Eosinophilic esophagitis is a newly diagnosed esophageal disease in adult and children. The clinical and pathological characteristics of this disease have been established and were recently summarized in the expert cl...Eosinophilic esophagitis is a newly diagnosed esophageal disease in adult and children. The clinical and pathological characteristics of this disease have been established and were recently summarized in the expert clinical guideline published in 2011. In spite of the wide knowledge accumulated on this disease, there are many areas where scientific data are missing, especially in regard to the disease's pathophysiology. Recent publications have suggested that other confounding factors modify the disease and may affect its clinicalphenotypic presentation. Those factors may include place of living, air pollution, race, genetic factors and other. In the present report we discussed and review those confounding factors, the new developments, and what direction we should go to further advance our knowledge of this disease.展开更多
The necessity for understanding normal human cognitive processes and behavior, and themechanisrns which result in dysfunction in these processes are dependant on utilization of a suitable animal model. In order to dev...The necessity for understanding normal human cognitive processes and behavior, and themechanisrns which result in dysfunction in these processes are dependant on utilization of a suitable animal model. In order to develop pharmaceutical agents to alleviate mental disturbances and enable the individual to cope within the norms of society, it is incumbent upon investigators to choose a species in which pharmacokinetic principles are established and resemble those of hurnans. The choice of rats in cognition research studies has specific advantages in that these anirnals possess similar pharrnacodynamic parameters to hurnans. Further advantages include availability, low cost, ease of breeding, maintenance and an extensive literature database which enable comparisons to present findings. However, there are substantial differences in the perforrnance of various rat strains in tasks of learning, memory, attention, and responses to stress or drugs. In addition to rat strain, quantity of thed also exerts profound consequences on animal behavior. The aim of this review is to demonstrate that there are differences in the central nervous systern responsivencess of rat strains to chemicals and these could be related to factors such as source of supplier, type and quantity of feed, or season of the year. It is also evident that the genotype differs amongst strains and this may be responsible for the observed differences in CNS sensitivity to chemicals. Strain differences must be identified and taken into consideration in interpretation of assessrnent of neurobehavioural functions. It is also incumbent upon the investigators to utilize healthy (diet-controlled) animal models.展开更多
提升Q学习(Q-learning)算法在复杂环境中的数据效率与决策准确度,无疑是算法性能优化所面临的关键挑战。将因果模型引入Q学习算法,通过揭示变量间的因果关系,从而提高Q学习算法的性能是新兴且热门的研究方向。该文提出一种基于因果模型...提升Q学习(Q-learning)算法在复杂环境中的数据效率与决策准确度,无疑是算法性能优化所面临的关键挑战。将因果模型引入Q学习算法,通过揭示变量间的因果关系,从而提高Q学习算法的性能是新兴且热门的研究方向。该文提出一种基于因果模型的Q学习算法,C-Q学习(Causal-model based Q-learning)算法。该算法包括基于智能体利用Q学习算法与环境交互过程中关键变量之间的因果关系,构建结构因果模型;采用因果推断理论中的后门调整的方法去除模型中影响奖励的混淆因子所引起的混淆效应,评估了更为准确的Q值,并且精准识别出每个状态下可能获得最高奖励的动作,优化Q学习算法的动作选择过程。最后,将Q学习算法、Eva-Q学习算法、C-Q学习算法在栅格环境中进行仿真实验。仿真实验结果表明,C-Q学习算法在路径长度、规划时间、数据效率和决策准确度等多个指标上均优于其余两种算法。展开更多
文摘Eosinophilic esophagitis is a newly diagnosed esophageal disease in adult and children. The clinical and pathological characteristics of this disease have been established and were recently summarized in the expert clinical guideline published in 2011. In spite of the wide knowledge accumulated on this disease, there are many areas where scientific data are missing, especially in regard to the disease's pathophysiology. Recent publications have suggested that other confounding factors modify the disease and may affect its clinicalphenotypic presentation. Those factors may include place of living, air pollution, race, genetic factors and other. In the present report we discussed and review those confounding factors, the new developments, and what direction we should go to further advance our knowledge of this disease.
文摘The necessity for understanding normal human cognitive processes and behavior, and themechanisrns which result in dysfunction in these processes are dependant on utilization of a suitable animal model. In order to develop pharmaceutical agents to alleviate mental disturbances and enable the individual to cope within the norms of society, it is incumbent upon investigators to choose a species in which pharmacokinetic principles are established and resemble those of hurnans. The choice of rats in cognition research studies has specific advantages in that these anirnals possess similar pharrnacodynamic parameters to hurnans. Further advantages include availability, low cost, ease of breeding, maintenance and an extensive literature database which enable comparisons to present findings. However, there are substantial differences in the perforrnance of various rat strains in tasks of learning, memory, attention, and responses to stress or drugs. In addition to rat strain, quantity of thed also exerts profound consequences on animal behavior. The aim of this review is to demonstrate that there are differences in the central nervous systern responsivencess of rat strains to chemicals and these could be related to factors such as source of supplier, type and quantity of feed, or season of the year. It is also evident that the genotype differs amongst strains and this may be responsible for the observed differences in CNS sensitivity to chemicals. Strain differences must be identified and taken into consideration in interpretation of assessrnent of neurobehavioural functions. It is also incumbent upon the investigators to utilize healthy (diet-controlled) animal models.
文摘提升Q学习(Q-learning)算法在复杂环境中的数据效率与决策准确度,无疑是算法性能优化所面临的关键挑战。将因果模型引入Q学习算法,通过揭示变量间的因果关系,从而提高Q学习算法的性能是新兴且热门的研究方向。该文提出一种基于因果模型的Q学习算法,C-Q学习(Causal-model based Q-learning)算法。该算法包括基于智能体利用Q学习算法与环境交互过程中关键变量之间的因果关系,构建结构因果模型;采用因果推断理论中的后门调整的方法去除模型中影响奖励的混淆因子所引起的混淆效应,评估了更为准确的Q值,并且精准识别出每个状态下可能获得最高奖励的动作,优化Q学习算法的动作选择过程。最后,将Q学习算法、Eva-Q学习算法、C-Q学习算法在栅格环境中进行仿真实验。仿真实验结果表明,C-Q学习算法在路径长度、规划时间、数据效率和决策准确度等多个指标上均优于其余两种算法。
