Evaluation of three different adhesive systems using a bacterial method to develop secondary caries in vitro

Purpose: To assess the effects of three different dental adhesive systems on the formation of secondary root caries, in vitro, with a standardized interfacial gap in a filled cavity model. Methods: 40 sound human molars were selected and randomly assigned to four experimental groups: Clearfil SE Bond (CSEB), Xeno III (X-III), Scotchbond Multi-Purpose Plus (SBMP) and negative control (NC) without an adhesive system. After the standardized Class V cavity preparations on the buccal and lingual surfaces, restorations were placed with resin composite (Filtek Z250) using a standardized interfacial gap, using a 3 x 2 mm piece of 50 mu m metal matrix. The teeth were sterilized with gamma irradiation and exposed to a cariogenic challenge using a bacterial system with Streptococcus mutans. Depth and extension of wall lesions formed and the depth of outer lesions were measured by software coupled with light microscopy. Results: For wall lesion extension the ANOVA test showed differences between groups except between X-HI and SBMP (P= 0.294). The Tukey`s test of confidence intervals indicated smaller values for the CSEB group than for the others. For wall lesion depth the CSEB group also presented the smallest mean values of wall lesion depth when compared to the others (P< 0.0001) for all comparisons using Tukey`s test. Regarding outer lesion depth, all adhesives showed statistically similar behavior. SEM evaluation of the morphologic appearance of caries lesions confirmed the statistical results showing small caries lesion development for cavities restored with CSEB adhesive system, which may suggest that this adhesive system interdiffusion zone promoted a good interaction with subjacent dentin protecting the dental tissues from recurrent caries. (Am J Dent 2010;23:93-97).

Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.

Salt content in bread and dough from northern Portugal: method development and comparison

In the initial stage of this work, two potentiometric methods were used to determine the salt (sodium chloride) content in bread and dough samples from several cities in the north of Portugal. A reference method (potentiometric precipitation titration) and a newly developed ion-selective chloride electrode (ISE) were applied. Both methods determine the sodium chloride content through the quantification of chloride. To evaluate the accuracy of the ISE, bread and respective dough samples were analyzed by both methods. Statistical analysis (0.05 significance level) indicated that the results of these methods did not differ significantly. Therefore the ISE is an adequate alternative for the determination of chloride in the analyzed samples. To compare the results of these chloride-based methods with a sodium-based method, sodium was quantified in the same samples by a reference method (atomic absorption spectrometry). Significant differences between the results were verified. In several cases the sodium chloride content exceeded the legal limit when the chloride-based methods were used, but when the sodium-based method was applied this was not the case. This could lead to the erroneous application of fines and therefore the authorities should supply additional information regarding the analytical procedure for this particular control.

Square-wave voltametric method for determination of molinate concentration in a biological process using a hanging mercury drop electrode

A square-wave voltammetric (SWV) method using a hanging mercury drop electrode (HMDE) has been developed for determination of the herbicide molinate in a biodegradation process. The method is based on controlled adsorptive accumulation of molinate for 10 s at a potential of -0.8 V versus AgCl/Ag. An anodic peak, due to oxidation of the adsorbed pesticide, was observed in the cyclic voltammogram at ca. -0.320 V versus AgCl/Ag; a very small cathodic peak was also detected. The SWV calibration plot was established to be linear in the range 5.0x10-6 to 9.0x10-6 mol L-1; this corresponded to a detection limit of 3.5x10-8 mol L-1. This electroanalytical method was used to monitor the decrease of molinate concentration in river waters along a biodegradation process using a bacterial mixed culture. The results achieved with this voltammetric method were compared with those obtained by use of a chromatographic method (HPLC–UV) and no significant statistical differences were observed.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

The effect of method, standard and sample components on the total antioxidant capacity of commercial waters assessed by optical conventional assays

The total antioxidant capacity (TAC) of 28 flavoured water samples was assessed by ferric reducing antioxidant potential (FRAP), oxygen radical absorbance capacity (ORAC), trolox equivalent antioxidant capacity (TEAC) and total reactive antioxidant potential (TRAP) methods. It was observed that flavoured waters had higher antioxidant activity than the corresponding natural ones. The observed differences were attributed to flavours, juice and vitamins. Generally, higher TAC contents were obtained on lemon waters and lower values on guava and raspberry flavoured waters. Lower and higher TACs were obtained by TRAP and ORAC method, respectively. Statistical analysis suggested that vitamins and flavours increased the antioxidant content of the commercial waters.

