No / Objectives Previous studies have demonstrated that intravesical administration of apaziquone (EOquin) has ablative activity against superficial bladder cancer marker lesions with 8 out of 12 complete responses recorded. We present a comparison between the rates of tumor recurrence before and after treatment with apaziquone. Methods The rate of tumor recurrence after treatment with apaziquone was compared with each patient's historical record of recurrences obtained from a retrospective analysis of the patients' case notes. The time to each recurrence event before apaziquone treatment and the time to the first recurrence after apaziquone treatment were recorded, and the data were analyzed using a population-averaged linear regression model using Stata Release, version 9.2, software. Results Of the eight complete responses obtained in the Phase I study, tumor recurrence occurred in 4 patients and the remaining 4 patients remained disease free after a median follow-up of 31 months. The time to the first recurrence after apaziquone treatment was significantly longer (P <0.001) compared with the historical pattern and recurrence interval before apaziquone. Before apaziquone instillation, the mean ± SE recurrence rate and tumor rate per year was 1.5 ± 0.2 and 4.8 ± 1.2, respectively, and these decreased to 0.6 ± 0.25 and 1.5 ± 0.8, respectively, after apaziquone treatment (P <0.05). Conclusions The results of this study indicate that early recurrences after treatment with apaziquone are infrequent and the interval to recurrence is significantly greater compared with the historical recurrence times for these patients. Larger prospective randomised trials are warranted to confirm these results. Aapaziquone (EOquin, USAN, E09, 3-hydroxy-5-aziridinyl-1-methyl-2[indole-4,7-dione]¿prop-¿-en-¿-ol) belongs to a class of anticancer agents known as bioreductive drugs that require metabolism by cellular reductases to generate a cytotoxic species.1 Although it is chemically related to mitomycin C, apaziquone has a distinctly different mechanism of action and preclinical activity profile.1 and 2 The initial optimism generated by its preclinical activity profile rapidly evaporated after the demonstration that intravenously administered apaziquone was clinically inactive against a range of solid tumors in Phase II clinical trials.3 and 4 Several possible explanations were considered for its lack of efficacy, but poor drug delivery to the tumor because of the rapid pharmacokinetic elimination of apaziquone in conjunction with relatively poor penetration through avascular tissue was considered to be the principal reason.5 On the basis of the rationale that intravesical administration would circumvent the problem of drug delivery and any apaziquone absorbed into the blood stream would be rapidly cleared,6 a Phase I-II clinical pilot study of intravesical administration of apaziquone to superficial bladder tumors was established.7 The results of that trial demonstrated that intravesically administered apaziquone has ablative activity against superficial bladder transitional cell carcinoma (TCC) marker lesions.7 These results were confirmed and extended in a Phase II clinical trial of 47 patients with superficial bladder TCC, in which complete responses were obtained in 67% of patients.8 Because all the enrolled patients in the original trial7 had had multiple recurrences after previous intravesical chemotherapy and/or immunotherapy, the purpose of the present study was, first, to report the recurrences that occurred after apaziquone treatment and, second, to study the effect of apaziquone instillation on the recurrence rate by statistically comparing these results with the historical pattern of recurrences for each patient before treatment with apaziquone.

Traffic congestion on urban road networks has been recognized as one of the most serious problems with which modern cities are confronted. It is generally anticipated that Dynamic Route Guidance Systems (DRGS) will play an important role in reducing urban traffic congestion and improving traffic flows and safety. One of the most critical issues in designing these systems is in the development of optimal routing strategies that would maximize the benefits to overall system as well as individual users. Infrastructure based DRGS have advantage of pursuing system optimal routing strategy, which is more essential under abnormal traffic conditions such as non-recurrent congestion and natural disaster. However user compliance could be a problem under such a strategy, particularly when some of equipped drivers are urged not to choose minimum travel time path for the sake of improving the total network travel time. On the other hand, In-vehicle based DRGS can utilize the user-specified route selection criteria to avoid "Braess Paradox" under normal traffic conditions. However, it may be of little use under abnormal traffic conditions and high DRGS market penetration. In conducting the comparative analysis between system optimal strategy and user equilibrium strategy, significant differences were found within the mid-range traffic demand. The maximum total travel time difference occurs when the level of traffic demand is half of the system capacity. At this point, system optimal route guidance strategy can save more than 11% of the total travel time of user equilibrium route guidance strategy. The research proposes an adaptive routing strategy as an efficient dynamic route guidance under non-recurrent traffic congestion. Computation results show that there is no need to implement system optimal routing strategy at the initial stage of the incident. However, it is critical to use system optimal routing strategy as freeway and arterial are getting congested and the queue delay in freeway increases. The adaptive routing strategy is evaluated using Traffic simulation model, INTEGRATION. According to simulation results using an ideal network, the travel time saving ratio is maximum when both arterial and freeway have normal traffic demand under incident. In case of a realistic network, the adaptive routing strategy also proved to save the total travel time between 3% to 10% over the traditional user equilibrium routing strategy. The reduction of total travel time increases as the incident duration increases. Consequently, it is concluded that the adaptive routing strategy for DRGS is more efficient than using user equilibrium routing strategy alone. / Ph. D.

