Intracellular chemical messengers and neuronal function: Two examples

Two distinct classes of neurons have been examined in the nervous system of Aplysia. The membrane properties of these neurons are regulated by intracellular signalling molecules in both a short-term and a long-term fashion.^ The role of the phosphatidylinositol cycle in the control of neuronal properties was studied in a class of bursting pacemaker cells, the left upper-quadrant bursting neurons (cells L2, L3, L4, and L6) of the abdominal ganglion of Aplysia. These cells display a regular burst-firing pattern that is controlled by cyclic changes of intracellular Ca$\sp{2+}$ that occur during the bursting rhythm. The characteristic bursting pattern of these neurons occurs within a range of membrane potentials ($-35$ to $-50$ mV) called the pacemaker range. Intracellular pressure injection of inositol 1,4,5-trisphosphate (IP$\sb3$) altered the bursting rhythm of the bursting cells. Injection of IP$\sb3$ induced a brief depolarization that was followed by a long-lasting (2-15 min) hyperpolarization. When cells were voltage-clamped at potentials within the pacemaker range, injection of IP$\sb3$ generally induced a biphasic response that had a total duration of 2-15 min. An initial inward shift in holding current (I$\sb{\rm in}$), which lasted 5-120 sec, was followed by a slow outward shift in holding current (I$\sb{\rm out}$). At membrane potentials more negative than $-40$ mV, I$\sb{\rm in}$ was associated with a small and relatively voltage-independent increase in membrane conductance. I$\sb{\rm in}$ was not blocked by bath application of TTX or Co$\sp{2+}$. Although I$\sb{\rm in}$ was activated by injection of IP$\sb3$, it was not blocked by iontophoretic injection of ethyleneglycol-bis-(beta-aminoethyl ether), N, N$\sp\prime$-tetraacetic acid (EGTA) sufficient to block the Ca$\sp{2+}$-activated inward tail current (I$\sb{\rm B}$).^ Long-term (lasting at least 24 hours) effects of adenylate cyclase activation were examined in a well characterized class of mechanosensory neurons in Aplysia. The injected cells were analyzed 24 hours later by two-electrode voltage-clamp techniques. We found that K$\sp+$ currents of these cells were reduced 24 hours after injection of cAMP. The currents that were reduced by cAMP were very similar to those found to be reduced 24 hours after behavioral sensitization. These results suggest that cAMP is part of the intracellular signal that induces long-term sensitization in Aplysia. (Abstract shortened with permission of author.) ^

Detection of the pan neuronal marker PGP9.5 by immuno-histochemistry and quantitative PCR in eutopic endometrium from women with and without endometriosis.

PURPOSE To assess endometrial gene as well as protein expression of neuroendocrine and supposedly endometriosis-associated product PGP9.5 and pain symptoms in women with endometriosis and controls undergoing laparoscopy, using molecular biological and immuno-histochemical approaches in the same patients. METHODS Biopsy of eutopic endometrium from 29 patients by sharp curettage, and preparation of paraffin blocks. Determination of PGP9.5 gene expression and protein abundance using qPCR and immuno-histochemistry. RESULTS qPCR; The PGP9.5 mRNA expression level between women with (N = 16) and without (N = 13) endometriosis was not different, regardless of pain symptoms or menstrual cycle phase. PGP9.5 expression was higher in women who reported pain compared to those who did not; however, this association was not statistically significant. The expression of PGP9.5 mRNA was higher in women with endometriosis and pain during the proliferative than in the secretory phase (P = 0.03). Furthermore, in the first half of the cycle, the abundance of the PGP9.5 transcript was also significantly higher in endometriosis patients compared to those without (P = 0.03). Immuno-histochemistry; Thirteen of the 16 endometriosis patients showed positive PGP9.5 immuno-reactivity in the endometrium, whereas no such signal was observed in women without endometriosis. The absolute number of nerve fibres per mm(2) in women with endometriosis was similar, regardless of the pain symptoms. CONCLUSIONS PGP9.5 mRNA expression is increased in the proliferative phase of endometriotic women with pain. The presence of nerve fibres was demonstrated by a PGP9.5 protein signal in immuno-histochemistry and restricted to patients with endometriosis. Based on these results, however, there did not appear to be a direct association between the gene expression and protein abundance in women with and without endometriosis or those that experienced pain.

