Bicycling as an alternate mode of transportation has been on the rise. It is environmentally friendly in nature and the associated health benefits have made it a popular choice for many types of trips. The purpose of this research is to increase understanding of the impacts of implementing bicycle corridors (as part of the Utah Department of Transportation's (UDOT) Inclusion of Active Transportation policy) on bicycle rate as a function of roadway characteristics. The results of this research will be used in determining when and where bicycle corridors will enhance the transportation system and an estimate of the overall impact of bicycle corridors on travel demand in Utah. Data collection was fundamental in this research project in determining the impacts of bicycle corridors on travel demands in the state of Utah. With limited amount of commuting bicycle data available throughout the state, it was necessary to gather bicycle volume data on corridors with and without bicycle infrastructure. In order to accomplish this data collection effort, two primary methods were used to collect bicycle volume data. The first method was to use automatic bicycle counters on roadways that had bicycle infrastructure. The second method was to gather bicycle volume data through manual counts on roads with and without bicycle infrastructure. After the bicycle volume data were collected the data were analyzed to identify trends. The first step in the analysis was to convert the bicycle volumes into rates to provide a more uniform comparison. Several analyses were run including an analysis of bicycle rate compared to Annual Average Daily Traffic (AADT), bicycle rate compared to posted speed limit, bicycle rate compared to number of vehicle lanes, and bicycle rate compared to roadway classification. A comparison of sites with bicycle infrastructure to sites without bicycle infrastructure (non-bicycle infrastructure) was also conducted to identify relationships. Comparison of bicycle rates to AADT resulted in no correlation or statistical relationship in the data but the data do suggest trends. Statistically significant results did occur when comparing bicycle rates to posted speed limits. No statistically significant relationships occurred when comparing bicycle rates to the number of lanes or roadway classification. It was determined that roadways with bicycle infrastructure tend to yield higher bicycle rates than roadways that do not have bicycle infrastructure. Lastly, using shared use path data it is determined that bicycle rates on shared use paths have increased between 1.7 to 7.5 percent from 2013 to 2014 and it is assumed that a similar trend would exist on bicycle infrastructure in the communities.

No description available.

The goal of this thesis was to investigate the factors that influence the adoption of big data analytics by public sector employees based on the adapted Unified Theory of Acceptance and Use of Technology (UTAUT) model. A mixed method of survey and interviews were used to collect data from employees of a Canadian provincial government ministry. The results show that performance expectancy and facilitating conditions have significant positive effects on the adoption intention of big data analytics, while effort expectancy has a significant negative effect on the adoption intention of big data analytics. The result shows that social influence does not have a significant effect on adoption intention. In terms of moderating variables, the results show that gender moderates the effects of effort expectancy, social influence and facilitating condition; data experience moderates the effects of performance expectancy, effort expectancy and facilitating condition; and leadership moderates the effect of social influence. The moderation effects of age on performance expectancy, effort expectancy is significant for only employees in the 40 to 49 age group while the moderation effects of age on social influence is significant for employees that are 40 years and more. Based on the results, implications for public sector organizations planning to implement big data analytics were discussed and suggestions for further research were made. This research contributes to existing studies on the user adoption of big data analytics.

In this thesis we develop data mining techniques to analyze sleep irregularities in humans. We investigate the effects of several demographic, behavioral and emotional factors on sleep progression and on patient's susceptibility to sleep-related and other disorders. Mining is performed over subjective and objective data collected from patients visiting the UMass Medical Center and the Day Kimball Hospital for treatment. Subjective data are obtained from patient responses to questions posed in a sleep questionnaire. Objective data comprise observations and clinical measurements recorded by sleep technicians using a suite of instruments together called polysomnogram. We create suitable filters to capture significant events within sleep epochs. We propose and employ a Window-based Association Rule Mining Algorithm to discover associations among sleep progression, pathology, demographics and other factors. This algorithm is a modified and extended version of the Set-and-Sequences Association Rule Mining Algorithm developed at WPI to support the mining of association rules from complex data types. We analyze both the medical as well as the statistical significance of the associations discovered by our algorithm. We also develop predictive classification models using logistic regression and compare the results with those obtained through association rule mining.

