Microarray analysis of autoimmune diseases by machine learning procedures

—Microarray-based global gene expression profiling, with the use of sophisticated statistical algorithms is providing new insights into the pathogenesis of autoimmune diseases. We have applied a novel statistical technique for gene selection based on machine learning approaches to analyze microarray expression data gathered from patients with systemic lupus erythematosus (SLE) and primary antiphospholipid syndrome (PAPS), two autoimmune diseases of unknown genetic origin that share many common features. The methodology included a combination of three data discretization policies, a consensus gene selection method, and a multivariate correlation measurement. A set of 150 genes was found to discriminate SLE and PAPS patients from healthy individuals. Statistical validations demonstrate the relevance of this gene set from an univariate and multivariate perspective. Moreover, functional characterization of these genes identified an interferon-regulated gene signature, consistent with previous reports. It also revealed the existence of other regulatory pathways, including those regulated by PTEN, TNF, and BCL-2, which are altered in SLE and PAPS. Remarkably, a significant number of these genes carry E2F binding motifs in their promoters, projecting a role for E2F in the regulation of autoimmunity.

Explicit/implicit training of cooperative learning with junior elementary students /

This experimental study examined the effects of cooperative learning and expliciUimpliGit instruction on student achievement and attitudes toward working in cooperative groups. Specifically, fourth- and fifth-grade students (n=48) were randomly assigned to two conditions: cooperative learning with explicit instruction and cooperative learning with implicit instruction. All participants were given initial training either explicitly or implicitly in cooperative learning procedures via 10 one-hour sessions. Following the instruction period, all students participated in completing a group project related to a famous artists unit. It was hypothesized that the explicit instruction training would enhance students' scores on the famous artists test and the group projects, as well as improve students' attitudes toward cooperative learning. Although the explicit training group did not achieve significantly higher scores on the famous artists test, significant differences were found in group project results between the explicit and implicit groups. The explicit group also exhibited more favourable and positive attitudes toward cooperative learning. The findings of this study demonstrate that combining cooperative learning with explicit instruction is an effective classroom strategy and a useful practice for presenting and learning new information, as well as working in groups with success.

Cooperative learning and elaborative interrogation: effects on children's learning

This experimental study examined the effects of cooperative learning and a question-answering strategy called elaborative interrogation ("Why is this fact true?") on the learning of factual information about familiar animals. Retention gains were compared across four study conditions: elaborative-interrogation-plus-cooperative learning, cooperative-learning, elaborative-interrogation, and reading-control. Sixth-grade students (n=68) were randomly assigned to the four conditions. All participants were given initial training and practice in cooperative learning procedures via three 45-minute sessions. After studying 36 facts about six animals, students' retention gains were measured via immediate free recall, immediate matched association, 30-day, and GO-day matched association tests. A priori comparisons were made to analyze the data. For immediate free recall and immediate matched association, significant differences were found between students in the three experimental conditions versus those in the control condition. Elaborative-interrogation and elaborativeinterrogation- plus-cooperative-learning also promoted longterm retention (measured via 30-day matched association) of the material relative to repetitive reading with elaborative-interrogation promoting the most durable gains (measured via GO-day matched association). The relationship between the types of elaborative responses and probability of subsequent retention was also examined. Even when students were unable to provide adequate answers to the why questions, learning was facilitated more so than repetitive reading. In general, generation of adequate elaborations was associated with greater probability of recall than was provision of inadequate answers. The findings of the study demonstrate that cooperative learning and the use of elaborative interrogation, both individually and collaboratively, are effective classroom procedures for facilitating children's learning of new information.

The Cascade Neo-Fuzzy Architecture and its Online Learning Algorithm

In the paper learning algorithm for adjusting weight coefficients of the Cascade Neo-Fuzzy Neural Network (CNFNN) in sequential mode is introduced. Concerned architecture has the similar structure with the Cascade-Correlation Learning Architecture proposed by S.E. Fahlman and C. Lebiere, but differs from it in type of artificial neurons. CNFNN consists of neo-fuzzy neurons, which can be adjusted using high-speed linear learning procedures. Proposed CNFNN is characterized by high learning rate, low size of learning sample and its operations can be described by fuzzy linguistic “if-then” rules providing “transparency” of received results, as compared with conventional neural networks. Using of online learning algorithm allows to process input data sequentially in real time mode.

