p22phox C242T Single-Nucleotide Polymorphism Inhibits Inflammatory Oxidative Damage to Endothelial Cells and Vessels.

BACKGROUND: The NADPH oxidase, by generating reactive oxygen species, is involved in the pathophysiology of many cardiovascular diseases and represents a therapeutic target for the development of novel drugs. A single-nucleotide polymorphism, C242T of the p22(phox) subunit of NADPH oxidase, has been reported to be negatively associated with coronary heart disease and may predict disease prevalence. However, the underlying mechanisms remain unknown. METHODS AND RESULTS: With the use of computer molecular modeling, we discovered that C242T single-nucleotide polymorphism causes significant structural changes in the extracellular loop of p22(phox) and reduces its interaction stability with Nox2 subunit. Gene transfection of human pulmonary microvascular endothelial cells showed that C242T p22(phox) significantly reduced Nox2 expression but had no significant effect on basal endothelial O2 (.-) production or the expression of Nox1 and Nox4. When cells were stimulated with tumor necrosis factor-α (or high glucose), C242T p22(phox) significantly inhibited tumor necrosis factor-α-induced Nox2 maturation, O2 (.-) production, mitogen-activated protein kinases and nuclear factor κB activation, and inflammation (all P<0.05). These C242T effects were further confirmed using p22(phox) short-hairpin RNA-engineered HeLa cells and Nox2(-/-) coronary microvascular endothelial cells. Clinical significance was investigated by using saphenous vein segments from non-coronary heart disease subjects after phlebotomies. TT (C242T) allele was common (prevalence of ≈22%) and, in comparison with CC, veins bearing TT allele had significantly lower levels of Nox2 expression and O2 (.-) generation in response to high-glucose challenge. CONCLUSIONS: C242T single-nucleotide polymorphism causes p22(phox) structural changes that inhibit endothelial Nox2 activation and oxidative response to tumor necrosis factor-α or high-glucose stimulation. C242T single-nucleotide polymorphism may represent a natural protective mechanism against inflammatory cardiovascular diseases

Cross-Platform Comparison of Microarray-Based Multiple-Class Prediction

High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets

Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays

Train rescheduling after a perturbation is a challenging task and is an important concern of the railway industry as delayed trains can lead to large fines, disgruntled customers and loss of revenue. Sometimes not just one delay but several unrelated delays can occur in a short space of time which makes the problem even more challenging. In addition, the problem is a dynamic one that changes over time for, as trains are waiting to be rescheduled at the junction, more timetabled trains will be arriving, which will change the nature of the problem. The aim of this research is to investigate the application of several different ant colony optimization (ACO) algorithms to the problem of a dynamic train delay scenario with multiple delays. The algorithms not only resequence the trains at the junction but also resequence the trains at the stations, which is considered to be a first step towards expanding the problem to consider a larger area of the railway network. The results show that, in this dynamic rescheduling problem, ACO algorithms with a memory cope with dynamic changes better than an ACO algorithm that uses only pheromone evaporation to remove redundant pheromone trails. In addition, it has been shown that if the ant solutions in memory become irreparably infeasible it is possible to replace them with elite immigrants, based on the best-so-far ant, and still obtain a good performance

Static non-reciprocity in mechanical metamaterials

Reciprocity is a fundamental principle governing various physical systems, which ensures that the transfer function between any two points in space is identical, regardless of geometrical or material asymmetries. Breaking this transmission symmetry offers enhanced control over signal transport, isolation and source protection. So far, devices that break reciprocity have been mostly considered in dynamic systems, for electromagnetic, acoustic and mechanical wave propagation associated with spatio-temporal variations. Here we show that it is possible to strongly break reciprocity in static systems, realizing mechanical metamaterials that, by combining large nonlinearities with suitable geometrical asymmetries, and possibly topological features, exhibit vastly different output displacements under excitation from different sides, as well as one-way displacement amplification. In addition to extending non-reciprocity and isolation to statics, our work sheds new light on the understanding of energy propagation in non-linear materials with asymmetric crystalline structures and topological properties, opening avenues for energy absorption, conversion and harvesting, soft robotics, prosthetics and optomechanics.Comment: 19 pages, 3 figures, Supplementary information (11 pages and 5 figures

