EMD: A novel technique for the study of tremor time series

This paper introduces the Hilbert Analysis (HA), which is a novel digital signal processing technique, for the investigation of tremor. The HA is formed by two complementary tools, i.e. the Empirical Mode Decomposition (EMD) and the Hilbert Spectrum (HS). In this work we show that the EMD can automatically detect and isolate tremulous and voluntary movements from experimental signals collected from 31 patients with different conditions. Our results also suggest that the tremor may be described by a new class of mathematical functions defined in the HA framework. In a further study, the HS was employed for visualization of the energy activities of signals. This tool introduces the concept of instantaneous frequency in the field of tremor. In addition, it could provide, in a time-frequency-energy plot, a clear visualization of local activities of tremor energy over the time. The HA demonstrated to be very useful to perform objective measurements of any kind of tremor and can therefore be used to perform functional assessment.

Application of EMD as a novel technique for the study of tremor time series

This paper introduces the Hilbert Analysis (HA), which is a novel digital signal processing technique, for the investigation of tremor. The HA is formed by two complementary tools, i.e. the Empirical Mode Decomposition (EMD) and the Hilbert Spectrum (HS). In this work we show that the EMD can automatically detect and isolate tremulous and voluntary movements from experimental signals collected from 31 patients with different conditions. Our results also suggest that the tremor may be described by a new class of mathematical functions defined in the HA framework. In a further study, the HS was employed for visualization of the energy activities of signals. This tool introduces the concept of instantaneous frequency in the field of tremor. In addition, it could provide, in a time-frequency energy plot, a clear visualization of local activities of tremor energy over the time. The HA demonstrated to be very useful to perform objective measurements of any kind of tremor and can therefore be used to perform functional assessment.

Neural correlates of true and false memory in mild cognitive impairment

The goal of this research was to investigate the changes in neural processing in mild cognitive impairment. We measured phase synchrony, amplitudes, and event-related potentials in veridical and false memory to determine whether these differed in participants with mild cognitive impairment compared with typical, age-matched controls. Empirical mode decomposition phase locking analysis was used to assess synchrony, which is the first time this analysis technique has been applied in a complex cognitive task such as memory processing. The technique allowed assessment of changes in frontal and parietal cortex connectivity over time during a memory task, without a priori selection of frequency ranges, which has been shown previously to influence synchrony detection. Phase synchrony differed significantly in its timing and degree between participant groups in the theta and alpha frequency ranges. Timing differences suggested greater dependence on gist memory in the presence of mild cognitive impairment. The group with mild cognitive impairment had significantly more frontal theta phase locking than the controls in the absence of a significant behavioural difference in the task, providing new evidence for compensatory processing in the former group. Both groups showed greater frontal phase locking during false than true memory, suggesting increased searching when no actual memory trace was found. Significant inter-group differences in frontal alpha phase locking provided support for a role for lower and upper alpha oscillations in memory processing. Finally, fronto-parietal interaction was significantly reduced in the group with mild cognitive impairment, supporting the notion that mild cognitive impairment could represent an early stage in Alzheimer’s disease, which has been described as a ‘disconnection syndrome’.

Final warming of the Southern Hemisphere polar vortex in high- and low-top CMIP5 models

The final warming date of the polar vortex is a key component of Southern Hemisphere stratospheric and tropospheric variability in spring and summer. We examine the effect of external forcings on Southern Hemisphere final warming date, and the sensitivity of any projected changes to model representation of the stratosphere. Final warming date is calculated using a temperature-based diagnostic for ensembles of high- and low-top CMIP5 models, under the CMIP5 historical, RCP4.5, and RCP8.5 forcing scenarios. The final warming date in the models is generally too late in comparison with those from reanalyses: around two weeks too late in the low-top ensemble, and around one week too late in the high-top ensemble. Ensemble Empirical Mode Decomposition (EEMD) is used to analyse past and future change in final warming date. Both the low- and high-top ensemble show characteristic behaviour expected in response to changes in greenhouse gas and stratospheric ozone concentrations. In both ensembles, under both scenarios, an increase in final warming date is seen between 1850 and 2100, with the latest dates occurring in the early twenty-first century, associated with the minimum in stratospheric ozone concentrations in this period. However, this response is more pronounced in the high-top ensemble. The high-top models show a delay in final warming date in RCP8.5 that is not produced by the low-top models, which are shown to be less responsive to greenhouse gas forcing. This suggests that it may be necessary to use stratosphere resolving models to accurately predict Southern Hemisphere surface climate change.

Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles

Background: The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. New method: We propose a complete pipeline for the cluster analysis of ERP data. To increase the signalto-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA)to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). Results: After validating the pipeline on simulated data, we tested it on data from two experiments – a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership.

EMD performance comparison: single vs double floating points

Empirical mode decomposition (EMD) is a data-driven method used to decompose data into oscillatory components. This paper examines to what extent the defined algorithm for EMD might be susceptible to data format. Two key issues with EMD are its stability and computational speed. This paper shows that for a given signal there is no significant difference between results obtained with single (binary32) and double (binary64) floating points precision. This implies that there is no benefit in increasing floating point precision when performing EMD on devices optimised for single floating point format, such as graphical processing units (GPUs).

Summertime precipitation variability over europe and its links to atmospheric dynamics and evaporation

Gridded monthly precipitation data for 1979-2006 from the Global Precipitation Climatology Project are used to investigate interannual summer precipitation variability over Europe and its links to regional atmospheric circulation and evaporation. The first empirical orthogonal function (EOF) mode of European precipitation, explaining 17.2%-22.8% of its total variance, is stable during the summer season and is associated with the North Atlantic Oscillation. The spatialtemporal structure of the second EOF mode is less stable and shows monthtomonth variations during the summer season. This mode is linked to the Scandinavian teleconnection pattern. Analysis of links between leading EOF modes of regional precipitation and evaporation has revealed a significant link between precipitation and evaporation from the European land surface, thus, indicating an important role of the local processes in summertime precipitation variability over Europe. Weaker, but statistically significant links have been found for evaporation from the surface of the Mediterranean and Baltic Seas. Finally, in contrast to winter, no significant links have been revealed between European precipitation and evaporation in the North Atlantic during the summer season.

The FunFOLD2 server for the prediction of protein-ligand interactions

The FunFOLD2 server is a new independent server that integrates our novel protein–ligand binding site and quality assessment protocols for the prediction of protein function (FN) from sequence via structure. Our guiding principles were, first, to provide a simple unified resource to make our function prediction software easily accessible to all via a simple web interface and, second, to produce integrated output for predictions that can be easily interpreted. The server provides a clean web interface so that results can be viewed on a single page and interpreted by non-experts at a glance. The output for the prediction is an image of the top predicted tertiary structure annotated to indicate putative ligand-binding site residues. The results page also includes a list of the most likely binding site residues and the types of predicted ligands and their frequencies in similar structures. The protein–ligand interactions can also be interactively visualized in 3D using the Jmol plug-in. The raw machine readable data are provided for developers, which comply with the Critical Assessment of Techniques for Protein Structure Prediction data standards for FN predictions. The FunFOLD2 webserver is freely available to all at the following web site: http://www.reading.ac.uk/bioinf/FunFOLD/FunFOLD_form_2_0.html.

Genetic, structural, and molecular insights into the function of ras of complex proteins domains

Ras of complex proteins (ROC) domains were identified in 2003 as GTP binding modules in large multidomain proteins from Dictyostelium discoideum. Research into the function of these domains exploded with their identification in a number of proteins linked to human disease, including leucine-rich repeat kinase 2 (LRRK2) and death-associated protein kinase 1 (DAPK1) in Parkinson’s disease and cancer, respectively. This surge in research has resulted in a growing body of data revealing the role that ROC domains play in regulating protein function and signaling pathways. In this review, recent advances in the structural informa- tion available for proteins containing ROC domains, along with insights into enzymatic function and the integration of ROC domains as molecular switches in a cellular and organismal context, are explored.

Regulation of gene and protein expression in cardiac myocyte hypertrophy and apoptosis

Considerable efforts have been expended in elucidating the inter-cellular and intra-cellular signaling pathways which elicit cardiac myocyte hypertrophy or apoptosis, and in identifying the changes which are associated with the end-stage of the response. The challenge now is to link the two. Although some of the signaling effects will be the acute modulation of existing protein function, long-term effects which bring about and maintain the hypertrophic state or which culminate in cell death are mediated at the level of gene and protein expression. With the advances in micro-array technology and genome sequencing, it is now possible to obtain a picture of the global gene expression profile in myocytes or in whole heart which dictates the proteins which could be made. This is not the final picture since additional regulation at the level of translation modulates the relative proportions of each protein that can be made from the transcriptome. Even here, further regulation of protein stability and turnover means that ultimately it is still necessary to examine the proteome to determine what may cause the functional changes in a cell. Thus, in order to gain a full picture of events which regulate the response and gain some insight into possible points of intervention for therapy, it is necessary to examine gene expression, mRNA translation and protein expression in concert.

