Automated grading of left ventricular segmental wall motion by an artificial neural network using color kinesis images

The present study describes an auxiliary tool in the diagnosis of left ventricular (LV) segmental wall motion (WM) abnormalities based on color-coded echocardiographic WM images. An artificial neural network (ANN) was developed and validated for grading LV segmental WM using data from color kinesis (CK) images, a technique developed to display the timing and magnitude of global and regional WM in real time. We evaluated 21 normal subjects and 20 patients with LVWM abnormalities revealed by two-dimensional echocardiography. CK images were obtained in two sets of viewing planes. A method was developed to analyze CK images, providing quantitation of fractional area change in each of the 16 LV segments. Two experienced observers analyzed LVWM from two-dimensional images and scored them as: 1) normal, 2) mild hypokinesia, 3) moderate hypokinesia, 4) severe hypokinesia, 5) akinesia, and 6) dyskinesia. Based on expert analysis of 10 normal subjects and 10 patients, we trained a multilayer perceptron ANN using a back-propagation algorithm to provide automated grading of LVWM, and this ANN was then tested in the remaining subjects. Excellent concordance between expert and ANN analysis was shown by ROC curve analysis, with measured area under the curve of 0.975. An excellent correlation was also obtained for global LV segmental WM index by expert and ANN analysis (R² = 0.99). In conclusion, ANN showed high accuracy for automated semi-quantitative grading of WM based on CK images. This technique can be an important aid, improving diagnostic accuracy and reducing inter-observer variability in scoring segmental LVWM.

Collective Memory Embodied in Poetry as Poison. The Mnemonic Role of Romantic Poets Discussed on the Basis of Adam Mickiewicz’ Konrad Wallenrod and Forefathers (Part III)

This thesis constitutes an interdisciplinary approach to the Polish Romanticism combining literature studies with memory studies, nationalism research and psychoanalysis. This phenomenon-based study attempts to answer the question, how the Polish national poet Adam Mickiewicz (1798–1855) – or more exactly the implied authors in his works – perceived the role of poetry in mnemonic terms and how it changes in course of time. Consequently, ‘memory in literature’ (Astrin Erll and Ansger Nünning) is discussed here. Two pieces of writing by Mickiewicz – Konrad Wallenrod [1828] and the third part of Forefathers [1832], where a bard respectively a poetic genius appears – are seen as meta-texts defining goals of poets in time of the political non-existence of a state. Poetry is supposed to keep memory of the glorious past alive, kindle the love for the motherland, support the collective identity of a group and initiate a liberation movement. Poets function as memory guards, leaders of the nation and prophets. Thus, literature is a medium of collective memory – it stores crucial contents, transmits them and acts as a cue. Nevertheless, shifting the focus from the community towards well-being of individuals, which is consistent with the postmodern thinking, the impact that poetry has on members of a given memory culture (Jan Assmann) can be described in ‘vampiric’ terms (Maria Janion). Poetry embodying collective memory may be compared to ‘poison’, ‘infecting’ people with a nationalistic way of thinking to their disadvantage as far as their personal lives are concerned.

Reduced hippocampal GABAergic function in Wistar audiogenic rats

Epilepsy is a neurological disorder associated with excitatory and inhibitory imbalance within the underlying neural network. This study evaluated inhibitory γ-amino-butyric acid (GABA)ergic modulation in the CA1 region of the hippocampus of male Wistar rats and Wistar audiogenic rats (aged 90 ± 3 days), a strain of inbred animals susceptible to audiogenic seizures. Field excitatory postsynaptic potentials and population spike complexes in response to Schaffer collateral fiber stimulation were recorded in hippocampal slices before and during application of picrotoxin (50 µM, 60 min), a GABA A antagonist, and the size of the population spike was quantified by measuring its amplitude and slope. In control audiogenic-resistant Wistar rats (N = 9), picrotoxin significantly increased both the amplitude of the population spike by 51 ± 19% and its maximum slope by 73 ± 21%. In contrast, in slices from Wistar audiogenic rats (N = 6), picrotoxin caused no statistically significant change in population spike amplitude (33 ± 46%) or slope (11 ± 29%). Data are reported as means ± SEM. This result indicates a functional reduction of GABAergic neurotransmission in hippocampal slices from Wistar audiogenic rats.

