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The seemingly random and unpredictable nature of seizures is a major debilitating factor for people with epilepsy. An increasing body of evidence demonstrates that the epileptic brain exhibits long-term fluctuations in seizure susceptibility, and seizure emergence seems to be a consequence of processes operating over multiple temporal scales. A deeper insight into the mechanisms responsible for long-term seizure fluctuations may provide important information for understanding the complex nature of seizure genesis. In this study, we explored the long-term dynamics of seizures in the tetanus toxin model of temporal lobe epilepsy. The results demonstrate the existence of long-term fluctuations in seizure probability, where seizures form clusters in time and are then followed by seizure-free periods. Within each cluster, seizure distribution is non-Poissonian, as demonstrated by the progressively increasing inter-seizure interval (ISI), which marks the approaching cluster termination. The lengthening of ISIs is paralleled by: increasing behavioral seizure severity, the occurrence of convulsive seizures, recruitment of extra-hippocampal structures and the spread of electrographic epileptiform activity outside of the limbic system. The results suggest that repeated non-convulsive seizures obey the ‘seizures-beget-seizures’ principle, leading to the occurrence of convulsive seizures, which decrease the probability of a subsequent seizure and, thus, increase the following ISI. The cumulative effect of repeated convulsive seizures leads to cluster termination, followed by a long inter-cluster period. We propose that seizures themselves are an endogenous factor that contributes to long-term fluctuations in seizure susceptibility and their mutual interaction determines the future evolution of disease activity.

With the purpose of studying data on spontaneous customary changes in diabetic rats, we induced diabetes in 28 Wistar rats with streptozotocin. The animals were observed for 27 weeks in an attempt to characterize spontaneous customary changes that could suggest signs of chronic pain. Morphine, as a central-acting potent analgesic and its specific antagonist naloxone, were used. Our results evidenced in the animals a clinical syndrome similar to human diabetes. Long-term customary analysis revealed a significant (pCom o objetivo de estudar dados sobre mudança de comportamento em ratos com neuropatia diabética dolorosa, induzimos diabetes em 28 ratos Wistar com estreptozotocina. Os animais foram então observados ao longo de 27 semanas com o propósito de caracterizar mudanças espontâneas em seus hábitos que pudessem sugerir sinais de dor crônica. Morfina como um potente analgésico de ação central e seu antagonista específico naloxona foram utilizados. Nossos resultados evidenciaram nos animais uma síndrome clínica semelhante ao diabetes humano (poliúria, catarata, perda de peso, neuropatia sensitivo-motora de predomínio distal). A análise comportamental revelou aumento dos comportamentos de coçar-se e descansar/dormir, e diminuição das atividades motoras e hábitos de comer e limpar-se. Além disso, os testes térmicos revelaram sinais de hiperalgesia em 43% desses animais, o que corrobora o significado de coçar-se como sinal de dor. Os testes farmacológicos com morfina evidenciaram inibição significativa (p

Highlights: [•] An experimental study on user interest evolution in real-world recommender systems. [•] Integrating the long-term and short-term reading preferences of users. [•] Selecting news from the user-item affinity graph using absorbing random walk model. [•] Extensive empirical experiments on news data obtained from popular news websites. [ABSTRACT FROM AUTHOR]

Abstract: In many E-commerce recommender systems, a special class of recommendation involves recommending items to users in a life cycle. For example, customers who have babies will shop on Diapers.com within a relatively long period, and purchase different products for babies within different growth stages. Traditional recommendation algorithms produce recommendation lists similar to items that the target user has accessed before (content filtering), or compute recommendation by analyzing the items purchased by the users who are similar to the target user (collaborative filtering). Such recommendation paradigms cannot effectively resolve the situation with a life cycle, i.e., the need of customers within different stages might vary significantly. In this paper, we model users’ behavior with life cycles by employing hand-crafted item taxonomies, of which the background knowledge can be tailored for the computation of personalized recommendation. In particular, our method first formalizes a user’s long-term behavior using the item taxonomy, and then identifies the exact stage of the user. By incorporating collaborative filtering into recommendation, we can easily provide a personalized item list to the user through other similar users within the same stage. An empirical evaluation conducted on a purchasing data collection obtained from Diapers.com demonstrates the efficacy of our proposed method. [Copyright &y& Elsevier]

The seemingly random and unpredictable nature of seizures is a major debilitating factor for people with epilepsy. An increasing body of evidence demonstrates that the epileptic brain exhibits long-term fluctuations in seizure susceptibility, and seizure emergence seems to be a consequence of processes operating over multiple temporal scales. A deeper insight into the mechanisms responsible for long-term seizure fluctuations may provide important information for understanding the complex nature of seizure genesis. In this study, we explored the long-term dynamics of seizures in the tetanus toxin model of temporal lobe epilepsy. The results demonstrate the existence of long-term fluctuations in seizure probability, where seizures form clusters in time and are then followed by seizure-free periods. Within each cluster, seizure distribution is non-Poissonian, as demonstrated by the progressively increasing inter-seizure interval (ISI), which marks the approaching cluster termination. The lengthening of ISIs is paralleled by: increasing behavioral seizure severity, the occurrence of convulsive seizures, recruitment of extra-hippocampal structures and the spread of electrographic epileptiform activity outside of the limbic system. The results suggest that repeated non-convulsive seizures obey the 'seizures-beget-seizures' principle, leading to the occurrence of convulsive seizures, which decrease the probability of a subsequent seizure and, thus, increase the following ISI. The cumulative effect of repeated convulsive seizures leads to cluster termination, followed by a long inter-cluster period. We propose that seizures themselves are an endogenous factor that contributes to long-term fluctuations in seizure susceptibility and their mutual interaction determines the future evolution of disease activity. • Seizure occurrence is determined by processes operating over long temporal scales. • The 'seizures beget seizures' principle contributes to seizure clustering. • Seizure severity and spatial expansion of epileptic activity determine the probability of future seizure occurrence. • Seizure clustering may be exclusively governed by seizures themselves, with no external contributing factors. • Intra-cluster evolution of seizure properties is accompanied by changes in interictal activity. [ABSTRACT FROM AUTHOR]