Understanding the edge effect in TASEP with mean-field theoretic approaches

We study a totally asymmetric simple exclusion process (TASEP) with one defect site, hopping rate $q<1$, near the system boundary. Regarding our system as a pair of uniform TASEP's coupled through the defect, we study various methods to match a \emph{finite} TASEP and an \emph{infinite} one across a common boundary. Several approximation schemes are investigated. Utilizing the finite segment mean-field (FSMF) method, we set up a framework for computing the steady state current $J$ as a function of the entry rate $$ and $q$. For the case where the defect is located at the entry site, we obtain an analytical expression for $J(\alpha, q)$ which is in good agreement with Monte Carlo simulation results. When the defect is located deeper in the bulk, we refined the scheme of MacDonald, et.al. [Biopolymers, \textbf{6}, 1 (1968)] and find reasonably good fits to the density profiles before the defect site. We discuss the strengths and limitations of each method, as well as possible avenues for further studies.Comment: 16 pages, 4 figure

Methods for detecting associations between phenotype and aggregations of rare variants

Although genome-wide association studies have uncovered variants associated with more than 150 traits, the percentage of phenotypic variation explained by these associations remains small. This has led to the search for the dark matter that explains this missing genetic component of heritability. One potential explanation for dark matter is rare variants, and several statistics have been devised to detect associations resulting from aggregations of rare variants in relatively short regions of interest, such as candidate genes. In this paper we investigate the feasibility of extending this approach in an agnostic way, in which we consider all variants within a much broader region of interest, such as an entire chromosome or even the entire exome. Our method searches for subsets of variant sites using either Markov chain Monte Carlo or genetic algorithms. The analysis was performed with knowledge of the Genetic Analysis Workshop 17 answers

Radial Growth of Qilian Juniper on the Northeast Tibetan Plateau and Potential Climate Associations

There is controversy regarding the limiting climatic factor for tree radial growth at the alpine treeline on the northeastern Tibetan Plateau. In this study, we collected 594 increment cores from 331 trees, grouped within four altitude belts spanning the range 3550 to 4020 m.a.s.l. on a single hillside. We have developed four equivalent ring-width chronologies and shown that there are no significant differences in their growth-climate responses during 1956 to 2011 or in their longer-term growth patterns during the period AD 1110–2011. The main climate influence on radial growth is shown to be precipitation variability. Missing ring analysis shows that tree radial growth at the uppermost treeline location is more sensitive to climate variation than that at other elevations, and poor tree radial growth is particularly linked to the occurrence of serious drought events. Hence water limitation, rather than temperature stress, plays the pivotal role in controlling the radial growth of Sabina przewalskii Kom. at the treeline in this region. This finding contradicts any generalisation that tree-ring chronologies from high-elevation treeline environments are mostly indicators of temperature changes

Ptch2/Gas1 and Ptch1/Boc differentially regulate Hedgehog signalling in murine primordial germ cell migration.

Gas1 and Boc/Cdon act as co-receptors in the vertebrate Hedgehog signalling pathway, but the nature of their interaction with the primary Ptch1/2 receptors remains unclear. Here we demonstrate, using primordial germ cell migration in mouse as a developmental model, that specific hetero-complexes of Ptch2/Gas1 and Ptch1/Boc mediate the process of Smo de-repression with different kinetics, through distinct modes of Hedgehog ligand reception. Moreover, Ptch2-mediated Hedgehog signalling induces the phosphorylation of Creb and Src proteins in parallel to Gli induction, identifying a previously unknown Ptch2-specific signal pathway. We propose that although Ptch1 and Ptch2 functionally overlap in the sequestration of Smo, the spatiotemporal expression of Boc and Gas1 may determine the outcome of Hedgehog signalling through compartmentalisation and modulation of Smo-downstream signalling. Our study identifies the existence of a divergent Hedgehog signal pathway mediated by Ptch2 and provides a mechanism for differential interpretation of Hedgehog signalling in the germ cell niche

Construction of large-volume tissue mimics with 3D functional vascular networks

We used indirect stereolithography (SL) to form inner-layered fluidic networks in a porous scaffold by introducing a hydrogel barrier on the luminal surface, then seeded the networks separately with human umbilical vein endothelial cells and human lung fibroblasts to form a tissue mimic containing vascular networks. The artificial vascular networks provided channels for oxygen transport, thus reducing the hypoxic volume and preventing cell death. The endothelium of the vascular networks significantly retarded the occlusion of channels during whole-blood circulation. The tissue mimics have the potential to be used as an in vitro platform to examine the physiologic and pathologic phenomena through vascular architecture.ope