On the use of qualitative reasoning to simulate and identify metabolic pathways

King, R.D., Garrett, S.M., Coghill, G.M. (2005). On the use of qualitative reasoning to simulate and identify metabolic pathways. Bioinformatics 21(9):2017-2026 RAE2008

L'utilisation de la musique comme support de nouveaux apprentissages dans le vieillissement normal et la maladie d'Alzheimer

L’objectif de ce travail de thèse est d’évaluer le potentiel de la musique comme support mnémotechnique pour l’acquisition de nouvelles informations chez des personnes âgées saines et atteintes de la maladie d’Alzheimer (MA). Les bénéfices de la musique sur la cognition ont souvent été mis en évidence, y compris chez des populations âgées ou atteintes de démence. Parallèlement, chez des sujets jeunes, l’idée que la musique peut servir de support pour la mémoire a été largement débattue. Pourtant, très peu d’études ont posé cette question auprès de populations âgées ou dans la démence, malgré le besoin persistant de stratégies d’intervention dans ce domaine. Dans le présent travail, deux études sont menées dans une cohorte de 8 participants atteints d’un stade léger de la maladie d’Alzheimer, et 7 participants âgés sains appariés en âge et niveau de scolarité. La première étude porte sur la mémoire verbale, et compare l’apprentissage et la rétention de paroles (textes inconnus) présentées de manière récitée ou chantée. Lorsque les paroles sont chantées, différents degrés de familiarité de la mélodie sont contrastés. Aussi, l’action motrice étant intimement liée à l’écoute musicale, nous contrastons deux procédures d’apprentissage impliquant (ou non) la production synchronisée des paroles à mémoriser pendant l’encodage : le participant est invité à chanter à l’unisson avec un modèle (ou à écouter simplement sans chanter). Les résultats de cette étude sont présentés et discutés dans les deux premiers articles de la partie expérimentale. Ils suggèrent globalement que la musique n’aide pas l’apprentissage en rappel immédiat ; un effet délétère est même observé lorsque la mélodie utilisée est non familière. Par contre, la musique favorise la rétention à long terme des paroles, principalement pour les participants MA. Elle ne semble cependant pas interagir avec la procédure d’apprentissage impliquant le chant à l’unisson. La seconde étude porte sur l’apprentissage de séquences de gestes. Suivant la même logique que dans la première étude, nous explorons l’influence d’un accompagnement musical (versus apprentissage en silence) et d’une procédure d’apprentissage avec production synchronisée (versus observation) des gestes durant l’encodage. Les résultats (article 3) ne montrent pas non plus d’interaction entre l’accompagnement et la procédure d’apprentissage, mais différents effets de chaque composante sur les deux groupes de participants. Effectuer les gestes en synchronie avec un modèle lors de l’encodage est bénéfique pour les sujets Contrôles, mais plutôt délétère pour les participants MA. Par contre, l’accompagnement musical favorise davantage l’apprentissage chez les sujet MA que chez les Contrôles. En discussion générale, nous discutons les implications de ces résultats pour la neuropsychologie fondamentale et clinique, et proposons notamment différentes recommandations visant à maximiser ces effets et à les rendre pertinents pour l’usage thérapeutique en stimulation cognitive.

Improving accuracy and speed of optimum-path forest classifier using combination of disjoint training subsets

The Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OPF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure. The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OPF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more. © 2011 Springer-Verlag.

Psicoterapia analítico-funcional: efeitos de um curso sobre a interação terapêutica

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Emergência de leitura recombinativa de frases em crianças de escolas públicas

