Spatio-temporal forecasting using wavelet transform-based decision trees with application to air quality and covid-19 forecasting.

We develop a new method that combines a decision tree with a wavelet transform to forecast time series data with spatial spillover effects. The method can not only improve prediction but also give good interpretability of the time series mechanism. As a feature exploration method, the wavelet transform represents information at different resolution levels, which may improve the performance of decision trees. The method is applied to simulated data, air pollution and COVID time series data sets. In the simulation, Haar, LA8, D4 and D6 wavelets are compared, with the Haar wavelet having the best performance. In the air pollution application, by using wavelet transform-based decision trees, the temporal effect of air quality index including autoregressive and seasonal effects can be described as well as the spatial correlation effect. To describe the spillover spatial effect in contiguous regions, a spatial weight is constructed to improve the modeling performance. The results show that air quality index has autoregressive, seasonal and spatial spillover effects. The wavelet transformed variables have a better forecasting performance and enhanced interpretability than the original variables. For the COVID time series of cumulative cases, spatial weighted variables are not selected which shows the lock-down policies are truly effective.

Sparse modelling of cancer patients' survival based on genomic copy number alterations.

Copy number alterations (CNA) are structural variation in the genome, in which some regions exhibit more or less than the normal two chromosomal copies. This genomic CNA profile provides critical information in tumour progression and is therefore informative for patients' survival. It is currently a statistical challenge to model patients' survival using their genomic CNA profiles while at the same time identify regions in the genome that are associated with patients' survival. Some methods have been proposed, including Cox proportional hazard (PH) model with ridge, lasso, or elastic net penalties. However, these methods do not take the general dependencies between genomic regions into account and produce results that are difficult to interpret. In this paper, we extend the elastic net penalty by introducing additional penalty that takes into account general dependencies between genomic regions. This new model produces smooth parameter estimates while simultaneously performs variable selection via sparse solution. The results indicate that the proposed method shows a better prediction performance than other models in our simulation study, while enabling us to investigate regions in the genome that are associated with the patients' survival with sensible interpretation. We illustrate the method using a real dataset from a lung cancer cohort and simulated data.

Density estimation for circular data observed with errors.

Until now the problem of estimating circular densities when data are observed with errors has been mainly treated by Fourier series methods. We propose kernel-based estimators exhibiting simple construction and easy implementation. Specifically, we consider three different approaches: the first one is based on the equivalence between kernel estimators using data corrupted with different levels of error. This proposal appears to be totally unexplored, despite its potential for application also in the Euclidean setting. The second approach relies on estimators whose weight functions are circular deconvolution kernels. Due to the periodicity of the involved densities, it requires ad hoc mathematical tools. Finally, the third one is based on the idea of correcting extra bias of kernel estimators which use contaminated data and is essentially an adaptation of the standard theory to the circular case. For all the proposed estimators, we derive asymptotic properties, provide some simulation results, and also discuss some possible generalizations and extensions. Real data case studies are also included.

Geometry-based distance for clustering amino acids.

Clustering amino acids is one of the most challenging problems in functional and structural prediction of protein. Previous studies have proposed clusters based on measurements of physical and biochemical characteristics of the amino acids such as volume, area, hydrophilicity, polarity, hydrogen bonding, shape, and charge. These characteristics, although important, are less directly related to the protein structure compared to geometrical characteristics such as dihedral angles between amino acids. We propose using the p-value from a test of equality of dihedral-angle distributions as the basis of a distance measure for the clustering. In this novel approach, an energy test is modified to deal with bivariate angular data and the p-value is obtained via a permutation method. The results indicate that the clusters of amino acids have sensible interpretation where Glycine, Proline, and Asparagine each forms a distinct cluster. A simulation study suggests that this approach has good working characteristics to cluster amino acids.

Fluid shear stress stimulates breast cancer cells to display invasive and chemoresistant phenotypes while upregulating PLAU in a 3D bioreactor.

Breast cancer cells experience a range of shear stresses in the tumor microenvironment (TME). However most current in vitro three-dimensional (3D) models fail to systematically probe the effects of this biophysical stimuli on cancer cell metastasis, proliferation, and chemoresistance. To investigate the roles of shear stress within the mammary and lung pleural effusion TME, a bioreactor capable of applying shear stress to cells within a 3D extracellular matrix was designed and characterized. Breast cancer cells were encapsulated within an interpenetrating network hydrogel and subjected to shear stress of 5.4 dynes cm-2 for 72 hr. Finite element modeling assessed shear stress profiles within the bioreactor. Cells exposed to shear stress had significantly higher cellular area and significantly lower circularity, indicating a motile phenotype. Stimulated cells were more proliferative than static controls and showed higher rates of chemoresistance to the anti-neoplastic drug paclitaxel. Fluid shear stress-induced significant upregulation of the PLAU gene and elevated urokinase activity was confirmed through zymography and activity assay. Overall, these results indicate that pulsatile shear stress promotes breast cancer cell proliferation, invasive potential, chemoresistance, and PLAU signaling.

Estimating optimal window size for analysis of low-coverage next-generation sequence data.

MOTIVATION: Current high-throughput sequencing has greatly transformed genome sequence analysis. In the context of very low-coverage sequencing (<0.1×), performing 'binning' or 'windowing' on mapped short sequences ('reads') is critical to extract genomic information of interest for further evaluation, such as copy-number alteration analysis. If the window size is too small, many windows will exhibit zero counts and almost no pattern can be observed. In contrast, if the window size is too wide, the patterns or genomic features will be 'smoothed out'. Our objective is to identify an optimal window size in between the two extremes. RESULTS: We assume the reads density to be a step function. Given this model, we propose a data-based estimation of optimal window size based on Akaike's information criterion (AIC) and cross-validation (CV) log-likelihood. By plotting the AIC and CV log-likelihood curve as a function of window size, we are able to estimate the optimal window size that minimizes AIC or maximizes CV log-likelihood. The proposed methods are of general purpose and we illustrate their application using low-coverage next-generation sequence datasets from real tumour samples and simulated datasets. AVAILABILITY AND IMPLEMENTATION: An R package to estimate optimal window size is available at http://www1.maths.leeds.ac.uk/∼arief/R/win/.

A generative, probabilistic model of local protein structure.

Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence-structure correlations in the native state. Our method represents a significant theoretical and practical improvement over the widely used fragment assembly technique by avoiding the drawbacks associated with a discrete and nonprobabilistic approach.

Protein bioinformatics and mixtures of bivariate von Mises distributions for angular data.

A fundamental problem in bioinformatics is to characterize the secondary structure of a protein, which has traditionally been carried out by examining a scatterplot (Ramachandran plot) of the conformational angles. We examine two natural bivariate von Mises distributions--referred to as Sine and Cosine models--which have five parameters and, for concentrated data, tend to a bivariate normal distribution. These are analyzed and their main properties derived. Conditions on the parameters are established which result in bimodal behavior for the joint density and the marginal distribution, and we note an interesting situation in which the joint density is bimodal but the marginal distributions are unimodal. We carry out comparisons of the two models, and it is seen that the Cosine model may be preferred. Mixture distributions of the Cosine model are fitted to two representative protein datasets using the expectation maximization algorithm, which results in an objective partition of the scatterplot into a number of components. Our results are consistent with empirical observations; new insights are discussed.

Air ionisation and colonisation/infection with methicillin-resistant Staphylococcus aureus and Acinetobacter species in an intensive care unit.

OBJECTIVE: To determine effect of negative air ions on colonisation/infection with methicillin-resistant Staphylococcus aureus (MRSA) and Acinetobacter species in an intensive care unit. DESIGN: Prospective single-centre cross-over study in an adult general intensive care unit. PATIENTS: 201 patients whose stay on the unit exceeded 48 hour's duration. INTERVENTION: Six negative air ionisers were installed on the unit but not operational for the first 5 months of the study (control period). Devices were then operational for the following 5.5 months. MEASUREMENTS AND RESULTS: 30 and 13 patients were colonised/infected with MRSA and Acinetobacter spp., respectively, over 10.5 months. No change in MRSA colonisation/infection was observed compared with the 5 month control period. Acinetobacter cases were reduced from 11 to 2 (p=0.007). CONCLUSION: Ionisers may have a role in the prevention of Acinetobacter infections.