Use of Bayesian networks to dissect the complexity of genetic disease: application to the Genetic Analysis Workshop 17 simulated data

Complex diseases are often the downstream event of a number of risk factors, including both environmental and genetic variables. To better understand the mechanism of disease onset, it is of great interest to systematically investigate the crosstalk among various risk factors. Bayesian networks provide an intuitive graphical interface that captures not only the association but also the conditional independence and dependence structures among the variables, resulting in sparser relationships between risk factors and the disease phenotype than traditional correlation-based methods. In this paper, we apply a Bayesian network to dissect the complex regulatory relationships among disease traits and various risk factors for the Genetic Analysis Workshop 17 simulated data. We use the Bayesian network as a tool for the risk prediction of disease outcome

Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies.

Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or unknown subtle environmental perturbations. These factors can induce a pronounced artifactual correlation structure in the expression profiles, which may create spurious false associations or mask real genetic association signals. Here, we report PANAMA (Probabilistic ANAlysis of genoMic dAta), a novel probabilistic model to account for confounding factors within an eQTL analysis. In contrast to previous methods, PANAMA learns hidden factors jointly with the effect of prominent genetic regulators. As a result, this new model can more accurately distinguish true genetic association signals from confounding variation. We applied our model and compared it to existing methods on different datasets and biological systems. PANAMA consistently performs better than alternative methods, and finds in particular substantially more trans regulators. Importantly, our approach not only identifies a greater number of associations, but also yields hits that are biologically more plausible and can be better reproduced between independent studies. A software implementation of PANAMA is freely available online at http://ml.sheffield.ac.uk/qtl/

Imaging Electronic Correlations in Twisted Bilayer Graphene near the Magic Angle

Twisted bilayer graphene with a twist angle of around 1.1{\deg} features a pair of isolated flat electronic bands and forms a strongly correlated electronic platform. Here, we use scanning tunneling microscopy to probe local properties of highly tunable twisted bilayer graphene devices and show that the flat bands strongly deform when aligned with the Fermi level. At half filling of the bands, we observe the development of gaps originating from correlated insulating states. Near charge neutrality, we find a previously unidentified correlated regime featuring a substantially enhanced flat band splitting that we describe within a microscopic model predicting a strong tendency towards nematic ordering. Our results provide insights into symmetry breaking correlation effects and highlight the importance of electronic interactions for all filling factors in twisted bilayer graphene.Comment: Main text 9 pages, 4 figures; Supplementary Information 25 page

Large-scale risk prediction applied to Genetic Analysis Workshop 17 mini-exome sequence data

We consider the application of Efron’s empirical Bayes classification method to risk prediction in a genome-wide association study using the Genetic Analysis Workshop 17 (GAW17) data. A major advantage of using this method is that the effect size distribution for the set of possible features is empirically estimated and that all subsequent parameter estimation and risk prediction is guided by this distribution. Here, we generalize Efron’s method to allow for some of the peculiarities of the GAW17 data. In particular, we introduce two ways to extend Efron’s model: a weighted empirical Bayes model and a joint covariance model that allows the model to properly incorporate the annotation information of single-nucleotide polymorphisms (SNPs). In the course of our analysis, we examine several aspects of the possible simulation model, including the identity of the most important genes, the differing effects of synonymous and nonsynonymous SNPs, and the relative roles of covariates and genes in conferring disease risk. Finally, we compare the three methods to each other and to other classifiers (random forest and neural network)

Work-Related Mental Health and Job Performance: Can Mindfulness Help?

Work-related mental health issues such as work-related stress and addiction to work impose a significant health and economic burden to the employee, the employing organization, and the country of work more generally. Interventions that can be empirically shown to improve levels of work-related mental health – especially those with the potential to concurrently improve employee levels of work performance – are of particular interest to occupational stakeholders. One such broad-application interventional approach currently of interest to occupational stakeholders in this respect is mindfulness-based interventions (MBIs). Following a brief explication of the mindfulness construct, this paper critically discusses current research directions in the utilization of mindfulness in workplace settings and assesses its suitability for operationalization as an organization-level work-related mental health intervention. By effecting a perceptual-shift in the mode of responding and relating to sensory and cognitive-affective stimuli, employees that undergo mindfulness training may be able to transfer the locus of control for stress from external work conditions to internal metacognitive and attentional resources. Therefore, MBIs may constitute cost-effective organization-level interventions due to not actually requiring any modifications to human resource management systems and practises. Based on preliminary empirical findings and on the outcomes of MBI studies with clinical populations, it is concluded that MBIs appear to be viable interventional options for organizations wishing to improve the mental health of their employees