Proteomic analysis of peach fruit mesocarp softening and chilling injury using difference gel electrophoresis (DIGE)

Background: Peach fruit undergoes a rapid softening process that involves a number of metabolic changes. Storing fruit at low temperatures has been widely used to extend its postharvest life. However, this leads to undesired changes, such as mealiness and browning, which affect the quality of the fruit. In this study, a 2-D DIGE approach was designed to screen for differentially accumulated proteins in peach fruit during normal softening as well as under conditions that led to fruit chilling injury. Results:The analysis allowed us to identify 43 spots -representing about 18% of the total number analyzed- that show statistically significant changes. Thirty-nine of the proteins could be identified by mass spectrometry. Some of the proteins that changed during postharvest had been related to peach fruit ripening and cold stress in the past. However, we identified other proteins that had not been linked to these processes. A graphical display of the relationship between the differentially accumulated proteins was obtained using pairwise average-linkage cluster analysis and principal component analysis. Proteins such as endopolygalacturonase, catalase, NADP-dependent isocitrate dehydrogenase, pectin methylesterase and dehydrins were found to be very important for distinguishing between healthy and chill injured fruit. A categorization of the differentially accumulated proteins was performed using Gene Ontology annotation. The results showed that the 'response to stress', 'cellular homeostasis', 'metabolism of carbohydrates' and 'amino acid metabolism' biological processes were affected the most during the postharvest. Conclusions: Using a comparative proteomic approach with 2-D DIGE allowed us to identify proteins that showed stage-specific changes in their accumulation pattern. Several proteins that are related to response to stress, cellular homeostasis, cellular component organization and carbohydrate metabolism were detected as being differentially accumulated. Finally, a significant proportion of the proteins identified had not been associated with softening, cold storage or chilling injury-altered fruit before; thus, comparative proteomics has proven to be a valuable tool for understanding fruit softening and postharvest.

Role of the superior parietal lobules in letter-identity processing within strings: FMRI evidence from skilled and dyslexic readers.

Traditionally, the ventral occipito-temporal (vOT) area, but not the superior parietal lobules (SPLs), is thought as belonging to the neural system of visual word recognition. However, some dyslexic children who exhibit a visual attention span disorder - i.e. poor multi-element parallel processing - further show reduced SPLs activation when engaged in visual multi-element categorization tasks. We investigated whether these parietal regions further contribute to letter-identity processing within strings. Adult skilled readers and dyslexic participants with a visual attention span disorder were administered a letter-string comparison task under fMRI. Dyslexic adults were less accurate than skilled readers to detect letter identity substitutions within strings. In skilled readers, letter identity differs related to enhanced activation of the left vOT. However, specific neural responses were further found in the superior and inferior parietal regions, including the SPLs bilaterally. Two brain regions that are specifically related to substituted letter detection, the left SPL and the left vOT, were less activated in dyslexic participants. These findings suggest that the left SPL, like the left vOT, may contribute to letter string processing.

Absence of progression as assessed by response evaluation criteria in solid tumors predicts survival in advanced GI stromal tumors treated with imatinib mesylate: the intergroup EORTC-ISG-AGITG phase III trial.

PURPOSE: From February 2001 to February 2002, 946 patients with advanced GI stromal tumors (GISTs) treated with imatinib were included in a controlled EORTC/ISG/AGITG (European Organisation for Research and Treatment of Cancer/Italian Sarcoma Group/Australasian Gastro-Intestinal Trials Group) trial. This analysis investigates whether the response classification assessed by RECIST (Response Evaluation Criteria in Solid Tumors), predicts for time to progression (TTP) and overall survival (OS). PATIENTS AND METHODS: Per protocol, the first three disease assessments were done at 2, 4, and 6 months. For the purpose of the analysis (landmark method), disease response was subclassified in six categories: partial response (PR; > 30% size reduction), minor response (MR; 10% to 30% reduction), no change (NC) as either NC- (0% to 10% reduction) or NC+ (0% to 20% size increase), progressive disease (PD; > 20% increase/new lesions), and subjective PD (clinical progression). RESULTS: A total of 906 patients had measurable disease at entry. At all measurement time points, complete response (CR), PR, and MR resulted in similar TTP and OS; this was also true for NC- and NC+, and for PD and subjective PD. Patients were subsequently classified as responders (CR/PR/MR), NC (NC+/NC-), or PD. This three-class response categorization was found to be highly predictive of further progression or survival for the first two measurement points. After 6 months of imatinib, responders (CR/PR/MR) had the same survival prognosis as patients classified as NC. CONCLUSION: RECIST perfectly enables early discrimination between patients who benefited long term from imatinib and those who did not. After 6 months of imatinib, if the patient is not experiencing PD, the pattern of radiologic response by tumor size criteria has no prognostic value for further outcome. Imatinib needs to be continued as long as there is no progression according to RECIST.