Uma das principais queixas acadêmicas refere-se à dificuldade de compreensão de leitura, apresentada por uma parcela considerável do corpo estudantil brasileiro, em diversos níveis de ensino. O paradigma de equivalência tem contribuído para o entendimento dos processos comportamentais envolvidos na aquisição do repertório de leitura de textos com compreensão. Os objetivos do estudo foram, por meio de replicação sistemática: 1) verificar o efeito do ensino das discriminações de sílabas na emergência da leitura textual de palavras e frases de ensino e recombinadas; 2) investigar o efeito de um procedimento de ensino de discriminação de palavras ditadas e impressas (AC) na emergência da leitura com compreensão de palavras e frases de ensino e recombinadas; 3) programar um procedimento de ensino que produza poucos ou nenhum erro; 4) aprimorar os procedimentos utilizados por estudos anteriores, tornando-os mais eficientes e econômicos e com menor variabilidade de desempenho entre participantes. Os estímulos foram auditivos, visuais e auditivo-visuais (sílabas, palavras e frases faladas e impressas e figuras impressas). Foi realizado o ensino das discriminações condicionais entre palavras/ frases faladas e figuras (relação AB) sílabas/ palavras/ frases faladas e estímulos impressos (relações AC). Foram programadas seis fases experimentais. A unidade de leitura foi ampliada gradualmente durante as fases, as quais foram compostas, na Fase V, de pronomes demonstrativos, substantivos concretos, adjetivos e verbos intransitivos. Todos os participantes demonstraram a leitura textual das sílabas simples e complexas e a emergência imediata com compreensão das palavras de ensino. A maioria dos participantes demonstrou prontamente a leitura textual das palavras. Todos os participantes, exceto um, demonstrou a emergência da leitura textual de todas as frases com quatro palavras. A maioria dos participantes apresentou prontamente a leitura com compreensão das palavras e das frases. Na Fase III, a maioria dos participantes apresentou a leitura das frases com duas palavras na primeira exposição e uma participante leu corretamente na segunda exposição. Na Fase IV, cinco participantes apresentaram a leitura textual das frases com três palavras na primeira exposição e uma participante após a emergência da leitura com compreensão. No teste de manutenção de repertório, a maioria dos participantes leu todas as palavras e frases do estudo, com exceção de duas participantes na leitura das frases de duas e três palavras. O melhor desempenho dos participantes deu-se na leitura das frases com quatro palavras. Esses resultados indicam que se as discriminações entre sílabas forem ensinadas diretamente, ocorrerá a emergência da leitura generalizada recombinativa de palavras e frases com até quatro componentes sem estabelecer o controle parcial e sem a necessidade de procedimentos especiais de ensino.

Avaliando conhecimento em DST de graduandos em medicina segundo a taxonomia de bloom

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Elektrophysiologische, molekularbiologische und verhaltensbiologische Untersuchungen zur Funktion von N-Methyl-D-Aspartat-Rezeptoren bei Gehirnentwicklung und Lernvorgängen

Im Zentralnervensystem der Säuger steuern N-Methyl-D-Aspartat-(NMDA)-Rezeptoren viele neuronale Prozesse, insbesondere während der Ontogenese sowie bei Lern- und Gedächtnisvorgängen. In der vorliegenden Arbeit wurde die Bedeutung dieser Rezeptoren während der Kortexentwicklung und bei Lernvorgängen mittels elektrophysiologischer, molekularbiologischer, pharmakologischer, histologischer, genetischer und verhaltensbiologischer Methoden an der Maus untersucht. Oszillatorische Netzwerkaktivität ist für die gesunde Entwicklung des Kortex essentiell. Mittels gepaarter patch-clamp Experimente an neonatalen Subplattenzellen wurde festgestellt, dass diese Neurone elektrisch gekoppelt sind. Damit könnten sie einen wichtigen Beitrag zur Entstehung bzw. Verstärkung von Netzwerkoszillationen leisten. Subplattenzellen erhalten afferenten Eingang aus dem Thalamus sowie von benachbarten Subplattenzellen. Die funktionellen und molekularen Eigenschaften dieser Synapsen differierten in eingangsspezifischer Weise. Subplatteninterne Verbindungen besaßen Integrations- und Summationsfähigkeiten, wenig synaptische Ermüdung, Paarpulsfazilitierung und einen erhöhten NR2D-Anteil in ihren NMDA-Rezeptoren. CA1-Pyramidenzellen des adulten Hippocampus zeigten eine den Subplattenzellen vergleichbare eingangsspezifische Verteilung der NMDA-Rezeptor-Untereinheiten. Synapsen von Schaffer-Kollateralen besaßen einen höheren NR2B-Anteil als temporo-ammonische Verbindungen. Die Aktivierung von Dopamin-Rezeptoren potenzierte NR2B-vermittelte synaptische Ströme in CA1-Neuronen. Bei komplexen Lernvorgängen, wie der Extinktion einer traumatischen Erinnerung, spielten NMDA-Rezeptoren von hippocampalen CA1-Zellen eine entscheidende Rolle. CA1-NMDA-Rezeptor-ko-Mäuse zeigten erhebliche Extinktionsdefizite nach Angstkonditionierung. Zudem entwickelten diese Mäuse erhöhte Ängstlichkeit und Hyperaktivität. Das sind beim Menschen Symptome für psychiatrische Angststörungen. Daher könnten CA1-NMDA-Rezeptor-ko-Mäuse als neues Tiermodell für solche Störungen dienen, die durch ein traumatisches Erlebnis ausgelöst werden, wie beim Posttraumatischen Stresssyndrom (PTSD).

