Proton capture cross section of Sr isotopes and their importance for nucleosynthesis of proton-rich nuclides

The (p,$\gamma$) cross sections of three stable Sr isotopes have been measured in the astrophysically relevant energy range. These reactions are important for the $p$-process in stellar nucleosynthesis and, in addition, the reaction cross sections in the mass region up to 100 are also of importance concerning the $rp$-process associated with explosive hydrogen and helium burning. It is speculated that this $rp$-process could be responsible for a certain amount of $p$-nuclei in this mass region. The (p,$\gamma$) cross sections of $^{84,86,87}$Sr isotopes were determined using an activation technique. The measurements were carried out at the 5 MV Van de Graaff accelerator of the ATOMKI, Debrecen. The resulting cross sections are compared with the predictions of statistical model calculations. The predictions are in good agreement with the experimental results for $^{84}$Sr(p,$\gamma$)$^{85}$Y whereas the other two reactions exhibit differences that increase with mass number. The corresponding astrophysical reaction rates have also been computed.Comment: Phys. Rev. C in pres

Highly site-specific H2 adsorption on vicinal Si(001) surfaces

Experimental and theoretical results for the dissociative adsorption of H_2 on vicinal Si(001) surfaces are presented. Using optical second-harmonic generation, sticking probabilities at the step sites are found to exceed those on the terraces by up to six orders of magnitude. Density functional theory calculations indicate the presence of direct adsorption pathways for monohydride formation but with a dramatically lowered barrier for step adsorption due to an efficient rehybridization of dangling orbitals.Comment: 5 pages, 4 figures, submitted to Phys. Rev. Lett. (1998). Other related publications can be found at http://www.fhi-berlin.mpg.de/th/paper.htm

GeneSrF and varSelRF: a web-based tool and R package for gene selection and classification using random forest

<p>Abstract</p> <p>Background</p> <p>Microarray data are often used for patient classification and gene selection. An appropriate tool for end users and biomedical researchers should combine user friendliness with statistical rigor, including carefully avoiding selection biases and allowing analysis of multiple solutions, together with access to additional functional information of selected genes. Methodologically, such a tool would be of greater use if it incorporates state-of-the-art computational approaches and makes source code available.</p> <p>Results</p> <p>We have developed GeneSrF, a web-based tool, and varSelRF, an R package, that implement, in the context of patient classification, a validated method for selecting very small sets of genes while preserving classification accuracy. Computation is parallelized, allowing to take advantage of multicore CPUs and clusters of workstations. Output includes bootstrapped estimates of prediction error rate, and assessments of the stability of the solutions. Clickable tables link to additional information for each gene (GO terms, PubMed citations, KEGG pathways), and output can be sent to PaLS for examination of PubMed references, GO terms, KEGG and and Reactome pathways characteristic of sets of genes selected for class prediction. The full source code is available, allowing to extend the software. The web-based application is available from <url>http://genesrf2.bioinfo.cnio.es</url>. All source code is available from Bioinformatics.org or The Launchpad. The R package is also available from CRAN.</p> <p>Conclusion</p> <p>varSelRF and GeneSrF implement a validated method for gene selection including bootstrap estimates of classification error rate. They are valuable tools for applied biomedical researchers, specially for exploratory work with microarray data. Because of the underlying technology used (combination of parallelization with web-based application) they are also of methodological interest to bioinformaticians and biostatisticians.</p

Pseudo-single crystal electrochemistry on polycrystalline electrodes : visualizing activity at grains and grain boundaries on platinum for the Fe2+/Fe3+ redox reaction

