Stochastic point process models for Next Generation Sequencing

<p>The Next Generation Sequencing (NGS) revolutionized the quality and quantity of the genetic data delivered. To extract all the benefits of the new technique there is an urge of precise inference rules built from a strong theoretical basis. In the presentation I will provide a novel, extended way of looking at NGS data. The NGS experiment can<br>be interpreted as a process of mapping short fragments of sequences (short reads) to a genome region of interest (exon , gene, gene family or even whole chromosome) and the activity of a region, is derived from the number of successful mappings. The increased reliability and the design of the NGS experiments allows for a more sophisticated<br>mathematical framework which uses not only the intensity of expression but also the position of particular reads aligned to the genomic region. To account for both aspects, in my presentation I introduce the Poisson point process framework for the NGS experiments. In this approach the reference genome coordinate information of the mapped reads implies that the differences in activity can arise also in changes of read positioning. Using the<br>inference tools for stochastic point processes combined with functional data analysis I provide a method to quantify the activity differences in terms of both - the intensity and positioning - through the phase-amplitude separation. As a consequence I  revisit the problem of the variability in NGS data and indicate, how it can be understood through the phase-amplitude dichotomy. Finally I will show that the new approach can reveal additional<br>information in the genetic data. The proposed method can be effectively utilized in detecting events of alternative splicing, exon blocking, exon skipping, can be also thought of as a new setting for inference on NGS data.</p

A Comparison of Euclidean metrics in spike train space

<p>Spike trains are observables when investigating neural activity - represent the response of a neuron to stimuli and are often modeled as realizations of stochastic point processes. The spike train space is non-euclidean, recently, however, two L 2<br>- like distances were introduced on that space:<br>the Elastic distance and Generalized Victor-Purpura (GVP) distance.</p> <p><br>On this poster we briefly review these two distances and run several comparisons, including construction of the summary statistics, corresponding in ideas to mean and variance as well as classification capabilities. To allow comparisons between<br>metrics we propose an efficient algorithm for GVP summary statistics.</p

Seniority Number in Valence Bond Theory

In this work, a hierarchy of valence bond (VB) methods based on the concept of seniority number, defined as the number of singly occupied orbitals in a determinant or an orbital configuration, is proposed and applied to the studies of the potential energy curves (PECs) of H₈, N₂, and C₂ molecules. It is found that the seniority-based VB expansion converges more rapidly toward the full configuration interaction (FCI) or complete active space self-consistent field (CASSCF) limit and produces more accurate PECs with smaller nonparallelity errors than its molecular orbital (MO) theory-based analogue. Test results reveal that the nonorthogonal orbital-based VB theory provides a reverse but more efficient way to truncate the complete active Hilbert space by seniority numbers

A Comparison of Imputation Strategies for Ordinal Missing Data on Likert Scale Variables

<div><p>This article compares a variety of imputation strategies for ordinal missing data on Likert scale variables (number of categories = 2, 3, 5, or 7) in recovering reliability coefficients, mean scale scores, and regression coefficients of predicting one scale score from another. The examined strategies include imputing using normal data models with naïve rounding/without rounding, using latent variable models, and using categorical data models such as discriminant analysis and binary logistic regression (for dichotomous data only), multinomial and proportional odds logistic regression (for polytomous data only). The result suggests that both the normal model approach without rounding and the latent variable model approach perform well for either dichotomous or polytomous data regardless of sample size, missing data proportion, and asymmetry of item distributions. The discriminant analysis approach also performs well for dichotomous data. Naïvely rounding normal imputations or using logistic regression models to impute ordinal data are not recommended as they can potentially lead to substantial bias in all or some of the parameters.</p></div

Additional file 2: Table S2. of Analysis of microRNA (miRNA) expression profiles reveals 11 key biomarkers associated with non-small cell lung cancer

TF list of miRNA targets. (XLSX 33 kb

Preparation of Few-Layer MoS₂ Nanosheets via an Efficient Shearing Exfoliation Method