Clasificación de tráfico de Internet mediante análisis de datos

Poder clasificar de manera precisa la aplicación o programa del que provienen los flujos que conforman el tráfico de uso de Internet dentro de una red permite tanto a empresas como a organismos una útil herramienta de gestión de los recursos de sus redes, así como la posibilidad de establecer políticas de prohibición o priorización de tráfico específico. La proliferación de nuevas aplicaciones y de nuevas técnicas han dificultado el uso de valores conocidos (well-known) en puertos de aplicaciones proporcionados por la IANA (Internet Assigned Numbers Authority) para la detección de dichas aplicaciones. Las redes P2P (Peer to Peer), el uso de puertos no conocidos o aleatorios, y el enmascaramiento de tráfico de muchas aplicaciones en tráfico HTTP y HTTPS con el fin de atravesar firewalls y NATs (Network Address Translation), entre otros, crea la necesidad de nuevos métodos de detección de tráfico. El objetivo de este estudio es desarrollar una serie de prácticas que permitan realizar dicha tarea a través de técnicas que están más allá de la observación de puertos y otros valores conocidos. Existen una serie de metodologías como Deep Packet Inspection (DPI) que se basa en la búsqueda de firmas, signatures, en base a patrones creados por el contenido de los paquetes, incluido el payload, que caracterizan cada aplicación. Otras basadas en el aprendizaje automático de parámetros de los flujos, Machine Learning, que permite determinar mediante análisis estadísticos a qué aplicación pueden pertenecer dichos flujos y, por último, técnicas de carácter más heurístico basadas en la intuición o el conocimiento propio sobre tráfico de red. En concreto, se propone el uso de alguna de las técnicas anteriormente comentadas en conjunto con técnicas de minería de datos como son el Análisis de Componentes Principales (PCA por sus siglas en inglés) y Clustering de estadísticos extraídos de los flujos procedentes de ficheros de tráfico de red. Esto implicará la configuración de diversos parámetros que precisarán de un proceso iterativo de prueba y error que permita dar con una clasificación del tráfico fiable. El resultado ideal sería aquel en el que se pudiera identificar cada aplicación presente en el tráfico en un clúster distinto, o en clusters que agrupen grupos de aplicaciones de similar naturaleza. Para ello, se crearán capturas de tráfico dentro de un entorno controlado e identificando cada tráfico con su aplicación correspondiente, a continuación se extraerán los flujos de dichas capturas. Tras esto, parámetros determinados de los paquetes pertenecientes a dichos flujos serán obtenidos, como por ejemplo la fecha y hora de llagada o la longitud en octetos del paquete IP. Estos parámetros serán cargados en una base de datos MySQL y serán usados para obtener estadísticos que ayuden, en un siguiente paso, a realizar una clasificación de los flujos mediante minería de datos. Concretamente, se usarán las técnicas de PCA y clustering haciendo uso del software RapidMiner. Por último, los resultados obtenidos serán plasmados en una matriz de confusión que nos permitirá que sean valorados correctamente. ABSTRACT. Being able to classify the applications that generate the traffic flows in an Internet network allows companies and organisms to implement efficient resource management policies such as prohibition of specific applications or prioritization of certain application traffic, looking for an optimization of the available bandwidth. The proliferation of new applications and new technics in the last years has made it more difficult to use well-known values assigned by the IANA (Internet Assigned Numbers Authority), like UDP and TCP ports, to identify the traffic. Also, P2P networks and data encapsulation over HTTP and HTTPS traffic has increased the necessity to improve these traffic analysis technics. The aim of this project is to develop a number of techniques that make us able to classify the traffic with more than the simple observation of the well-known ports. There are some proposals that have been created to cover this necessity; Deep Packet Inspection (DPI) tries to find signatures in the packets reading the information contained in them, the payload, looking for patterns that can be used to characterize the applications to which that traffic belongs; Machine Learning procedures work with statistical analysis of the flows, trying to generate an automatic process that learns from those statistical parameters and calculate the likelihood of a flow pertaining to a certain application; Heuristic Techniques, finally, are based in the intuition or the knowledge of the researcher himself about the traffic being analyzed that can help him to characterize the traffic. Specifically, the use of some of the techniques previously mentioned in combination with data mining technics such as Principal Component Analysis (PCA) and Clustering (grouping) of the flows extracted from network traffic captures are proposed. An iterative process based in success and failure will be needed to configure these data mining techniques looking for a reliable traffic classification. The perfect result would be the one in which the traffic flows of each application is grouped correctly in each cluster or in clusters that contain group of applications of similar nature. To do this, network traffic captures will be created in a controlled environment in which every capture is classified and known to pertain to a specific application. Then, for each capture, all the flows will be extracted. These flows will be used to extract from them information such as date and arrival time or the IP length of the packets inside them. This information will be then loaded to a MySQL database where all the packets defining a flow will be classified and also, each flow will be assigned to its specific application. All the information obtained from the packets will be used to generate statistical parameters in order to describe each flow in the best possible way. After that, data mining techniques previously mentioned (PCA and Clustering) will be used on these parameters making use of the software RapidMiner. Finally, the results obtained from the data mining will be compared with the real classification of the flows that can be obtained from the database. A Confusion Matrix will be used for the comparison, letting us measure the veracity of the developed classification process.

