Wnt-3a and Wnt-3 differently stimulate proliferation and neurogenesis of spinal neural precursors and promote neurite outgrowth by canonical signaling

Wnt factors regulate neural stem cell development and neuronal connectivity. Here we investigated whether Wnt-3a and Wnt-3, expressed in the developing spinal cord, regulate proliferation and the neuronal differentiation of spinal cord neural precursors (SCNP). Wnt-3a promoted a sustained increase of SCNP proliferation, whereas Wnt-3 enhanced SCNP proliferation transiently and increased neurogenesis through β-catenin signaling. Consistent with this, Wnt-3a and Wnt-3 differently regulate the expression of Cyclin-dependent kinase inhibitors. Furthermore, Wnt-3a and Wnt-3 stimulated neurite outgrowth in SCNP-derived neurons through ß-catenin and TCF4-dependent transcription. GSK-3ß inhibitors mimicked Wnt signaling and promoted neurite outgrowth in established cultures. We conclude that Wnt-3a and Wnt-3 signal through the canonical Wnt/β-catenin pathway to regulate different aspects of SCNP development. These findings may be of therapeutic interest for the treatment of neurodegenerative diseases and nerve injury.

Implementación y entrenamiento de un modelo clasificatorio de red neural sobre la base de datos IGBADAT para la clasificación de las rocas basálticas de acuerdo a las clases del sistema de clasificación tradicional de Yoder and Tiller.

Aquest treball vol implementar un projecte de mineria de dades en l'àrea de la petrologia ígnia, especialitat englobada dins la geologia clàssica.

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

Volatility smile extrapolation with an artificial neural network

I use a multi-layer feedforward perceptron, with backpropagation learning implemented via stochastic gradient descent, to extrapolate the volatility smile of Euribor derivatives over low-strikes by training the network on parametric prices.

Anàlisi tèrmica de l'obtenció d'òxids de ceri a partir de precursors moleculars

El projecte consisteix en l’anàlisi tèrmica de l’obtenció d’òxids de ceri a partirde precursors moleculars com són el propionat de ceri (III), el propionat de ceri (III)dopat amb gadolini i el propionat de ceri (III) dopat amb zirconi. Els òxids resultantssón materials superconductors que ofereixen una resistència nul•la al pas del correnten determinades condicions. Per realitzar l’estudi hem fet servir quatre tècniques diferents: termogravimetria, calorimetria diferencial, espectrometria de masses i difracció de raigs-x

Anàlisi tèrmica de l’obtenció d’òxids de bari a partir de precursors moleculars

Aquest projecte, que col•labora amb el Grup de Recerca en Materials i termodinàmica,tractarà de com obtenir òxids de bari purs per incloure en la matriu del superconductorYBCO a partir de diversos precursors moleculars tals com l’acetat de bari, el propionatde bari i l’etil hexaonat de bari. La dificultat rau en la descomposició tèrmica d’aquestsprecursors a òxid, ja que solen descompondre’s a carbonat. L’estudi consistirà encercar les condicions que evitin la formació de carbonat i per tant afavoreixin l’obtencióde l’òxid.L’estudi es realitza fent ús de les tècniques d’anàlisi tèrmica tals com latermogravimetria i l’anàlisi tèrmica diferencial. A posteriori, també hem utilitzat ladifracció de Raigs-X que ens permetrà analitzar el residu sòlid resultat de ladescomposició i per tant saber quin compost tenim, si òxid o carbonat. Tot i això, caltenir en compte que aquesta tècnica s’ha d’aplicar en finalitzar l’anàlisi tèrmica ja quesinó l’òxid que es forma reacciona amb el CO2 de l’atmosfera amb molta facilitat ialeshores s’observa només carbonat en el resultat