Prevalence and factors associated with incidents related to medication in surgical patients

The aim of this study was to estimate the prevalence and factors associated with the occurrence of incidents related to medication, registered in the medical records of patients admitted to a Surgical Clinic, in 2010. This is a cross-sectional study, conducted at a university hospital, with a sample of 735 hospitalizations. Was performed the categorization of types of incidents, multivariate analysis of regression logistic and calculated the prevalence. The prevalence of drug-related incidents was estimated at 48.0% and were identified, as factors related to the occurrence of these incidents: length of hospitalization more than four days, prescribed three or more medications per day and realization of surgery intervention. It is expected to have contributed for the professionals and area managers can identify risky situations and rethink their actions.

Children and adolescents on hemodialysis: attributes associated with quality of life


The aim of this study was to estimate the prevalence and factors associated with the occurrence of incidents related to medication, registered in the medical records of patients admitted to a Surgical Clinic, in 2010. This is a cross-sectional study, conducted at a university hospital, with a sample of 735 hospitalizations. Was performed the categorization of types of incidents, multivariate analysis of regression logistic and calculated the prevalence. The prevalence of drug-related incidents was estimated at 48.0% and were identified, as factors related to the occurrence of these incidents: length of hospitalization more than four days, prescribed three or more medications per day and realization of surgery intervention. It is expected to have contributed for the professionals and area managers can identify risky situations and rethink their actions.


Construction and validation of nursing diagnoses for individuals with diabetes in specialized care

OBJECTIVE To construct statements of nursing diagnoses related to nursing practice for individuals with diabetes in Specialized Care, on the basis of the Database of Nursing Practice Terms related to diabetes, in the International Classification for Nursing Practice (ICNP®) and in the Theory of Basic Human Needs and to validate them with specialist nurses in the area. METHOD Methodological research, structured into sequential stages of construction, cross-mapping, validation and categorization of nursing diagnoses. RESULTS A list was indicated of 115 statements of diagnostic, including positive, negative and improvement statements; 59 nursing diagnoses present in and 56 nursing diagnoses absent from the ICNP® Version 2011. 66 diagnoses with CVI ≥ 0.50 were validated, being categorized on the basis of human needs. CONCLUSION It was observed that the use of the ICNP® 2011 favored the specifications of the concepts of professional practice in care with individuals with diabetes.

Perceptual and semantic contributions to repetition priming of environmental sounds.

Repetition of environmental sounds, like their visual counterparts, can facilitate behavior and modulate neural responses, exemplifying plasticity in how auditory objects are represented or accessed. It remains controversial whether such repetition priming/suppression involves solely plasticity based on acoustic features and/or also access to semantic features. To evaluate contributions of physical and semantic features in eliciting repetition-induced plasticity, the present functional magnetic resonance imaging (fMRI) study repeated either identical or different exemplars of the initially presented object; reasoning that identical exemplars share both physical and semantic features, whereas different exemplars share only semantic features. Participants performed a living/man-made categorization task while being scanned at 3T. Repeated stimuli of both types significantly facilitated reaction times versus initial presentations, demonstrating perceptual and semantic repetition priming. There was also repetition suppression of fMRI activity within overlapping temporal, premotor, and prefrontal regions of the auditory "what" pathway. Importantly, the magnitude of suppression effects was equivalent for both physically identical and semantically related exemplars. That the degree of repetition suppression was irrespective of whether or not both perceptual and semantic information was repeated is suggestive of a degree of acoustically independent semantic analysis in how object representations are maintained and retrieved.

