Optimal perfusion computed tomographic thresholds for ischemic core and penumbra are not time dependent in the clinically relevant time window.

BACKGROUND AND PURPOSE: This study aims to determine whether perfusion computed tomographic (PCT) thresholds for delineating the ischemic core and penumbra are time dependent or time independent in patients presenting with symptoms of acute stroke. METHODS: Two hundred seventeen patients were evaluated in a retrospective, multicenter study. Patients were divided into those with either persistent occlusion or recanalization. All patients received admission PCT and follow-up imaging to determine the final ischemic core, which was then retrospectively matched to the PCT images to identify optimal thresholds for the different PCT parameters. These thresholds were assessed for significant variation over time since symptom onset. RESULTS: In the persistent occlusion group, optimal PCT parameters that did not significantly change with time included absolute mean transit time, relative mean transit time, relative cerebral blood flow, and relative cerebral blood volume when time was restricted to 15 hours after symptom onset. Conversely, the recanalization group showed no significant time variation for any PCT parameter at any time interval. In the persistent occlusion group, the optimal threshold to delineate the total ischemic area was the relative mean transit time at a threshold of 180%. In patients with recanalization, the optimal parameter to predict the ischemic core was relative cerebral blood volume at a threshold of 66%. CONCLUSIONS: Time does not influence the optimal PCT thresholds to delineate the ischemic core and penumbra in the first 15 hours after symptom onset for relative mean transit time and relative cerebral blood volume, the optimal parameters to delineate ischemic core and penumbra.

Optimal culture incubation time in orthopedic device-associated infections: a retrospective analysis of prolonged 14-day incubation.

Accurate diagnosis of orthopedic device-associated infections can be challenging. Culture of tissue biopsy specimens is often considered the gold standard; however, there is currently no consensus on the ideal incubation time for specimens. The aim of our study was to assess the yield of a 14-day incubation protocol for tissue biopsy specimens from revision surgery (joint replacements and internal fixation devices) in a general orthopedic and trauma surgery setting. Medical records were reviewed retrospectively in order to identify cases of infection according to predefined diagnostic criteria. From August 2009 to March 2012, 499 tissue biopsy specimens were sampled from 117 cases. In 70 cases (59.8%), at least one sample showed microbiological growth. Among them, 58 cases (82.9%) were considered infections and 12 cases (17.1%) were classified as contaminations. The median time to positivity in the cases of infection was 1 day (range, 1 to 10 days), compared to 6 days (range, 1 to 11 days) in the cases of contamination (P < 0.001). Fifty-six (96.6%) of the infection cases were diagnosed within 7 days of incubation. In conclusion, the results of our study show that the incubation of tissue biopsy specimens beyond 7 days is not productive in a general orthopedic and trauma surgery setting. Prolonged 14-day incubation might be of interest in particular situations, however, in which the prevalence of slow-growing microorganisms and anaerobes is higher.

Artificial evolution on network structures : how time and space influence dynamics