Early Life History and a Modeling Framework for Lobster (Homarus Americanus) Populations in the Gulf of Maine

Beginning in the late 1980s, lobster (Homarus americanus) landings for the state of Maine and the Bay of Fundy increased to levels more than three times their previous 20-year means. Reduced predation may have permitted the expansion of lobsters into previously inhospitable territory, but we argue that in this region the spatial patterns of recruitment and the abundance of lobsters are substantially driven by events governing the earliest life history stages, including the abundance and distribution of planktonic stages and their initial settlement as Young-of-Year (YOY) lobsters. Settlement densities appear to be strongly driven by abundance of the pelagic postlarvae. Postlarvae and YOY show large-scale spatial patterns commensurate with coastal circulation, but also multi-year trends in abundance and abrupt shifts in abundance and spatial patterns that signal strong environmental forcing. The extent of the coastal shelf that defines the initial settlement grounds for lobsters is important to future population modeling. We address one part of this definition by examining patterns of settlement with depth, and discuss a modeling framework for the full life history of lobsters in the Gulf of Maine.

Diversity of neuronal connexins in the mammalian retina

Many neurons in the mammalian retina are electrically coupled by intercellular channels or gap junctions, which are assembled from a family of proteins called connexins. Numerous studies indicate that gap junctions differ in properties such as conductance and tracer permeability. For example, A-type horizontal cell gap junctions are permeable to Lucifer Yellow, but B-type horizontal cell gap junctions are not. This suggests the two cell types express different connexins. My hypothesis is that multiple neuronal connexins are expressed in the mammalian retina in a cell type specific manner. Immunohistochemical techniques and confocal microscopy were used to localize certain connexins within well-defined neuronal circuits. The results of this study can be summarized as follows: AII amacrine cells, which receive direct input from rod bipolar cells, are well-coupled to neighboring AIIs. In addition, AII amacrine cells also form gap junctions with ON cone bipolar cells. This is a complex heterocellular network. In both rabbit and primate retina, connexin36 occurs at dendritic crossings in the AII matrix as well as between AIIs and ON cone bipolar cells. Coupling in the AII network is thought to reduce noise in the rod pathway while AII/bipolar gap junctions are required for the transmission of rod signals to ON ganglion cells. In the outer plexiform layer, connexin36 forms gap junctions between cones and between rods and cones via cone telodendria. Cone to cone coupling is thought to reduce noise and is partly color selective. Rod to cone coupling forms an alternative rod pathway thought to operate at intermediate light intensity. A-type horizontal cells in the rabbit retina are strongly coupled via massive low resistance gap junctions composed from Cx50. Coupling dramatically extends the receptive field of horizontal cells and the modulation of coupling is thought to change the strength of the feedback signal from horizontal cells to cones. Finally, there are other coupled networks, such as B-type horizontal cells and S1/S2 amacrine cells, which do not use either connexin36 or Cx50. These results confirm the hypothesis that multiple neuronal connexins are expressed in the mammalian retina and these connexins are localized to particular retinal circuits. ^

Integrating sequence information in microarray data analysis by free energy modeling

Microarray technology is a high-throughput method for genotyping and gene expression profiling. Limited sensitivity and specificity are one of the essential problems for this technology. Most of existing methods of microarray data analysis have an apparent limitation for they merely deal with the numerical part of microarray data and have made little use of gene sequence information. Because it's the gene sequences that precisely define the physical objects being measured by a microarray, it is natural to make the gene sequences an essential part of the data analysis. This dissertation focused on the development of free energy models to integrate sequence information in microarray data analysis. The models were used to characterize the mechanism of hybridization on microarrays and enhance sensitivity and specificity of microarray measurements. ^ Cross-hybridization is a major obstacle factor for the sensitivity and specificity of microarray measurements. In this dissertation, we evaluated the scope of cross-hybridization problem on short-oligo microarrays. The results showed that cross hybridization on arrays is mostly caused by oligo fragments with a run of 10 to 16 nucleotides complementary to the probes. Furthermore, a free-energy based model was proposed to quantify the amount of cross-hybridization signal on each probe. This model treats cross-hybridization as an integral effect of the interactions between a probe and various off-target oligo fragments. Using public spike-in datasets, the model showed high accuracy in predicting the cross-hybridization signals on those probes whose intended targets are absent in the sample. ^ Several prospective models were proposed to improve Positional Dependent Nearest-Neighbor (PDNN) model for better quantification of gene expression and cross-hybridization. ^ The problem addressed in this dissertation is fundamental to the microarray technology. We expect that this study will help us to understand the detailed mechanism that determines sensitivity and specificity on the microarrays. Consequently, this research will have a wide impact on how microarrays are designed and how the data are interpreted. ^