Application of multivariate statistical process control to fuel cell manufacturing

Univariate statistical control charts, such as the Shewhart chart, do not satisfy the requirements for process monitoring on a high volume automated fuel cell manufacturing line. This is because of the number of variables that require monitoring. The risk of elevated false alarms, due to the nature of the process being high volume, can present problems if univariate methods are used. Multivariate statistical methods are discussed as an alternative for process monitoring and control. The research presented is conducted on a manufacturing line which evaluates the performance of a fuel cell. It has three stages of production assembly that contribute to the final end product performance. The product performance is assessed by power and energy measurements, taken at various time points throughout the discharge testing of the fuel cell. The literature review performed on these multivariate techniques are evaluated using individual and batch observations. Modern techniques using multivariate control charts on Hotellings T2 are compared to other multivariate methods, such as Principal Components Analysis (PCA). The latter, PCA, was identified as the most suitable method. Control charts such as, scores, T2 and DModX charts, are constructed from the PCA model. Diagnostic procedures, using Contribution plots, for out of control points that are detected using these control charts, are also discussed. These plots enable the investigator to perform root cause analysis. Multivariate batch techniques are compared to individual observations typically seen on continuous processes. Recommendations, for the introduction of multivariate techniques that would be appropriate for most high volume processes, are also covered.

Inter-informant agreement and prevalence estimates for substance use disorders: direct interview versus family history method.

OBJECTIVES: Family studies typically use multiple sources of information on each individual including direct interviews and family history information. The aims of the present study were to: (1) assess agreement for diagnoses of specific substance use disorders between direct interviews and the family history method; (2) compare prevalence estimates according to the two methods; (3) test strategies to approximate prevalence estimates according to family history reports to those based on direct interviews; (4) determine covariates of inter-informant agreement; and (5) identify covariates that affect the likelihood of reporting disorders by informants. METHODS: Analyses were based on family study data which included 1621 distinct informant (first-degree relatives and spouses) - index subject pairs. RESULTS: Our main findings were: (1) inter-informant agreement was fair to good for all substance disorders, except for alcohol abuse; (2) the family history method underestimated the prevalence of drug but not alcohol use disorders; (3) lowering diagnostic thresholds for drug disorders and combining multiple family histories increased the accuracy of prevalence estimates for these disorders according to the family history method; (4) female sex of index subjects was associated with higher agreement for nearly all disorders; and (5) informants who themselves had a history of the same substance use disorder were more likely to report this disorder in their relatives, which entails the risk of overestimation of the size of familial aggregation. CONCLUSION: Our findings have important implications for the best-estimate procedure applied in family studies.

Parent-child agreement and prevalence estimates of diagnoses in childhood: direct interview versus family history method.

Diagnostic information on children is typically elicited from both children and their parents. The aims of the present paper were to: (1) compare prevalence estimates according to maternal reports, paternal reports and direct interviews of children [major depressive disorder (MDD), anxiety and attention-deficit and disruptive behavioural disorders]; (2) assess mother-child, father-child and inter-parental agreement for these disorders; (3) determine the association between several child, parent and familial characteristics and the degree of diagnostic agreement or the likelihood of parental reporting; (4) determine the predictive validity of diagnostic information provided by parents and children. Analyses were based on 235 mother-offspring, 189 father-offspring and 128 mother-father pairs. Diagnostic assessment included the Kiddie-schedule for Affective Disorders and Schizophrenia (K-SADS) (offspring) and the Diagnostic Interview for Genetic Studies (DIGS) (parents and offspring at follow-up) interviews. Parental reports were collected using the Family History - Research Diagnostic Criteria (FH-RDC). Analyses revealed: (1) prevalence estimates for internalizing disorders were generally lower according to parental information than according to the K-SADS; (2) mother-child and father-child agreement was poor and within similar ranges; (3) parents with a history of MDD or attention deficit hyperactivity disorder (ADHD) reported these disorders in their children more frequently; (4) in a sub-sample followed-up into adulthood, diagnoses of MDD, separation anxiety and conduct disorder at baseline concurred with the corresponding lifetime diagnosis at age 19 according to the child rather than according to the parents. In conclusion, our findings support large discrepancies of diagnostic information provided by parents and children with generally lower reporting of internalizing disorders by parents, and differential reporting of depression and ADHD by parental disease status. Follow-up data also supports the validity of information provided by adolescent offspring.