L'acide rétinoïque (AR) est un morphogène dérivé de la vitamine A, qui intervient dans le contrôle de l'organogenèse, de la prolifération et de la différenciation cellulaires chez les Chordés. Dans ce contexte, nous avons étudié les régulations spatio-temporelles de la voie de signalisation de l’AR au cours du développement de l’amphioxus, en mettant l'accent sur l’espèce européenne Branchiostoma lanceolatum.Nous avons tout d'abord inhibé ou activé la voie de signalisation de l’AR lors du développement embryonnaire en traitant des embryons d’amphioxus à des doses variables de composés pharmacologiques interférant avec le métabolisme des rétinoïdes. Grâce à l’utilisation d’outils mathématiques spécifiques, nous avons établi un schéma détaillé des effets des traitements effectués sur le développement du système nerveux central (SNC) et du pharynx chez l’amphioxus en nous basant sur l’expression de gènes marqueurs de tissus spécifiques. À l’issue de cette première analyse, nous avons par la suite étudié les effets d’une perturbation de la signalisation de l’AR à des points clés du développement chez l’amphioxus lors de la régionalisation du SNC et du pharynx. Nous avons ainsi montré que la voie de signalisation de l’AR intervient dans la régionalisation de l’axe antéro-postérieur via le contrôle des gènes hox dès le stade gastrula et jusqu’aux stades larvaires. En outre, nous avons réalisé l'étude préliminaire du gène homologue chez l’amphioxus du gène aldh1a2 des Vertébrés, et avons démontré que la régulation du niveau de synthèse de l’AR au cour du développement est conservée entre l’amphioxus et les Vertébrés. Finalement, nous avons montré que la voie de l’AR participe également à la morphogenèse caudale chez l’amphioxus, et que le mécanisme impliqué semble différent de celui proposé chez les Vertébrés où l’AR contrôle la structuration de la nageoire caudale par le ciblage des tissus mésenchymateux. / Retinoic acid (RA) is an endogenous vitamin A-derived morphogen. In this context, we studied the spatiotemporal roles of RA signaling in amphioxus development, focusing on the European amphioxus species: Branchiostoma lanceolatum. We first created excess and insufficiency models of RA signaling by exposing amphioxus embryos to series of doses of different pharmacological compounds targeting either the RA receptors or the RA metabolism machinery. By introducing the important mathematical concept of a Cartesian coordinate system founded by René Descartes, we created detailed diagrams of the concentration-dependent defects caused by RA signaling in the central nervous system (CNS) and pharynx of amphioxus by evaluating the statistical significances of tissue-specific marker gene expression in labeled embryos. This analysis yielded a very detailed description of the sensitivities of the developing amphioxus CNS and pharynx to altered RA signaling levels. Following this initial challenge, we correlated the effects of altered RA signaling levels with key amphioxus developmental stages characterized by structural transitions in CNS and pharynx. We show that hox-mediated RA signaling in axial patterning is active beyond the gastrula stage and might be maintained until at least early larval stage, with possible roles in more regionalized axis formation and organ induction. In addition, we carried out a preliminary study on a RA synthesizing gene in amphioxus, called aldh1a, a possible homolog of the vertebrate aldh1a2 gene, demonstrating that the feedback between RA signaling and RA synthesizing levels has emerged before the split of the cephalochordate and vertebrate lineages. Moreover, we are able to show that RA signaling also participates in tail fin morphogenesis in amphioxus by a mechanism that is probably not comparable to that in vertebrates, where RA modulates caudal fin patterning through targeting mesenchymal derivatives.

