El perfil de clima escolar: estudio transcultural de la validez de una batería de cuestionarios para evaluar el clima escolar

This paper studies the validity of the School Climate Battery of Questionnaires for Secondary and High School Teachers (SCBQSHST). The battery includes five questionnaires: Quality of Leadership, Quality of Teachers' Support, School Motivational Orientation, Quality of Students' Attitude, and Quality of Parental Support. A total of 178 teachers from Costa Rica were compared with 343 Spanish teachers. Confirmatory factor analyses showed that the questionnaires allow assessing teachers' perceptions of the different dimensions of school climate in a valid and reliable way in both Spain and Costa Rica. Differences in the perception of Spanish and Costa Rican teachers about school climate shown by multigroup confirmatory factor analyses are discussed as well as theoretical and practical implications.Este artículo estudia la validez de la Batería de cuestionarios sobre clima escolar para profesores de Secundaria y Bachillerato (BQCE-SB). La batería incluye cinco cuestionarios: calidad de liderazgo, calidad de apoyo docente, orientación motivacional del centro, calidad de las actitudes de los estudiantes y calidad del apoyo de los padres. Un total de 178 docentes de Costa Rica se compararon con 343 docentes españoles. Los análisis factoriales confirmatorios mostraron que los cuestionarios permiten evaluar de manera válida y confiable las percepciones de los docentes sobre las diferentes dimensiones del clima escolar, tanto en España como en Costa Rica. Se discuten las diferencias en la percepción de los docentes españoles y costarricenses sobre el clima escolar que muestran los análisis factoriales confirmatorios multigrupo, así como sus implicaciones teóricas y práctica

Annotation and overview of the Pseudomonas savastanoi pv. savastanoi NCPPB 3335 draft genome reveals the virulence gene complement of a tumour-inducing pathogen of woody hosts

Pseudomonas savastanoi pv. savastanoi is a tumour-inducing pathogen of Olea europaea L. causing oliveknot disease. Bioinformatic analysis of the draftgenome sequence of strain NCPPB 3335, whichencodes 5232 predicted coding genes on a totallength of 5856 998 bp and a 57.12% G + C, revealed alarge degree of conservation with Pseudomonassyringae pv. phaseolicola 1448A and P. syringae pv.tabaci 11528. However, NCPPB 3335 contains twelvevariable genomic regions, which are absent in all pre-viously sequenced P. syringae strains. Various fea-tures that could contribute to the ability of this strainto survive in a woody host were identified, includingbroad catabolic and transport capabilities for degrad-ing plant-derived aromatic compounds, the duplica-tion of sequences related to the biosynthesis of thephytohormone indoleacetic acid (iaaM, iaaH) and itsamino acid conjugate indoleacetic acid-lysine (iaaLgene), and the repertoire of strain-specific putativetype III secretion system effectors. Access to thisseventh genome sequence belonging to the ‘P. syrin-gae complex’ allowed us to identify 73 predictedcoding genes that are NCPPB 3335-specific. Resultsshown here provide the basis for detailed functionalanalysis of a tumour-inducing pathogen of woodyhosts and for the study of specific adaptations of a P.savastanoi pathovar

An application of generalized matrix learning vector quantization in neuroimaging

Background and objective: Neurodegenerative diseases like Parkinson’s disease often take several years before they can be diagnosed reliably based on clinical grounds. Imaging techniques such as MRI are used to detect anatomical (structural) pathological changes. However, these kinds of changes are usually seen only late in the development. The measurement of functional brain activity by means of [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) can provide useful information, but its interpretation is more difficult. The scaled sub-profile model principal component analysis (SSM/PCA) was shown to provide more useful information than other statistical techniques. Our objective is to improve the performance further by combining SSM/PCA and prototype-based generalized matrix learning vector quantization (GMLVQ). Methods: We apply a combination of SSM/PCA and GMLVQ as a classifier. In order to demonstrate the combination’s validity, we analyze FDG-PET data of Parkinson’s disease (PD) patients collected at three different neuroimaging centers in Europe. We determine the diagnostic performance by performing a ten times repeated ten fold cross validation. Additionally, discriminant visualizations of the data are included. The prototypes and relevance of GMLVQ are transformed back to the original voxel space by exploiting the linearity of SSM/PCA. The resulting prototypes and relevance profiles have then been assessed by three neurologists. Results: One important finding is that discriminative visualization can help to identify disease-related properties as well as differences which are due to center-specific factors. Secondly, the neurologist assessed the interpretability of the method and confirmed that prototypes are similar to known activity profiles of PD patients. Conclusion: We have shown that the presented combination of SSM/PCA and GMLVQ can provide useful means to assess and better understand characteristic differences in FDG-PET data from PD patients and HCs. Based on the assessments by medical experts and the results of our computational analysis we conclude that the first steps towards a diagnostic support system have been taken successfully

An application of generalized matrix learning vector quantization in neuroimaging

Background and objective: Neurodegenerative diseases like Parkinson’s disease often take several years before they can be diagnosed reliably based on clinical grounds. Imaging techniques such as MRI are used to detect anatomical (structural) pathological changes. However, these kinds of changes are usually seen only late in the development. The measurement of functional brain activity by means of [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) can provide useful information, but its interpretation is more difficult. The scaled sub-profile model principal component analysis (SSM/PCA) was shown to provide more useful information than other statistical techniques. Our objective is to improve the performance further by combining SSM/PCA and prototype-based generalized matrix learning vector quantization (GMLVQ). Methods: We apply a combination of SSM/PCA and GMLVQ as a classifier. In order to demonstrate the combination’s validity, we analyze FDG-PET data of Parkinson’s disease (PD) patients collected at three different neuroimaging centers in Europe. We determine the diagnostic performance by performing a ten times repeated ten fold cross validation. Additionally, discriminant visualizations of the data are included. The prototypes and relevance of GMLVQ are transformed back to the original voxel space by exploiting the linearity of SSM/PCA. The resulting prototypes and relevance profiles have then been assessed by three neurologists. Results: One important finding is that discriminative visualization can help to identify disease-related properties as well as differences which are due to center-specific factors. Secondly, the neurologist assessed the interpretability of the method and confirmed that prototypes are similar to known activity profiles of PD patients. Conclusion: We have shown that the presented combination of SSM/PCA and GMLVQ can provide useful means to assess and better understand characteristic differences in FDG-PET data from PD patients and HCs. Based on the assessments by medical experts and the results of our computational analysis we conclude that the first steps towards a diagnostic support system have been taken successfully