Orexigenic Gene Expression in Late Gestation Ovine Foetal Hypothalamus is Sensitive to Maternal Undernutrition and Realimentation

Acknowledgements Research was funded by the Scottish Government's Rural and Environment Science and Analytical Services Division (RESAS), including the Strategic Partnership for Animal Science Excellence (SPASE). The authors have no conflicts of interest to declare.

The interplay between neuronal activity and actin dynamics mimic the setting of an LTD synaptic tag

Persistent forms of plasticity, such as long-term depression (LTD), are dependent on the interplay between activity-dependent synaptic tags and the capture of plasticity-related proteins. We propose that the synaptic tag represents a structural alteration that turns synapses permissive to change. We found that modulation of actin dynamics has different roles in the induction and maintenance of LTD. Inhibition of either actin depolymerisation or polymerization blocks LTD induction whereas only the inhibition of actin depolymerisation blocks LTD maintenance. Interestingly, we found that actin depolymerisation and CaMKII activation are involved in LTD synaptic-tagging and capture. Moreover, inhibition of actin polymerisation mimics the setting of a synaptic tag, in an activity-dependent manner, allowing the expression of LTD in non-stimulated synapses. Suspending synaptic activation also restricts the time window of synaptic capture, which can be restored by inhibiting actin polymerization. Our results support our hypothesis that modulation of the actin cytoskeleton provides an input-specific signal for synaptic protein capture.

The molecular basis of Marinesco-Sjögren syndrome

Marinesco-Sjögren syndrome (MSS) is a rare autosomal recessive neurodegenerative disorder characterized by cerebellar ataxia due to cerebellar cortical atrophy, infantile- or childhood-onset bilateral cataracts, progressive myopathy, and mild to severe mental retardation. Additional features include hypergonadotropic hypogonadism, various skeletal abnormalities, short stature, and strabismus. The neuroradiologic hallmarks are hypoplasia of both the vermis and cerebellar hemispheres. The histopathologic findings include severe cerebellar atrophy and loss of Purkinje and granule cells. The common pathologic findings in muscle biopsy are variation in muscle fiber size, atrophic fibers, fatty replacement, and rimmed vacuole formation. The presence of marked cerebellar atrophy with myopathy distinguishes MSS from another rare syndrome, the congenital cataracts, facial dysmorphism, and neuropathy syndrome (CCFDN). Previously, work by others had resulted in the identification of an MSS locus on chromosome 5q31. A subtype of MSS with myoglobinuria and neuropathy had been linked to the CCFDN locus on chromosome 18qter, at which mutations in the CTDP1 gene had been identified. We confirmed linkage to the previously identified locus on chromosome 5q31 in two Finnish families with eight affected individuals, reduced the critical region by fine-mapping, and identified SIL1 as a gene underlying MSS. We found a common homozygous founder mutation in all Finnish patients. The same mutation was also present in patient samples from Norway and Sweden. Altogether, we identified eight mutations in SIL1, including nonsense, frameshift, splice site alterations, and one missense mutation. SIL1 encodes a nucleotide exchange factor for the endoplasmic reticulum (ER) resident heat-shock protein 70 chaperone GRP78. GRP78 functions in protein synthesis and quality control of the newly synthesized polypeptides. It senses and responds to stressful cellular conditions. We showed that in mice, SIL1 and GRP78 show highly similar spatial and temporal tissue expression in developing and mature brain, eye, and muscle. Studying endogenous proteins in mouse primary hippocampal neurons, we found that SIL1 and GRP78 colocalize and that SIL1 localizes to the ER. We studied the subcellular localization of two mutant proteins, a missense mutant found in two patients and an artificial mutant lacking the ER retrieval signal, and found that both mutant proteins formed aggregates within the ER. Well in line with our findings and the clinical features of MSS, recent work by Zhao et al. showed that a truncation of SIL1 causes ataxia and cerebellar Purkinje cell loss in the naturally occurring woozy mutant mouse. Prior to Purkinje cell degeneration, the unfolded protein response is initiated and abnormal protein accumulations are present. MSS thus joins the group of protein misfolding and accumulation diseases. These findings highlight the importance of SIL1 and the role of the ER in neuronal function and survival. The results presented in this thesis provide tools for the molecular genetic diagnostics of MSS and give a basis for future studies on the molecular pathogenesis of MSS. Understanding the mechanisms behind this pleiotropic syndrome may provide insights into more common forms of ataxia, myopathy, and neurodegeneration.

