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).

Atenuação de ruídos coerentes utilizando decomposição em modos empíricos

The seismic processing technique has the main objective to provide adequate picture of geological structures from subsurface of sedimentary basins. Among the key steps of this process is the enhancement of seismic reflections by filtering unwanted signals, called seismic noise, the improvement of signals of interest and the application of imaging procedures. The seismic noise may appear random or coherent. This dissertation will present a technique to attenuate coherent noise, such as ground roll and multiple reflections, based on Empirical Mode Decomposition method. This method will be applied to decompose the seismic trace into Intrinsic Mode Functions. These functions have the properties of being symmetric, with local mean equals zero and the same number of zero-crossing and extremes. The developed technique was tested on synthetic and real data, and the results were considered encouraging

Classificação de sinais acústicos utilizando a transformada wavelet discreta e a decomposição de modo empírico: aplicações na área de alimentos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Global and regional sea level variations in the recent past and future: insight from observations and modeling

This work is focused on the analysis of sea–level change (last century), based mainly on instrumental observations. During this period, individual components of sea–level change are investigated, both at global and regional scales. Some of the geophysical processes responsible for current sea-level change such as glacial isostatic adjustments and current melting terrestrial ice sources, have been modeled and compared with observations. A new value of global mean sea level change based of tide gauges observations has been independently assessed in 1.5 mm/year, using corrections for glacial isostatic adjustment obtained with different models as a criterion for the tide gauge selection. The long wavelength spatial variability of the main components of sea–level change has been investigated by means of traditional and new spectral methods. Complex non–linear trends and abrupt sea–level variations shown by tide gauges records have been addressed applying different approaches to regional case studies. The Ensemble Empirical Mode Decomposition technique has been used to analyse tide gauges records from the Adriatic Sea to ascertain the existence of cyclic sea-level variations. An Early Warning approach have been adopted to detect tipping points in sea–level records of North East Pacific and their relationship with oceanic modes. Global sea–level projections to year 2100 have been obtained by a semi-empirical approach based on the artificial neural network method. In addition, a model-based approach has been applied to the case of the Mediterranean Sea, obtaining sea-level projection to year 2050.

A novel detection method for determining a surgeon's fatigue and hand tremor

The thesis presents new methodology and algorithms that can be used to analyse and measure the hand tremor and fatigue of surgeons while performing surgery. This will assist them in deriving useful information about their fatigue levels, and make them aware of the changes in their tool point accuracies. This thesis proposes that muscular changes of surgeons, which occur through a day of operating, can be monitored using Electromyography (EMG) signals. The multi-channel EMG signals are measured at different muscles in the upper arm of surgeons. The dependence of EMG signals has been examined to test the hypothesis that EMG signals are coupled with and dependent on each other. The results demonstrated that EMG signals collected from different channels while mimicking an operating posture are independent. Consequently, single channel fatigue analysis has been performed. In measuring hand tremor, a new method for determining the maximum tremor amplitude using Principal Component Analysis (PCA) and a new technique to detrend acceleration signals using Empirical Mode Decomposition algorithm were introduced. This tremor determination method is more representative for surgeons and it is suggested as an alternative fatigue measure. This was combined with the complexity analysis method, and applied to surgically captured data to determine if operating has an effect on a surgeon’s fatigue and tremor levels. It was found that surgical tremor and fatigue are developed throughout a day of operating and that this could be determined based solely on their initial values. Finally, several Nonlinear AutoRegressive with eXogenous inputs (NARX) neural networks were evaluated. The results suggest that it is possible to monitor surgeon tremor variations during surgery from their EMG fatigue measurements.

Towards a unified understanding of event-related changes in the EEG:the Firefly model of synchronization through cross-frequency phase modulation

Although event-related potentials (ERPs) are widely used to study sensory, perceptual and cognitive processes, it remains unknown whether they are phase-locked signals superimposed upon the ongoing electroencephalogram (EEG) or result from phase-alignment of the EEG. Previous attempts to discriminate between these hypotheses have been unsuccessful but here a new test is presented based on the prediction that ERPs generated by phase-alignment will be associated with event-related changes in frequency whereas evoked-ERPs will not. Using empirical mode decomposition (EMD), which allows measurement of narrow-band changes in the EEG without predefining frequency bands, evidence was found for transient frequency slowing in recognition memory ERPs but not in simulated data derived from the evoked model. Furthermore, the timing of phase-alignment was frequency dependent with the earliest alignment occurring at high frequencies. Based on these findings, the Firefly model was developed, which proposes that both evoked and induced power changes derive from frequency-dependent phase-alignment of the ongoing EEG. Simulated data derived from the Firefly model provided a close match with empirical data and the model was able to account for i) the shape and timing of ERPs at different scalp sites, ii) the event-related desynchronization in alpha and synchronization in theta, and iii) changes in the power density spectrum from the pre-stimulus baseline to the post-stimulus period. The Firefly Model, therefore, provides not only a unifying account of event-related changes in the EEG but also a possible mechanism for cross-frequency information processing.

