Early identification of stimulant treatment responders, partial responders and non-responders using objective measures in children and adolescents with hyperkinetic disorder

Background: The aim of this study was to evaluate stimulant medication response following a single dose of methylphenidate (MPH) in children and young people with hyperkinetic disorder using infrared motion analysis combined with a continuous performance task (QbTest system) as objective measures. The hypothesis was put forward that a moderate testdose of stimulant medication could determine a robust treatment response, partial response and non-response in relation to activity, attention and impulse control measures. Methods: The study included 44 children and young people between the ages of 7-18 years with a diagnosis of hyperkinetic disorder (F90 & F90.1). A single dose-protocol incorporated the time course effects of both immediate release MPH and extended release MPH (Concerta XL, Equasym XL) to determine comparable peak efficacy periods post intake. Results: A robust treatment response with objective measures reverting to the population mean was found in 37 participants (84%). Three participants (7%) demonstrated a partial response to MPH and four participants (9%) were determined as non-responders due to deteriorating activity measures together with no improvements in attention and impulse control measures. Conclusion: Objective measures provide early into prescribing the opportunity to measure treatment response and monitor adverse reactions to stimulant medication. Most treatment responders demonstrated an effective response to MPH on a moderate testdose facilitating a swift and more optimal titration process.

Identification of partial resetting using De as a function of illumination time

Modern age samples from various depositional environments were examined for signal resetting. For 19 modern aeolian/beach samples all De values obtained were View the MathML source, with ∼70% having View the MathML source. For 21 fluvial/colluvial samples, all De values were View the MathML source with ∼80% being View the MathML source. De as a function of illumination (OSL measurement) time (De(t)) plots were examined for all samples. Based on previous laboratory experiments, increases in De(t) were expected for partially reset samples, and constant De(t) for fully reset samples. All aeolian samples, both modern age and additional ‘young’ samples (<1000 years), showed constant (flat) De(t) while all modern, non-zero De, fluvial/colluvial samples showed increasing De(t). ‘Replacement plots’, where a regenerated signal is substituted for the natural, yielded constant (flat) De(t). These findings support strongly the use of De(t) as a method of identifying incomplete resetting in fluvial samples. Potential complicating factors, such as illumination (bleaching) spectrum, thermal instability and component composition are discussed and a series of internal checks on the applicability of the De(t) for each individual aliquot/grain level are outlined.

Multiscale Fractal Descriptors and Polynomial Classifier for Partial Pixels Identification in Regions of Interest of Mammographic Images

Computer systems are used to support breast cancer diagnosis, with decisions taken from measurements carried out in regions of interest (ROIs). We show that support decisions obtained from square or rectangular ROIs can to include background regions with different behavior of healthy or diseased tissues. In this study, the background regions were identified as Partial Pixels (PP), obtained with a multilevel method of segmentation based on maximum entropy. The behaviors of healthy, diseased and partial tissues were quantified by fractal dimension and multiscale lacunarity, calculated through signatures of textures. The separability of groups was achieved using a polynomial classifier. The polynomials have powerful approximation properties as classifiers to treat characteristics linearly separable or not. This proposed method allowed quantifying the ROIs investigated and demonstrated that different behaviors are obtained, with distinctions of 90% for images obtained in the Cranio-caudal (CC) and Mediolateral Oblique (MLO) views.

Partial Discharge Phenomena in Converter and Traction Transformers: Identification and Reliability

After the development of power electronics converters, the number of transformers subjected to non-sinusoidal stresses (including DC) has increased in applications such as HVDC links and traction (electric train power cars). The effects of non-sinusoidal voltages on transformer insulation have been investigated by many researchers, but still now, there are some issues that must be understood. Some of those issues are tackled in this Thesis, studying PD phenomena behavior in Kraft paper, pressboard and mineral oil at different voltage conditions like AC, DC, AC+DC, notched AC and square waveforms. From the point of view of converter transformers, it was found that the combined effect of AC and DC voltages produces higher stresses in the pressboard that those that are present under pure DC voltages. The electrical conductivity of the dielectric systems in DC and AC+DC conditions has demonstrated to be a critical parameter, so, its measurement and analysis was also taken into account during all the experiments. Regarding notched voltages, the RMS reduction caused by notches (depending on firing and overlap angles) seems to increase the PDIV. However, the experimental results show that once PD activity has incepted, the notches increase PD repetition rate and magnitude, producing a higher degradation rate of paper. On the other hand, the reduction of mineral oil stocks, their relatively low flash point as well as environmental issues, are factors that are pushing towards the use of esters as transformer insulating fluids. This PhD Thesis also covers the study of two different esters with the scope to validate their use in traction transformers. Mineral oil was used as benchmark. The complete set of dielectric tests performed in the three fluids, show that esters behave better than mineral oil in practically all the investigated conditions, so, their application in traction transformers is possible and encouraged.