Η διπλωματική εργασία με τίτλο «Μέθοδοι για Ανίχνευση και Χαρακτηρισμό Βιοσημάτων σε Θορυβώδεις Χρονοσειρές βασισμένοι στο Μετασχηματισμό Hilbert-Huang» μελετάει ζητήματα που σχετίζονται με βιοϊατρικά σήματα και την ανάλυση τους. Γίνεται διερεύνηση των διαθέσιμων τεχνικών και μεθόδων ανάλυσης βιοϊατρικών σημάτων, επισημαίνονται τα ιδιαίτερα χαρακτηριστικά των χρονοσειρών που προκύπτουν από την παρατήρηση και καταγραφή των σημάτων και έμφαση δίνεται στη μη στασιμότητα, την μη γραμμικότητα των υποκείμενων φυσικών διεργασιών και την ανάγκη προσαρμοστικότητας της μεθόδου. Μια μέθοδος που ικανοποιεί αυτές τις απαιτήσεις είναι η εμπειρική μέθοδος αποσύνθεσης η οποία αναλύει ένα σήμα σε ένα σύνολο συνιστωσών (IMFs) από τις οποίες ένα υποσύνολο θεωρείται ότι έχει φυσική σημασία. Επιπλέον, με το μετασχηματισμό Hilbert ανιχνεύονται οι στιγμιαίες συχνότητες και διαμορφώνεται η χρονοσυχνοτική κατανομή του σήματος. Τα θέματα που διερευνώνται αναφορικά με την εμπειρική μέθοδο αποσύνθεσης αφορούν τη στατιστική σημαντικότητα των IMFs, την αποθορυβοποίηση βιοϊατρικών σημάτων, την εξαγωγή χαρακτηριστικών από ηλεκτροκαρδιογράφημα και την απόδοση της μεθόδου. Ειδικά η απόδοση της εμπειρικής μεθόδου αποσύνθεσης είναι κρίσιμη παράμετρος για συστήματα με περιορισμένους πόρους όπως είναι οι κόμβοι ασύρματων δικτύων αισθητήρων ή τα ενσωματωμένα συστήματα. Η μοντελοποίηση μεθόδων που υλοποιούνται στο επίπεδο κόμβων ασύρματου δικτύου αισθητήρων είναι απαραίτητη για τη βέλτιστη διαχείριση πόρων και τον προγραμματισμό διεργασιών ώστε να μην διαταραχθεί η λειτουργία και λειτουργικότητα του συστήματος / This diploma thesis entitled "Methods for Identification and Characterization of Biosignals in Noise corrupted Time Series based on Hilbert-Huang Transform " studies issues concerning biomedical signal analysis. There is a review of the available techniques and methods for biomedical signal analysis pointing at certain characteristics of biomedical time series such as non stationarity, the non linearity of the underlying physical process and the need for the adaptive nature of the analysis method. One method that meets these requirements is considered to be the Empirical Mode Decomposition (EMD) which decomposes a signal into a set of components (IMFs) that a subset of them is believed to have a physical meaning. Application of Hilbert Transform on these IMFs provides the instantaneous frequencies and forms the time-frequency distribution of the signal. Issues studied are related to the statistical significance of the IMFs, denoising of biomedical signals, characteristics extraction and feature selection out of the electrocardiogram as well as the performance of the method. Particularly, the performance of empirical mode decomposition is considered to be a critical parameter especially in the case of implementation on nodes of wireless sensor networks or generally embedded systems due to the limited amount of resources available onboard. Modeling method's performance and demand for resources is a significant task facilitating the optimum resource management and task execution schedule of these systems.

