Science journalism: the importance of shaping the communication channel between scientists and the general public

Não importa o quão útil, complexa ou surpreendente seja uma descoberta relacionada com o cérebro, ela afeta magicamente a opinião pública. Para além do entendimento dos mecanismos neurais estão a cura de doenças neurológicas e psiquiátricas e, ainda mais atraente, o poder de compreender e modificar o comportamento das pessoas. Enquanto os avanços têm sido informados à comunidade científica através de meios tradicionais, o público em geral receber estas notícias através da mídia. Neste trabalho, analisamos diferentes casos paradigmáticos em que estratégias inadequadas de comunicação e suas consequências tiveram um impacto negativo na sociedade. Junto com a apresentação desses casos, aconselhamos sobre a necessidade de incorporar os jornalistas ao círculo de descoberta e comunicação, a fim de garantir a compreensão, pelo público em geral, das descobertas e progresso da neurociênciaNo matter how useful, complex or astonishing a discovery related to the brain is, it impacts magically on public opinion. Beyond the pure understanding of the brain mechanisms are the cure of neurological and psychiatric disorders and, even more attractive, the power to understand and modify people’s behaviour. While breakthroughs have been communicated to the scientific community by traditional means, general public receive these news through the media. In this work we analyse different paradigmatic cases where wrong communicative strategies and their consequences impacted negatively on the society. Along with the presentation of those cases, we advise over the necessity of incorporating journalists to the scientific loop of discovery and communication, in order to guarantee the general public understanding of neuroscience discoveries and progres

State-dependent changes of connectivity patterns and functional brain network topology in Autism Spectrum Disorder

Anatomical and functional brain studies have converged to the hypothesis that Autism Spectrum Disorders (ASD) are associated with atypical connectivity. Using a modified resting-state paradigm to drive subjects' attention, we provide evidence of a very marked interaction between ASD brain functional connectivity and cognitive state. We show that functional connectivity changes in opposite ways in ASD and typicals as attention shifts from external world towards one's body generated information. Furthermore, ASD subject alter more markedly than typicals their connectivity across cognitive states. Using differences in brain connectivity across conditions, we classified ASD subjects at a performance around 80% while classification based on the connectivity patterns in any given cognitive state were close to chance. Connectivity between the Anterior Insula and dorsal-anterior Cingulate Cortex showed the highest classification accuracy and its strength increased with ASD severity. These results pave the path for diagnosis of mental pathologies based on functional brain networks obtained from a library of mental states

A Computational Theory for the Learning of Equivalence Relations

Equivalence relations (ERs) are logical entities that emerge concurrently with the development of language capabilities. In this work we propose a computational model that learns to build ERs by learning simple conditional rules. The model includes visual areas, dopaminergic, and noradrenergic structures as well as prefrontal and motor areas, each of them modeled as a group of continuous valued units that simulate clusters of real neurons. In the model, lateral interaction between neurons of visual structures and top-down modulation of prefrontal/premotor structures over the activity of neurons in visual structures are necessary conditions for learning the paradigm. In terms of the number of neurons and their interaction, we show that a minimal structural complexity is required for learning ERs among conditioned stimuli. Paradoxically, the emergence of the ER drives a reduction in the number of neurons needed to maintain those previously specific stimulus–response learned rules, allowing an efficient use of neuronal resources

High mutual cooperation rates in rats learning reciprocal altruism: The role of payoff matrix

Cooperation is one of the most studied paradigms for the understanding of social interactions. Reciprocal altruism -a special type of cooperation that is taught by means of the iterated prisoner dilemma game (iPD)- has been shown to emerge in different species with different success rates. When playing iPD against a reciprocal opponent, the larger theoretical long-term reward is delivered when both players cooperate mutually. In this work, we trained rats in iPD against an opponent playing a Tit for Tat strategy, using a payoff matrix with positive and negative reinforcements, that is food and timeout respectively. We showed for the first time, that experimental rats were able to learn reciprocal altruism with a high average cooperation rate, where the most probable state was mutual cooperation (85%). Although when subjects defected, the most probable behavior was to go back to mutual cooperation. When we modified the matrix by increasing temptation rewards (T) or by increasing cooperation rewards (R), the cooperation rate decreased. In conclusion, we observe that an iPD matrix with large positive reward improves less cooperation than one with small rewards, shown that satisfying the relationship among iPD reinforcement was not enough to achieve high mutual cooperation behavior. Therefore, using positive and negative reinforcements and an appropriate contrast between rewards, rats have cognitive capacity to learn reciprocal altruism. This finding allows to infer that the learning of reciprocal altruism has early appeared in evolution.Fil: Delmas, Guillermo Ezequiel. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lew, Sergio Eduardo. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Zanutto, Bonifacio Silvano. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentin

Plasticity in the Rat Prefrontal Cortex: Linking Gene Expression and an Operant Learning with a Computational Theory

The plasticity in the medial Prefrontal Cortex (mPFC) of rodents or lateral prefrontal cortex in non human primates (lPFC), plays a key role neural circuits involved in learning and memory. Several genes, like brain-derived neurotrophic factor (BDNF), cAMP response element binding (CREB), Synapsin I, Calcium/calmodulin-dependent protein kinase II (CamKII), activity-regulated cytoskeleton-associated protein (Arc), c-jun and c-fos have been related to plasticity processes. We analysed differential expression of related plasticity genes and immediate early genes in the mPFC of rats during learning an operant conditioning task. Incompletely and completely trained animals were studied because of the distinct events predicted by our computational model at different learning stages. During learning an operant conditioning task, we measured changes in the mRNA levels by Real-Time RT-PCR during learning; expression of these markers associated to plasticity was incremented while learning and such increments began to decline when the task was learned. The plasticity changes in the lPFC during learning predicted by the model matched up with those of the representative gene BDNF. Herein, we showed for the first time that plasticity in the mPFC in rats during learning of an operant conditioning is higher while learning than when the task is learned, using an integrative approach of a computational model and gene expression

