Enhanced sequential carrier capture into individual quantum dots and quantum posts controlled by surface acoustic waves

Individual self-assembled Quantum Dots and Quantum Posts are studied under the influence of a surface acoustic wave. In optical experiments we observe an acoustically induced switching of the occupancy of the nanostructures along with an overall increase of the emission intensity. For Quantum Posts, switching occurs continuously from predominantely charged excitons (dissimilar number of electrons and holes) to neutral excitons (same number of electrons and holes) and is independent on whether the surface acoustic wave amplitude is increased or decreased. For quantum dots, switching is non-monotonic and shows a pronounced hysteresis on the amplitude sweep direction. Moreover, emission of positively charged and neutral excitons is observed at high surface acoustic wave amplitudes. These findings are explained by carrier trapping and localization in the thin and disordered two-dimensional wetting layer on top of which Quantum Dots nucleate. This limitation can be overcome for Quantum Posts where acoustically induced charge transport is highly efficient in a wide lateral Matrix-Quantum Well.Comment: 11 pages, 5 figure

Stokes' Drift of linear Defects

A linear defect, viz. an elastic string, diffusing on a planar substrate traversed by a travelling wave experiences a drag known as Stokes' drift. In the limit of an infinitely long string, such a mechanism is shown to be characterized by a sharp threshold that depends on the wave parameters, the string damping constant and the substrate temperature. Moreover, the onset of the Stokes' drift is signaled by an excess diffusion of the string center of mass, while the dispersion of the drifting string around its center of mass may grow anomalous.Comment: 14 pages, no figures, to be published in Phys.Rev.

A robust measure of correlation between two genes on a microarray

<p>Abstract</p> <p>Background</p> <p>The underlying goal of microarray experiments is to identify gene expression patterns across different experimental conditions. Genes that are contained in a particular pathway or that respond similarly to experimental conditions could be co-expressed and show similar patterns of expression on a microarray. Using any of a variety of clustering methods or gene network analyses we can partition genes of interest into groups, clusters, or modules based on measures of similarity. Typically, Pearson correlation is used to measure distance (or similarity) before implementing a clustering algorithm. Pearson correlation is quite susceptible to outliers, however, an unfortunate characteristic when dealing with microarray data (well known to be typically quite noisy.)</p> <p>Results</p> <p>We propose a resistant similarity metric based on Tukey's biweight estimate of multivariate scale and location. The resistant metric is simply the correlation obtained from a resistant covariance matrix of scale. We give results which demonstrate that our correlation metric is much more resistant than the Pearson correlation while being more efficient than other nonparametric measures of correlation (e.g., Spearman correlation.) Additionally, our method gives a systematic gene flagging procedure which is useful when dealing with large amounts of noisy data.</p> <p>Conclusion</p> <p>When dealing with microarray data, which are known to be quite noisy, robust methods should be used. Specifically, robust distances, including the biweight correlation, should be used in clustering and gene network analysis.</p

A Comprehensive and Universal Method for Assessing the Performance of Differential Gene Expression Analyses

The number of methods for pre-processing and analysis of gene expression data continues to increase, often making it difficult to select the most appropriate approach. We present a simple procedure for comparative estimation of a variety of methods for microarray data pre-processing and analysis. Our approach is based on the use of real microarray data in which controlled fold changes are introduced into 20% of the data to provide a metric for comparison with the unmodified data. The data modifications can be easily applied to raw data measured with any technological platform and retains all the complex structures and statistical characteristics of the real-world data. The power of the method is illustrated by its application to the quantitative comparison of different methods of normalization and analysis of microarray data. Our results demonstrate that the method of controlled modifications of real experimental data provides a simple tool for assessing the performance of data preprocessing and analysis methods

Assessing Monkeypox Virus Prevalence in Small Mammals at the Human-Animal Interface in the Democratic Republic of the Congo