The present article describes a method to quantify the magnitude of the differences between two measures and/or the degree of the effect of a variable about criteria, and it is named likethe effect size measure, d. Use it use in research and applied contexts provides a quitedescriptive complementary information, improving the interpretation of the results obtained bythe traditional methods that emphasize the statistical significance. Severa) forms there are of interpreting the d, and an example taken of an experimental research, is presented to clarify the concepts and necessary calculations. This method is not robust to sorne conditions that they candistort its interpretation, for example, the non normality of the data; alternative methods are mentioned to the statistical d. We ending with sorne conclusions that will notice about the appropriate use of it. / El presente artículo describe un método para cuantificar la magnitud de las diferencias entredos mediciones y/o el grado del efecto de una variable sobre un criterio, y es llamado lamedida de la magnitud del efecto, de su uso en contextos de investigación y aplicados proporciona un información complementaria bastante descriptiva, mejorando la interpretaciónde los resultados obtenidos por los métodos tradicionales que enfatizan la significación estadística. Existen varias formas de interpretar el estadístico d, y se presenta un ejemplo,tomado de una investigación experimental, para aclarar los conceptos y cálculos necesarios.Este método no es robusto a ciertas condiciones que pueden distorsionar su interpretación, por ejemplo, la no normalidad de los datos entre otros; se mencionan métodos alternativos alestadístico d. Finalizamos con unas conclusiones que advierten sobre su apropiado uso.

Apresenta-se, neste trabalho, o desenvolvimento de um modelo bioinspirado a partir do circuito neural de insetos. Este modelo é obtido através da análise de primeira ordem dada pelo STA (Spike Triggered Average) e pela transferência de informação entre os sinais neurais. São aplicadas técnicas baseadas na identificação dos atrasos de tempo da máxima coerência da informação. Utilizam-se, para esta finalidade, os conceitos da teoria de informação: a DMI (Delayed Mutual Information) e a TE (Transfer Entropy). Essas duas abordagens têm aplicação em transferência de informação, cada uma com suas particularidades. A DMI é uma ferramenta mais simples do que a TE, do ponto de vista computacional, pois depende da análise estatística de funções densidades de probabilidades de segunda ordem, enquanto que a TE, de funções de terceira ordem. Dependendo dos recursos computacionais disponíveis, este é um fator que deve ser levado em consideração. Os resultados de atraso da informação são muito bem identificados pela DMI. No entanto, a DMI falha em distinguir a direção do fluxo de informação, quando se tem sistemas com transferência de informação indireta e com sobreposição da informação. Nesses casos, a TE é a ferramenta mais indicada para a determinação da direção do fluxo de informação, devido à dependência condicional imposta pelo histórico comum entre os sinais analisados. Em circuitos neurais, estas questões ocorrem em diversos casos. No gânglio metatorácico de insetos, os interneurônios locais possuem diferentes padrões de caminhos com sobreposição da informação, pois recebem sinais de diferentes neurônios sensores para o movimento das membros locomotores desses animais. O principal objetivo deste trabalho é propor um modelo do circuito neural do inseto, para mapear como os sinais neurais se comportam, quando sujeitos a um conjunto de movimentos aleatórios impostos no membro do inseto. As respostas neurais são reflexos provocados pelo estímulo táctil, que gera o movimento na junção femorotibial do membro posterior. Nestes circuitos neurais, os sinais neurais são processados por interneurônios locais dos tipos spiking e nonspiking que operam em paralelo para processar a informação vinda dos neurônios sensores. Esses interneurônios recebem sinais de entrada de mecanorreceptores do membro posterior e da junção motora dos insetos. A principal característica dos interneurônios locais é a sua capacidade de se comunicar com outros neurônios, tendo ou não a presença de impulsos nervosos (spiking e nonspiking). Assim, forma-se um circuito neural com sinais de entradas (neurônios sensores) e saídas (neurônios motores). Neste trabalho, os algoritmos propostos analisam desde a geração aleatória dos movimentos mecânicos e os estímulos nos neurônios sensores que chegam até o gânglio metatorácico, incluindo suas respostas nos neurônios motores. São implementados os algoritmos e seus respectivos pseudocódigos para a DMI e para a TE. É utilizada a técnica de Surrogate Data para inferir as medidas de significância estatística em relação à máxima coerência de informação entre os sinais neurais. Os resultados a partir dos Surrogate Data são utilizados para a compensação dos erros de desvio das medidas de transferência de informação. Um