In silico identification and characterization of protein-ligand binding sites

Protein–ligand binding site prediction methods aim to predict, from amino acid sequence, protein–ligand interactions, putative ligands, and ligand binding site residues using either sequence information, structural information, or a combination of both. In silico characterization of protein–ligand interactions has become extremely important to help determine a protein’s functionality, as in vivo-based functional elucidation is unable to keep pace with the current growth of sequence databases. Additionally, in vitro biochemical functional elucidation is time-consuming, costly, and may not be feasible for large-scale analysis, such as drug discovery. Thus, in silico prediction of protein–ligand interactions must be utilized to aid in functional elucidation. Here, we briefly discuss protein function prediction, prediction of protein–ligand interactions, the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated EvaluatiOn (CAMEO) competitions, along with their role in shaping the field. We also discuss, in detail, our cutting-edge web-server method, FunFOLD for the structurally informed prediction of protein–ligand interactions. Furthermore, we provide a step-by-step guide on using the FunFOLD web server and FunFOLD3 downloadable application, along with some real world examples, where the FunFOLD methods have been used to aid functional elucidation.

Critical flux in ultrafiltration of skimmed milk

he best operating conditions, using the critical flux concept during ultrafiltration of skimmed milk, were evaluated for tubular membranes. It was found that irreversible fouling was greatly reduced by operating at or below the critical flux, but was not totally eliminated. The critical flux of skimmed milk was found to be the weak form. The critical flux at cross flow velocity 3.4 in s(-1) for MWCO 200 kDa membrane was 56.9 kg m(-2) h(-1) while for MWCO 25 kDa membranes it was 45 kg m(2) h(-1) suggesting that membrane pore size influenced the flux. The critical flux increased with increasing wall shear stress and decreased with increasing protein concentration. Empirical equations, for predicting the critical flux (J(crit)) for skimmed milk with a protein concentration (c(b)) in the range 3-7% w/w and wall shear stress (tau(w)) in the range 7-60 Pa for MWCO 200 kDa and 25 kDa membranes were J(crit) = 5.1 (tau(w)/c(b)) and J(crit) = 4.0 (tau(w)/c(b)) respectively. In general, the rejections of protein and lactose at the critical flux were not affected by protein concentration, wall shear stress and membrane used, and they were similar to those found when operating at the limiting flux.

The use of proteomics to identify novel therapeutic targets for the treatment of disease

The completion of the Human Genome Project has revealed a multitude of potential avenues for the identification of therapeutic targets. Extensive sequence information enables the identification of novel genes but does not facilitate a thorough understanding of how changes in gene expression control the molecular mechanisms underlying the development and regulation of a cell or the progression of disease. Proteomics encompasses the study of proteins expressed by a population of cells, and evaluates changes in protein expression, post-translational modifications, protein interactions, protein structure and splice variants, all of which are imperative for a complete understanding of protein function within the cell. From the outset, proteomics has been used to compare the protein profiles of cells in healthy and diseased states and as such can be used to identify proteins associated with disease development and progression. These candidate proteins might provide novel targets for new therapeutic agents or aid the development of assays for disease biomarkers. This review provides an overview of the current proteomic techniques available and focuses on their application in the search for novel therapeutic targets for the treatment of disease.

Proteins and their interacting partners: an introduction to protein–ligand binding site prediction methods

Elucidating the biological and biochemical roles of proteins, and subsequently determining their interacting partners, can be difficult and time consuming using in vitro and/or in vivo methods, and consequently the majority of newly sequenced proteins will have unknown structures and functions. However, in silico methods for predicting protein–ligand binding sites and protein biochemical functions offer an alternative practical solution. The characterisation of protein–ligand binding sites is essential for investigating new functional roles, which can impact the major biological research spheres of health, food, and energy security. In this review we discuss the role in silico methods play in 3D modelling of protein–ligand binding sites, along with their role in predicting biochemical functionality. In addition, we describe in detail some of the key alternative in silico prediction approaches that are available, as well as discussing the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated Model EvaluatiOn (CAMEO) projects, and their impact on developments in the field. Furthermore, we discuss the importance of protein function prediction methods for tackling 21st century problems.

The vibrational spectra of natural and perdeuterated monochlorogallane, H2Ga([mu]-Cl)2GaH2, and an empirical harmonic potential function

The infrared and Raman spectra of monochlorogallane and its fully deuterated isotopomer are recorded and assigned on the basis of the dimeric structures. H2Ga(μ-Cl)2GaH2 and D2Ga(μ-Cl)2GaD2, conforming to D2 symmetry. The observed frequencies are corrected for anharmonicity and fitted to a potential function in which 19 of the 33 independent force constants are refined.