Deep learning applied to EEG source-data reveals both ventral and dorsal visual stream involvement in holistic processing of social stimuli

Perception of social stimuli (faces and bodies) relies on “holistic” (i.e., global) mechanisms, as supported by picture-plane inversion: perceiving inverted faces/bodies is harder than perceiving their upright counterpart. Albeit neuroimaging evidence suggested involvement of face-specific brain areas in holistic processing, their spatiotemporal dynamics and selectivity for social stimuli is still debated. Here, we investigate the spatiotemporal dynamics of holistic processing for faces, bodies and houses (adopted as control non-social category), by applying deep learning to high-density electroencephalographic signals (EEG) at source-level. Convolutional neural networks were trained to classify cortical EEG responses to stimulus orientation (upright/inverted), separately for each stimulus type (faces, bodies, houses), resulting to perform well above chance for faces and bodies, and close to chance for houses. By explaining network decision, the 150–200 ms time interval and few visual ventral-stream regions were identified as mostly relevant for discriminating face and body orientation (lateral occipital cortex, and for face only, precuneus cortex, fusiform and lingual gyri), together with two additional dorsal-stream areas (superior and inferior parietal cortices). Overall, the proposed approach is sensitive in detecting cortical activity underlying perceptual phenomena, and by maximally exploiting discriminant information contained in data, may reveal spatiotemporal features previously undisclosed, stimulating novel investigations

Caracterización de los factores familiares de riesgo en el consumo de sustancias, en estudiantes de enseñanza media

Una de las epidemias sociales de mayor y más rápida extensión en la pasada centuria y con probabilidad de extenderse y hacerse aún más grave, es el problema mundial de las drogas, fenómeno que representa una amenaza para la salud y el bienestar de los seres humanos, al menoscabar las bases socio-económicas, culturales y políticas de una sociedad (32). En el presente estudio se realiza una caracterización de los alumnos de enseñanza media a través de la Encuesta Nacional 2005 realizada por SEDRONAR, a los fines de observar los factores los factores de riesgo y su relación al consumo de drogas. Se analizaron 4593 encuestas correspondientes a la Provincia de Córdoba. Del total de los encuestados el 12,1% reconoció haber probado alguna vez en la vida droga. En el análisis en relación a sí el consumo de droga está relacionado con el turno escolar al cual concurre, se obtuvo que tanto los que consumieran una vez en su vida como los que lo hicieron en el último mes, resultaron significativos siendo el turno tarde y noche de mayor riesgo en ambos casos. En cuanto a la edad, donde más se consumió drogas tanto en los que los hicieron una vez o el último mes resultó altamente significativo para los de 15 años o más, y en menor para los de 14 años o menos. Mientras que el análisis en relación del consumo de droga y la situación de convivencia de los padres se observa que una familia constituida es altamente significativa y establece una relación con los factores de protección, siendo mayor su significación para aquellos que la probaron alguna vez en la vida. Por todo lo anteriormente analizado es que podemos afirmar que los factores de riesgo familiares son los más significativos. Se determinó que los factores de riesgo familiar son fuertes componentes al momento de probar la droga

Advances and Perspectives in Genetics of Congenital Thyroid Disorders

Congenital hypothyroidism (CH) is the most frequent endocrine disease in infants, affects about 1 in 3,000 newborns and is characterized by elevated levels of thyroidstimulating hormone (TSH) as a consequence of reduced thyroid function. It is also one of the most common preventable causes of cognitive and motor deficits. Prevention of CH is based on carrier identification, genetic counseling and prenatal diagnosis. In neonates a complete diagnosis of CH should include clinical examination, biochemical thyroid tests, thyroid ultrasound, radioiodine or technetium scintigraphy and perchlorate discharge test (PDT). In the last two decades, considerable progress has been made in identifying the genetic and molecular causes of CH. Knowing the prevalence of mutations in each population will facilitate greatly the molecular genetic testing. The classification based on the genetic alterations divides CH into two main categories caused: (a) by disorders of thyroid gland development (dysembriogenesis or thyroid dysgenesis group) or (b) by defects in any of the steps of thyroid hormone synthesis (dyshormonogenesis group) [1]. The dysembryogenesis or thyroid dysgenesis group, which accounts for the 80-85% of the cases, results from a thyroid gland that is completely absent in orthotopic or ectopic location (agenesis or athyreosis), severely reduced in size but in the proper position in the neck (orthotopic hypoplasia) or located in an unusual position (thyroid ectopy) at the base of the tongue or along the thyroglossal tract [1]. In only 5% of the patients, the CH is associated with mutations in genes responsible for the development or growth of thyroid cells: NKX2.1 (also known as TTF1 or T/EBP), FOXE1 (also known as TTF2 or FKHL15), paired box transcription factor 8 (PAX-8), NKX2.5, and TSHR genes [1]. Consequently, the genetic mechanisms underlying the defects in thyroid organogenesis in the majority of the cases remain to be elucidated. Epigenetic mechanisms leading to stochastic variations in the expression of multiple loci could be responsible for the sporadic characteristic of thyroid dysgenesis