algoritmo, baseado na IAAFT (Iterative Amplitude Adjusted Fourier Transform), gera os dados substitutos, com mesmo espectro de potência e diferentes distribuições dos sinais originais. Os resultados da DMI e da TE com os Surrogate Data fornecem os valores das linhas de base quando ocorre a mínima transferência de informação. Além disso, foram utilizados dados simulados, para uma discussão sobre os efeitos dos tamanhos das amostras e as forças de associação da informação. Os sinais neurais coletados estão disponíveis em um banco de dados com diversos experimentos no gânglio metatorácico dos gafanhotos. No entanto, cada experimento possui poucos sinais coletados simultaneamente; assim, para diferentes experimentos, os sinais ficam sujeitos às variações de tamanho de amostras, além de ruídos que interferem nas medidas absolutas de transferência de informação. Para se mapear essas conexões neurais, foi utilizada a metodologia baseada na normalização e compensação dos erros de desvio para os cálculos da transferência de informação. As normalizações das medidas utilizam as entropias totais do sistema. Para a DMI, utiliza-se a média geométrica das entropias de X e Y , para a TE aplica-se a CMI (Conditional Mutual Information) para a sua normalização. Após a aplicação dessas abordagens, baseadas no STA e na transferência de informação, apresenta-se o modelo estrutural do circuito neural do sistema neuromotor de gafanhotos. São apresentados os resultados com o STA e a DMI para os neurônios sensores, dos quais são levantadas algumas hipóteses sobre o funcionamento desta parte do FeCO (Femoral Chordotonal Organ). Para cada tipo de neurônio foram identificados múltiplos caminhos no circuito neural, através dos tempos de atraso e dos valores de máxima coerência da informação. Nos interneurônios spiking obtiveram-se dois padrões de caminhos, enquanto que para os interneurônios nonspiking identificaram-se três padrões distintos. Esses resultados são obtidos computacionalmente e condizem com que é esperado a partir dos modelos biológicos descritos em Burrows (1996). / Herein, we present the development of a bioinspired model by the neural circuit of insects. This model is obtained by analyzing the first order from STA (Spike Triggered Average) and the transfer of information among neural signals. Techniques are applied based on the identification of the time delays of the information maximum coherence. For this purpose we use the concepts of the theory of information: DMI (Delayed Mutual Information) and TE (Transfer Entropy). These two approaches have applications on information transfer and each one has peculiarities. The DMI is a simpler tool than the TE, from the computational point of view. Therefore, DMI depends on the statistical analysis of second order probability density functions, whereas the TE depends on third order functions. If computational resources are a problem, those questions can be taken into consideration. The results of the information delay are very effective for DMI. However, DMI fails to distinguish the direction of the information flow when we have systems subjected to indirect information transfer and superposition of the information. In these cases, the TE is the most appropriate tool for determining the direction of the information flow, due to the conditional dependence imposed by a common history among the signals. In neural circuits, those issues occur in many cases. For example, in metathoracic ganglion of insects, the local interneurons have different pathways with superposition of the information. Therefore, the interneurons receive signals from different sensory neurons for moving the animals legs . The main objective of this work is propose a model of the neural circuit from an insect. Additionally, we map the neural signals when the hind leg is subjected to a set of movements. Neural responses are reflexes caused by tactile stimulus, which generates the movement of femoro-tibial joint of the hind leg. In these neural circuits, the signals are processed by neural spiking and nonspiking local interneurons. These types of neurons operate in parallel processing of the information from the sensory neurons. Interneurons receive input signals from mechanoreceptors by the leg and the insect knees. The main feature of local interneurons is their ability to communicate with others neurons. It can occur with or without of the presence of impulses (spiking and nonspiking). Thus, they form a neural circuit with input signals (sensory neurons) and outputs (motor neurons). The proposed algorithms analyze the random generation of movements and mechanical stimuli in sensory neurons. Which are processing in the metathoracic ganglion, including their responses in the motor neurons. The algorithms and the pseudo-code are implemented for TE and DMI. The technique of Surrogate Data is applied to infer the measures of statistical significance related to the information maximum coherence among neural signals. The results of the Surrogate Data are used for