Reconstruction of Random Colourings

Reconstruction problems have been studied in a number of contexts including biology, information theory and and statistical physics. We consider the reconstruction problem for random $k$-colourings on the $\Delta$-ary tree for large $k$. Bhatnagar et. al. showed non-reconstruction when $\Delta \leq \frac12 k\log k - o(k\log k)$ and reconstruction when $\Delta \geq k\log k + o(k\log k)$. We tighten this result and show non-reconstruction when $\Delta \leq k[\log k + \log \log k + 1 - \ln 2 -o(1)]$ and reconstruction when $\Delta \geq k[\log k + \log \log k + 1+o(1)]$.Comment: Added references, updated notatio

Gene expression and matrix turnover in overused and damaged tendons

Chronic, painful conditions affecting tendons, frequently known as tendinopathy, are very common types of sporting injury. The tendon extracellular matrix is substantially altered in tendinopathy, and these changes are thought to precede and underlie the clinical condition. The tendon cell response to repeated minor injuries or “overuse” is thought to be a major factor in the development of tendinopathy. Changes in matrix turnover may also be effected by the cellular response to physical load, altering the balance of matrix turnover and changing the structure and composition of the tendon. Matrix turnover is relatively high in tendons exposed to high mechanical demands, such as the supraspinatus and Achilles, and this is thought to represent either a repair or tissue maintenance function. Metalloproteinases are a large family of enzymes capable of degrading all of the tendon matrix components, and these are thought to play a major role in the degradation of matrix during development, adaptation and repair. It is proposed that some metalloproteinase enzymes are required for the health of the tendon, and others may be damaging, leading to degeneration of the tissue. Further research is required to investigate how these enzyme activities are regulated in tendon and altered in tendinopathy. A profile of all the metalloproteinases expressed and active in healthy and degenerate tendon is required and may lead to the development of new drug therapies for these common and debilitating sports injuries

Intercultural New Media Studies: The Next Frontier in intercultural Communication

New media (ICT\u27s) are transforming communication across cultures. Despite this revolution in cross cultural contact, communication researchers have largely ignored the impact of new media on intercultural communication. This groundbreaking article defines the parameters of a new field of inquiry called Intercultural New Media Studies (INMS), which explores the intersection between ICT\u27s and intercultural communication. Composed of two research areas—(1) new media and intercultural communication theory and (2) culture and new media—INMS investigates new digital theories of intercultural contact as well as refines and expands twentieth-century intercultural communication theories, examining their salience in a digital world. INMS promises to increase our understanding of intercultural communication in a new media age and is the next frontier in intercultural communication

Approximation via Correlation Decay when Strong Spatial Mixing Fails

Approximate counting via correlation decay is the core algorithmic technique used in the sharp delineation of the computational phase transition that arises in the approximation of the partition function of antiferromagnetic 2-spin models. Previous analyses of correlation-decay algorithms implicitly depended on the occurrence of strong spatial mixing. This, roughly, means that one uses worst-case analysis of the recursive procedure that creates the subinstances. In this paper, we develop a new analysis method that is more refined than the worst-case analysis. We take the shape of instances in the computation tree into consideration and we amortize against certain “bad” instances that are created as the recursion proceeds. This enables us to show correlation decay and to obtain a fully polynomial-time approximation scheme (FPTAS) even when strong spatial mixing fails. We apply our technique to the problem of approximately counting independent sets in hypergraphs with degree upper bound $\Delta$ and with a lower bound $k$ on the arity of hyperedges. Liu and Lin gave an FPTAS for $k\geq2$ and $\Delta\leq5$ (lack of strong spatial mixing was the obstacle preventing this algorithm from being generalized to $\Delta=6$). Our technique gives a tight result for $\Delta=6$, showing that there is an FPTAS for $k\geq3$ and $\Delta\leq6$. The best previously known approximation scheme for $\Delta=6$ is the Markov-chain simulation based fully polynomial-time randomized approximation scheme (FPRAS) of Bordewich, Dyer, and Karpinski, which only works for $k\geq8$. Our technique also applies for larger values of $k$, giving an FPTAS for $k\geq\Delta$. This bound is not substantially stronger than existing randomized results in the literature. Nevertheless, it gives the first deterministic approximation scheme in this regime. Moreover, unlike existing results, it leads to an FPTAS for counting dominating sets in regular graphs with sufficiently large degree. We further demonstrate that in the hypergraph independent set model, approximating the partition function is NP-hard even within the uniqueness regime. Also, approximately counting dominating sets of bounded-degree graphs (without the regularity restriction) is NP-hard

Prediction models for the development of COPD: A systematic review

Early identification of people at risk of developing COPD is crucial for implementing preventive strategies. We aimed to systematically review and assess the performance of all published models that predicted development of COPD. A search was conducted to identify studies that developed a prediction model for COPD development. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was followed when extracting data and appraising the selected studies. Of the 4,481 records identified, 30 articles were selected for full-text review, and only four of these were eligible to be included in the review. The only consistent predictor across all four models was a measure of smoking. Sex and age were used in most models; however, other factors varied widely. Two of the models had good ability to discriminate between people who were correctly or incorrectly classified as at risk of developing COPD. Overall none of the models were particularly useful in accurately predicting future risk of COPD, nor were they good at ruling out future risk of COPD. Further studies are needed to develop new prediction models and robustly validate them in external cohorts