CAV2 vector gene transfer into central nervious System

Estudi realitzat a partir d’una estada al Institut de Génétique Moléculaire de Montpellier, França, entre 2010 i 2012. En aquest projecte s’ha avaluat les avantatges dels vectors adenovirals canins tipus 2 (CAV2) com a vectors de transferència gènica al sistema nerviós central (SNC) en un model primat no-humà i en un model caní del síndrome de Sly (mucopolisacaridosis tipus 7, MPS VII), malaltia monogènica que cursa amb neurodegeneració. En una primera part del projecte s’ha avaluat la biodistribució, l’eficàcia i la durada de l’expressió del transgen en un model primat no humà, (Microcebus murinus). Com ha vector s’ha utilitzat un CAV2 de primera generació que expressa la proteïna verda fluorescent (CAVGFP). Els resultats aportats en aquesta memòria demostren que en primats no humans, com en d’altres espècies testades anteriorment per l’equip de l’EJ Kremer, la injecció intracerebral de CAV2 resulta en una extensa transducció del SNC, siguent les neurones i els precursors neuronals les cèl•lules preferencialment transduïdes. Els vectors canins, servint-se de vesícules intracel•lulars són transportats, majoritàriament, des de les sinapsis cap al soma neuronal, aquest transport intracel•lular permet una extensa transducció del SNC a partir d’una única injecció intracerebral dels vectors virals. En una segona part d’aquest projecte s’ha avaluat l’ús terapèutic dels CAV2. S’ha injectat un vector helper-dependent que expressa el gen la b-glucuronidasa i el gen de la proteïna verda fluorescent (HD-RIGIE), en el SNC del model caní del síndrome de Sly (MPS VII). La biodistribució i la eficàcia terapèutica han estat avaluades. Els nivells d’activitat enzimàtica en animals malalts injectats amb el vector terapèutic va arribar a valors similars als dels animals no afectes. A més a més s’ha observat una reducció en la quantitat dels GAGs acumulats en les cèl•lules dels animals malalts tractats amb el vector terapèutic, demostrant la potencialitat terapèutica dels CAV2 per a malalties que afecten al SNC. Els resultats aportats en aquest treball ens permeten dir que els CAV2 són unes bones eines terapèutiques per al tractament de malalties que afecten al SNC.

Cannabinoid addiction: behavioral models and neural correlates

The use of cannabis sativa preparations as recreational drugs can be traced back to the earliest civilizations. However, animal models of cannabinoid addiction allowing the exploration of neural correlates of cannabinoid abuse have been developed only recently. We review these models and the role of the CB1 cannabinoid receptor, the main target of natural cannabinoids, and its interaction with opioid and dopamine transmission in reward circuits. Extensive reviews on the molecular basis of cannabinoid action are available elsewhere (Piomelli et al., 2000;Schlicker and Kathmann, 2001).

What can spike train distances tell us about the neural code?

Time scale parametric spike train distances like the Victor and the van Rossum distancesare often applied to study the neural code based on neural stimuli discrimination.Different neural coding hypotheses, such as rate or coincidence coding,can be assessed by combining a time scale parametric spike train distance with aclassifier in order to obtain the optimal discrimination performance. The time scalefor which the responses to different stimuli are distinguished best is assumed to bethe discriminative precision of the neural code. The relevance of temporal codingis evaluated by comparing the optimal discrimination performance with the oneachieved when assuming a rate code.We here characterize the measures quantifying the discrimination performance,the discriminative precision, and the relevance of temporal coding. Furthermore,we evaluate the information these quantities provide about the neural code. Weshow that the discriminative precision is too unspecific to be interpreted in termsof the time scales relevant for encoding. Accordingly, the time scale parametricnature of the distances is mainly an advantage because it allows maximizing thediscrimination performance across a whole set of measures with different sensitivitiesdetermined by the time scale parameter, but not due to the possibility toexamine the temporal properties of the neural code.

Validation procedures in radiological diagnostic models. Neural network and logistic regression

The objective of this paper is to compare the performance of twopredictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules. The neural network classifier performs better than the one based on logistic regression. This advantage is well detected by three-fold cross-validation, but remains unnoticed when leave-one-out or bootstrap algorithms are used.