bias error compensation from information transfer. An algorithm, based on IAAFT (Iterative Amplitude Adjusted Fourier Transform), generates Surrogate Data with the same power spectrum and different distributions of the original signals. The results of the surrogate data, for DMI and TE, achieve the values of baselines when there are minimum information transfer. Additionally, we used simulated data to discuss the effects of sample sizes and different strengths of information connectivity. The collected neural signals are available from one database based on several experiments of the locusts metathoracic ganglion. However, each experiment has few simultaneously collected signals and the signals are subjected of variations in sample size and absolute measurements noisy of information transfer. We used a methodology based on normalization and compensation of the bias errors for computing the information transfer. The normalization of the measures uses the total entropy of the system. For the DMI, we applied the geometric mean of X and Y . Whereas, for the TE is computed the CMI (Conditional Mutual Information) for the normalization. We present the neural circuit structural model of the locusts neuromotor system, from those approaches based on STA and the information transfer. Some results are presented from STA and DMI for sensory neurones. Then, we achieve some new hypothesis about the neurophisiology function of FeCO. For each type of neuron, we identify multiple pathways in neural circuit through the time delay and the information maximum coherence. The spiking interneurons areyielded by two pathways, whereas the nonspiking interneurons has revealed three distinct patterns. These results are obtained computationally and are consistent with biological models described in Burrows (1996).

Apresenta-se, neste trabalho, o desenvolvimento de um modelo bioinspirado a partir do circuito neural de insetos. Este modelo é obtido através da análise de primeira ordem dada pelo STA (Spike Triggered Average) e pela transferência de informação entre os sinais neurais. São aplicadas técnicas baseadas na identificação dos atrasos de tempo da máxima coerência da informação. Utilizam-se, para esta finalidade, os conceitos da teoria de informação: a DMI (Delayed Mutual Information) e a TE (Transfer Entropy). Essas duas abordagens têm aplicação em transferência de informação, cada uma com suas particularidades. A DMI é uma ferramenta mais simples do que a TE, do ponto de vista computacional, pois depende da análise estatística de funções densidades de probabilidades de segunda ordem, enquanto que a TE, de funções de terceira ordem. Dependendo dos recursos computacionais disponíveis, este é um fator que deve ser levado em consideração. Os resultados de atraso da informação são muito bem identificados pela DMI. No entanto, a DMI falha em distinguir a direção do fluxo de informação, quando se tem sistemas com transferência de informação indireta e com sobreposição da informação. Nesses casos, a TE é a ferramenta mais indicada para a determinação da direção do fluxo de informação, devido à dependência condicional imposta pelo histórico comum entre os sinais analisados. Em circuitos neurais, estas questões ocorrem em diversos casos. No gânglio metatorácico de insetos, os interneurônios locais possuem diferentes padrões de caminhos com sobreposição da informação, pois recebem sinais de diferentes neurônios sensores para o movimento das membros locomotores desses animais. O principal objetivo deste trabalho é propor um modelo do circuito neural do inseto, para mapear como os sinais neurais se comportam, quando sujeitos a um conjunto de movimentos aleatórios impostos no membro do inseto. As respostas neurais são reflexos provocados pelo estímulo táctil, que gera o movimento na junção femorotibial do membro posterior. Nestes circuitos neurais, os sinais neurais são processados por interneurônios locais dos tipos spiking e nonspiking que operam em paralelo para processar a informação vinda dos neurônios sensores. Esses interneurônios recebem sinais de entrada de mecanorreceptores do membro posterior e da junção motora dos insetos. A principal característica dos interneurônios locais é a sua capacidade de se comunicar com outros neurônios, tendo ou não a presença de impulsos nervosos (spiking e nonspiking). Assim, forma-se um circuito neural com sinais de entradas (neurônios sensores) e saídas (neurônios motores). Neste trabalho, os algoritmos propostos analisam desde a geração aleatória dos movimentos mecânicos e os estímulos nos neurônios sensores que chegam até o gânglio metatorácico, incluindo suas respostas nos neurônios motores. São implementados os algoritmos e seus respectivos pseudocódigos para a DMI e para a TE. É utilizada a técnica de Surrogate Data para inferir as medidas de significância estatística em relação à máxima coerência de informação entre os sinais neurais. Os resultados a partir dos Surrogate Data são utilizados para a compensação dos erros de desvio das