BackgroundBatch effects are a persistent and pervasive form of measurement noise which undermine the scientific utility of high-throughput genomic datasets. At their most benign, they reduce the power of statistical tests resulting in actual effects going unidentified. At their worst, they constitute confounds and render datasets useless. Attempting to remove batch effects will result in some of the biologically meaningful component of the measurement (i.e. signal) being lost. We present and benchmark a novel technique, called Harman. Harman maximises the removal of batch noise with the constraint that the risk of also losing biologically meaningful component of the measurement is kept to a fraction which is set by the user.ResultsAnalyses of three independent publically available datasets reveal that Harman removes more batch noise and preserves more signal at the same time, than the current leading technique. Results also show that Harman is able to identify and remove batch effects no matter what their relative size compared to other sources of variation in the dataset. Of particular advantage for meta-analyses and data integration is Harman’s superior consistency in achieving comparable noise suppression - signal preservation trade-offs across multiple datasets, with differing number of treatments, replicates and processing batches.ConclusionHarman’s ability to better remove batch noise, and better preserve biologically meaningful signal simultaneously within a single study, and maintain the user-set trade-off between batch noise rejection and signal preservation across different studies makes it an effective alternative method to deal with batch effects in high-throughput genomic datasets. Harman is flexible in terms of the data types it can process. It is available publically as an R package (https://bioconductor.org/packages/release/bioc/html/Harman.html), as well as a compiled Matlab package (http://www.bioinformatics.csiro.au/harman/) which does not require a Matlab license to run.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1212-5) contains supplementary material, which is available to authorized users.

BackgroundThe application of sunscreen is a critical component of a sun-safe strategy, however the possibility of unexpected, adverse outcomes resulting from long-term use of sunscreens containing nanoparticles of titanium dioxide (TiO2) and zinc oxide (ZnO) has not yet been examined. Here, immune-competent hairless mice were exposed over a 36-week period to weekly topical applications of sunscreens containing nanoparticles of ZnO or TiO2, or no metal oxide nanoparticles, with or without subsequent exposure to ultraviolet radiation (UVR). Control groups received no sunscreen applications, with or without UVR.ResultsMice exposed to UVR in the absence of sunscreen developed statistically significant incidences of histologically-diagnosed malignant and benign skin neoplasms, whereas no statistically significant adverse biological outcomes were found in mice treated with the sunscreens containing ZnO or TiO2 nanoparticles. Elevated levels of Ti were detected in the livers of mice treated with sunscreen containing TiO2 nanoparticles compared to untreated control, but total Zn concentrations did not significantly alter in any major organs except for the skin of mice treated with ZnO sunscreen. Exposure to UVR did not have a significant impact on examined tissue concentrations of Zn or Ti. Few to no transcriptional changes were found in ZnO or TiO2-treated groups, but mice treated with the sunscreen containing only organic filters showed substantial gene disregulation.ConclusionsTaken together with previous work, this long-term study provided no basis to avoid the use of sunscreens containing metal oxide nanoparticles.

The exponential growth of the Web documents has constituted the need for automatic document summarization. In this context, extractive document summarization, i.e., that task of extracting the most relevant information, removing redundancy and presenting the remained data in a coherent and cohesive structure, is a challenging task. In this paper, we propose a novel intelligent approach, namely ExDoS, that harvests benefits of both supervised and unsupervised algorithms simultaneously. To the best of our knowledge, ExDoS is the first approach to combine both supervised and unsupervised algorithms in a single framework and an interpretable manner for document summarization purpose. ExDoS iteratively minimizes the error rate of the classifier in each cluster with the help of dynamic local feature weighting. Moreover, this approach specifies the contribution of features to discriminate each class, which is a challenging issue in the summarization task. Therefore, in addition to summarizing text, ExDoS is also able to measure the importance of each feature in the summarization process. We evaluate our model both automatically (in terms of ROUGE factor) and empirically (human analysis) on the benchmark datasets: the DUC2002 and CNN/DailyMail. Results show that our model obtains higher ROUGE scores comparing to most state-of-the-art models. The human evaluation also demonstrates that our model is capable of generating informative and readable summaries.

No abstract

Abstract:The use of multiple antennas at both transmitting and receiving sides of communication channel has increased the spectral efficiency to near the Shannon bound. However algorithmic complexity in the realization of the receiver is a major problem for its hardware implementation. In this paper we investigate a near optimal algorithm for V-BLAST detection in MIMO wireless communication systems based on QR factorization, offering remarkable reduction in the hardware complexity.Specifically, we analyze some hardware implementation aspects of the selected algorithm through MATLAB simulations and demonstrate its robustness. This technique can be used in an efficient fixed point VLSI implementation of the algorithm.