Abstract The main objective of this work is to show how the choice of the temporal dimension and of the spatial structure of the population influences an artificial evolutionary process. In the field of Artificial Evolution we can observe a common trend in synchronously evolv¬ing panmictic populations, i.e., populations in which any individual can be recombined with any other individual. Already in the '90s, the works of Spiessens and Manderick, Sarma and De Jong, and Gorges-Schleuter have pointed out that, if a population is struc¬tured according to a mono- or bi-dimensional regular lattice, the evolutionary process shows a different dynamic with respect to the panmictic case. In particular, Sarma and De Jong have studied the selection pressure (i.e., the diffusion of a best individual when the only selection operator is active) induced by a regular bi-dimensional structure of the population, proposing a logistic modeling of the selection pressure curves. This model supposes that the diffusion of a best individual in a population follows an exponential law. We show that such a model is inadequate to describe the process, since the growth speed must be quadratic or sub-quadratic in the case of a bi-dimensional regular lattice. New linear and sub-quadratic models are proposed for modeling the selection pressure curves in, respectively, mono- and bi-dimensional regu¬lar structures. These models are extended to describe the process when asynchronous evolutions are employed. Different dynamics of the populations imply different search strategies of the resulting algorithm, when the evolutionary process is used to solve optimisation problems. A benchmark of both discrete and continuous test problems is used to study the search characteristics of the different topologies and updates of the populations. In the last decade, the pioneering studies of Watts and Strogatz have shown that most real networks, both in the biological and sociological worlds as well as in man-made structures, have mathematical properties that set them apart from regular and random structures. In particular, they introduced the concepts of small-world graphs, and they showed that this new family of structures has interesting computing capabilities. Populations structured according to these new topologies are proposed, and their evolutionary dynamics are studied and modeled. We also propose asynchronous evolutions for these structures, and the resulting evolutionary behaviors are investigated. Many man-made networks have grown, and are still growing incrementally, and explanations have been proposed for their actual shape, such as Albert and Barabasi's preferential attachment growth rule. However, many actual networks seem to have undergone some kind of Darwinian variation and selection. Thus, how these networks might have come to be selected is an interesting yet unanswered question. In the last part of this work, we show how a simple evolutionary algorithm can enable the emrgence o these kinds of structures for two prototypical problems of the automata networks world, the majority classification and the synchronisation problems. Synopsis L'objectif principal de ce travail est de montrer l'influence du choix de la dimension temporelle et de la structure spatiale d'une population sur un processus évolutionnaire artificiel. Dans le domaine de l'Evolution Artificielle on peut observer une tendence à évoluer d'une façon synchrone des populations panmictiques, où chaque individu peut être récombiné avec tout autre individu dans la population. Déjà dans les année '90, Spiessens et Manderick, Sarma et De Jong, et Gorges-Schleuter ont observé que, si une population possède une structure régulière mono- ou bi-dimensionnelle, le processus évolutionnaire montre une dynamique différente de celle d'une population panmictique. En particulier, Sarma et De Jong ont étudié la pression de sélection (c-à-d la diffusion d'un individu optimal quand seul l'opérateur de sélection est actif) induite par une structure régulière bi-dimensionnelle de la population, proposant une modélisation logistique des courbes de pression de sélection. Ce modèle suppose que la diffusion d'un individu optimal suit une loi exponentielle. On montre que ce modèle est inadéquat pour décrire ce phénomène, étant donné que la vitesse de croissance doit obéir à une loi quadratique ou sous-quadratique dans le cas d'une structure régulière bi-dimensionnelle. De nouveaux modèles linéaires et sous-quadratique sont proposés pour des structures mono- et bi-dimensionnelles. Ces modèles sont étendus pour décrire des processus évolutionnaires asynchrones. Différentes dynamiques de la population impliquent strategies différentes de recherche de l'algorithme résultant lorsque le processus évolutionnaire est utilisé pour résoudre des problèmes d'optimisation. Un ensemble de problèmes discrets et continus est utilisé pour étudier les charactéristiques de recherche des différentes topologies et mises à jour des populations. Ces dernières années, les études de Watts et Strogatz ont montré que beaucoup de réseaux, aussi bien dans les mondes biologiques et sociologiques que dans les structures produites par l'homme, ont des propriétés mathématiques qui les séparent à la fois des structures régulières et des structures aléatoires. En particulier, ils ont introduit la notion de graphe sm,all-world et ont montré que cette nouvelle famille de structures possède des intéressantes propriétés dynamiques. Des populations ayant ces nouvelles topologies sont proposés, et leurs dynamiques évolutionnaires sont étudiées et modélisées. Pour des populations ayant ces structures, des méthodes d'évolution asynchrone sont proposées, et la dynamique résultante est étudiée. Beaucoup de réseaux produits par l'homme se sont formés d'une façon incrémentale, et des explications pour leur forme actuelle ont été proposées, comme le preferential attachment de Albert et Barabàsi. Toutefois, beaucoup de réseaux existants doivent être le produit d'un processus de variation et sélection darwiniennes. Ainsi, la façon dont ces structures ont pu être sélectionnées est une question intéressante restée sans réponse. Dans la dernière partie de ce travail, on montre comment un simple processus évolutif artificiel permet à ce type de topologies d'émerger dans le cas de deux problèmes prototypiques des réseaux d'automates, les tâches de densité et de synchronisation.

Spatiotemporal neuromodulation therapies engaging muscle synergies improve motor control after spinal cord injury.

Electrical neuromodulation of lumbar segments improves motor control after spinal cord injury in animal models and humans. However, the physiological principles underlying the effect of this intervention remain poorly understood, which has limited the therapeutic approach to continuous stimulation applied to restricted spinal cord locations. Here we developed stimulation protocols that reproduce the natural dynamics of motoneuron activation during locomotion. For this, we computed the spatiotemporal activation pattern of muscle synergies during locomotion in healthy rats. Computer simulations identified optimal electrode locations to target each synergy through the recruitment of proprioceptive feedback circuits. This framework steered the design of spatially selective spinal implants and real-time control software that modulate extensor and flexor synergies with precise temporal resolution. Spatiotemporal neuromodulation therapies improved gait quality, weight-bearing capacity, endurance and skilled locomotion in several rodent models of spinal cord injury. These new concepts are directly translatable to strategies to improve motor control in humans.