Jäsenkategorisoinnin analyysi kulttuurisena metodina

Summary: Membership categorization device as a cultural method

La pré-configuration des débats publics : apports d'une approche multimodale et longitudinale des dispositifs de catégorisation

Most linguistic and interactional studies on public and media debates focus on the way talk-in- interaction is locally managed by the moderator. For example, they analyze the extent to which the questions asked are both sequentially and categorically relevant. The present paper aims at enriching these studies by discussing the profitability of a multimodal and longitudinal approach to membership categorization practices. The approach is multimodal in the sense that it does not focus exclusively on verbal features. The approach is also longitudinal by considering the extent to which a debate is pre- configured. To this end, particular attention is paid to the posters promoting the encounters and to the way the participants are positioned in physical space.

Voir le monde à travers les lunettes du métacontraste : un modèle mathématique inspiré de la psychologie sociale

Résumé La théorie de l'autocatégorisation est une théorie de psychologie sociale qui porte sur la relation entre l'individu et le groupe. Elle explique le comportement de groupe par la conception de soi et des autres en tant que membres de catégories sociales, et par l'attribution aux individus des caractéristiques prototypiques de ces catégories. Il s'agit donc d'une théorie de l'individu qui est censée expliquer des phénomènes collectifs. Les situations dans lesquelles un grand nombre d'individus interagissent de manière non triviale génèrent typiquement des comportements collectifs complexes qui sont difficiles à prévoir sur la base des comportements individuels. La simulation informatique de tels systèmes est un moyen fiable d'explorer de manière systématique la dynamique du comportement collectif en fonction des spécifications individuelles. Dans cette thèse, nous présentons un modèle formel d'une partie de la théorie de l'autocatégorisation appelée principe du métacontraste. À partir de la distribution d'un ensemble d'individus sur une ou plusieurs dimensions comparatives, le modèle génère les catégories et les prototypes associés. Nous montrons que le modèle se comporte de manière cohérente par rapport à la théorie et est capable de répliquer des données expérimentales concernant divers phénomènes de groupe, dont par exemple la polarisation. De plus, il permet de décrire systématiquement les prédictions de la théorie dont il dérive, notamment dans des situations nouvelles. Au niveau collectif, plusieurs dynamiques peuvent être observées, dont la convergence vers le consensus, vers une fragmentation ou vers l'émergence d'attitudes extrêmes. Nous étudions également l'effet du réseau social sur la dynamique et montrons qu'à l'exception de la vitesse de convergence, qui augmente lorsque les distances moyennes du réseau diminuent, les types de convergences dépendent peu du réseau choisi. Nous constatons d'autre part que les individus qui se situent à la frontière des groupes (dans le réseau social ou spatialement) ont une influence déterminante sur l'issue de la dynamique. Le modèle peut par ailleurs être utilisé comme un algorithme de classification automatique. Il identifie des prototypes autour desquels sont construits des groupes. Les prototypes sont positionnés de sorte à accentuer les caractéristiques typiques des groupes, et ne sont pas forcément centraux. Enfin, si l'on considère l'ensemble des pixels d'une image comme des individus dans un espace de couleur tridimensionnel, le modèle fournit un filtre qui permet d'atténuer du bruit, d'aider à la détection d'objets et de simuler des biais de perception comme l'induction chromatique. Abstract Self-categorization theory is a social psychology theory dealing with the relation between the individual and the group. It explains group behaviour through self- and others' conception as members of social categories, and through the attribution of the proto-typical categories' characteristics to the individuals. Hence, it is a theory of the individual that intends to explain collective phenomena. Situations involving a large number of non-trivially interacting individuals typically generate complex collective behaviours, which are difficult to anticipate on the basis of individual behaviour. Computer simulation of such systems is a reliable way of systematically exploring the dynamics of the collective behaviour depending on individual specifications. In this thesis, we present a formal model of a part of self-categorization theory named metacontrast principle. Given the distribution of a set of individuals on one or several comparison dimensions, the model generates categories and their associated prototypes. We show that the model behaves coherently with respect to the theory and is able to replicate experimental data concerning various group phenomena, for example polarization. Moreover, it allows to systematically describe the predictions of the theory from which it is derived, specially in unencountered situations. At the collective level, several dynamics can be observed, among which convergence towards consensus, towards frag-mentation or towards the emergence of extreme attitudes. We also study the effect of the social network on the dynamics and show that, except for the convergence speed which raises as the mean distances on the network decrease, the observed convergence types do not depend much on the chosen network. We further note that individuals located at the border of the groups (whether in the social network or spatially) have a decisive influence on the dynamics' issue. In addition, the model can be used as an automatic classification algorithm. It identifies prototypes around which groups are built. Prototypes are positioned such as to accentuate groups' typical characteristics and are not necessarily central. Finally, if we consider the set of pixels of an image as individuals in a three-dimensional color space, the model provides a filter that allows to lessen noise, to help detecting objects and to simulate perception biases such as chromatic induction.