Transduction motif analysis of gastric cancer based on a human signaling network

To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD) scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR,MAPK14, BCL2L1, KRT18,PTPN6, CASP3, TGFBR2,AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs.

Socially shared metacognitive regulation during collaborative learning processes in student dyads and small groups

Traditionally metacognition has been theorised, methodologically studied and empirically tested from the standpoint mainly of individuals and their learning contexts. In this dissertation the emergence of metacognition is analysed more broadly. The aim of the dissertation was to explore socially shared metacognitive regulation (SSMR) as part of collaborative learning processes taking place in student dyads and small learning groups. The specific aims were to extend the concept of individual metacognition to SSMR, to develop methods to capture and analyse SSMR and to validate the usefulness of the concept of SSMR in two different learning contexts; in face-to-face student dyads solving mathematical word problems and also in small groups taking part in inquiry-based science learning in an asynchronous computer-supported collaborative learning (CSCL) environment. This dissertation is comprised of four studies. In Study I, the main aim was to explore if and how metacognition emerges during problem solving in student dyads and then to develop a method for analysing the social level of awareness, monitoring, and regulatory processes emerging during the problem solving. Two dyads comprised of 10-year-old students who were high-achieving especially in mathematical word problem solving and reading comprehension were involved in the study. An in-depth case analysis was conducted. Data consisted of over 16 (30–45 minutes) videotaped and transcribed face-to-face sessions. The dyads solved altogether 151 mathematical word problems of different difficulty levels in a game-format learning environment. The interaction flowchart was used in the analysis to uncover socially shared metacognition. Interviews (also stimulated recall interviews) were conducted in order to obtain further information about socially shared metacognition. The findings showed the emergence of metacognition in a collaborative learning context in a way that cannot solely be explained by individual conception. The concept of socially-shared metacognition (SSMR) was proposed. The results highlighted the emergence of socially shared metacognition specifically in problems where dyads encountered challenges. Small verbal and nonverbal signals between students also triggered the emergence of socially shared metacognition. Additionally, one dyad implemented a system whereby they shared metacognitive regulation based on their strengths in learning. Overall, the findings suggested that in order to discover patterns of socially shared metacognition, it is important to investigate metacognition over time. However, it was concluded that more research on socially shared metacognition, from larger data sets, is needed. These findings formed the basis of the second study. In Study II, the specific aim was to investigate whether socially shared metacognition can be reliably identified from a large dataset of collaborative face-to-face mathematical word problem solving sessions by student dyads. We specifically examined different difficulty levels of tasks as well as the function and focus of socially shared metacognition. Furthermore, the presence of observable metacognitive experiences at the beginning of socially shared metacognition was explored. Four dyads participated in the study. Each dyad was comprised of high-achieving 10-year-old students, ranked in the top 11% of their fourth grade peers (n=393). Dyads were from the same data set as in Study I. The dyads worked face-to-face in a computer-supported, game-format learning environment. Problem-solving processes for 251 tasks at three difficulty levels taking place during 56 (30–45 minutes) lessons were video-taped and analysed. Baseline data for this study were 14 675 turns of transcribed verbal and nonverbal behaviours observed in four study dyads. The micro-level analysis illustrated how participants moved between different channels of communication (individual and interpersonal). The unit of analysis was a set of turns, referred to as an ‘episode’. The results indicated that socially shared metacognition and its function and focus, as well as the appearance of metacognitive experiences can be defined in a reliable way from a larger data set by independent coders. A comparison of the different difficulty levels of the problems suggested that in order to trigger socially shared metacognition in small groups, the problems should be more difficult, as opposed to moderately difficult or easy. Although socially shared metacognition was found in collaborative face-to-face problem solving among high-achieving student dyads, more research is needed in different contexts. This consideration created the basis of the research on socially shared metacognition in Studies III and IV. In Study III, the aim was to expand the research on SSMR from face-to-face mathematical problem solving in student dyads to inquiry-based science learning among small groups in an asynchronous computer-supported collaborative learning (CSCL) environment. The specific aims were to investigate SSMR’s evolvement and functions in a CSCL environment and to explore how SSMR emerges at different phases of the inquiry process. Finally, individual student participation in SSMR during the process was studied. An in-depth explanatory case study of one small group of four girls aged 12 years was