Event extraction is a type of information extraction(IE) that works on extracting the specific knowledge of certain incidents from texts. Nowadays the amount of available information (such as news, blogs, and social media) grows in exponential order. Therefore, it becomes imperative to develop algorithms that automatically extract the machine-readable information from large volumes of text data. In this dissertation, we focus on three problems in obtaining event-related information from news articles. (1) The first effort is to comprehensively analyze the performance and challenges in current large-scale event encoding systems. (2) The second problem involves event detection and critical information extractions from news articles. (3) Third, the efforts concentrate on event-encoding which aims to extract event extent and arguments from texts. We start by investigating the two large-scale event extraction systems (ICEWS and GDELT) in the political science domain. We design a set of experiments to evaluate the quality of the extracted events from the two target systems, in terms of reliability and correctness. The results show that there exist significant discrepancies between the outputs of automated systems and hand-coded system and the accuracy of both systems are far away from satisfying. These findings provide preliminary background and set the foundation for using advanced machine learning algorithms for event related information extraction. Inspired by the successful application of deep learning in Natural Language Processing (NLP), we propose a Multi-Instance Convolutional Neural Network (MI-CNN) model for event detection and critical sentences extraction without sentence level labels. To evaluate the model, we run a set of experiments on a real-world protest event dataset. The result shows that our model could be able to outperform the strong baseline models and extract the meaningful key sentences without domain knowledge and manually designed features. We also extend the MI-CNN model and propose an MIMTRNN model for event extraction with distant supervision to overcome the problem of lacking fine level labels and small size training data. The proposed MIMTRNN model systematically integrates the RNN, Multi-Instance Learning, and Multi-Task Learning into a unified framework. The RNN module aims to encode into the representation of entity mentions the sequential information as well as the dependencies between event arguments, which are very useful in the event extraction task. The Multi-Instance Learning paradigm makes the system does not require the precise labels in entity mention level and make it perfect to work together with distant supervision for event extraction. And the Multi-Task Learning module in our approach is designed to alleviate the potential overfitting problem caused by the relatively small size of training data. The results of the experiments on two real-world datasets(Cyber-Attack and Civil Unrest) show that our model could be able to benefit from the advantage of each component and outperform other baseline methods significantly. / Ph. D. / Nowadays the amount of available information (such as news, blogs, and social media) grows in exponential order. The demand of making use of the massive on-line information during decision making process becomes increasing intensive. Therefore, it is imperative to develop algorithms that automatically extract the formatted information from large volumes of the unstructured text data. In this dissertation, we focus on three problems in obtaining event-related information from news articles. (1) The first effort is to comprehensively analyze the performance and challenges in current large-scale event encoding systems. (2) The second problem involves detecting the event and extracting key information about the event in the article. (3) Third, the efforts concentrate on extracting the arguments of the event from the text. We found that there exist significant discrepancies between the outputs of automated systems and hand-coded system and the accuracy of current event extraction systems are far away from satisfying. These findings provide preliminary background and set the foundation for using advanced machine learning algorithms for event related information extraction. Our experiments on two real-world event extraction tasks (Cyber-Attack and Civil Unrest) show the effectiveness of our deep learning approaches for detecting and extracting the event information from unstructured text data.