Atenuação de ruídos coerentes utilizando decomposição em modos empíricos

The seismic processing technique has the main objective to provide adequate picture of geological structures from subsurface of sedimentary basins. Among the key steps of this process is the enhancement of seismic reflections by filtering unwanted signals, called seismic noise, the improvement of signals of interest and the application of imaging procedures. The seismic noise may appear random or coherent. This dissertation will present a technique to attenuate coherent noise, such as ground roll and multiple reflections, based on Empirical Mode Decomposition method. This method will be applied to decompose the seismic trace into Intrinsic Mode Functions. These functions have the properties of being symmetric, with local mean equals zero and the same number of zero-crossing and extremes. The developed technique was tested on synthetic and real data, and the results were considered encouraging

Condition assessment of timber utility poles based on a hierarchical data fusion model

This paper proposes a novel hierarchical data fusion technique for the non-destructive testing (NDT) and condition assessment of timber utility poles. The new method analyzes stress wave data from multisensor and multiexcitation guided wave testing using a hierarchical data fusion model consisting of feature extraction, data compression, pattern recognition, and decision fusion algorithms. The researchers validate the proposed technique using guided wave tests of a sample of in situ timber poles. The actual health states of these poles are known from autopsies conducted after the testing, forming a ground-truth for supervised classification. In the proposed method, a data fusion level extracts the main features from the sampled stress wave signals using power spectrum density (PSD) estimation, wavelet packet transform (WPT), and empirical mode decomposition (EMD). These features are then compiled to a feature vector via real-number encoding and sent to the next level for further processing. Principal component analysis (PCA) is also adopted for feature compression and to minimize information redundancy and noise interference. In the feature fusion level, two classifiers based on support vector machine (SVM) are applied to sensor separated data of the two excitation types and the pole condition is identified. In the decision making fusion level, the Dempster–Shafer (D-S) evidence theory is employed to integrate the results from the individual sensors obtaining a final decision. The results of the in situ timber pole testing show that the proposed hierarchical data fusion model was able to distinguish between healthy and faulty poles, demonstrating the effectiveness of the new method.

The Enamovirus P0 protein is a silencing suppressor which inhibits local and systemic RNA silencing through AGO1 degradation

The P0 protein of poleroviruses and P1 protein of sobemoviruses suppress the plant's RNA silencing machinery. Here we identified a silencing suppressor protein (SSP), P0PE, in the Enamovirus Pea enation mosaic virus-1 (PEMV-1) and showed that it and the P0s of poleroviruses Potato leaf roll virus and Cereal yellow dwarf virus have strong local and systemic SSP activity, while the P1 of Sobemovirus Southern bean mosaic virus supresses systemic silencing. The nuclear localized P0PE has no discernable sequence conservation with known SSPs, but proved to be a strong suppressor of local silencing and a moderate suppressor of systemic silencing. Like the P0s from poleroviruses, P0PE destabilizes AGO1 and this action is mediated by an F-box-like domain. Therefore, despite the lack of any sequence similarity, the poleroviral and enamoviral SSPs have a conserved mode of action upon the RNA silencing machinery. © 2012 Elsevier Inc.

Analysis of Sequence Periodicity in E. coli Proteins: Empirical Investigation of the ?Duplication and Divergence? Theory of Protein Evolution

Gatherer, D., and McEwan, N.R. (2003). Analysis of sequence periodicity in E. coli proteins: empirical investigation of the 'duplication and divergence' theory of protein evolution. Journal of Molecular Evolution 57, 149-158. RAE2008

Inhibition of G-protein betagamma-subunit functions by phosducin-like protein.

Phosducin is a cytosolic protein predominantly expressed in the retina and the pineal gland that can interact with the betagamma subunits of guanine nucleotide binding proteins (G proteins) and thereby may regulate transmembrane signaling. A cDNA encoding a phosducin-like protein (PhLP) has recently been isolated from rat brain [Miles, M. F., Barhite, S., Sganga, M. & Elliott, M. (1993) Proc. Natl. Acad. Sci. USA 90, 10831-10835. Here we report the expression of PhLP in Escherichia coli and its purification. Recombinant purified PUP inhibited multiple effects of G-protein betagamma subunits. First, it inhibited the betagamma-subunit-dependent ADP-ribosylation of purified alpha(o) by pertussis toxin. Second, it inhibited the GTPase activity of purified G(o). The IC50 value of PhLP in the latter assay was 89 nM, whereas phosducin caused half-maximal inhibition at 17 nM. And finally, PhLP antagonized the enhancement of rhodopsin phosphorylation by purified betagamma subunits. The N terminus of PhLP shows no similarity to the much longer N terminus of phosducin, the region shown to be critical for phosducin-betagamma-subunit interactions. Therefore, PhLP appears to bind to G-protein betagamma subunits by an as yet unknown mode of interaction and may represent an endogenous regulator of G-protein function.

PLIC: protein-ligand interaction clusters

Most of the biological processes are governed through specific protein-ligand interactions. Discerning different components that contribute toward a favorable protein-ligand interaction could contribute significantly toward better understanding protein function, rationalizing drug design and obtaining design principles for protein engineering. The Protein Data Bank (PDB) currently hosts the structure of similar to 68 000 protein-ligand complexes. Although several databases exist that classify proteins according to sequence and structure, a mere handful of them annotate and classify protein-ligand interactions and provide information on different attributes of molecular recognition. In this study, an exhaustive comparison of all the biologically relevant ligand-binding sites (84 846 sites) has been conducted using PocketMatch: a rapid, parallel, in-house algorithm. PocketMatch quantifies the similarity between binding sites based on structural descriptors and residue attributes. A similarity network was constructed using binding sites whose PocketMatch scores exceeded a high similarity threshold (0.80). The binding site similarity network was clustered into discrete sets of similar sites using the Markov clustering (MCL) algorithm. Furthermore, various computational tools have been used to study different attributes of interactions within the individual clusters. The attributes can be roughly divided into (i) binding site characteristics including pocket shape, nature of residues and interaction profiles with different kinds of atomic probes, (ii) atomic contacts consisting of various types of polar, hydrophobic and aromatic contacts along with binding site water molecules that could play crucial roles in protein-ligand interactions and (iii) binding energetics involved in interactions derived from scoring functions developed for docking. For each ligand-binding site in each protein in the PDB, site similarity information, clusters they belong to and description of site attributes are provided as a relational database-protein-ligand interaction clusters (PLIC).