carried out. The girls attended a class that has an entrance examination and conducts a language-enriched curriculum. The small group solved complex science problems in an asynchronous CSCL environment, participating in research-like processes of inquiry during 22 lessons (á 45–minute). Students’ network discussion were recorded in written notes (N=640) which were used as study data. A set of notes, referred to here as a ‘thread’, was used as the unit of analysis. The inter-coder agreement was regarded as substantial. The results indicated that SSMR emerges in a small group’s asynchronous CSCL inquiry process in the science domain. Hence, the results of Study III were in line with the previous Study I and Study II and revealed that metacognition cannot be reduced to the individual level alone. The findings also confirm that SSMR should be examined as a process, since SSMR can evolve during different phases and that different SSMR threads overlapped and intertwined. Although the classification of SSMR’s functions was applicable in the context of CSCL in a small group, the dominant function was different in the asynchronous CSCL inquiry in the small group in a science activity than in mathematical word problem solving among student dyads (Study II). Further, the use of different analytical methods provided complementary findings about students’ participation in SSMR. The findings suggest that it is not enough to code just a single written note or simply to examine who has the largest number of notes in the SSMR thread but also to examine the connections between the notes. As the findings of the present study are based on an in-depth analysis of a single small group, further cases were examined in Study IV, as well as looking at the SSMR’s focus, which was also studied in a face-to-face context. In Study IV, the general aim was to investigate the emergence of SSMR with a larger data set from an asynchronous CSCL inquiry process in small student groups carrying out science activities. The specific aims were to study the emergence of SSMR in the different phases of the process, students’ participation in SSMR, and the relation of SSMR’s focus to the quality of outcomes, which was not explored in previous studies. The participants were 12-year-old students from the same class as in Study III. Five small groups consisting of four students and one of five students (N=25) were involved in the study. The small groups solved ill-defined science problems in an asynchronous CSCL environment, participating in research-like processes of inquiry over a total period of 22 hours. Written notes (N=4088) detailed the network discussions of the small groups and these constituted the study data. With these notes, SSMR threads were explored. As in Study III, the thread was used as the unit of analysis. In total, 332 notes were classified as forming 41 SSMR threads. Inter-coder agreement was assessed by three coders in the different phases of the analysis and found to be reliable. Multiple methods of analysis were used. Results showed that SSMR emerged in all the asynchronous CSCL inquiry processes in the small groups. However, the findings did not reveal any significantly changing trend in the emergence of SSMR during the process. As a main trend, the number of notes included in SSMR threads differed significantly in different phases of the process and small groups differed from each other. Although student participation was seen as highly dispersed between the students, there were differences between students and small groups. Furthermore, the findings indicated that the amount of SSMR during the process or participation structure did not explain the differences in the quality of outcomes for the groups. Rather, when SSMRs were focused on understanding and procedural matters, it was associated with achieving high quality learning outcomes. In turn, when SSMRs were focused on incidental and procedural matters, it was associated with low level learning outcomes. Hence, the findings imply that the focus of any emerging SSMR is crucial to the quality of the learning outcomes. Moreover, the findings encourage the use of multiple research methods for studying SSMR. In total, the four studies convincingly indicate that a phenomenon of socially shared metacognitive regulation also exists. This means that it was possible to define the concept of SSMR theoretically, to investigate it methodologically and to validate it empirically in two different learning contexts across dyads and small groups. In-depth micro-level case analysis in Studies I and III showed the possibility to capture and analyse in detail SSMR during the collaborative process, while in Studies II and IV, the analysis validated the emergence of SSMR in larger data sets. Hence, validation was tested both between two environments and within the same environments with further cases. As a part of this dissertation, SSMR’s detailed functions and foci were revealed. Moreover, the findings showed the important role of observable metacognitive experiences as the starting point of SSMRs. It was apparent that problems dealt with by the groups should be rather difficult if SSMR is to be made clearly visible. Further, individual students’ participation was found to differ between students and groups. The multiple research methods employed revealed supplementary findings regarding SSMR. Finally, when SSMR was focused on understanding and procedural matters, this was seen to lead to higher quality learning outcomes. Socially shared metacognition regulation should therefore be taken into consideration in students’ collaborative learning at school similarly to how an individual’s metacognition is taken into account in individual learning.