Early termination of R&D projects

Early project termination is one of the most difficult decisions to be made by Research and Development managers. While there is the risk of terminating good projects, there is also the opposite risk of not terminating bad projects and overspend resources in unproductive research. Criteria used for identifying these projects are common subject of research in Business Administration. In addition, companies might take important lessons from its interrupted projects that could improve their overall portfolio technical and commercial success. Finally, the set and weight of criteria, as well as the procedures companies use for achieve learning from cancelled projects may vary depending on the project type. This research intends to contribute to the understanding of policies applied to projects that were once considered attractive, but by some reason is not appreciated anymore. The research addressed the question: How companies deal with projects that become unattractive? More specifically, this research tried to answer the following questions: (1) Are projects killed or (otherwise) they die naturally by lack of resources? (2) What criteria are used to terminate projects during development? (3) How companies learn from the terminated projects to improve the overall portfolio performance? (4) Are the criteria and learning procedures different for different types of projects? In order to answer these questions, we performed a multiple case study with four companies that are reference in business administration and innovation: (1) Oxiteno, considered the base case, (2) Natura, the literal replication, (3) Mahle and (4) AES, the theoretical replications. The case studies were performed using a semi-structured protocol for interviews, which were recorded and analyzed for comparison. We found that the criteria companies use for selecting projects for termination are very similar to those anticipated by the literature, except for a criteria related to compliance. We have evidences to confirm that the set of criteria is not altered when dealing with different project types, however the weight they are applied indeed varies. We also found that learning with cancelled projects is yet very incipient, with very few structured formal procedures being described for capturing learning with early-terminated projects. However, we could observe that these procedures are more common when dealing with projects labeled as innovative, risky, big and costly, while those smaller and cheaper derivative projects aren\'t subject of a complete investigation on the learning they brought to the company. For these, the most common learning route is the informal, where the project team learns and passes the knowledge though interpersonal information exchange. We explain that as a matter of cost versus benefit of spending time to deeply investigate projects with little potential to bring new knowledge to the project team and the organization

Using non-parametric search algorithms to forecast daily excess stock returns

Are the learning procedures of genetic algorithms (GAs) able to generate optimal architectures for artificial neural networks (ANNs) in high frequency data? In this experimental study,GAs are used to identify the best architecture for ANNs. Additional learning is undertaken by the ANNs to forecast daily excess stock returns. No ANN architectures were able to outperform a random walk,despite the finding of non-linearity in the excess returns. This failure is attributed to the absence of suitable ANN structures and further implies that researchers need to be cautious when making inferences from ANN results that use high frequency data.