Identification of SNP-containing regulatory motifs in the myelodysplastic syndromes model using SNP arrays ad gene expression arrays

Myelodysplastic syndromes have increased in frequency and incidence in the American population, but patient prognosis has not significantly improved over the last decade. Such improvements could be realized if biomarkers for accurate diagnosis and prognostic stratification were successfully identified. In this study, we propose a method that associates two state-of-the-art array technologies-single nucleotide polymor-phism (SNP) array and gene expression array-with gene motifs considered transcription factor-binding sites (TFBS). We are particularly interested in SNP-containing motifs introduced by genetic variation and mutation as TFBS. The potential regulation of SNP-containing motifs affects only when certain mutations occur. These motifs can be identified from a group of co-expressed genes with copy number variation. Then, we used a sliding window to identify motif candidates near SNPs on gene sequences. The candidates were filtered by coarse thresholding and fine statistical testing. Using the regression. based LARS-EN algorithm and a level. wise sequence combination procedure, we identified 28 SNP-containing motifs as candidate TFBS. We confirmed 21 of the 28 motifs with ChIP-chip fragments in the TRANSFAC database. Another six motifs were validated by TRANSFAC via searching binding fragments on co-regulated genes. The identified motifs and their location genes can be considered potential biomarkers for myelodysplastic syndromes. Thus, our proposed method, a novel strategy for associating two data categories, is capable of integrating information from different sources to identify reliable candidate regulatory SNP-containing motifs introduced by genetic variation and mutation

A hierarchical method for multi-class support vector machines

We introduce a framework, which we call Divide-by-2 (DB2), for extending support vector machines (SVM) to multi-class prob-lems. DB2 offers an alternative to the stan-dard one-against-one and one-against-rest al-gorithms. For an N class problem, DB2 pro-duces an N − 1 node binary decision tree where nodes represent decision boundaries formed by N−1 SVM binary classifiers. This tree structure allows us to present a gener-alization and a time complexity analysis of DB2. Our analysis and related experiments show that, DB2 is faster than one-against-one and one-against-rest algorithms in terms of testing time, significantly faster than one-against-rest in terms of training time, and that the cross-validation accuracy of DB2 is comparable to these two methods. 1

Explainable deep learning for insights in El Ni\~no and river flows

The El Ni\~no Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections

Acute effects of nicotine on visual search tasks in young adult smokers

Rationale Nicotine is known to improve performance on tests involving sustained attention and recent research suggests that nicotine may also improve performance on tests involving the strategic allocation of attention and working memory. Objectives We used measures of accuracy and response latency combined with eye-tracking techniques to examine the effects of nicotine on visual search tasks. Methods In experiment 1 smokers and non-smokers performed pop-out and serial search tasks. In experiment 2, we used a within-subject design and a more demanding search task for multiple targets. In both studies, 2-h abstinent smokers were asked to smoke one of their own cigarettes between baseline and tests. Results In experiment 1, pop-out search times were faster after nicotine, without a loss in accuracy. Similar effects were observed for serial searches, but these were significant only at a trend level. In experiment 2, nicotine facilitated a strategic change in eye movements resulting in a higher proportion of fixations on target letters. If the cigarette was smoked on the first trial (when the task was novel), nicotine additionally reduced the total number of fixations and refixations on all letters in the display. Conclusions Nicotine improves visual search performance by speeding up search time and enabling a better focus of attention on task relevant items. This appears to reflect more efficient inhibition of eye movements towards task irrelevant stimuli, and better active maintenance of task goals. When the task is novel, and therefore more difficult, nicotine lessens the need to refixate previously seen letters, suggesting an improvement in working memory