Evolutionary synthesis of stochastic gene network models using feature-based search spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic 'if-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

Frontal Lobe Function and Performance Monitoring following Total Sleep Deprivation

Imaging studies have shown reduced frontal lobe resources following total sleep deprivation (TSD). The anterior cingulate cortex (ACC) in the frontal region plays a role in performance monitoring and cognitive control; both error detection and response inhibition are impaired following sleep loss. Event-related potentials (ERPs) are an electrophysiological tool used to index the brain's response to stimuli and information processing. In the Flanker task, the error-related negativity (ERN) and error positivity (Pe) ERPs are elicited after erroneous button presses. In a Go/NoGo task, NoGo-N2 and NoGo-P3 ERPs are elicited during high conflict stimulus processing. Research investigating the impact of sleep loss on ERPs during performance monitoring is equivocal, possibly due to task differences, sample size differences and varying degrees of sleep loss. Based on the effects of sleep loss on frontal function and prior research, it was expected that the sleep deprivation group would have lower accuracy, slower reaction time and impaired remediation on performance monitoring tasks, along with attenuated and delayed stimulus- and response-locked ERPs. In the current study, 49 young adults (24 male) were screened to be healthy good sleepers and then randomly assigned to a sleep deprived (n = 24) or rested control (n = 25) group. Participants slept in the laboratory on a baseline night, followed by a second night of sleep or wake. Flanker and Go/NoGo tasks were administered in a battery at 1O:30am (i.e., 27 hours awake for the sleep deprivation group) to measure performance monitoring. On the Flanker task, the sleep deprivation group was significantly slower than controls (p's <.05), but groups did not differ on accuracy. No group differences were observed in post-error slowing, but a trend was observed for less remedial accuracy in the sleep deprived group compared to controls (p = .09), suggesting impairment in the ability to take remedial action following TSD. Delayed P300s were observed in the sleep deprived group on congruent and incongruent Flanker trials combined (p = .001). On the Go/NoGo task, the hit rate (i.e., Go accuracy) was significantly lower in the sleep deprived group compared to controls (p <.001), but no differences were found on false alarm rates (i.e., NoGo Accuracy). For the sleep deprived group, the Go-P3 was significantly smaller (p = .045) and there was a trend for a smaller NoGo-N2 compared to controls (p = .08). The ERN amplitude was reduced in the TSD group compared to controls in both the Flanker and Go/NoGo tasks. Error rate was significantly correlated with the amplitude of response-locked ERNs in control (r = -.55, p=.005) and sleep deprived groups (r = -.46, p = .021); error rate was also correlated with Pe amplitude in controls (r = .46, p=.022) and a trend was found in the sleep deprived participants (r = .39, p =. 052). An exploratory analysis showed significantly larger Pe mean amplitudes (p = .025) in the sleep deprived group compared to controls for participants who made more than 40+ errors on the Flanker task. Altered stimulus processing as indexed by delayed P3 latency during the Flanker task and smaller amplitude Go-P3s during the Go/NoGo task indicate impairment in stimulus evaluation and / or context updating during frontal lobe tasks. ERN and NoGoN2 reductions in the sleep deprived group confirm impairments in the monitoring system. These data add to a body of evidence showing that the frontal brain region is particularly vulnerable to sleep loss. Understanding the neural basis of these deficits in performance monitoring abilities is particularly important for our increasingly sleep deprived society and for safety and productivity in situations like driving and sustained operations.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Untangling the Organizational Ambiguities in Supply Chain Risk Recognition, Assessment, and Response: The Role of Network Embeddedness

