach.     

A Thermodynamic Funnel Drives Bacterial Lipopolysaccharide Transfer in the TLR4 Pathway

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Development of a Prototype System for Archiving Integrative/Hybrid Structure Models of Biological Macromolecules

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Personalized stent design for congenital heart defects using pulsatile blood flow simulations

Stent size selection and placement are among the most challenging tasks in the treatment of pulmonary artery stenosis in congenital heart defects (CHD). Patient-specific 3D model from CT or MR improves the understanding of the patient’s anatomy and information about the hemodynamics aid in patient risk assessment and treatment planning. This work presents a new approach for personalized stent design in pulmonary artery interventions combining personalized patient geometry and hemodynamic simulations. First, the stent position is initialized using a geometric approach. Second, the stent and artery expansion, including the foreshortening behavior of the stent is simulated. Two stent designs are considered, a regular stent and a Y-stent for bifurcations. Computational fluid dynamics (CFD) simulations of the blood flow in the initial and expanded artery models are performed using patient-specific boundary conditions in form of a pulsatile inflow waveform, 3-element Windkessel outflow conditions, and deformable vessel walls. The simulations have been applied to 16 patient cases with a large variability of anatomies. Finally, the simulations have been clinically validated using retrospective imaging from angiography and pressure measurements. The simulated pressure, volume flow and flow velocity values were on the same order of magnitude as the reference values obtained from clinical measurements, and the simulated stent placement showed a positive impact on the hemodynamic values. Simulation of geometric changes combined with CFD simulations offers the possibility to optimize stent type, size, and position by evaluating different configurations before the intervention, and eventually allow to test customized stent geometries and new deployment techniques in CHD.     

Morphing the feature-based multi-blocks of normative/healthy vertebral geometries to scoliosis vertebral geometries: development of personalized finite element models

Personalized Finite Element (FE) models and hexahedral elements are preferred for biomechanical investigations. Feature-based multi-block methods are used to develop anatomically accurate personalized FE models with hexahedral mesh. It is tedious to manually construct multi-blocks for large number of geometries on an individual basis to develop personalized FE models. Mesh-morphing method mitigates the aforementioned tediousness in meshing personalized geometries every time, but leads to element warping and loss of geometrical data. Such issues increase in magnitude when normative spine FE model is morphed to scoliosis-affected spinal geometry. The only way to bypass the issue of hex-mesh distortion or loss of geometry as a result of morphing is to rely on manually constructing the multi-blocks for scoliosis-affected spine geometry of each individual, which is time intensive. A method to semi-automate the construction of multi-blocks on the geometry of scoliosis vertebrae from the existing multi-blocks of normative vertebrae is demonstrated in this paper. High-quality hexahedral elements were generated on the scoliosis vertebrae from the morphed multi-blocks of normative vertebrae. Time taken was 3 months to construct the multi-blocks for normative spine and less than a day for scoliosis. Efforts taken to construct multi-blocks on personalized scoliosis spinal geometries are significantly reduced by morphing existing multi-blocks.     

Look in the trees: Hylobatids as evolutionary models for extinct hominins

Studying extant apes is of central importance to paleoanthropology. This approach is informative in inferring how hominin skeletal morphology reflects phylogeny, behavior, development, and ecological context. Traditionally, great apes have dominated the paleoanthropological literature as extant analogs for extinct hominins, to the exclusion of their phylogenetic sister group, the hylobatids. Phylogenetic proximity, large body size, and high encephalization quotients may have contributed to decisions to use great apes as models for hominins. However, if we reexamine hylobatids as extant models for extinct hominins—using modern phylogenetic, behavioral, and ecological data—this clade is uniquely poised to inform future frameworks in paleoanthropology. The following features make hylobatids strong analogs for extinct hominins: taxonomic diversity, the timing of diversification, hybridization between species, small body size, and reduced sexual dimorphism. Based on these shared features, hylobatids offer future opportunities to paleoanthropology, and provide a much richer extant analog than is currently recognized.     

Social perspectives on the use of reference conditions in restoration of fire‐adapted forest landscapes

As approaches to ecological restoration become increasingly large scale and collaborative, there is a need to better understand social aspects of restoration and how they influence land management. In this article, we examine social perspectives that influence the determination of ecological reference conditions in restoration. Our analysis is based on in‐depth interviews with diverse stakeholders involved in collaborative restoration of fire‐adapted forest landscapes. We conducted interviews with 86 respondents from six forest collaboratives that are part of the U.S. Forest Service's Collaborative Forest Landscape Restoration Program. Collaboratives use a variety of approaches to develop reference conditions, including historic, contemporary, and future scenarios. Historical conditions prior to European settlement (nineteenth century or “pre‐settlement” conditions), or prior to more recent grazing, logging, and exclusion of fire, were the predominant type of reference used in all sites. Stakeholders described benefits and limitations of reference conditions. Primary benefits include (1) providing a science‐based framework for bringing stakeholders together around a common vision; (2) gaining social understanding and acceptance of the underlying need for restoration; and (3) serving to neutralize otherwise value‐laden discussions about multiple, sometimes competing, resource objectives. Limitations stem from (1) concerns over social conflict when reference conditions are perceived to contradict other stakeholder values and interests, (2) differing interpretations of reference condition science, (3) inappropriate application or over‐generalization of reference information, and (4) limited relevance of historical references for current and future conditions in some ecosystems. At the same time, collaboratives are adopting innovative strategies to address conceptual and methodological limitations of reference conditions.