During 2012, 2013 and 2015, we collected small mammals within 25 km of the town of Boende in Tshuapa Province, the Democratic Republic of the Congo. The prevalence of monkeypox virus (MPXV) in this area is unknown; however, cases of human infection were previously confirmed near these collection sites. Samples were collected from 353 mammals (rodents, shrews, pangolins, elephant shrews, a potamogale, and a hyrax). Some rodents and shrews were captured from houses where human monkeypox cases have recently been identified, but most were trapped in forests and agricultural areas near villages. Real-time PCR and ELISA were used to assess evidence of MPXV infection and other Orthopoxvirus (OPXV) infections in these small mammals. Seven (2.0%) of these animal samples were found to be anti-orthopoxvirus immunoglobulin G (IgG) antibody positive (six rodents: two Funisciurus spp.; one Graphiurus lorraineus; one Cricetomys emini; one Heliosciurus sp.; one Oenomys hypoxanthus, and one elephant shrew Petrodromus tetradactylus); no individuals were found positive in PCR-based assays. These results suggest that a variety of animals can be infected with OPXVs, and that epidemiology studies and educational campaigns should focus on animals that people are regularly contacting, including larger rodents used as protein sources

Empirical Bayes models for multiple probe type microarrays at the probe level

<p>Abstract</p> <p>Background</p> <p>When analyzing microarray data a primary objective is often to find differentially expressed genes. With empirical Bayes and penalized t-tests the sample variances are adjusted towards a global estimate, producing more stable results compared to ordinary t-tests. However, for Affymetrix type data a clear dependency between variability and intensity-level generally exists, even for logged intensities, most clearly for data at the probe level but also for probe-set summarizes such as the MAS5 expression index. As a consequence, adjustment towards a global estimate results in an intensity-level dependent false positive rate.</p> <p>Results</p> <p>We propose two new methods for finding differentially expressed genes, Probe level Locally moderated Weighted median-t (PLW) and Locally Moderated Weighted-t (LMW). Both methods use an empirical Bayes model taking the dependency between variability and intensity-level into account. A global covariance matrix is also used allowing for differing variances between arrays as well as array-to-array correlations. PLW is specially designed for Affymetrix type arrays (or other multiple-probe arrays). Instead of making inference on probe-set summaries, comparisons are made separately for each perfect-match probe and are then summarized into one score for the probe-set.</p> <p>Conclusion</p> <p>The proposed methods are compared to 14 existing methods using five spike-in data sets. For RMA and GCRMA processed data, PLW has the most accurate ranking of regulated genes in four out of the five data sets, and LMW consistently performs better than all examined moderated t-tests when used on RMA, GCRMA, and MAS5 expression indexes.</p

Overexpression of S100A4 in human cancer cell lines resistant to methotrexate

Methotrexate is a chemotherapeutic drug that is used in therapy of a wide variety of cancers. The efficiency of treatment with this drug is compromised by the appearance of resistance. Combination treatments of MTX with other drugs that could modulate the expression of genes involved in MTX resistance would be an adequate strategy to prevent the development of this resistance. Methods: The differential expression pattern between sensitive and MTX-resistant cells was determined by whole human genome microarrays and analyzed with the GeneSpring GX software package. A global comparison of all the studied cell lines was performed in order to find out differentially expressed genes in the majority of the MTX-resistant cells. S100A4 mRNA and protein levels were determined by RT-Real-Time PCR and Western blot, respectively. Functional validations of S100A4 were performed either by transfection of an expression vector for S100A4 or a siRNA against S100A4. Transfection of an expression vector encoding for β-catenin was used to inquire for the possible transcriptional regulation of S100A4 through the Wnt pathway. Results: S100A4 is overexpressed in five out of the seven MTX-resistant cell lines studied. Ectopic overexpression of this gene in HT29 sensitive cells augmented both the intracellular and extracellular S100A4 protein levels and caused desensitization toward MTX. siRNA against S100A4 decreased the levels of this protein and caused a chemosensitization in combined treatments with MTX. β-catenin overexpression experiments support a possible involvement of the Wnt signaling pathway in S100A4 transcriptional regulation in HT29 cells. Conclusions: S100A4 is overexpressed in many MTX-resistant cells. S100A4 overexpression decreases the sensitivity of HT29 colon cancer human cells to MTX, whereas its knockdown causes chemosensitization toward MTX. Both approaches highlight a role for S100A4 in MTX resistanc