In this thesis, I focus on supply chain risk related ambiguity, which represents the ambiguities firms exhibit in recognizing, assessing, and responding to supply chain disruptions. I, primarily, argue that ambiguities associated with recognizing and responding to supply chain risk are information gathering and processing problems. Guided by the theoretical perspective of bounded rationality, I propose a typology of supply chain risk related ambiguity with four distinct dimensions. I, also, argue that the major contributor to risk related ambiguity is often the environment, specifically the web of suppliers. Hence, I focus on the characteristics of these supplier networks to examine the sources of ambiguity. I define three distinct elements of network embeddedness – relational, structural, and positional embeddedness – and argue that the ambiguity faced by a firm in appropriately identifying the nature or impacts of major disruptions is a function of these network properties. Based on a survey of large North American manufacturing firms, I found that the extent of the relational ties a firm has and its position in the network are significantly related to supply chain risk related ambiguity. However, this study did not provide any significant support for the hypothesized relationship between structural embeddedness and ambiguity. My research contributes towards the study of supply chain disruptions by using the idea of bounded rationality to understand supply chain risk related ambiguity and by providing evidence that the structure of supply chain networks influences the organizational understanding of and responses to supply chain disruptions.

Mécanismes cellulaires et moléculaires impliqués dans la régulation du développement des circuits d’interneurones GABAergiques dans le néocortex : rôle de la molécule d’adhésion cellulaire neurale (NCAM)

Les interneurones GABAergiques constituent une population mineure de cellules par rapport aux neurones glutamatergiques dans le néocortex. Cependant ils contrôlent fortement l'excitabilité neuronale, la dynamique des réseaux neuronaux et la plasticité synaptique. L'importance des circuits GABAergiques dans le processus fonctionnel et la plasticité des réseaux corticaux est soulignée par des résultats récents qui montrent que des modifications très précises et fiables des circuits GABAergiques sont associées à divers troubles du développement neurologique et à des défauts dans les fonctions cérébrales. De ce fait, la compréhension des mécanismes cellulaires et moléculaires impliquant le développement des circuits GABAergiques est la première étape vers une meilleure compréhension de la façon dont les anomalies de ces processus peuvent se produire. La molécule d’adhésion cellulaire neurale (NCAM) appartient à la super-famille des immunoglobulines de reconnaissance cellulaire et est impliquée dans des interactions homophiliques et hétérophiliques avec d’autres molécules. Même si plusieurs rôles de NCAM ont été démontrés dans la croissance neuronale, la fasciculation axonale, la formation et la maturation de synapses, de même que dans la plasticité cellulaire de plusieurs systèmes, le rôle de NCAM dans la formation des synapses GABAergiques reste inconnu. Ce projet visait donc à déterminer le rôle précis de NCAM dans le processus de maturation des synapses GABAergiques dans le néocortex, en modulant son expression à différentes étapes du développement. L’approche choisie a été de supprimer NCAM dans des cellules GABAergiques à paniers avant la maturation des synapses (EP12-18), pendant la maturation (EP16-24), ou durant le maintien de celles-ci (EP24-32). Les méthodes utilisées ont été le clonage moléculaire, l’imagerie confocale, la culture de coupes organotypiques et des techniques morphométriques de quantification de l’innervation GABAergique. Nos résultats montrent que l’inactivation de NCAM durant la phase de maturation des synapses périsomatiques (EP16-24) cause une réduction du nombre de synapses GABAergiques périsomatiques et du branchement de ces axones. En revanche, durant la phase de maintien (EP26-32), l’inactivation de NCAM n’a pas affecté ces paramètres des synapses GABAergiques. Or, il existe trois isoformes de NCAM (NCAM120, 140 et 180) qui pourraient jouer des rôles différents dans les divers types cellulaires ou à des stades développementaux différents. Nos données montrent que NCAM120 et 140 sont nécessaires à la maturation des synapses périsomatiques GABAergiques. Cependant, NCAM180, qui est l’isoforme la plus étudiée et caractérisée, ne semble pas être impliquée dans ce processus. De plus, l’inactivation de NCAM n’a pas affecté la densité des épines dendritiques ou leur longueur. Elle est donc spécifique aux synapses périsomatiques GABAeriques. Finalement, nos résultats suggèrent que le domaine conservé C-terminal KENESKA est essentiel à la maturation des synapses périsomatiques GABAergiques. Des expériences futures nous aiderons à mieux comprendre la mécanistique et les différentes voies de signalisation impliquées.