As an efficient energy storage technology, lithium-ion batteries play a key role in the ongoing electrification of the mobility sector. However, the required modelbased design process, including hardware in the loop solutions, demands precise battery models. In this work, an encoder-decoder model framework based on recurrent neural networks is developed and trained directly on unstructured battery data to replace time consuming characterisation tests and thus simplify the modelling process. A manifold pseudo-random bit stream dataset is used for model training and validation. A mean percentage error (MAPE) of 0.30% for the test dataset attests the proposed encoder-decoder model excellent generalisation capabilities. Instead of the recursive one-step prediction prevalent in the literature, the stage-wise trained encoder-decoder framework can instantaneously predict the battery voltage response for 2000 time steps and proves to be 120 times more time-efficient on the test dataset. Accuracy, generalisation capability and time efficiency of the developed battery model enable a potential online anomaly detection, power or range prediction. The fact that, apart from the initial voltage level, the battery model only relies on the current load as input and thus requires no estimated variables such as the state-of-charge (SOC) to predict the voltage response holds the potential of a battery ageing independent LIB modelling based on raw BMS signals. The intrinsically ageingindependent battery model is thus suitable to be used as a digital battery twin in virtual experiments to estimate the unknown battery SOH on purely BMS data basis.

No description available.

The neural net computer simulations which will be presented here are based on the acceptance of a set of assumptions that for the last twenty years have been expressed in the fields of information processing, neurophysiology and cognitive sciences. First of all, neural networks and their dynamical behaviors in terms of attractors is the natural way adopted by the brain to encode information. Any information item to be stored in the neural net should be coded in some way or another in one of the dynamical attractors of the brain and retrieved by stimulating the net so as to trap its dynamics in the desired item's basin of attraction. The second view shared by neural net researchers is to base the learning of the synaptic matrix on a local Hebbian mechanism. The last assumption is the presence of chaos and the benefit gained by its presence. Chaos, although very simply produced, inherently possesses an infinite amount of cyclic regimes that can be exploited for coding information. Moreover, the network randomly wanders around these unstable regimes in a spontaneous way, thus rapidly proposing alternative responses to external stimuli and being able to easily switch from one of these potential attractors to another in response to any coming stimulus. In this thesis, it is shown experimentally that the more information is to be stored in robust cyclic attractors, the more chaos appears as a regime in the back, erratically itinerating among brief appearances of these attractors. Chaos does not appear to be the cause but the consequence of the learning. However, it appears as an helpful consequence that widens the net's encoding capacity. To learn the information to be stored, an unsupervised Hebbian learning algorithm is introduced. By leaving the semantics of the attractors to be associated with the feeding data unprescribed, promising results have been obtained in term of storing capacity.

Les conditions de circulation sur un réseau routier subissent souvent de la congestion. Selon ses sources, la congestion routière peut être classée en deux catégories : la congestion récurrente déterminée par les lois de trafic et la congestion non-récurrente due aux incidents, accidents ou autres aléas sur la route. Grâce à l'avancement des technologies, notamment en informatique, communication et techniques de traitement des données, l'exploitant est devenu capable de détecter les perturbations, de mesurer les effets et même d'anticiper l'état du trafic afin de mieux adapter ses actions d'exploitation. L'information dynamique concernant les conditions de trafic permet aux usagers de réduire l'inconfort et d'effectuer leur choix d'itinéraire de manière plus raisonnable. Pour l'exploitant, le service d'information aux usagers peut servir à la gestion du trafic. Nous avons étudié la contribution potentielle de l'information dynamique au profit individuel des usagers et à la performance collective du système en prenant en compte : i) la congestion récurrente et non-récurrente ; ii) des différents comportements de choix d'itinéraire en fonction de l'accessibilité à l'information ; iii) d'autres actions de gestion du trafic menées par l'exploitant. Un modèle théorique avec une application analytique sur un réseau élémentaire de deux routes parallèles, une paire origine-destination et deux classes d'usagers respectivement informée ou non-informée nous a permis de retirer de nombreuses indications : i) la diffusion excessive de l'information avec un contenu « neutre » dégrade à la fois le profit individuel et la performance du système ; ii) l'information dynamique avec certain