Podocalyxin is a novel polysialylated neural adhesion protein with multiple roles in neural development and synapse formation

Neural development and plasticity are regulated by neural adhesion proteins, including the polysialylated form of NCAM (PSA-NCAM). Podocalyxin (PC) is a renal PSA-containing protein that has been reported to function as an anti-adhesin in kidney podocytes. Here we show that PC is widely expressed in neurons during neural development. Neural PC interacts with the ERM protein family, and with NHERF1/2 and RhoA/G. Experiments in vitro and phenotypic analyses of podxl-deficient mice indicate that PC is involved in neurite growth, branching and axonal fasciculation, and that PC loss-of-function reduces the number of synapses in the CNS and in the neuromuscular system. We also show that whereas some of the brain PC functions require PSA, others depend on PC per se. Our results show that PC, the second highly sialylated neural adhesion protein, plays multiple roles in neural development.

Characterization of Arabidopsis FPS isozymes and FPS gene expression analysis provide insight into the biosynthesis of isoprenoid precursors in seeds

Arabidopsis thaliana contains two genes encoding farnesyl diphosphate (FPP) synthase (FPS), the prenyl diphoshate synthase that catalyzes the synthesis of FPP from isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). In this study, we provide evidence that the two Arabidopsis short FPS isozymes FPS1S and FPS2 localize to the cytosol. Both enzymes were expressed in E. coli, purified and biochemically characterized. Despite FPS1S and FPS2 share more than 90% amino acid sequence identity, FPS2 was found to be more efficient as a catalyst, more sensitive to the inhibitory effect of NaCl, and more resistant to thermal inactivation than FPS1S. Homology modelling for FPS1S and FPS2 and analysis of the amino acid differences between the two enzymes revealed an increase in surface polarity and a greater capacity to form surface salt bridges of FPS2 compared to FPS1S. These factors most likely account for the enhanced thermostability of FPS2. Expression analysis of FPS::GUS genes in seeds showed that FPS1 and FPS2 display complementary patterns of expression particularly at late stages of seed development, which suggests that Arabidopsis seeds have two spatially segregated sources of FPP. Functional complementation studies of the Arabidopsis fps2 knockout mutant seed phenotypes demonstrated that under normal conditions FPS1S and FPS2 are functionally interchangeable. A putative role for FPS2 in maintaining seed germination capacity under adverse environmental conditions is discussed.

Analysis of S-rich CuIn(S,Se)2 layers for photovoltaic applications: Influence of the sulfurization temperature on the crystalline properties of electrodeposited and sulfurized CuInSe2 precursors

This paper reports the microstructural analysis of S-rich CuIn(S,Se)2 layers produced by electrodeposition of CuInSe2 precursors and annealing under sulfurizing conditions as a function of the temperature of sulfurization. The characterization of the layers by Raman scattering, scanning electron microscopy, Auger electron spectroscopy, and XRD techniques has allowed observation of the strong dependence of the crystalline quality of these layers on the sulfurization temperature: Higher sulfurization temperatures lead to films with improved crystallinity, larger average grain size, and lower density of structural defects. However, it also favors the formation of a thicker MoS2 interphase layer between the CuInS2 absorber layer and the Mo back contact. Decreasing the temperature of sulfurization leads to a significant decrease in the thickness of this intermediate layer and is also accompanied by significant changes in the composition of the interface region between the absorber and the MoS2 layer, which becomes Cu rich. The characterization of devices fabricated with these absorbers corroborates the significant impact of all these features on device parameters as the open circuit voltage and fill factor that determine the efficiency of the solar cells.

Quality coding by neural populations in the early olfactory pathway: analysis using information theory

In this article, we analyze the ability of the early olfactory system to detect and discriminate different odors by means of information theory measurements applied to olfactory bulb activity images. We have studied the role that the diversity and number of receptor neuron types play in encoding chemical information. Our results show that the olfactory receptors of the biological system are low correlated and present good coverage of the input space. The coding capacity of ensembles of olfactory receptors with the same receptive range is maximized when the receptors cover half of the odor input space - a configuration that corresponds to receptors that are not particularly selective. However, the ensemble's performance slightly increases when mixing uncorrelated receptors of different receptive ranges. Our results confirm that the low correlation between sensors could be more significant than the sensor selectivity for general purpose chemo-sensory systems, whether these are biological or biomimetic.