In this paper, we selected a less studied high-speed dispersive homogenizer as a shear-exfoliating device and selected NMP which matches the surface energy of MoS₂ as a solvent to prepare few-layer MoS₂ nanosheets. The effects of operating parameters on the concentration of few-layer MoS₂ nanosheets were systematically studied. The results showed that the change of operating conditions has a direct influence on the exfoliation effects. The concentration of MoS₂ nanosheets was 0.96 mg/mL in pure NMP under the optimized conditions. The concentration reached 1.44 mg/mL, and the highest yield was 4.8% after adding sodium citrate. Particularly, their lateral size is about 50–200 nm, in which almost 65% of MoS₂ nanosheets are less than four layers and 9% are monolayer. It was verified that the as-used exfoliation method is simple and highly efficient

Visible Light Responsive Liquid Crystal Polymers Containing Reactive Moieties with Good Processability

Two types of novel reactive linear liquid crystal polymers (LLCPs) with different azotolene concentrations have been synthesized and processed into films and fibers by solution and melting processing methods. Then, the LLCPs in the obtained monodomain fiber and polydomain film were easily cross-linked with difunctional primary amines. The resulted cross-linked liquid crystal polymers (CLCPs) underwent reversible photoinduced bending and unbending behaviors in response to 445 and 530 nm visible light at room temperature, respectively. The post-cross-linking method provides a facile way to prepare the CLCP films and fibers with different shapes from LLCPs, which can be processed by traditional melting and solution methods

Field-Assisted Splitting of Pure Water Based on Deep-Sub-Debye-Length Nanogap Electrochemical Cells

Owing to the low conductivity of pure water, using an electrolyte is common for achieving efficient water electrolysis. In this paper, we have fundamentally broken through this common sense by using deep-sub-Debye-length nanogap electrochemical cells to achieve efficient electrolysis of pure water (without any added electrolyte) at room temperature. A field-assisted effect resulted from overlapped electrical double layers can greatly enhance water molecules ionization and mass transport, leading to electron-transfer limited reactions. We have named this process “virtual breakdown mechanism” (which is completely different from traditional mechanisms) that couples the two half-reactions together, greatly reducing the energy losses arising from ion transport. This fundamental discovery has been theoretically discussed in this paper and experimentally demonstrated in a group of electrochemical cells with nanogaps between two electrodes down to 37 nm. On the basis of our nanogap electrochemical cells, the electrolysis current density from pure water can be significantly larger than that from 1 mol/L sodium hydroxide solution, indicating the much better performance of pure water splitting as a potential for on-demand clean hydrogen production

Promotion of Innovative Entrepreneurship in Czechia: the Role of Science and Technology Parks

Czechia seeks to respond to the challenges coming with the development of knowledge-based economy. It builds up innovation infrastructure with the aim of filling the gap between research institutions and application sector, especially private firms. Tools lowering the barriers for the diffusion of technological progress include science and technology parks (STP). STP operate as a platform interconnecting actors involved in the innovation process, they initiate a knowledge transfer and provide specialized business services, all aimed at developing innovative entrepreneurship and research commercialization. Flood of new STP in Czechia draws attention to their particular characteristics and their efficiency. An extensive survey analyzes the role of Czech STP in innovative entrepreneurship development. The evaluation consists of two levels. The first describes the strategic profile of each of the parks and search for common characteristics. The other observes innovation activity of tenant firm separately for the period before and after joining the STP. In addition, the thesis discusses socio-economic context from which innovation infrastructure originates, and factors influencing its efficiency. Author distinguishes 4 specific types in the sample of 22 STP. Evidence of 78 tenant firms suggests a positive effect..

Spherical Regression Models Using Projective Linear Transformations

<div><p>This article studies the problem of modeling relationship between two spherical (or directional) random variables in a regression setup. Here the predictor and the response variables are constrained to be on a unit sphere and, due to this nonlinear condition, the standard Euclidean regression models do not apply. Several past papers have studied this problem, termed spherical regression, by modeling the response variable with a von Mises-Fisher (VMF) density with the mean given by a rotation of the predictor variable. The few papers that go beyond rigid rotations are limited to one- or two-dimensional spheres. This article extends the mean transformations to a larger group—the projective linear group of transformations—on unit spheres of arbitrary dimensions, while keeping the VMF density to model the noise. It develops a Newton–Raphson algorithm on the special linear group for estimating the MLE of regression parameter and establishes its asymptotic properties when the sample-size becomes large. Through a variety of experiments, using data taken from projective shape analysis, cloud tracking, etc., and some simulations, this article demonstrates improvements in the prediction and modeling performance of the proposed framework over previously used models. Supplementary materials for this article are available online.</p></div