Identification of serotonin 5-HT1A receptor partial agonists in ginger

Animal studies suggest that ginger (Zingiber officinale Roscoe) reduces anxiety. In this study, bioactivity-guided fractionation of a ginger extract identified nine compounds that interact with the human serotonin 5-HT(1A) receptor with significant to moderate binding affinities (K(i)=3-20 microM). [(35)S]-GTP gamma S assays indicated that 10-shogaol, 1-dehydro-6-gingerdione, and particularly the whole lipophilic ginger extract (K(i)=11.6 microg/ml) partially activate the 5-HT(1A) receptor (20-60% of maximal activation). In addition, the intestinal absorption of gingerols and shogaols was simulated and their interactions with P-glycoprotein were measured, suggesting a favourable pharmacokinetic profile for the 5-HT(1A) active compounds.

Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

This paper proposes a method for the identification of different partial discharges (PDs) sources through the analysis of a collection of PD signals acquired with a PD measurement system. This method, robust and sensitive enough to cope with noisy data and external interferences, combines the characterization of each signal from the collection, with a clustering procedure, the CLARA algorithm. Several features are proposed for the characterization of the signals, being the wavelet variances, the frequency estimated with the Prony method, and the energy, the most relevant for the performance of the clustering procedure. The result of the unsupervised classification is a set of clusters each containing those signals which are more similar to each other than to those in other clusters. The analysis of the classification results permits both the identification of different PD sources and the discrimination between original PD signals, reflections, noise and external interferences. The methods and graphical tools detailed in this paper have been coded and published as a contributed package of the R environment under a GNU/GPL license.

Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

This paper proposes a method for the identification of different partial discharges (PDs) sources through the analysis of a collection of PD signals acquired with a PD measurement system. This method, robust and sensitive enough to cope with noisy data and external interferences, combines the characterization of each signal from the collection, with a clustering procedure, the CLARA algorithm. Several features are proposed for the characterization of the signals, being the wavelet variances, the frequency estimated with the Prony method, and the energy, the most relevant for the performance of the clustering procedure. The result of the unsupervised classification is a set of clusters each containing those signals which are more similar to each other than to those in other clusters. The analysis of the classification results permits both the identification of different PD sources and the discrimination between original PD signals, reflections, noise and external interferences. The methods and graphical tools detailed in this paper have been coded and published as a contributed package of the R environment under a GNU/GPL license.

A clustering technique for partial discharge and noise sources identification in power cables by means of waveform parameters

On-line partial discharge (PD) measurements have become a common technique for assessing the insulation condition of installed high voltage (HV) insulated cables. When on-line tests are performed in noisy environments, or when more than one source of pulse-shaped signals are present in a cable system, it is difficult to perform accurate diagnoses. In these cases, an adequate selection of the non-conventional measuring technique and the implementation of effective signal processing tools are essential for a correct evaluation of the insulation degradation. Once a specific noise rejection filter is applied, many signals can be identified as potential PD pulses, therefore, a classification tool to discriminate the PD sources involved is required. This paper proposes an efficient method for the classification of PD signals and pulse-type noise interferences measured in power cables with HFCT sensors. By using a signal feature generation algorithm, representative parameters associated to the waveform of each pulse acquired are calculated so that they can be separated in different clusters. The efficiency of the clustering technique proposed is demonstrated through an example with three different PD sources and several pulse-shaped interferences measured simultaneously in a cable system with a high frequency current transformer (HFCT).

Identification of partial loss of function p53 gene mutations utilizing a yeast-based functional assay

Missense mutations within the central DNA binding region of p53 are the most prevalent mutations found in human cancer. Numerous studies indicate that ‘hot-spot’ p53 mutants (which comprise ∼30% of human p53 gene mutations) are largely devoid of transcriptional activity. However, a growing body of evidence indicates that some non-hot-spot p53 mutants retain some degree of transcriptional activity in vivo, particularly against strong p53 binding sites. We have modified a previously described yeast-based p53 functional assay to readily identify such partial loss of function p53 mutants. We demonstrate the utility of this modified p53 functional assay using a diverse panel of p53 mutants.