Artificial neural-network technique for precipitation nowcasting from satellite imagery

The term nowcasting reflects the need of timely and accurate predictions of risky situations related to the development of severe meteorological events. In this work the objective is the very short term prediction of the rainfall field from geostationary satellite imagery entirely based on neural network approach. The very short-time prediction (or nowcasting) process consists of two steps: first, the infrared radiance field measured from geostationary satellite (Meteosat 7) is projected ahead in time (30 min or 1 h); secondly, the projected radiances are used to estimate the rainfall field by means of a calibrated microwave-based combined algorithm. The methodology is discussed and its accuracy is quantified by means of error indicators. An application to a satellite observation of a rainfall event over Central Italy is finally shown and evaluated

The Digital Image Correlation technique applied to the deformation behavior of welded sheet joints

The existence of a welded zone generally influences the local strain and stress distribution especially in case of welding defects. A method able to measure the local deformability can hence give many important information about the real stress and strain fields useful to improve the welded structure design. In this experimental work, some new generation automotive steels have been considered, because of the well known welding problems due to their unstable microstructural condition. Such materials, known as Q&P steels and available only as thin sheets, require a suitable quenching process able to give high mechanical resistance and satisfying deformability. Some sheet samples were welded by electron beam technique, because it is able to reduce the width of the heat affected zone where the main microstructural changes are concentrated. From such samples, tensile specimens were machined. During the tensile tests, the deformations were measured both by a traditional extensometer and by a 3D Digital Image Correlation (3D DIC) technique. A preliminary investigation of the melted and the heat affected zones resulted in small dimensions (about 10 mm) and hence the measuring setup has been optimized in order maximize the achievable measuring resolution minimizing the resulting uncertainty. This result can be achieved by a pattern generated by a suitable software and by an accurate preparation of the surface where the pattern will be deposited on

Probing the mechanism for hydrogel-based stasis induction in human pluripotent stem cells : is the chemical functionality of the hydrogel important?

It is well-known that pluripotent human embryonic stem cells (hPSC) can differentiate into any cell type. Recently, we reported that hPSC colonies enter stasis when immersed in an extremely soft hydrogel comprising hydroxyl-functional block copolymer worms (I. Canton, N. J. Warren, A. Chahal, K. Amps, A. Wood, R. Weightman, E. Wang, H. Moore and S. P. Armes, ACS Centr. Sci., 2016, 2, 65–74). The gel modulus and chemical structure of this synthetic hydrogel are similar to that of natural mucins, which are implicated in the mechanism of diapause for mammalian embryos. Does stasis induction occur merely because of the very soft nature of such hydrogels or does chemical functionality also play a role? Herein, we address this key question by designing a new hydrogel of comparable softness in which the PGMA stabilizer chains are replaced with non-hydroxylated poly(ethylene glycol) [PEG]. Immunolabeling studies confirm that hPSC colonies immersed in such PEG-based hydrogels do not enter stasis but instead proliferate (and differentiate if no adhesion substrate is present). However, pluripotency is retained if an appropriate adhesion substrate is provided. Thus, the chemical functionality of the hydrogel clearly plays a decisive role in the stasis induction mechanism

Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting

Wearable sensors play a significant role for monitoring the functional ability of the elderly and in general, promoting active ageing. One of the relevant variables to be tracked is the number of stair steps (single stair steps) performed daily, which is more challenging than counting flight of stairs and detecting stair climbing. In this study, we proposed a minimal complexity algorithm composed of a hierarchical classifier and a linear model to estimate the number of stair steps performed during everyday activities. The algorithm was calibrated on accelerometer and barometer recordings measured using a sensor platform worn at the wrist from 20 healthy subjects. It was then tested on 10 older people, specifically enrolled for the study. The algorithm was then compared with other three state-of-the-art methods, which used the accelerometer, the barometer or both. The experiments showed the good performance of our algorithm (stair step counting error: 13.8%), comparable with the best state-of-the-art (p > 0.05), but using a lower computational load and model complexity. Finally, the algorithm was successfully implemented in a low-power smartwatch prototype with a memory footprint of about 4 kB

Relation between toughness, infinite fatigue life and microstructure in large blooms for automotive plastic molds.

Presentazione orale al European Congress on Advanced Material and Processes (Euromat 2005), Praga (R. Ceca), 5/10/2005 - 8/10/200