Molecular biology of progressive myoclonus epilepsy of Unverricht-Lundborg type (EPM1)

Progressive myoclonus epilepsy of Unverricht-Lundborg type (EPM1) is an autosomal recessively inherited disorder characterized by age of onset at 6-15 years, stimulus-sensitive myoclonus, tonic-clonic epileptic seizures and a progressive course. Mutations in the cystatin B (CSTB) gene underlie EPM1. The most common mutation underlying EPM1 is a dodecamer repeat expansion in the promoter region of CSTB. In addition, nine other mutations have been identified. CSTB, a cysteine protease inhibitor, is a ubiquitously expressed inhibitor of cathepsins, but its physiological function is unknown. The purpose of this study was to investigate CSTB gene expression and CSTB protein function in normal and pathological conditions. The basal CSTB promoter was mapped and characterized using different promoter-luciferase gene constructs. The binding activity of transcription factors to one ARE half, five Sp1 and four AP1 sites in the CSTB promoter was demonstrated. The CSTB promoter activity was clearly decreased using a CSTB promoter with "premutation" repeat expansions and in individuals with alike expansions. The expression of CSTB mRNA and protein was markedly reduced in patient cells. The endogenous CSTB protein localized to the nucleus, cytoplasm and lysosomes, and in differentiated cells merely to the cytoplasm. This suggests that the subcellular distribution of CSTB is dependent on the differentation status of the cells. The proteins representing patient missense mutations failed to associate with lysosomes, implying the importance of the lysosomal association for the proper physiological function of CSTB. Several alternatively spliced CSTB isoforms were identified. Of these CSTB2 was widely expressed with very low levels whereas the other alternatively spliced forms seemed to have limited tissue expression. In patients CSTB2 expression was reduced similarly to that of CSTB. The physiological relevance of CSTB alternative splicing remains unknown. The mouse Cstb transcript was shown to be present in all embryonic stages and adult tissues examined. The expression was highest at embryonic day 7 and in thymus, as well as in postnatal brain in the cortex, caudate putamen, thalamus, hippocampus, and in the Purkinje cell layer of the cerebellum. Our data implies that CSTB expression is tightly temporally and spatially regulated. The data presented in my thesis lay the basis for further understanding of the role of CSTB in health and disease.

Electrophysiological, pharmacological and molecular profile of the transient outward rectifying conductance in neonatal rat symapthetic preganglionic neurons in vitro.

Transient outward rectifying conductances or A-like conductances in sympathetic preganglionic neurons (SPN) are prolonged, lasting for hundreds of milliseconds to seconds and are thought to play a key role in the regulation of SPN firing frequency. Here, a multidisciplinary electrophysiological, pharmacological and molecular single-cell rt-PCR approach was used to investigate the kinetics, pharmacological profile and putative K + channel subunits underlying the transient outward rectifying conductance expressed in SPN. SPN expressed a 4-aminopyridine (4-AP) sensitive transient outward rectification with significantly longer decay kinetics than reported for many other central neurons. The conductance and corresponding current in voltage-clamp conditions was also sensitive to the Kv4.2 and Kv4.3 blocker phrixotoxin-2 (1-10 µM) and the blocker of rapidly inactivating Kv channels, pandinotoxin-Ka (50 nM). The conductance and corresponding current was only weakly sensitive to the Kv1 channel blocker tityustoxin-Ka and insensitive to dendrotoxin I (200 nM) and the Kv3.4 channel blocker BDS-II (1 µM). Single-cell RT-PCR revealed mRNA expression for the a-subunits Kv4.1 and Kv4.3 in the majority and Kv1.5 in less than half of SPN. mRNA for accessory ß-subunits was detected for Kvß2 in all SPN with differential expression of mRNA for KChIP1, Kvß1 and Kvß3 and the peptidase homologue DPP6. These data together suggest that the transient outwardly rectifying conductance in SPN is mediated by members of the Kv4 subfamily (Kv4.1 and Kv4.3) in association with the ß-subunit Kvß2. Differential expression of the accessory ß subunits, which may act to modulate channel density and kinetics in SPN, may underlie the prolonged and variable time-course of this conductance in these neurons. © 2011 IBRO.