Identification and Partial Characterization of the Pectin Methyltransferase “Homogalacturonan-Methyltransferase” from Membranes of Tobacco Cell Suspensions1

A membrane preparation from tobacco (Nicotiana tabacum L.) cells contains at least one enzyme that is capable of transferring the methyl group from S-adenosyl-methionine (SAM) to the C6 carboxyl of homogalacturonan present in the membranes. This enzyme is named homogalacturonan-methyltransferase (HGA-MT) to distinguish it from methyltransferases that catalyze methyletherification of the pectic polysaccharides rhamnogalacturonan I or rhamnogalacturonan II. A trichloroacetic acid precipitation assay was used to measure HGA-MT activity, because published procedures to recover pectic polysaccharides via ethanol or chloroform:methanol precipitation lead to high and variable background radioactivity in the product pellet. Attempts to reduce the incorporation of the 14C-methyl group from SAM into pectin by the addition of the alternative methyl donor 5-methyltetrahydrofolate were unsuccessful, supporting the role of SAM as the authentic methyl donor for HGA-MT. The pH optimum for HGA-MT in membranes was 7.8, the apparent Michaelis constant for SAM was 38 μm, and the maximum initial velocity was 0.81 pkat mg−1 protein. At least 59% of the radiolabeled product was judged to be methylesterified homogalacturonan, based on the release of radioactivity from the product after a mild base treatment and via enzymatic hydrolysis by a purified pectin methylesterase. The released radioactivity eluted with a retention time identical to that of methanol upon fractionation over an organic acid column. Cleavage of the radiolabeled product by endopolygalacturonase into fragments that migrated as small oligomers of HGA during thin-layer chromatography, and the fact that HGA-MT activity in the membranes is stimulated by uridine 5′-diphosphate galacturonic acid, a substrate for HGA synthesis, confirms that the bulk of the product recovered from tobacco membranes incubated with SAM is methylesterified HGA.

Towards the in silico identification of class II restricted T-cell epitopes:a partial least squares iterative self-consistent algorithm for affinity prediction

Motivation: The immunogenicity of peptides depends on their ability to bind to MHC molecules. MHC binding affinity prediction methods can save significant amounts of experimental work. The class II MHC binding site is open at both ends, making epitope prediction difficult because of the multiple binding ability of long peptides. Results: An iterative self-consistent partial least squares (PLS)-based additive method was applied to a set of 66 pep- tides no longer than 16 amino acids, binding to DRB1*0401. A regression equation containing the quantitative contributions of the amino acids at each of the nine positions was generated. Its predictability was tested using two external test sets which gave r pred =0.593 and r pred=0.655, respectively. Furthermore, it was benchmarked using 25 known T-cell epitopes restricted by DRB1*0401 and we compared our results with four other online predictive methods. The additive method showed the best result finding 24 of the 25 T-cell epitopes. Availability: Peptides used in the study are available from http://www.jenner.ac.uk/JenPep. The PLS method is available commercially in the SYBYL molecular modelling software package. The final model for affinity prediction of peptides binding to DRB1*0401 molecule is available at http://www.jenner.ac.uk/MHCPred. Models developed for DRB1*0101 and DRB1*0701 also are available in MHC- Pred

Alternative analytical methods for the identification of cancer-related symptom clusters

Advances in symptom management strategies through a better understanding of cancer symptom clusters depend on the identification of symptom clusters that are valid and reliable. The purpose of this exploratory research was to investigate alternative analytical approaches to identify symptom clusters for patients with cancer, using readily accessible statistical methods, and to justify which methods of identification may be appropriate for this context. Three studies were undertaken: (1) a systematic review of the literature, to identify analytical methods commonly used for symptom cluster identification for cancer patients; (2) a secondary data analysis to identify symptom clusters and compare alternative methods, as a guide to best practice approaches in cross-sectional studies; and (3) a secondary data analysis to investigate the stability of symptom clusters over time. The systematic literature review identified, in 10 years prior to March 2007, 13 cross-sectional studies implementing multivariate methods to identify cancer related symptom clusters. The methods commonly used to group symptoms were exploratory factor analysis, hierarchical cluster analysis and principal components analysis. Common factor analysis methods were recommended as the best practice cross-sectional methods for cancer symptom cluster identification. A comparison of alternative common factor analysis methods was conducted, in a secondary analysis of a sample of 219 ambulatory cancer patients with mixed diagnoses, assessed within one month of commencing chemotherapy treatment. Principal axis factoring, unweighted least squares and image factor analysis identified five consistent symptom clusters, based on patient self-reported distress ratings of 42 physical symptoms. Extraction of an additional cluster was necessary when using alpha factor analysis to determine clinically relevant symptom clusters. The recommended approaches for symptom cluster identification using nonmultivariate normal data were: principal axis factoring or unweighted least squares for factor extraction, followed by oblique rotation; and use of the scree plot and Minimum Average Partial procedure to determine the number of factors. In contrast to other studies which typically interpret pattern coefficients alone, in these studies symptom clusters were determined on the basis of structure coefficients. This approach was adopted for the stability of the results as structure coefficients are correlations between factors and symptoms unaffected by the correlations between factors. Symptoms could be associated with multiple clusters as a foundation for investigating potential interventions. The stability of these five symptom clusters was investigated in separate common factor analyses, 6 and 12 months after chemotherapy commenced. Five qualitatively consistent symptom clusters were identified over time (Musculoskeletal-discomforts/lethargy, Oral-discomforts, Gastrointestinaldiscomforts, Vasomotor-symptoms, Gastrointestinal-toxicities), but at 12 months two additional clusters were determined (Lethargy and Gastrointestinal/digestive symptoms). Future studies should include physical, psychological, and cognitive symptoms. Further investigation of the identified symptom clusters is required for validation, to examine causality, and potentially to suggest interventions for symptom management. Future studies should use longitudinal analyses to investigate change in symptom clusters, the influence of patient related factors, and the impact on outcomes (e.g., daily functioning) over time.