Activité des cellules souches : identification de nouveaux effecteurs dans le système hématopoïétique

Les cellules souches somatiques présentent habituellement un comportement très différent des cellules souches pluripotentes. Les bases moléculaires de l’auto-renouvellement des cellules souches embryonnaires ont été récemment déchiffrées grâce à la facilité avec laquelle nous pouvons maintenant les purifier et les maintenir en culture durant de longues périodes de temps. Par contre, il en va tout autrement pour les cellules souches hématopoïétiques. Dans le but d’en apprendre davantage sur le fonctionnement moléculaire de l’auto-renouvellement des cellules souches hématopoïétiques, j’ai d’abord conçu une nouvelle méthode de criblage gain-de-fonction qui répond aux caprices particuliers de ces cellules. Partant d’une liste de plus de 700 facteurs nucléaires et facteurs de division asymétrique candidats, j’ai identifié 24 nouveaux facteurs qui augmentent l’activité des cellules souches hématopoïétiques lorsqu’ils sont surexprimés. J’ai par la suite démontré que neuf de ces facteurs agissent de manière extrinsèque aux cellules souches hématopoïétiques, c’est-à-dire que l’effet provient des cellules nourricières modifiées en co-culture. J’ai également mis à jour un nouveau réseau de régulation de transcription qui implique cinq des facteurs identifiés, c’est-à-dire PRDM16, SPI1, KLF10, FOS et TFEC. Ce réseau ressemble étrangement à celui soutenant l’ostéoclastogénèse. Ces résultats soulèvent l’hypothèse selon laquelle les ostéoclastes pourraient aussi faire partie de la niche fonctionnelle des cellules souches hématopoïétiques dans la moelle osseuse. De plus, j’ai identifié un second réseau de régulation impliquant SOX4, SMARCC1 et plusieurs facteurs identifiés précédemment dans le laboratoire, c’est-à-dire BMI1, MSI2 et KDM5B. D’autre part, plusieurs indices accumulés tendent à démontrer qu’il existe des différences fondamentales entre le fonctionnement des cellules souches hématopoïétiques murines et humaines.

Le gouvernement du Québec, les relations internationales et l'environnement économique international (1973-1994)

L'objectif de ce mémoire est de jeter les bases d'un nouveau récit historique portant sur les relations internationales du Québec, en rupture partielle avec le récit traditionnel, essentiellement issu des écrits provenant de la science politique, qui décrit l'activité internationale du gouvernement québécois en cherchant dans les particularités de l'histoire québécoise elle-même les forces de changement. Ce faisant, nous tentons plutôt de montrer que l'histoire des relations internationales du Québec est inexorablement enchâssée dans l'histoire canadienne, dans l'histoire nord-américaine et surtout dans l'histoire du capitalisme et du néolibéralisme. Depuis le début de son existence en tant qu'entité politique moderne, le gouvernement québécois entretient des liens avec des gouvernements étrangers, l'intensité de ceux-ci étant function des grandes transformations affectant le système capitaliste international au grand complet. Vers la fin de la période 1867-1973, la politique internationale du gouvernement québécois est institutionnalisée politiquement et juridiquement grâce à la formulation de la doctrine Gérin-Lajoie, qui s'appuie sur les dispositions constitutionnelles propres au Canada pour élaborer la politique internationale québécoise. Dans la période 1973-1981, les relations internationales du Québec se focalisent davantage sur des questions économiques, telles que la quête de capitaux étrangers et la projection d'une image de marque positive sur les marchés étrangers. Puis, dans la période 1981-1994, l'activité internationale du Québec est marquée par le virage néolibéral qui affecte tous les gouvernements capitalistes dans le monde. Ainsi, l'appareil diplomatique du Québec est de plus en plus perçu comme un outil de promotion des exportations des entreprises québécoises et d'adaptation à l'augmentation de l'interdépendance économique, et de moins en moins comme un réseau de contacts politiques et culturels. Afin de faire cette démonstration, nous puisons essentiellement dans deux types de sources primaires : d'une part, des sources gouvernementales, dont les rapports annuels des différents ministères associés aux relations internationales du Québec et du Canada au fil de la période et les livres blancs de politique internationale qu'ils ont publié, et d'autre part, des publications issues du milieu des affaires, dont la revue Les Affaires, excellent baromètre de la mentalité dominante de la classe entrepreneuriale du Québec et du Canada francophone.