The influence of electrode surface structure on electrochemical reaction rates and mechanisms is a major theme in electrochemical research, especially as electrodes with inherent structural heterogeneities are used ubiquitously. Yet, probing local electrochemistry and surface structure at complex surfaces is challenging. In this paper, high spatial resolution scanning electrochemical cell microscopy (SECCM) complemented with electron backscatter diffraction (EBSD) is demonstrated as a means of performing ‘pseudo-single-crystal’ electrochemical measurements at individual grains of a polycrystalline platinum electrode, while also allowing grain boundaries to be probed. Using the Fe2+/3+ couple as an illustrative case, a strong correlation is found between local surface structure and electrochemical activity. Variations in electrochemical activity for individual high index grains, visualized in a weakly adsorbing perchlorate medium, show that there is higher activity on grains with a significant (101) orientation contribution, compared to those with (001) and (111) contribution, consistent with findings on single-crystal electrodes. Interestingly, for Fe2+ oxidation in a sulfate medium a different pattern of activity emerges. Here, SECCM reveals only minor variations in activity between individual grains, again consistent with single-crystal studies, with a greatly enhanced activity at grain boundaries. This suggests that these sites may contribute significantly to the overall electrochemical behavior measured on the macroscale

Expanding the Understanding of Biases in Development of Clinical-Grade Molecular Signatures: A Case Study in Acute Respiratory Viral Infections

The promise of modern personalized medicine is to use molecular and clinical information to better diagnose, manage, and treat disease, on an individual patient basis. These functions are predominantly enabled by molecular signatures, which are computational models for predicting phenotypes and other responses of interest from high-throughput assay data. Data-analytics is a central component of molecular signature development and can jeopardize the entire process if conducted incorrectly. While exploratory data analysis may tolerate suboptimal protocols, clinical-grade molecular signatures are subject to vastly stricter requirements. Closing the gap between standards for exploratory versus clinically successful molecular signatures entails a thorough understanding of possible biases in the data analysis phase and developing strategies to avoid them.Using a recently introduced data-analytic protocol as a case study, we provide an in-depth examination of the poorly studied biases of the data-analytic protocols related to signature multiplicity, biomarker redundancy, data preprocessing, and validation of signature reproducibility. The methodology and results presented in this work are aimed at expanding the understanding of these data-analytic biases that affect development of clinically robust molecular signatures.Several recommendations follow from the current study. First, all molecular signatures of a phenotype should be extracted to the extent possible, in order to provide comprehensive and accurate grounds for understanding disease pathogenesis. Second, redundant genes should generally be removed from final signatures to facilitate reproducibility and decrease manufacturing costs. Third, data preprocessing procedures should be designed so as not to bias biomarker selection. Finally, molecular signatures developed and applied on different phenotypes and populations of patients should be treated with great caution

SignS: a parallelized, open-source, freely available, web-based tool for gene selection and molecular signatures for survival and censored data

<p>Abstract</p> <p>Background</p> <p>Censored data are increasingly common in many microarray studies that attempt to relate gene expression to patient survival. Several new methods have been proposed in the last two years. Most of these methods, however, are not available to biomedical researchers, leading to many re-implementations from scratch of ad-hoc, and suboptimal, approaches with survival data.</p> <p>Results</p> <p>We have developed SignS (Signatures for Survival data), an open-source, freely-available, web-based tool and R package for gene selection, building molecular signatures, and prediction with survival data. SignS implements four methods which, according to existing reviews, perform well and, by being of a very different nature, offer complementary approaches. We use parallel computing via MPI, leading to large decreases in user waiting time. Cross-validation is used to asses predictive performance and stability of solutions, the latter an issue of increasing concern given that there are often several solutions with similar predictive performance. Biological interpretation of results is enhanced because genes and signatures in models can be sent to other freely-available on-line tools for examination of PubMed references, GO terms, and KEGG and Reactome pathways of selected genes.</p> <p>Conclusion</p> <p>SignS is the first web-based tool for survival analysis of expression data, and one of the very few with biomedical researchers as target users. SignS is also one of the few bioinformatics web-based applications to extensively use parallelization, including fault tolerance and crash recovery. Because of its combination of methods implemented, usage of parallel computing, code availability, and links to additional data bases, SignS is a unique tool, and will be of immediate relevance to biomedical researchers, biostatisticians and bioinformaticians.</p