The validity of using ICD-9 codes and pharmacy records to identify patients with chronic obstructive pulmonary disease

Background: Administrative data is often used to identify patients with chronic obstructive pulmonary disease (COPD), yet the validity of this approach is unclear. We sought to develop a predictive model utilizing administrative data to accurately identify patients with COPD. Methods: Sequential logistic regression models were constructed using 9573 patients with postbronchodilator spirometry at two Veterans Affairs medical centers (2003-2007). COPD was defined as: 1) FEV1/FVC <0.70, and 2) FEV1/FVC < lower limits of normal. Model inputs included age, outpatient or inpatient COPD-related ICD-9 codes, and the number of metered does inhalers (MDI) prescribed over the one year prior to and one year post spirometry. Model performance was assessed using standard criteria. Results: 4564 of 9573 patients (47.7%) had an FEV1/FVC < 0.70. The presence of ≥1 outpatient COPD visit had a sensitivity of 76% and specificity of 67%; the AUC was 0.75 (95% CI 0.74-0.76). Adding the use of albuterol MDI increased the AUC of this model to 0.76 (95% CI 0.75-0.77) while the addition of ipratropium bromide MDI increased the AUC to 0.77 (95% CI 0.76-0.78). The best performing model included: ≥6 albuterol MDI, ≥3 ipratropium MDI, ≥1 outpatient ICD-9 code, ≥1 inpatient ICD-9 code, and age, achieving an AUC of 0.79 (95% CI 0.78-0.80). Conclusion: Commonly used definitions of COPD in observational studies misclassify the majority of patients as having COPD. Using multiple diagnostic codes in combination with pharmacy data improves the ability to accurately identify patients with COPD.Department of Veterans Affairs, Health Services Research and Development (DHA), American Lung Association (CI- 51755-N) awarded to DHA, the American Thoracic Society Fellow Career Development AwardPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/84155/1/Cooke - ICD9 validity in COPD.pd

Dynein structure and power stroke

Dynein ATPases are microtubule motors that are critical to diverse processes such as vesicle transport and the beating of sperm tails; however, their mechanism of force generation is unknown. Each dynein comprises a head, from which a stalk and a stem emerge. Here we use electron microscopy and image processing to reveal new structural details of dynein c, an isoform from Chlamydomonas reinhardtii flagella, at the start and end of its power stroke. Both stem and stalk are flexible, and the stem connects to the head by means of a linker approximately 10 nm long that we propose lies across the head. With both ADP and vanadate bound, the stem and stalk emerge from the head 10 nm apart. However, without nucleotide they emerge much closer together owing to a change in linker orientation, and the coiled-coil stalk becomes stiffer. The net result is a shortening of the molecule coupled to an approximately 15-nm displacement of the tip of the stalk. These changes indicate a mechanism for the dynein power stroke

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Supersymmetry in the shadow of photini

Additional neutral gauge fermions -- "photini" -- arise in string compactifications as superpartners of U(1) gauge fields. Unlike their vector counterparts, the photini can acquire weak-scale masses from soft SUSY breaking and lead to observable signatures at the LHC through mass mixing with the bino. In this work we investigate the collider consequences of adding photini to the neutralino sector of the MSSM. Relatively large mixing of one or more photini with the bino can lead to prompt decays of the lightest ordinary supersymmetric particle; these extra cascades transfer most of the energy of SUSY decay chains into Standard Model particles, diminishing the power of missing energy as an experimental handle for signal discrimination. We demonstrate that the missing energy in SUSY events with photini is reduced dramatically for supersymmetric spectra with MSSM neutralinos near the weak scale, and study the effects on limits set by the leading hadronic SUSY searches at ATLAS and CMS. We find that in the presence of even one light photino the limits on squark masses from hadronic searches can be reduced by 400 GeV, with comparable (though more modest) reduction of gluino mass limits. We also consider potential discovery channels such as dilepton and multilepton searches, which remain sensitive to SUSY spectra with photini and can provide an unexpected route to the discovery of supersymmetry. Although presented in the context of photini, our results apply in general to theories in which additional light neutral fermions mix with MSSM gauginos.Comment: 23 pages, 8 figures, references adde