Study of interfaces chemistry in type-II GaSb/InAs superlattice structures

There is a considerable interest in type-II GaSb/InAs superlattice system due to several modern applications including infrared detectors. In these studies X-ray Photoelectron Spectroscopy (XPS) and Spectroscopic Ellipsometry (SE) have been used to extensive characterization of the surface and interface of GaSb/InAs superlattice. Application of XPS and SE techniques provide precise information from topmost layers of structure and allow excluding presence of GaAs-type interfaces in GaSb/InAs superlattices. Simultaneously, these results indicate that InSb-type or GaInSb-type interfaces have been detected in the structures studied

Helicobacter pylori Seropositivity: Prevalence, Associations, and the Impact on Incident Metabolic Diseases/Risk Factors in the Population-Based KORA Study

Introduction:Helicobacter pylori (H. pylori) is a common infection and known risk factor for gastric cancer. We assessed cross-sectional and longitudinal associations to study the impact of H. pylori seropositivity on metabolic diseases.Methods:Helicobacter pylori seropositivity in serum samples of the KORA study was analyzed by multiplex serology. We calculated sex-specific prevalence of H. pylori seropositivity for the year 2007 based on the first follow-up survey (termed F4) of the KORA study S4. We identified factors associated with H. pylori seropositivity in the F4 survey. Further, we assessed relative risks of incident metabolic diseases/risk factors at the time of the second follow-up survey of S4 (termed FF4) and H. pylori seropositivity at the F4 survey as a determinant. Models were adjusted for age, sex, overweight status, physical activity, smoking status, education level, alcohol intake, and other metabolic diseases.Results: Based on 3,037 persons aged 32 to 82 years, the H. pylori prevalence for 2007 was 30.2% in men (n = 1,465) and 28.1% in women (n = 1,572). Increasing age, current smoking, low education and no alcohol intake were significantly associated with H. pylori seropositivity in the F4 survey. However, no association between H. pylori seropositivity and BMI, metabolic diseases (type 2 diabetes, hypertension and dyslipidemia, gout or increased uric acid) and gastrointestinal diseases (gastritis, inflammatory bowel disease, and gastric or duodenal ulcer) was observed. No significant associations between H. pylori seropositivity and one of the five investigated incident metabolic diseases/risk factors were detected in the longitudinal analysis.Conclusion: We identified associations between age, smoking, education and alcohol intake and H. pylori seropositivity but no impact of H. pylori seropositivity on incident metabolic diseases/risk factors

The SDO Education and Outreach (E/PO) Program: Changing Perceptions One Program at a Time

The Solar Dynamics Observatory (SDO) Education and Public Outreach (E/PO) program began as a series of discrete efforts implemented by each of the instrument teams and has evolved into a well-rounded program with a full suite of national and international programs. The SDO E/PO team has put forth much effort in the past few years to increase our cohesiveness by adopting common goals and increasing the amount of overlap between our programs. In this paper, we outline the context and overall philosophy for our combined programs, present a brief overview of all SDO E/PO programs along with more detailed highlight of a few key programs, followed by a review of our results up to date. Concluding is a summary of the successes, failures, and lessons learned that future missions can use as a guide, while further incorporating their own content to enhance the public's knowledge and appreciation of NASA?s science and technology as well as its benefit to society

The Solar Dynamics Observatory (SDO) Education and Outreach (E/PO) Program: Changing Perceptions One Program at a Time

We outline the context and overall philosophy for the combined Solar Dynamics Observatory (SDO) Education and Public Outreach (E/PO) program, present a brief overview of all SDO E/PO programs along with more detailed highlights of a few key programs, followed by a review of our results to date, conclude a summary of the successes, failures, and lessons learned, which future missions can use as a guide, while incorporating their own content to enhance the public's knowledge and appreciation of science and technology as well as its benefit to society

Comparison of Population-Based Association Study Methods Correcting for Population Stratification

Population stratification can cause spurious associations in population–based association studies. Several statistical methods have been proposed to reduce the impact of population stratification on population–based association studies. We simulated a set of stratified populations based on the real haplotype data from the HapMap ENCODE project, and compared the relative power, type I error rates, accuracy and positive prediction value of four prevailing population–based association study methods: traditional case-control tests, structured association (SA), genomic control (GC) and principal components analysis (PCA) under various population stratification levels. Additionally, we evaluated the effects of sample sizes and frequencies of disease susceptible allele on the performance of the four analytical methods in the presence of population stratification. We found that the performance of PCA was very stable under various scenarios. Our comparison results suggest that SA and PCA have comparable performance, if sufficient ancestral informative markers are used in SA analysis. GC appeared to be strongly conservative in significantly stratified populations. It may be better to apply GC in the stratified populations with low stratification level. Our study intends to provide a practical guideline for researchers to select proper study methods and make appropriate inference of the results in population-based association studies

Intrinsic Capability of Budding Yeast Cofilin to Promote Turnover of Tropomyosin-Bound Actin Filaments

The ability of actin filaments to function in cell morphogenesis and motility is closely coupled to their dynamic properties. Yeast cells contain two prominent actin structures, cables and patches, both of which are rapidly assembled and disassembled. Although genetic studies have shown that rapid actin turnover in patches and cables depends on cofilin, how cofilin might control cable disassembly remains unclear, because tropomyosin, a component of actin cables, is thought to protect actin filaments against the depolymerizing activity of ADF/cofilin. We have identified cofilin as a yeast tropomyosin (Tpm1) binding protein through Tpm1 affinity column and mass spectrometry. Using a variety of assays, we show that yeast cofilin can efficiently depolymerize and sever yeast actin filaments decorated with either Tpm1 or mouse tropomyosins TM1 and TM4. Our results suggest that yeast cofilin has the intrinsic ability to promote actin cable turnover, and that the severing activity may rely on its ability to bind Tpm1

Neural networks for modeling gene-gene interactions in association studies

<p>Abstract</p> <p>Background</p> <p>Our aim is to investigate the ability of neural networks to model different two-locus disease models. We conduct a simulation study to compare neural networks with two standard methods, namely logistic regression models and multifactor dimensionality reduction. One hundred data sets are generated for each of six two-locus disease models, which are considered in a low and in a high risk scenario. Two models represent independence, one is a multiplicative model, and three models are epistatic. For each data set, six neural networks (with up to five hidden neurons) and five logistic regression models (the null model, three main effect models, and the full model) with two different codings for the genotype information are fitted. Additionally, the multifactor dimensionality reduction approach is applied.</p> <p>Results</p> <p>The results show that neural networks are more successful in modeling the structure of the underlying disease model than logistic regression models in most of the investigated situations. In our simulation study, neither logistic regression nor multifactor dimensionality reduction are able to correctly identify biological interaction.</p> <p>Conclusions</p> <p>Neural networks are a promising tool to handle complex data situations. However, further research is necessary concerning the interpretation of their parameters.</p