A multivariate approach to the identification of surrogate parameters for heavy metals in stormwater

Stormwater is a potential and readily available alternative source for potable water in urban areas. However, its direct use is severely constrained by the presence of toxic pollutants, such as heavy metals (HMs). The presence of HMs in stormwater is of concern because of their chronic toxicity and persistent nature. In addition to human health impacts, metals can contribute to adverse ecosystem health impact on receiving waters. Therefore, the ability to predict the levels of HMs in stormwater is crucial for monitoring stormwater quality and for the design of effective treatment systems. Unfortunately, the current laboratory methods for determining HM concentrations are resource intensive and time consuming. In this paper, applications of multivariate data analysis techniques are presented to identify potential surrogate parameters which can be used to determine HM concentrations in stormwater. Accordingly, partial least squares was applied to identify a suite of physicochemical parameters which can serve as indicators of HMs. Datasets having varied characteristics, such as land use and particle size distribution of solids, were analyzed to validate the efficacy of the influencing parameters. Iron, manganese, total organic carbon, and inorganic carbon were identified as the predominant parameters that correlate with the HM concentrations. The practical extension of the study outcomes to urban stormwater management is also discussed.

Putative full-length clones of the genomic DNA segments of subterranean clover stunt virus and identification of the segment coding for the viral coat protein

Subterranean clover stunt disease is an economically important aphid-borne virus disease affecting certain pasture and grain legumes in Australia. The virus associated with the disease, subterranean clover stunt virus (SCSV), was previously found to be representative of a new type of single-stranded DNA virus. Analysis of the virion DNA and restriction mapping of double-stranded cDNA synthesized from virion DNA suggested that SCSV has a segmented genome composed of 3 or 4 different species of circular ssDNA each of about 850-880 nucleotides. To further investigate the complexity of the SCSV genome, we have isolated the replicative form DNA from infected pea and from it prepared putative full-length clones representing the SCSV genome segments. Analysis of these clones by restriction mapping indicated that clones representing at least 4 distinct genomic segments were obtained. This method is thus suitable for generating an extensive genomic library of novel ssDNA viruses containing multiple genome segments such as SCSV and banana bunchy top virus. The N-terminal amino acid sequence and amino acid composition of the coat protein of SCSV were determined. Comparison of the amino acid sequence with partial DNA sequence data, and the distinctly different restriction maps obtained for the full-length clones suggested that only one of these clones contained the coat protein gene. The results confirmed that SCSV has a functionally divided genome composed of several distinct ssDNA circles each of about 1 kb.

Multi-shot person re-identification via relational stein divergence

Person re-identification is particularly challenging due to significant appearance changes across separate camera views. In order to re-identify people, a representative human signature should effectively handle differences in illumination, pose and camera parameters. While general appearance-based methods are modelled in Euclidean spaces, it has been argued that some applications in image and video analysis are better modelled via non-Euclidean manifold geometry. To this end, recent approaches represent images as covariance matrices, and interpret such matrices as points on Riemannian manifolds. As direct classification on such manifolds can be difficult, in this paper we propose to represent each manifold point as a vector of similarities to class representers, via a recently introduced form of Bregman matrix divergence known as the Stein divergence. This is followed by using a discriminative mapping of similarity vectors for final classification. The use of similarity vectors is in contrast to the traditional approach of embedding manifolds into tangent spaces, which can suffer from representing the manifold structure inaccurately. Comparative evaluations on benchmark ETHZ and iLIDS datasets for the person re-identification task show that the proposed approach obtains better performance than recent techniques such as Histogram Plus Epitome, Partial Least Squares, and Symmetry-Driven Accumulation of Local Features.