Illicit drug profiling, reflection on statistical comparisons

This paper presents reflexions about statistical considerations on illicit drug profiling and more specifically about the calculation of threshold for determining of the seizure are linked or not. The specific case of heroin and cocaine profiling is presented with the necessary details on the target profiling variables (major alkaloids) selected and the analytical method used. Statistical approach to compare illicit drug seizures is also presented with the introduction of different scenarios dealing with different data pre-treatment or transformation of variables.The main aim consists to demonstrate the influence of data pre-treatment on the statistical outputs. A thorough study of the evolution of the true positive rate (TP) and the false positive rate (FP) in heroin and cocaine comparison is then proposed to investigate this specific topic and to demonstrate that there is no universal approach available and that the calculations have to be revaluate for each new specific application.

A study of the cambial zone and conductive phloem of common beech (Fagus sylvatica L.) using an image analysis method. I. Influence of tree age on the structure

Quantification is a major problem when using histology to study the influence of ecological factors on tree structure. This paper presents a method to prepare and to analyse transverse sections of cambial zone and of conductive phloem in bark samples. The following paper (II) presents the automated measurement procedure. Part I here describes and discusses the preparation method, and the influence of tree age on the observed structure. Highly contrasted images of samples extracted at breast height during dormancy were analysed with an automatic image analyser. Between three young (38 years) and three old (147 years) trees, age-related differences were identified by size and shape parameters, at both cell and tissue levels. In the cambial zone, older trees had larger and more rectangular fusiform initials. In the phloem, sieve tubes were also larger, but their shape did not change and the area for sap conduction was similar in both categories. Nevertheless, alterations were limited, and demanded statistical analysis to be identified and ascertained. The physiological implications of the structural changes are discussed.

Statistical evaluation of the reproducibility and the influence of paper on the analysis of black gel pen ink using laser desorption ionisation mass spectrometry

Laser desorption ionisation mass spectrometry (LDI-MS) has demonstrated to be an excellent analytical method for the forensic analysis of inks on a questioned document. The ink can be analysed directly on its substrate (paper) and hence offers a fast method of analysis as sample preparation is kept to a minimum and more importantly, damage to the document is minimised. LDI-MS has also previously been reported to provide a high power of discrimination in the statistical comparison of ink samples and has the potential to be introduced as part of routine ink analysis. This paper looks into the methodology further and evaluates statistically the reproducibility and the influence of paper on black gel pen ink LDI-MS spectra; by comparing spectra of three different black gel pen inks on three different paper substrates. Although generally minimal, the influences of sample homogeneity and paper type were found to be sample dependent. This should be taken into account to avoid the risk of false differentiation of black gel pen ink samples. Other statistical approaches such as principal component analysis (PCA) proved to be a good alternative to correlation coefficients for the comparison of whole mass spectra.

Assessment of statistical iterative reconstructions to optimize CT examination protocols for children and young adults