medidas de transferência de informação. Um algoritmo, baseado na IAAFT (Iterative Amplitude Adjusted Fourier Transform), gera os dados substitutos, com mesmo espectro de potência e diferentes distribuições dos sinais originais. Os resultados da DMI e da TE com os Surrogate Data fornecem os valores das linhas de base quando ocorre a mínima transferência de informação. Além disso, foram utilizados dados simulados, para uma discussão sobre os efeitos dos tamanhos das amostras e as forças de associação da informação. Os sinais neurais coletados estão disponíveis em um banco de dados com diversos experimentos no gânglio metatorácico dos gafanhotos. No entanto, cada experimento possui poucos sinais coletados simultaneamente; assim, para diferentes experimentos, os sinais ficam sujeitos às variações de tamanho de amostras, além de ruídos que interferem nas medidas absolutas de transferência de informação. Para se mapear essas conexões neurais, foi utilizada a metodologia baseada na normalização e compensação dos erros de desvio para os cálculos da transferência de informação. As normalizações das medidas utilizam as entropias totais do sistema. Para a DMI, utiliza-se a média geométrica das entropias de X e Y , para a TE aplica-se a CMI (Conditional Mutual Information) para a sua normalização. Após a aplicação dessas abordagens, baseadas no STA e na transferência de informação, apresenta-se o modelo estrutural do circuito neural do sistema neuromotor de gafanhotos. São apresentados os resultados com o STA e a DMI para os neurônios sensores, dos quais são levantadas algumas hipóteses sobre o funcionamento desta parte do FeCO (Femoral Chordotonal Organ). Para cada tipo de neurônio foram identificados múltiplos caminhos no circuito neural, através dos tempos de atraso e dos valores de máxima coerência da informação. Nos interneurônios spiking obtiveram-se dois padrões de caminhos, enquanto que para os interneurônios nonspiking identificaram-se três padrões distintos. Esses resultados são obtidos computacionalmente e condizem com que é esperado a partir dos modelos biológicos descritos em Burrows (1996). / Herein, we present the development of a bioinspired model by the neural circuit of insects. This model is obtained by analyzing the first order from STA (Spike Triggered Average) and the transfer of information among neural signals. Techniques are applied based on the identification of the time delays of the information maximum coherence. For this purpose we use the concepts of the theory of information: DMI (Delayed Mutual Information) and TE (Transfer Entropy). These two approaches have applications on information transfer and each one has peculiarities. The DMI is a simpler tool than the TE, from the computational point of view. Therefore, DMI depends on the statistical analysis of second order probability density functions, whereas the TE depends on third order functions. If computational resources are a problem, those questions can be taken into consideration. The results of the information delay are very effective for DMI. However, DMI fails to distinguish the direction of the information flow when we have systems subjected to indirect information transfer and superposition of the information. In these cases, the TE is the most appropriate tool for determining the direction of the information flow, due to the conditional dependence imposed by a common history among the signals. In neural circuits, those issues occur in many cases. For example, in metathoracic ganglion of insects, the local interneurons have different pathways with superposition of the information. Therefore, the interneurons receive signals from different sensory neurons for moving the animals legs . The main objective of this work is propose a model of the neural circuit from an insect. Additionally, we map the neural signals when the hind leg is subjected to a set of movements. Neural responses are reflexes caused by tactile stimulus, which generates the movement of femoro-tibial joint of the hind leg. In these neural circuits, the signals are processed by neural spiking and nonspiking local interneurons. These types of neurons operate in parallel processing of the information from the sensory neurons. Interneurons receive input signals from mechanoreceptors by the leg and the insect knees. The main feature of local interneurons is their ability to communicate with others neurons. It can occur with or without of the presence of impulses (spiking and nonspiking). Thus, they form a neural circuit with input signals (sensory neurons) and outputs (motor neurons). The proposed algorithms analyze the random generation of movements and mechanical stimuli in sensory neurons. Which are processing in the metathoracic ganglion, including their responses in the motor neurons. The algorithms and the pseudo-code are implemented for TE and DMI. The technique of Surrogate Data is applied to infer the measures of statistical significance related to the information maximum coherence among neural signals. The