Brain Glutamate Dehydrogenase Changes In Streptozotocin Diabetic Rats As A Function Of Age

Kinetic parameters of brain glutamate dehydrogenase (GDH) were compared in the brain stem, cerebellum and cerebral cortex of three weeks and one year old streptozotocin (STZ) induced four day diabetic rats with respective controls. A single intrafemoral dose of STZ (60mg/Kg body weight) was administered to induce diabetes in both age groups. After four days the blood glucose levels showed a significant increase in the diabetic animals of both age groups compared with the respective controls. The increase in blood glucose was significant in one year old compared to the three weeks old diabetic rats. The Vmm of the enzyme was decreased in all the brain regions studied, of the three weeks old diabetic rats without any significant change in the Km. In the adult the Vmax of GDH was increased in cerebellum and brain stem but was unchanged in the cerebral cortex. The K. was unchanged in cerebellum and cerebral cortex but was increased in the brain stem. These results suggest there may be an important regulatory role of the glutamate pathway in brain neural network disturbances and neuronal degeneration in diabetes as a function of age.

Adrenergic and serotonergic function in dna synthesis during rat liver regeneration and in hepatocyte cultures

The adult mammalian liver is predominantly in a quiescent state with respect to cell division. This quiescent state changes dramatically, however, if the liver is injured by toxic, infectious or mechanic agents (Ponder, 1996). Partial hepatectomy (PH) which consists of surgical removal of two-thirds of the liver, has been used to stimulate hepatocyte proliferation (Higgins & Anderson 1931). This experimental model of liver regeneration has been the target of many studies to probe the mechanisms responsible for liver cell growth control (Michalopoulos, 1990; Taub, 1996). After PH most of the remaining cells in the renmant liver respond with co-ordinated waves of DNA synthesis and divide in a process called compensatory hyperplasia. Hence, liver regeneration is a model of relatively synchronous cell cycle progression in vivo. In contrast to hepatomas, cell division is terminated under some intrinsic control when the original cellular mass has been regained. This has made liver regeneration a useful model to dissect the biochemical and molecular mechanisms of cell division regulation. The liver is thus, one of the few adult organs that demonstrates a physiological growth rewonse (Fausto & Mead, 1989; Fausto & Webber, 1994). The regulation of liver cell proliferation involves circulating or intrahepatic factors that are involved in either the priming of hepatocytes to enter the cell cycle (Go to G1) or progression through the cell cycle. In order to understand the basis of liver regeneration it is mandatory to define the mechanisms which (a) trigger division, (b) allow the liver to concurrently grow and maintain dilferentiated fimction and (c) terminate cell proliferation once the liver has reached the appropriate mass. Studies on these aspects of liver regeneration will provide basic insight of cell growth and dilferentiation, liver diseases like viral hepatitis, toxic damage and liver transplant where regeneration of the liver is essential. In the present study, Go/G1/S transition of hepatocytes re-entering the cell cycle after PH was studied with special emphasis on the involvement of neurotransmitters, their receptors and second messenger function in the control of cell division during liver regeneration

“Reconstruction of Gene Regulatory Network from Expression Profile of Plasma RNA Data of Colorectal Cancer Patients using Soft Computing Techniques”

Microarray data analysis is one of data mining tool which is used to extract meaningful information hidden in biological data. One of the major focuses on microarray data analysis is the reconstruction of gene regulatory network that may be used to provide a broader understanding on the functioning of complex cellular systems. Since cancer is a genetic disease arising from the abnormal gene function, the identification of cancerous genes and the regulatory pathways they control will provide a better platform for understanding the tumor formation and development. The major focus of this thesis is to understand the regulation of genes responsible for the development of cancer, particularly colorectal cancer by analyzing the microarray expression data. In this thesis, four computational algorithms namely fuzzy logic algorithm, modified genetic algorithm, dynamic neural fuzzy network and Takagi Sugeno Kang-type recurrent neural fuzzy network are used to extract cancer specific gene regulatory network from plasma RNA dataset of colorectal cancer patients. Plasma RNA is highly attractive for cancer analysis since it requires a collection of small amount of blood and it can be obtained at any time in repetitive fashion allowing the analysis of disease progression and treatment response.