Investigation of the seasonality of N. pachyderma in the North Atlantic Ocean during Heinrich Stadial 1 using an ecosystem modeling approach

Fossil shells of planktonic foraminifera serve as the prime source of information on past changes in surface ocean conditions. Because the population size of planktonic foraminifera species changes throughout the year, the signal preserved in fossil shells is biased towards the conditions when species production was at its maximum. The amplitude of the potential seasonal bias is a function of the magnitude of the seasonal cycle in production. Here we use a planktonic foraminifera model coupled to an ecosystem model to investigate to what degree seasonal variations in production of the species Neogloboquadrina pachyderma may affect paleoceanographic reconstructions during Heinrich Stadial 1 (~18-15 cal. ka B.P.) in the North Atlantic Ocean. The model implies that during Heinrich Stadial 1 the maximum seasonal production occurred later in the year compared to the Last Glacial Maximum (~21-19 cal. ka B.P.) and the pre-industrial era north of 30 ºN. A diagnosis of the model output indicates that this change reflects the sensitivity of the species to the seasonal cycle of sea-ice cover and food supply, which collectively lead to shifts in the modeled maximum production from the Last Glacial Maximum to Heinrich Stadial 1 by up to six months. Assuming equilibrium oxygen isotopic incorporation in the shells of N. pachyderma, the modeled changes in seasonality would result in an underestimation of the actual magnitude of the meltwater isotopic signal recorded by fossil assemblages of N. pachyderma wherever calcification is likely to take place.

A region based approach to background modeling in a wavelet multi-resolution framework

In the field of detection and monitoring of dynamic objects in quasi-static scenes, background subtraction techniques where background is modeled at pixel-level, although showing very significant limitations, are extensively used. In this work we propose a novel approach to background modeling that operates at region-level in a wavelet based multi-resolution framework. Based on a segmentation of the background, characterization is made for each region independently as a mixture of K Gaussian modes, considering the model of the approximation and detail coefficients at the different wavelet decomposition levels. Background region characterization is updated along time, and the detection of elements of interest is carried out computing the distance between background region models and those of each incoming image in the sequence. The inclusion of the context in the modeling scheme through each region characterization makes the model robust, being able to support not only gradual illumination and long-term changes, but also sudden illumination changes and the presence of strong shadows in the scene

Modeling Reflective Bistability in Vertical-Cavity Semiconductor Optical Amplifiers

The characteristics of optical bistability in a vertical- cavity semiconductor optical amplifier (VCSOA) operated in reflection are reported. The dependences of the optical bistability in VCSOAs on the initial phase detuning and on the applied bias current are analyzed. The optical bistability is also studied for different numbers of superimposed periods in the top distributed bragg reflector (DBR) that conform the internal cavity of the device. The appearance of the X-bistable and the clockwise bistable loops is predicted theoretically in a VCSOA operated in reflection for the first time, to the best of our knowledge. Moreover, it is also predicted that the control of the VCSOA’s top reflectivity by the addition of new superimposed periods in its top DBR reduces by one order of magnitude the input power needed for the assessment of the X- and the clockwise bistable loop, compared to that required in in-plane semiconductor optical amplifiers. These results, added to the ease of fabricating two-dimensional arrays of this kind of device could be useful for the development of new optical logic or optical signal regeneration devices.

Analysis of neuronal functions based on feynman diagrams

A method to study some neuronal functions, based on the use of the Feynman diagrams, employed in many-body theory, is reported. An equation obtained from the neuron cable theory is the basis for the method. The Green's function for this equation is obtained under some simple boundary conditions. An excitatory signal, with different conditions concerning high and pulse duration, is employed as input signal. Different responses have been obtained

Radio propagation modeling and measurements for ZigBee based indoor wireless sensor networks