contenu « coopératif » peut contribuer l'optimisation du système sans causer le problème d'acceptabilité ; iii) l'information dynamique et d'autres mesures de gestion dynamique s'interagissent de manière complémentaire à l'optimisation du trafic / Traffic conditions on a road network often suffer from congestion. According to sources, the traffic congestion can be classified into two categories : recurrent congestion determined by the physic laws of traffic and non-recurrent congestion due to incidents, accidents or other hazards on the road. Thanks to the advancement of technologies, including computers, communications and data processing, the traffic operator is now able to detect disturbances, to measure the effects and even to anticipate traffic conditions to better match traffic management activities. Dynamic information on traffic conditions enables users to reduce discomfort and make their route choice decision more reasonable. For the operator, the service user information may be used as a traffic management tool. We investigated the potential contribution of dynamic traffic information for the benefit of individual users and system performance by taking into account : i) recurring congestion and non-recurring ; ii) different route choice behaviours based on accessibility to information service ; iii) other traffic management actions taken by the traffic operator. A theoretical model with an analytical application on a simple two-parallel-road network, an origin-destination pairs and two user classes, respectively-informed or non-informed has given many conclusions : i) an excessive distribution of traffic information with a « neutral » content damages both the individual profit and system performance ; ii) traffic information with some « cooperative » content may help optimize the system performance without causing acceptability problem ; and iii) dynamic information and other traffic management tools interplay in a complementary manner to optimize the traffic

La plupart des molécules d’ARN doivent se replier en structure tertiaire complexe afin d’accomplir leurs fonctions biologiques. Cependant, les déterminants d’une chaîne de polynucléotides qui sont nécessaires à son repliement et à ses interactions avec d’autres éléments sont essentiellement inconnus. L’établissement des relations structure-fonction dans les grandes molécules d’ARN passe inévitablement par l’analyse de chaque élément de leur structure de façon individuelle et en contexte avec d’autres éléments. À l’image d’une construction d’immeuble, une structure d’ARN est composée d’unités répétitives assemblées de façon spécifique. Les motifs récurrents d’ARN sont des arrangements de nucléotides retrouvés à différents endroits d’une structure tertiaire et possèdent des conformations identiques ou très similaires. Ainsi, une des étapes nécessaires à la compréhension de la structure et de la fonction des molécules d’ARN consiste à identifier de façon systématique les motifs récurrents et d’en effectuer une analyse comparative afin d’établir la séquence consensus. L’analyse de tous les cas d’empaquetage de doubles hélices dans la structure du ribosome a permis l’identification d’un nouvel arrangement nommé motif d’empaquetage le long du sillon (AGPM) (along-groove packing motif). Ce motif est retrouvé à 14 endroits dans la structure du ribosome de même qu’entre l’ARN ribosomique 23S et les molécules d’ARN de transfert liées aux sites ribosomaux P et E. Le motif se forme par l’empaquetage de deux doubles hélices via leur sillon mineur. Le squelette sucre-phosphate d’une hélice voyage le long du sillon mineur de l’autre hélice et vice versa. Dans chacune des hélices, la région de contact comprend quatre paires de bases. L’empaquetage le plus serré est retrouvé au centre de l’arrangement où l’on retrouve souvent une paire de bases GU dans une hélice interagissant avec une paire de bases Watson-Crick (WC) dans l’autre hélice. Même si la présence des paires de bases centrales GU versus WC au centre du motif augmente sa stabilité, d’autres alternatives existent pour différents représentants du motif. L’analyse comparative de trois librairies combinatoires de gènes d’AGPM, où les paires de bases centrales ont été variées de manière complètement aléatoire, a montré que le contexte structural influence l’étendue de la variabilité des séquences de nucléotides formant les paires de bases centrales. Le fait que l’identité des paires de bases centrales puisse varier suggérait la présence d’autres déterminants responsables au maintien de l’intégrité du motif. L’analyse de tous les contacts entre les hélices a révélé qu’en dehors du centre du motif, les interactions entre les squelettes sucre-phosphate s’effectuent via trois contacts ribose-ribose. Pour chacun de ces contacts, les riboses des nucléotides qui interagissent ensemble doivent adopter des positions particulières afin d’éviter qu’ils entrent en collision. Nous montrons que la position de ces riboses est modulée par des conformations spécifiques des paires de bases auxquelles ils appartiennent. Finalement, un autre motif récurrent identifié à l’intérieur même de la structure de trois cas d’AGPM a été nommé « adenosine-wedge ». Son analyse a révélé que ce dernier est lui-même composé d’un autre