La tomodensitométrie (CT) est une technique d'imagerie dont l'intérêt n'a cessé de croître depuis son apparition dans le début des années 70. Dans le domaine médical, son utilisation est incontournable à tel point que ce système d'imagerie pourrait être amené à devenir victime de son succès si son impact au niveau de l'exposition de la population ne fait pas l'objet d'une attention particulière. Bien évidemment, l'augmentation du nombre d'examens CT a permis d'améliorer la prise en charge des patients ou a rendu certaines procédures moins invasives. Toutefois, pour assurer que le compromis risque - bénéfice soit toujours en faveur du patient, il est nécessaire d'éviter de délivrer des doses non utiles au diagnostic.¦Si cette action est importante chez l'adulte elle doit être une priorité lorsque les examens se font chez l'enfant, en particulier lorsque l'on suit des pathologies qui nécessitent plusieurs examens CT au cours de la vie du patient. En effet, les enfants et jeunes adultes sont plus radiosensibles. De plus, leur espérance de vie étant supérieure à celle de l'adulte, ils présentent un risque accru de développer un cancer radio-induit dont la phase de latence peut être supérieure à vingt ans. Partant du principe que chaque examen radiologique est justifié, il devient dès lors nécessaire d'optimiser les protocoles d'acquisitions pour s'assurer que le patient ne soit pas irradié inutilement. L'avancée technologique au niveau du CT est très rapide et depuis 2009, de nouvelles techniques de reconstructions d'images, dites itératives, ont été introduites afin de réduire la dose et améliorer la qualité d'image.¦Le présent travail a pour objectif de déterminer le potentiel des reconstructions itératives statistiques pour réduire au minimum les doses délivrées lors d'examens CT chez l'enfant et le jeune adulte tout en conservant une qualité d'image permettant le diagnostic, ceci afin de proposer des protocoles optimisés.¦L'optimisation d'un protocole d'examen CT nécessite de pouvoir évaluer la dose délivrée et la qualité d'image utile au diagnostic. Alors que la dose est estimée au moyen d'indices CT (CTDIV0| et DLP), ce travail a la particularité d'utiliser deux approches radicalement différentes pour évaluer la qualité d'image. La première approche dite « physique », se base sur le calcul de métriques physiques (SD, MTF, NPS, etc.) mesurées dans des conditions bien définies, le plus souvent sur fantômes. Bien que cette démarche soit limitée car elle n'intègre pas la perception des radiologues, elle permet de caractériser de manière rapide et simple certaines propriétés d'une image. La seconde approche, dite « clinique », est basée sur l'évaluation de structures anatomiques (critères diagnostiques) présentes sur les images de patients. Des radiologues, impliqués dans l'étape d'évaluation, doivent qualifier la qualité des structures d'un point de vue diagnostique en utilisant une échelle de notation simple. Cette approche, lourde à mettre en place, a l'avantage d'être proche du travail du radiologue et peut être considérée comme méthode de référence.¦Parmi les principaux résultats de ce travail, il a été montré que les algorithmes itératifs statistiques étudiés en clinique (ASIR?, VEO?) ont un important potentiel pour réduire la dose au CT (jusqu'à-90%). Cependant, par leur fonctionnement, ils modifient l'apparence de l'image en entraînant un changement de texture qui pourrait affecter la qualité du diagnostic. En comparant les résultats fournis par les approches « clinique » et « physique », il a été montré que ce changement de texture se traduit par une modification du spectre fréquentiel du bruit dont l'analyse permet d'anticiper ou d'éviter une perte diagnostique. Ce travail montre également que l'intégration de ces nouvelles techniques de reconstruction en clinique ne peut se faire de manière simple sur la base de protocoles utilisant des reconstructions classiques. Les conclusions de ce travail ainsi que les outils développés pourront également guider de futures études dans le domaine de la qualité d'image, comme par exemple, l'analyse de textures ou la modélisation d'observateurs pour le CT.¦-¦Computed tomography (CT) is an imaging technique in which interest has been growing since it first began to be used in the early 1970s. In the clinical environment, this imaging system has emerged as the gold standard modality because of its high sensitivity in producing accurate diagnostic images. However, even if a direct benefit to patient healthcare is attributed to CT, the dramatic increase of the number of CT examinations performed has raised concerns about the potential negative effects of ionizing radiation on the population. To insure a benefit - risk that works in favor of a patient, it is important to balance image quality and dose in order to avoid unnecessary patient exposure.¦If this balance is important for adults, it should be an absolute priority for children undergoing CT examinations, especially for patients suffering from diseases requiring several follow-up examinations over the patient's lifetime. Indeed, children and young adults are more sensitive to ionizing radiation and have an extended life span in comparison to adults. For this population, the risk of developing cancer, whose latency period exceeds 20 years, is significantly higher than for adults. Assuming that each patient examination is justified, it then becomes a priority to optimize CT acquisition protocols in order to minimize the delivered dose to the patient. Over the past few years, CT advances have been developing at a rapid pace. Since 2009, new iterative image reconstruction techniques, called statistical iterative reconstructions, have been introduced in order to decrease patient exposure and improve image quality.¦The goal of the present work was to determine the potential of statistical iterative reconstructions to reduce dose as much as possible without compromising image quality and maintain diagnosis of children and young adult examinations.¦The optimization step requires the evaluation of the delivered dose and image quality useful to perform diagnosis. While the dose is estimated using CT indices (CTDIV0| and DLP), the particularity of this research was to use two radically different approaches to evaluate image quality. The first approach, called the "physical approach", computed physical metrics (SD, MTF, NPS, etc.) measured on phantoms in well-known conditions. Although this technique has some limitations because it does not take radiologist perspective into account, it enables the physical characterization of image properties in a simple and timely way. The second approach, called the "clinical approach", was based on the evaluation of anatomical structures (diagnostic criteria) present on patient images. Radiologists, involved in the assessment step, were asked to score image quality of structures for diagnostic purposes using a simple rating scale. This approach is relatively complicated to implement and also time-consuming. Nevertheless, it has the advantage of being very close to the practice of radiologists and is considered as a reference method.¦Primarily, this work revealed that the statistical iterative reconstructions studied in clinic (ASIR? and VECO have a strong potential to reduce CT dose (up to -90%). However, by their mechanisms, they lead to a modification of the image appearance with a change in image texture which may then effect the quality of the diagnosis. By comparing the results of the "clinical" and "physical" approach, it was showed that a change in texture is related to a modification of the noise spectrum bandwidth. The NPS analysis makes possible to anticipate or avoid a decrease in image quality. This project demonstrated that integrating these new statistical iterative reconstruction techniques can be complex and cannot be made on the basis of protocols using conventional reconstructions. The conclusions of this work and the image quality tools developed will be able to guide future studies in the field of image quality as texture analysis or model observers dedicated to CT.