A temporal hierarchy for conspecific vocalization discrimination in humans.

The ability to discriminate conspecific vocalizations is observed across species and early during development. However, its neurophysiologic mechanism remains controversial, particularly regarding whether it involves specialized processes with dedicated neural machinery. We identified spatiotemporal brain mechanisms for conspecific vocalization discrimination in humans by applying electrical neuroimaging analyses to auditory evoked potentials (AEPs) in response to acoustically and psychophysically controlled nonverbal human and animal vocalizations as well as sounds of man-made objects. AEP strength modulations in the absence of topographic modulations are suggestive of statistically indistinguishable brain networks. First, responses were significantly stronger, but topographically indistinguishable to human versus animal vocalizations starting at 169-219 ms after stimulus onset and within regions of the right superior temporal sulcus and superior temporal gyrus. This effect correlated with another AEP strength modulation occurring at 291-357 ms that was localized within the left inferior prefrontal and precentral gyri. Temporally segregated and spatially distributed stages of vocalization discrimination are thus functionally coupled and demonstrate how conventional views of functional specialization must incorporate network dynamics. Second, vocalization discrimination is not subject to facilitated processing in time, but instead lags more general categorization by approximately 100 ms, indicative of hierarchical processing during object discrimination. Third, although differences between human and animal vocalizations persisted when analyses were performed at a single-object level or extended to include additional (man-made) sound categories, at no latency were responses to human vocalizations stronger than those to all other categories. Vocalization discrimination transpires at times synchronous with that of face discrimination but is not functionally specialized.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Psychosocial analysis of the collective processes in the United States after September 11

This article studies alterations in the values, attitudes, and behaviors that emerged among U.S. citizens as a consequence of, and as a response to, the attacks of September 11, 2001. The study briefly examines the immediate reaction to the attack, before focusing on the collective reactions that characterized the behavior of the majority of the population between the events of 9/11 and the response to it in the form of intervention in Afghanistan. In studying this period an eight-phase sequential model (Botcharova, 2001) is used, where the initial phases center on the nation as the ingroup and the latter focus on the enemy who carried out the attack as the outgroup. The study is conducted from a psychosocial perspective and uses "social identity theory" (Tajfel & Turner, 1979, 1986) as the basic framework for interpreting and accounting for the collective reactions recorded. The main purpose of this paper is to show that the interpretation of these collective reactions is consistent with the postulates of social identity theory. The application of this theory provides a different and specific analysis of events. The study is based on data obtained from a variety of rigorous academic studies and opinion polls conducted in relation to the events of 9/11. In line with social identity theory, 9/11 had a marked impact on the importance attached by the majority of U.S. citizens to their identity as members of a nation. This in turn accentuated group differentiation and activated ingroup favoritism and outgroup discrimination (Tajfel & Turner, 1979, 1986). Ingroup favoritism strengthened group cohesion, feelings of solidarity, and identification with the most emblematic values of the U.S. nation, while outgroup discrimination induced U.S. citizens to conceive the enemy (al-Qaeda and its protectors) as the incarnation of evil, depersonalizing the group and venting their anger on it, and to give their backing to a military response, the eventual intervention in Afghanistan. Finally, and also in line with the postulates of social identity theory, as an alternative to the virtual bipolarization of the conflict (U.S. vs al-Qaeda), the activation of a higher level of identity in the ingroup is proposed, a group that includes the United States and the largest possible number of countries¿ including Islamic states¿in the search for a common, more legitimate and effective solution.

Psychosocial analysis of ETA's violence legitimation discourse

This study analyses the fundamental components shaping the violence legitimation discourse of ETA (Euskadi Ta Askasuna). With this aim, a category system has been built, which organizes the psychosocial processes identified in previous studies related to violence legitimation. Based on the proposed category system, a content analysis was conducted on 21 statements of ETA, released between 1998 and 2011. An intraobserver and inter-observer reliability analysis reveals high level stability and replicability of the categorization. The results show, firstly, that outgroup components have a predominant presence over ingroup components. Secondly, in the components hierarchy, we observe that elements referring to identity come in first place, followed in similar frequencies by those related to violence representation and the definition of the situation.