arrangement, nommé motif triangle-NAG (NAG-triangle). Nous montrons que le motif « adenosine-wedge » représente un arrangement complexe d’ARN composé de quatre éléments répétitifs, c’est-à-dire des motifs AGPM, « hook-turn », « A-minor » et triangle-NAG. Ceci illustre clairement l’arrangement hiérarchique des structures d’ARN qui peut aussi être observé pour d’autres motifs d’ARN. D’un point de vue plus global, mes résultats enrichissent notre compréhension générale du rôle des différents types d’interactions tertiaires dans la formation des molécules d’ARN complexes. / Most RNA molecules have to adopt a complex tertiary structure to accomplish their biological functions. However, the important determinants of a polynucleotide chain that are required for its proper folding and its interactions with other elements are essentially unknown. The establishment of structure-function relationships in large RNA molecules goes inevitably through the analysis of each element of their structure separately and in context with other elements. Like a building, an RNA structure is built of repetitive pieces that are glued together in a specific way. These repetitive elements, instead of being bricks, are recurrent motifs. Recurrent RNA motifs are arrangements of nucleotides found in different parts of a tertiary structure and have identical or very similar conformations. Thus, a necessary step toward the understanding of RNA structure and function consists in the systematic identification of recurrent motifs, followed by their comparative analysis and establishment of their sequence consensus. The analysis of all instances of helical packing within the ribosome structure led to the identification of a new structural arrangement, named the along-groove packing motif (AGPM), which is found in 14 places of the ribosome structure as well as between the 23S ribosomal RNA and the transfer RNA molecules bound to the P and E sites. The motif is formed by the packing of two double helices via their minor grooves. The sugar-phosphate backbone of one helix goes along the minor groove of the other helix and vice versa. In each helix, the contact region includes four base pairs. The closest packing occurs in the center where one can often see a GU base pair packed against a WC base pair. While the presence of the central base pairs GU versus WC in the core of the motif enhances its stability, other alternatives are also present among available structures of the motif. A comparative analysis of three different combinatorial gene libraries of AGPM, in which the central base pairs were fully randomized, shows that the structural context influences the scope of nucleotide sequence variability of the central base pairs. The fact that the identity of the central base pairs can vary suggested that there are other determinants responsible of the motif’s integrity. Analysis of all other inter-helix contacts has shown that outside the center of the motif the interactions between backbones are made via three ribose-ribose contacts. Within each of these contacts, the riboses of the nucleotides that are in touch adopt particular positions in order to provide for collision-free interactions between them. We show that the position of these riboses is modulated by the specific base pair conformation in which it belongs. Finally, another recurrent arrangement that occurs within the structure of three cases of AGPM was identified and called the adenosine-wedge. Analysis has shown that the latter motif is itself composed of a smaller arrangement, called the NAG-triangle motif. We show that the adenosine-wedge motif represents a complex RNA arrangement composed of four repetitive elements, AGPM, the hook-turn, the A-minor and the NAG-triangle, which clearly illustrates the hierarchical organisation of the structure that could also occur in other RNA motifs as well. Altogether, my results enrich our general understanding of the role of different types of tertiary interactions in the formation of large RNA molecules.

OBJECTIF: Évaluer si la prééclampsie (PE) récidivante présente un taux de prématurité (< 37 semaines de gestation) plus élevé qu'une première PE. Les critères de jugement secondaires étaient le retard de croissance intra-utérin (RCIU) et la morbidité maternelle. MÉTHODES: Il s'agit d'une étude rétrospective de cohorte conduite sur 383 femmes avec un diagnostic de prééclampsie et ayant accouché au CHU Sainte-Justine à Montréal (Canada) entre 2001 et 2011. Parmi elles, 128 ont développé une récidive de PE à la grossesse successive. RÉSULTATS: Chez les femmes récidivantes (n = 128), les taux de prématurité et de RCIU étaient similaires dans les 2 épisodes de PE, bien que plus atteintes d'hypertension chronique (p = 0.001) et de diabète gestationnel (p = 0.021) dans leur seconde PE. Comparativement aux femmes non récidivantes (n = 255), les récidivantes (n = 128) présentaient, à leur première PE, un profil clinique caractérisé par un taux élevé de PE sévère (p < 0.001), éclampsie et critères adverses (p = 0.007). Le risque relatif de récidive de PE chez une femme avec ce profil clinique à sa première PE a été évalué à 1, 60 (95%IC: 1, 17 – 2, 18). CONCLUSION: La récidive de PE est associée à des taux similairement élevés de prématurité et de RCIU comparativement à la première PE. Les femmes qui à leur première PE ont un profil clinique défini par prééclampsie sévère, éclampsie ou présence de critères adverses sont plus à risque de récidive de PE à la grossesse subséquente. Mots clés: prééclampsie, récidive, issues périnatales, prématurité / OBJECTIVE: To assess whether a recurrent preeclampsia compared to the preeclampsia that occurred at the first pregnancy was more at risk of preterm delivery at < 37 weeks of gestation. Secondary outcomes were intrauterine growth restriction (IUGR) and maternal morbidity. STUDY DESIGN: We conducted a retrospective cohort study including 383 women with preeclampsia who delivered at Sainte-Justine Hospital in Montreal (Canada) from 2001 to 2011. Among these, 128 women developed a recurrent preeclampsia in their subsequent pregnancy. RESULTS: Among women with a recurrent preeclampsia (n = 128), no significant differences were found in the rates of preterm delivery and IUGR between the first and the subsequent pregnancy. Women with a recurrent preeclampsia were more at risk of chronic hypertension (p = 0.001) and gestational diabetes (p = 0.02) in their second pregnancy. Furthermore, women with recurrent preeclampsia had, at their first pregnancy, a higher rate of severe preeclampsia (p < 0.001), eclampsia and adverse criteria (p = 0.007), than women who experienced a single preeclampsia (n = 255). The Relative Risk to experience a recurrent preeclampsia at the second pregnancy, if a woman had the above criteria at the first pregnancy, was 1.60 (95%CI 1.17 - 2.18). CONCLUSION: Recurrent preeclampsia was not associated with a higher rate of preterm delivery. Women who experienced a severe preeclampsia, an eclampsia or adverse criteria in a first pregnancy were more at risk to have a recurrent preeclampsia in the subsequent pregnancy. Key words: preeclampsia, recurrence, perinatal outcome, and preterm delivery

Les primates doivent pouvoir reconnaître de nouvelles situations pour pouvoir s'y adapter. La représentation de ces situations dans l'activité du cortex est le sujet de cette thèse. Les situations complexes s'expliquent souvent par l'interaction entre des informations sensorielles, internes et motrices. Des activités unitaires dénommées sélectivité mixte, qui sont très présentes dans le cortex préfrontal (CPF), sont un mécanisme possible pour représenter n'importe quelle interaction entre des informations. En parallèle, le Reservoir Computing a démontré que des réseaux récurrents ont la propriété de recombiner des entrées actuelles et passées dans un espace de plus haute dimension, fournissant ainsi un pré-codage potentiellement universel de combinaisons pouvant être ensuite sélectionnées et utilisées en fonction de leur pertinence pour la tâche courante. En combinant ces deux approches, nous soutenons que la nature fortement récurrente de la connectivité locale du CPF est à l'origine d'une forme dynamique de sélectivité mixte. De plus, nous tentons de démontrer qu'une simple régression linéaire, implémentable par un neurone seul, peut extraire n'importe qu'elle information/contingence encodée dans ces combinaisons complexes et dynamiques. Finalement, les entrées précédentes, qu'elles soient sensorielles ou motrices, à ces réseaux du CPF doivent être maintenues pour pouvoir influencer les traitements courants. Nous soutenons que les représentations de ces contextes définis par ces entrées précédentes doivent être exprimées explicitement et retournées aux réseaux locaux du CPF pour influencer les combinaisons courantes à l'origine de la représentation des contingences / In order to adapt to new situations, primates must be able to recognize these situations. How the cortex represents contingencies in its activity is the main subject of this thesis. First, complex new situations are often explained by the interaction between sensory, internal and motor information. Recent studies have shown that single-neuron activities referred to as mixed selectivity which are ubiquitous in the prefrontal cortex (PFC) are a possible mechanism to represent arbitrary interaction between information defining a contingency. In parallel, a recent area of reasearch referred to as Reservoir Computing has demonstrated that recurrent neural networks have the property of recombining present and past inputs into a higher dimensional space thereby providing a pre-coding of an essentially universal set of combinations which can then be selected and used arbitrarily for their relevance to the task at hand. Combining these two approaches we argue that the highly recurrent nature of local prefrontal connectivity is at the origin of dynamic form of mixed selectivity. Also, we attempt to demonstrate that a simple linear regression, implementable by a single neuron, can extract any information/ contingency encoded in these highly complex and dynamic combinations. In addition, previous inputs, whether sensory or motor, to these PFC networks must be maintained in order to influence current processing and behavioral demand. We argue that representations of contexts defined by these past inputs must be expressed explicitely and fed back to the local PFC networks in order to influence the current combinations at the origin of contingencies representation