Cutoff for the Ising model on the lattice

Introduced in 1963, Glauber dynamics is one of the most practiced and extensively studied methods for sampling the Ising model on lattices. It is well known that at high temperatures, the time it takes this chain to mix in $L^1$ on a system of size $n$ is $O(\log n)$. Whether in this regime there is cutoff, i.e. a sharp transition in the $L^1$-convergence to equilibrium, is a fundamental open problem: If so, as conjectured by Peres, it would imply that mixing occurs abruptly at $(c+o(1))\log n$ for some fixed $c>0$, thus providing a rigorous stopping rule for this MCMC sampler. However, obtaining the precise asymptotics of the mixing and proving cutoff can be extremely challenging even for fairly simple Markov chains. Already for the one-dimensional Ising model, showing cutoff is a longstanding open problem. We settle the above by establishing cutoff and its location at the high temperature regime of the Ising model on the lattice with periodic boundary conditions. Our results hold for any dimension and at any temperature where there is strong spatial mixing: For $\Z^2$ this carries all the way to the critical temperature. Specifically, for fixed $d\geq 1$, the continuous-time Glauber dynamics for the Ising model on $(\Z/n\Z)^d$ with periodic boundary conditions has cutoff at $(d/2\lambda_\infty)\log n$, where $\lambda_\infty$ is the spectral gap of the dynamics on the infinite-volume lattice. To our knowledge, this is the first time where cutoff is shown for a Markov chain where even understanding its stationary distribution is limited. The proof hinges on a new technique for translating $L^1$ to $L^2$ mixing which enables the application of log-Sobolev inequalities. The technique is general and carries to other monotone and anti-monotone spin-systems.Comment: 34 pages, 3 figure

Constitutive Activation of the Src Family Kinase Hck Results in Spontaneous Pulmonary Inflammation and an Enhanced Innate Immune Response

To identify the physiological role of Hck, a functionally redundant member of the Src family of tyrosine kinases expressed in myelomonocytic cells, we generated HckF/F “knock-in” mice which carry a targeted tyrosine (Y) to phenylalanine (F) substitution of the COOH-terminal, negative regulatory Y499-residue in the Hck protein. Unlike their Hck−/− “loss-of-function” counterparts, HckF/F “gain-of-function” mice spontaneously acquired a lung pathology characterized by extensive eosinophilic and mononuclear cell infiltration within the lung parenchyma, alveolar airspaces, and around blood vessels, as well as marked epithelial mucus metaplasia in conducting airways. Lungs from HckF/F mice showed areas of mild emphysema and pulmonary fibrosis, which together with inflammation resulted in altered lung function and respiratory distress in aging mice. When challenged transnasally with lipopolysaccharide (LPS), HckF/F mice displayed an exaggerated pulmonary innate immune response, characterized by excessive release of matrix metalloproteinases and tumor necrosis factor (TNF)α. Similarly, HckF/F mice were highly sensitive to endotoxemia after systemic administration of LPS, and macrophages and neutrophils derived from HckF/F mice exhibited enhanced effector functions in vitro (e.g., nitric oxide and TNFα production, chemotaxis, and degranulation). Based on the demonstrated functional association of Hck with leukocyte integrins, we propose that constitutive activation of Hck may mimic adhesion-dependent priming of leukocytes. Thus, our observations collectively suggest an enhanced innate immune response in HckF/F mice thereby skewing innate immunity from a reversible physiological host defense response to one causing irreversible tissue damage

Induction of Asthma and the Environment: What We Know and Need to Know

The prevalence of asthma has increased dramatically over the last 25 years in the United States and in other nations as a result of ill-defined changes in living conditions in modern society. On 18 and 19 October 2004 the U.S. Environmental Protection Agency and the National Institute of Environmental Health Sciences sponsored the workshop “Environmental Influences on the Induction and Incidence of Asthma” to review current scientific evidence with respect to factors that may contribute to the induction of asthma. Participants addressed two broad questions: a) What does the science suggest that regulatory and public health agencies could do now to reduce the incidence of asthma? and b) What research is needed to improve our understanding of the factors that contribute to the induction of asthma and our ability to manage this problem? In this article (one of four articles resulting from the workshop), we briefly characterize asthma and its public health and economic impacts, and intervention strategies that have been successfully used to prevent induction of asthma in the workplace. We conclude with the findings of seven working groups that focus on ambient air, indoor pollutants (biologics), occupational exposures, early life stages, older adults, intrinsic susceptibility, and lifestyle. These groups found strong scientific support for public health efforts to limit in utero and postnatal exposure to cigarette smoke. However, with respect to other potential types of interventions, participants noted many scientific questions, which are summarized in this article. Research to address these questions could have a significant public health and economic impact that would be well worth the investment

Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as asthma endotypes. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies