Real-time localised forecasting of the Madden-Julian Oscillation using neural network models

Existing statistical forecast models of the Madden-Julian Oscillation (MJO) are generally of very low order and predict the evolution of a small number (typically two) of principal components (PCs). While such models are skilful up to 25 days lead time, by design they only predict the very largest-scale features of the MJO. Here we present a higher-order MJO statistical forecast model that is able to predict MJO variability on smaller, more localised scales, that will be of more direct benefit to national weather agencies and regional government planning. The model is based on daily outgoing long-wave radiation (OLR) data that are intraseasonally filtered using a recently developed technique of empirical mode decomposition that can be used in real time. A standard truncated PC analysis is then used to isolate the maximum amount of variance in a finite number of modes. The evolution of these modes is then forecast using a neural network model, which does not suffer from the parametrisation problems of other statistical forecast techniques when applied to a higher number of modes. Compared to a standard 2-PC model, the higher-order PC model showed improved skill over the whole MJO domain, with substantial improvements over the western Pacific, Arabian Sea, Bay of Bengal, South China Sea and Phillipine Sea

Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data

The availability of large quantities of transcriptomic data in the form of RNA-seq count data has necessitated the development of methods to identify genes differentially expressed between experimental conditions. Many existing approaches apply a parametric model of gene expression and so place strong assumptions on the distribution of the data. Here we explore an alternate nonparametric approach that applies an empirical likelihood framework, allowing us to define likelihoods without specifying a parametric model of the data. We demonstrate the performance of our method when applied to gold standard datasets, and to existing experimental data. Our approach outperforms or closely matches performance of existing methods in the literature, and requires modest computational resources. An R package, EmpDiff implementing the methods described in the paper is available from: http://homepages.inf.ed.ac.uk/tthorne/software/packages/EmpDiff_0.99.tar.gz

Real-time extraction of the Madden-Julian oscillation using empirical mode decomposition and statistical forecasting with a VARMA model

A simple guide to the new technique of empirical mode decomposition (EMD) in a meteorological-climate forecasting context is presented. A single application of EMD to a time series essentially acts as a local high-pass filter. Hence, successive applications can be used to produce a bandpass filter that is highly efficient at extracting a broadband signal such as the Madden-Julian Oscillation (MJO). The basic EMD method is adapted to minimize end effects, such that it is suitable for use in real time. The EMD process is then used to efficiently extract the MJO signal from gridded time series of outgoing longwave radiation (OLR) data. A range of statistical models from the general class of vector autoregressive moving average (VARMA) models was then tested for their suitability in forecasting the MJO signal, as isolated by the EMD. A VARMA (5, 1) model was selected and its parameters determined by a maximum likelihood method using 17 yr of OLR data from 1980 to 1996. Forecasts were then made on the remaining independent data from 1998 to 2004. These were made in real time, as only data up to the date the forecast was made were used. The median skill of forecasts was accurate (defined as an anomaly correlation above 0.6) at lead times up to 25 days

CD36 maintains the gastric mucosa and associates with gastric disease

The gastric epithelium is often exposed to injurious elements and failure of appropriate healing predisposes to ulcers, hemorrhage, and ultimately cancer. We examined the gastric function of CD36, a protein linked to disease and homeostasis. We used the tamoxifen model of gastric injury in mice null for Cd36 (Cd3

Bilateral Assessment of Functional Tasks for Robot-assisted Therapy Applications

This article presents a novel evaluation system along with methods to evaluate bilateral coordination of arm function on activities of daily living tasks before and after robot-assisted therapy. An affordable bilateral assessment system (BiAS) consisting of two mini-passive measuring units modeled as three degree of freedom robots is described. The process for evaluating functional tasks using the BiAS is presented and we demonstrate its ability to measure wrist kinematic trajectories. Three metrics, phase difference, movement overlap, and task completion time, are used to evaluate the BiAS system on a bilateral symmetric (bi-drink) and a bilateral asymmetric (bi-pour) functional task. Wrist position and velocity trajectories are evaluated using these metrics to provide insight into temporal and spatial bilateral deficits after stroke. The BiAS system quantified movements of the wrists during functional tasks and detected differences in impaired and unimpaired arm movements. Case studies showed that stroke patients compared to healthy subjects move slower and are less likely to use their arm simultaneously even when the functional task requires simultaneous movement. After robot-assisted therapy, interlimb coordination spatial deficits moved toward normal coordination on functional tasks

CD36 maintains the gastric mucosa and associates with gastric disease.

The gastric epithelium is often exposed to injurious elements and failure of appropriate healing predisposes to ulcers, hemorrhage, and ultimately cancer. We examined the gastric function of CD36, a protein linked to disease and homeostasis. We used the tamoxifen model of gastric injury in mice null for Cd36 (Cd36-/-), with Cd36 deletion in parietal cells (PC-Cd36-/-) or in endothelial cells (EC-Cd36-/-). CD36 expresses on corpus ECs, on PC basolateral membranes, and in gastrin and ghrelin cells. Stomachs of Cd36-/- mice have altered gland organization and secretion, more fibronectin, and inflammation. Tissue respiration and mitochondrial efficiency are reduced. Phospholipids increased and triglycerides decreased. Mucosal repair after injury is impaired in Cd36-/- and EC-Cd36-/-, not in PC-Cd36-/- mice, and is due to defect of progenitor differentiation to PCs, not of progenitor proliferation or mature PC dysfunction. Relevance to humans is explored in the Vanderbilt BioVu using PrediXcan that links genetically-determined gene expression to clinical phenotypes, which associates low CD36 mRNA with gastritis, gastric ulcer, and gastro-intestinal hemorrhage. A CD36 variant predicted to disrupt an enhancer site associates (p < 10-17) to death from gastro-intestinal hemorrhage in the UK Biobank. The findings support role of CD36 in gastric tissue repair, and its deletion associated with chronic diseases that can predispose to malignancy

Genetic analysis of the naked trait in panicles of hexaploid oat

The aim of this study was to estimate the number of genes that control the naked (hull-less) trait and the mode of expression of this characteristic in panicles of hexaploid white oat. Parents and the segregating population (in the F2 and F3 generations) were evaluated in regard to the presence and distribution of naked grains in panicles of individual oat plants. For each plant, a drawing of the main panicle was developed. From the drawings obtained in the progenies of the F2 population, six distinct phenotypic classes were produced. The expected phenotypic proportion of 3:9:4 (naked:segregating:hulled) was that which best fit by the Chi-square test. In the F3 generation, the results showed agreement with the hypothesis observed in the F2 generation. The naked trait in oat is passed on by two genes and the greatest expression of this trait occurs in the upper third of the panicles. Expression of this trait in oats is not complete, even in homozygous genotypes

Modular synthesis of semiconducting graft co-polymers to achieve ‘clickable’ fluorescent nanoparticles with long circulation and specific cancer targeting

Semiconducting polymer nanoparticles (SPNs) are explored for applications in cancer theranostics because of their high absorption coefficients, photostability, and biocompatibility. However, SPNs are susceptible to aggregation and protein fouling in physiological conditions, which can be detrimental for in vivo applications. Here, a method for achieving colloidally stable and low-fouling SPNs is described by grafting poly(ethylene glycol) (PEG) onto the backbone of the fluorescent semiconducting polymer, poly(9,9′-dioctylfluorene-5-fluoro-2,1,3-benzothiadiazole), in a simple one-step substitution reaction, postpolymerization. Further, by utilizing azide-functionalized PEG, anti-human epidermal growth factor receptor 2 (HER2) antibodies, antibody fragments, or affibodies are site-specifically “clicked” onto the SPN surface, which allows the functionalized SPNs to specifically target HER2-positive cancer cells. In vivo, the PEGylated SPNs are found to have excellent circulation efficiencies in zebrafish embryos for up to seven days postinjection. SPNs functionalized with affibodies are then shown to be able to target HER2 expressing cancer cells in a zebrafish xenograft model. The covalent PEGylated SPN system described herein shows great potential for cancer theranostics