Molecular determinants of binding of a wasp toxin (PMTXs) and its analogs in the Na+ channels proteins

The structural specificity of alpha-PMTX, a novel peptide toxin derived from wasp venom has been studied on the neuromuscular synapse in the walking leg of the lobster. alpha-PMTX is known to induce repetitive action potentials in the presynaptic axon due to sodium channel inactivation. We synthesized 29 analogs of alpha-PMTX by substituting one or two amino acids and compared threshold concentrations of these mutant toxins for inducing repetitive action potentials. In 13 amino acid residues of alpha-PMTX, Arg-1, Lys-3 and Lys-12 regulate the toxic activity because substitution of these basic amino acid residues with other amino acid residues greatly changed the potency. Determining the structure-activity relationships of PMTXs will help clarifying the molecular mechanism of sodium channel inactivation. (C) 2000 Elsevier B.V. Ireland Ltd. All rights reserved.

Molecular and biochemical analysis of the {\it Drosophila\/} segmentation gene {\it runt\/}

Genetic evidence has indicated that the segmentation gene runt plays a key role in regulating gene expression of the pair-rule genes hairy, even-skipped, and fushi tarazu. In contrast to other pair-rule genes, sequence data of the runt open reading frame did not reveal homologies to DNA-binding motifs of known transcriptional regulatory proteins. This thesis project examined several properties of the runt gene based on the sequence of the transcription unit, including the subcellular localization of the protein in vivo, its ability to bind DNA, and the functionality of a putative nucleotide binding domain.^ A runt-specific antibody was generated and used to demonstrate that runt is localized in the nucleus. Since the precise overlap of the pair-rule stripes is thought to be critical for the determination of cellular identity along the anterior-posterior axis, phasing of early runt expression in the blastoderm was examined with regard to the segmentation genes hairy, even-skipped, and fushi tarazu. runt was also expressed at later stages of embryogenesis, including expression in neuroblasts, and ganglion mother cells of the developing nervous system. Expression at this stage was required for the subsequent formation of specific neurons and runt was extensively expressed in the central and peripheral nervous systems.^ Several experiments were done to address the biochemical function of the runt protein. A direct interaction of runt with DNA was first examined. Although bacterial expressed runt was found to bind dsDNA-cellulose, subsequent experiments failed to detect sequence-specific interactions with DNA. Inter-species conservation of the putative nucleotide binding domain suggested that this region was functionally important, and runt protein bound a labeled ATP analog with high affinity in vitro. Finally, the effect of substitution of a critical residue of the nucleotide binding domain on runt activity was examined in vivo. Ectopic expression of the mutant protein indicated that this conserved substitution altered, but did not eliminate, runt activity as evaluated by segmentation phenotype and viability. ^

Biologic and molecular analysis of tumor suppressor loci in human glioblastoma multiforme

Molecular and cytogenetic analyses of human glioblastomas have revealed frequent genetic alterations, including major deletions in chromosomes 9, 10, and 17, suggesting the presence of glioma-associated tumor suppressor genes on these chromosomes. To examine this hypothesis, copies of chromosomes 2, 4, and 10 derived from a human fibroblast cell line were independently introduced into a human glioma cell line, U251, by microcell-mediated chromosomal transfer. Successful transfer of chromosomes in each case was confirmed by resistance to the drug G418, indicating the presence of the neomycin-resistance gene previously integrated into each transferred chromosome. The presence of novel chromosomes and or chromosomal fragments was also demonstrated by molecular and karyotypic analyses. The hybrid clones containing either a novel chromosome 4 or chromosome 10 displayed suppression of the tumorigenic phenotype in vivo and suppression of the transformed phenotype in vitro, while cells containing a transferred chromosome 2 failed to alter their tumorigenic phenotype. The hybrid cells containing chromosome 4 or 10 exhibited a significant decrease in their saturation density, altered cellular morphology at high cell density, but only a slight decrease in their exponential growth rate. A dramatic decrease was observed in growth of cells with chromosome 4 or 10 in soft agarose, with the number and size of the colonies being greatly reduced, compared to the parental or chromosome 2 containing cells. The introduction of chromosome 4 or 10 also completely suppressed tumor formation in nude mice. These studies indicate that chromosome 10, as hypothesized, and chromosome 4, a novel finding for gliomas, harbor tumor suppressor loci that may be directly involved in the initiation or progression of normal glial precursors to human glioblastoma multiforme. ^