results of the Surrogate Data are used for bias error compensation from information transfer. An algorithm, based on IAAFT (Iterative Amplitude Adjusted Fourier Transform), generates Surrogate Data with the same power spectrum and different distributions of the original signals. The results of the surrogate data, for DMI and TE, achieve the values of baselines when there are minimum information transfer. Additionally, we used simulated data to discuss the effects of sample sizes and different strengths of information connectivity. The collected neural signals are available from one database based on several experiments of the locusts metathoracic ganglion. However, each experiment has few simultaneously collected signals and the signals are subjected of variations in sample size and absolute measurements noisy of information transfer. We used a methodology based on normalization and compensation of the bias errors for computing the information transfer. The normalization of the measures uses the total entropy of the system. For the DMI, we applied the geometric mean of X and Y . Whereas, for the TE is computed the CMI (Conditional Mutual Information) for the normalization. We present the neural circuit structural model of the locusts neuromotor system, from those approaches based on STA and the information transfer. Some results are presented from STA and DMI for sensory neurones. Then, we achieve some new hypothesis about the neurophisiology function of FeCO. For each type of neuron, we identify multiple pathways in neural circuit through the time delay and the information maximum coherence. The spiking interneurons areyielded by two pathways, whereas the nonspiking interneurons has revealed three distinct patterns. These results are obtained computationally and are consistent with biological models described in Burrows (1996).

Stem cells differentiate through an organized hierarchy of intermediate cell types to terminally differentiated cell types. This process is largely guided by master transcriptional regulators, but it also depends on the expression of many other types of genes. The discrete cell types in the differentiation hierarchy are often identified based on the expression or non-expression of certain marker genes. Historically, these have often been various cell-surface proteins, which are fairly easy to assay biochemically but are not necessarily causative of the cell type, in the sense of being master transcriptional regulators. This raises important questions about how gene expression across the whole genome controls or reflects cell state, and in particular, differentiation hierarchies. Traditional approaches to understanding gene expression patterns across multiple conditions, such as principal components analysis or K-means clustering, can group cell types based on gene expression, but they do so without knowledge of the differentiation hierarchy. Hierarchical clustering and maximization of parsimony can organize the cell types into a tree, but in general this tree is different from the differentiation hierarchy. Using hematopoietic differentiation as an example, we demonstrate how many genes other than marker genes are able to discriminate between different branches of the differentiation tree by proposing two models for detecting genes that are up-regulated or down-regulated in distinct lineages. We then propose a novel approach to solving the following problem: Given the differentiation hierarchy and gene expression data at each node, construct a weighted Euclidean distance metric such that the minimum spanning tree with respect to that metric is precisely the given differentiation hierarchy. We provide a set of linear constraints that are provably sufficient for the desired construction and a linear programming framework to identify sparse sets of weights, effectively identifying genes that are most relevant for discriminating different parts of the tree. We apply our method to microarray gene expression data describing 38 cell types in the hematopoiesis hierarchy, constructing a sparse weighted Euclidean metric that uses just 175 genes. These 175 genes are different than the marker genes that were used to identify the 38 cell types, hence offering a novel alternative way of discriminating different branches of the tree. A DAVID functional annotation analysis shows that the 175 genes reflect major processes and pathways active in different parts of the tree. However, we find that there are many alternative sets of weights that satisfy the linear constraints. Thus, in the style of random-forest training, we also construct metrics based on random subsets of the genes and compare them to the metric of 175 genes. Our results show that the 175 genes frequently appear in the random metrics, implicating their significance from an empirical point of view as well. Finally, we show how our linear programming method is able to identify columns that were selected to build minimum spanning trees on the nodes of random variable-size matrices.