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis.

Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analysis and interpretation. We present a method, which allows automatic detection of epileptiform events and discrimination of them from eye blinks, and is based on features derived using a novel application of independent component analysis. The algorithm was trained and cross validated using seven EEGs with epileptiform activity. For epileptiform events with compensation for eyeblinks, the sensitivity was 65 +/- 22% at a specificity of 86 +/- 7% (mean +/- SD). With feature extraction by PCA or classification of raw data, specificity reduced to 76 and 74%, respectively, for the same sensitivity. On exactly the same data, the commercially available software Reveal had a maximum sensitivity of 30% and concurrent specificity of 77%. Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG.

Splitting statistical potentials into meaningful scoring functions: testing the prediction of near-native structures from decoy conformations

Background: Recent advances on high-throughput technologies have produced a vast amount of protein sequences, while the number of high-resolution structures has seen a limited increase. This has impelled the production of many strategies to built protein structures from its sequence, generating a considerable amount of alternative models. The selection of the closest model to the native conformation has thus become crucial for structure prediction. Several methods have been developed to score protein models by energies, knowledge-based potentials and combination of both.Results: Here, we present and demonstrate a theory to split the knowledge-based potentials in scoring terms biologically meaningful and to combine them in new scores to predict near-native structures. Our strategy allows circumventing the problem of defining the reference state. In this approach we give the proof for a simple and linear application that can be further improved by optimizing the combination of Zscores. Using the simplest composite score () we obtained predictions similar to state-of-the-art methods. Besides, our approach has the advantage of identifying the most relevant terms involved in the stability of the protein structure. Finally, we also use the composite Zscores to assess the conformation of models and to detect local errors.Conclusion: We have introduced a method to split knowledge-based potentials and to solve the problem of defining a reference state. The new scores have detected near-native structures as accurately as state-of-art methods and have been successful to identify wrongly modeled regions of many near-native conformations.