The deployment of nodes in Wireless Sensor Networks (WSNs) arises as one of the biggest challenges of this field, which involves in distributing a large number of embedded systems to fulfill a specific application. The connectivity of WSNs is difficult to estimate due to the irregularity of the physical environment and affects the WSN designers? decision on deploying sensor nodes. Therefore, in this paper, a new method is proposed to enhance the efficiency and accuracy on ZigBee propagation simulation in indoor environments. The method consists of two steps: automatic 3D indoor reconstruction and 3D ray-tracing based radio simulation. The automatic 3D indoor reconstruction employs unattended image classification algorithm and image vectorization algorithm to build the environment database accurately, which also significantly reduces time and efforts spent on non-radio propagation issue. The 3D ray tracing is developed by using kd-tree space division algorithm and a modified polar sweep algorithm, which accelerates the searching of rays over the entire space. Signal propagation model is proposed for the ray tracing engine by considering both the materials of obstacles and the impact of positions along the ray path of radio. Three different WSN deployments are realized in the indoor environment of an office and the results are verified to be accurate. Experimental results also indicate that the proposed method is efficient in pre-simulation strategy and 3D ray searching scheme and is suitable for different indoor environments.

Simplified small-signal stability analysis for optimized power system architecture

The optimization of power architectures is a complex problem due to the plethora of different ways to connect various system components. This issue has been addressed by developing a methodology to design and optimize power architectures in terms of the most fundamental system features: size, cost and efficiency. The process assumes various simplifications regarding the utilized DC/DC converter models in order to prevent the simulation time to become excessive and, therefore, stability is not considered. The objective of this paper is to present a simplified method to analyze small-signal stability of a system in order to integrate it into the optimization methodology. A black-box modeling approach, applicable to commercial converters with unknown topology and components, is based on frequency response measurements enabling the system small-signal stability assessment. The applicability of passivity-based stability criterion is assessed. The stability margins are stated utilizing a concept of maximum peak criteria derived from the behavior of the impedance-based sensitivity function that provides a single number to state the robustness of the stability of a well-defined minor-loop gain.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure (versión actualizada)

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Bayesian network modeling of the consensus between experts: an application to neuron classification

Neuronal morphology is hugely variable across brain regions and species, and their classification strategies are a matter of intense debate in neuroscience. GABAergic cortical interneurons have been a challenge because it is difficult to find a set of morphological properties which clearly define neuronal types. A group of 48 neuroscience experts around the world were asked to classify a set of 320 cortical GABAergic interneurons according to the main features of their three-dimensional morphological reconstructions. A methodology for building a model which captures the opinions of all the experts was proposed. First, one Bayesian network was learned for each expert, and we proposed 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 was induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts was built. A thorough analysis of the consensus model identified different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types was defined by performing inference in the Bayesian multinet. These findings were used to validate the model and to gain some insights into neuron morphology.

Coordinate regulation of gonadotropin-releasing hormone neuronal firing patterns by cytosolic calcium and store depletion

Elevation of cytosolic free Ca2+ concentration ([Ca2+]i) in excitable cells often acts as a negative feedback signal on firing of action potentials and the associated voltage-gated Ca2+ influx. Increased [Ca2+]i stimulates Ca2+-sensitive K+ channels (IK-Ca), and this, in turn, hyperpolarizes the cell and inhibits Ca2+ influx. However, in some cells expressing IK-Ca the elevation in [Ca2+]i by depletion of intracellular stores facilitates voltage-gated Ca2+ influx. This phenomenon was studied in hypothalamic GT1 neuronal cells during store depletion caused by activation of gonadotropin-releasing hormone (GnRH) receptors and inhibition of endoplasmic reticulum (Ca2+)ATPase with thapsigargin. GnRH induced a rapid spike increase in [Ca2+]i accompanied by transient hyperpolarization, followed by a sustained [Ca2+]i plateau during which the depolarized cells fired with higher frequency. The transient hyperpolarization was caused by the initial spike in [Ca2+]i and was mediated by apamin-sensitive IK-Ca channels, which also were operative during the subsequent depolarization phase. Agonist-induced depolarization and increased firing were independent of [Ca2+]i and were not mediated by inhibition of K+ current, but by facilitation of a voltage-insensitive, Ca2+-conducting inward current. Store depletion by thapsigargin also activated this inward depolarizing current and increased the firing frequency. Thus, the pattern of firing in GT1 neurons is regulated coordinately by apamin-sensitive SK current and store depletion-activated Ca2+ current. This dual control of pacemaker activity facilitates voltage-gated Ca2+ influx at elevated [Ca2+]i levels, but also protects cells from Ca2+ overload. This process may also provide a general mechanism for the integration of voltage-gated Ca2+ influx into receptor-controlled Ca2+ mobilization.