Glycosylation of TRPM4 and TRPM5 channels: molecular determinants and functional aspects

The transient receptor potential channel, TRPM4, and its closest homolog, TRPM5, are non-selective cation channels that are activated by an increase in intracellular calcium. They are expressed in many cell types, including neurons and myocytes. Although the electrophysiological and pharmacological properties of these two channels have been previously studied, less is known about their regulation, in particular their post-translational modifications. We, and others, have reported that wild-type (WT) TRPM4 channels expressed in HEK293 cells, migrated on SDS-PAGE gel as doublets, similar to other ion channels and membrane proteins. In the present study, we provide evidence that TRPM4 and TRPM5 are each N-linked glycosylated at a unique residue, Asn(992) and Asn(932), respectively. N-linked glycosylated TRPM4 is also found in native cardiac cells. Biochemical experiments using HEK293 cells over-expressing WT TRPM4/5 or N992Q/N932Q mutants demonstrated that the abolishment of N-linked glycosylation did not alter the number of channels at the plasma membrane. In parallel, electrophysiological experiments demonstrated a decrease in the current density of both mutant channels, as compared to their respective controls, either due to the Asn to Gln mutations themselves or abolition of glycosylation. To discriminate between these possibilities, HEK293 cells expressing TRPM4 WT were treated with tunicamycin, an inhibitor of glycosylation. In contrast to N-glycosylation signal abolishment by mutagenesis, tunicamycin treatment led to an increase in the TRPM4-mediated current. Altogether, these results demonstrate that TRPM4 and TRPM5 are both N-linked glycosylated at a unique site and also suggest that TRPM4/5 glycosylation seems not to be involved in channel trafficking, but mainly in their functional regulation.

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

Origins of molecular neurobiology:The role of the physicists

The revolution in the foundations of physics at the beginning of the twentieth century suggested to several of its most prominent workers that biology was ripe for something similar. In consequence, a number of physicists moved into biology. They were highly influential in initiating a molecular biology in the 1950s. Two decades later it seemed to several of these migrants, and those they had influenced, that the major problems in molecular biology had been solved, and that it was time to move on to what seemed to them the final problem: the nervous system, consciousness, and the age-old mind-body problem. This paper reviews this "double migration" and shows how the hopes of the first generation of physicist-biologists were both realized and dashed. No new physical principles were discovered at work in the foundations of biology or neuroscience. On the other hand, the mind-set of those trained in physics proved immensely valuable in analyzing fundamental issues in both biology and neuroscience. It has been argued that the outcome of the molecular biology of the 1950s was a change in the concept of the gene from that of "a mysterious entity into that of a real molecular object" (Watson, 1965, p.6); the gates and channels which play such crucial roles in the functioning of nervous systems have been transformed in a similar way. Studies on highly simplified systems have also opened the prospect of finding the neural correlatives of numerous behaviors and neuropathologies. This increasing understanding at the molecular level is invaluable not only in devising rational therapies but also, by defining the material substrate of consciousness, in bringing the mind-body problem into sharper focus. Copyright © Taylor & Francis Inc.

Elements of molecular neurobiology

This edition of the popular text incorporates recent advances in neurobiology enabled by modern molecular biology techniques. Understanding how the brain works from a molecular level allows research to better understand behaviours, cognition, and neuropathologies. Since the appearance six years ago of the second edition, much more has been learned about the molecular biology of development and its relations with early evolution. This "evodevo" (as it has come to be known) framework also has a great deal of bearing on our understanding of neuropathologies as dysfunction of early onset genes can cause neurodegeneration in later life. Advances in our understanding of the genomes and proteomes of a number of organisms also greatly influence our understanding of neurobiology. This book will be of particular interest to biomedical undergraduates undertaking a neuroscience unit, neuroscience postgraduates, physiologists, pharmacologists. It is also a useful basic reference for university libraries.