A copula-based method for synthetic microarray data generation

In this work, we propose a copula-based method to generate synthetic gene expression data that account for marginal and joint probability distributions features captured from real data. Our method allows us to implant significant genes in the synthetic dataset in a controlled manner, giving the possibility of testing new detection algorithms under more realistic environments

Gene filtering with optimal threshold selection

Gene filtering is a useful preprocessing technique often applied to microarray datasets. However, it is no common practice because clear guidelines are lacking and it bears the risk of excluding some potentially relevant genes. In this work, we propose to model microarray data as a mixture of two Gaussian distributions that will allow us to obtain an optimal filter threshold in terms of the gene expression level.Fil: Bau Macia, Josep. Universidad de Vic; EspañaFil: Sole Casals, Jordi. Universidad de Vic; EspañaFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Lew, Sergio Eduardo. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; ArgentinaThe Barcelona International Conference on Advances in StatisticsBarcelonaEspañaUniversidad Autónoma de Barcelon

Differential representation of sunflower ESTs in enriched organ-specific cDNA libraries in a small scale sequencing project

BACKGROUND: Subtractive hybridization methods are valuable tools for identifying differentially regulated genes in a given tissue avoiding redundant sequencing of clones representing the same expressed genes, maximizing detection of low abundant transcripts and thus, affecting the efficiency and cost effectiveness of small scale cDNA sequencing projects aimed to the specific identification of useful genes for breeding purposes. The objective of this work is to evaluate alternative strategies to high-throughput sequencing projects for the identification of novel genes differentially expressed in sunflower as a source of organ-specific genetic markers that can be functionally associated to important traits. RESULTS: Differential organ-specific ESTs were generated from leaf, stem, root and flower bud at two developmental stages (R1 and R4). The use of different sources of RNA as tester and driver cDNA for the construction of differential libraries was evaluated as a tool for detection of rare or low abundant transcripts. Organ-specificity ranged from 75 to 100% of non-redundant sequences in the different cDNA libraries. Sequence redundancy varied according to the target and driver cDNA used in each case. The R4 flower cDNA library was the less redundant library with 62% of unique sequences. Out of a total of 919 sequences that were edited and annotated, 318 were non-redundant sequences. Comparison against sequences in public databases showed that 60% of non-redundant sequences showed significant similarity to known sequences. The number of predicted novel genes varied among the different cDNA libraries, ranging from 56% in the R4 flower to 16 % in the R1 flower bud library. Comparison with sunflower ESTs on public databases showed that 197 of non-redundant sequences (60%) did not exhibit significant similarity to previously reported sunflower ESTs. This approach helped to successfully isolate a significant number of new reported sequences putatively related to responses to important agronomic traits and key regulatory and physiological genes. CONCLUSIONS: The application of suppressed subtracted hybridization technology not only enabled the cost effective isolation of differentially expressed sequences but it also allowed the identification of novel sequences in sunflower from a relative small number of analyzed sequences when compared to major sequencing projects

¿Qué es la Inteligencia Artificial?

Muy probablemente, el lector haya descubierto el término Inteligencia Artificial (IA) a través de la literatura fantástica o producciones cinematográficas que, frecuentemente, aluden a un futuro dominado por la tecnología y plagado de robots humanoides. Sin duda, esas obras de ciencia ficción estuvieron inspiradas en discusiones científicas iniciadas a partir de mediados del siglo XX con la aparición de las primeras computadoras y con la idea de que éstas pudieran imitar, y hasta superar, las habilidades intelectuales de los humanos. No es casual que Isaac Asimov, autor del libro de ciencia ficción pionero en IA publicado en 1950: ?Yo, Robot? (Asimov, 2001), fuera profesor de la Universidad de Boston, doctorado en Química, cuyo desempeño en la academia le permitió estar al tanto de los avances en las por entonces florecientes ciencias de la computación. Si bien en el pasado reciente la IA pertenecía casi exclusivamente al mundo de la ciencia ficción o era materia de estudio de un puñado de científicos, durante los últimos años hemos comenzado a familiarizarnos con este término que ya forma parte de nuestra vida cada día. Nuestros teléfonos celulares están dotados de IA, nos sugieren itinerarios óptimos, nos recomiendan artículos para comprar y nos identifican en una fotografía tomada por un contacto en una red social, entre otras acciones cotidianas. La IA, ha dejado de ser una idea futurística para formar parte de nuestras vidas, además de tener un rol relevante en el desarrollo de la ciencia moderna. Nos permite descubrir inteligentemente nuevas drogas para tratamientos de enfermedades, ayuda a los médicos a diagnosticar enfermedades a partir de imágenes, asiste a los astrónomos en el análisis de grandes volúmenes de datos para validar nuevas teorías científicas, entre otras aplicaciones a la ciencia. Pero ¿de qué hablamos exactamente cuando nos referimos a la IA? En este breve artículo, se cuenta brevemente la historia de esta tecnología con una introducción a sus principios fundamentales y utilidades.Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Lew, Sergio Eduardo. Universidad de Buenos Aires. Facultad de Ingeniería; Argentin