Anodal Transcranial Direct Current Stimulation Can Improve Spatial Learning and Memory and Attenuate Aß42 Burden at the Early Stage of Alzheimer's Disease in APP/PS1 Transgenic Mice.

Alzheimer's disease (AD) is an irreversible progressive neurodegenerative disease. Intervention in the early stage of AD is a new path for AD treatment that is being explored. The behavioral and pathological effects of anodal transcranial direct current stimulation (AtDCS) at the early stage of AD in the mouse model, amyloid precursor protein (APP)/presenilin-1 (PS1) transgenic mice, were investigated based on our previous studies. Thirty-three 6-month-old male APP/PS1 mice were randomly divided into the model group (AD group), model + sham stimulation group (ADST group) and stimulation group (ADT group). Eleven 6-month-old male C57 wild-type mice were randomly selected as a control group (CTL group). The ADT group received 10 AtDCS sessions. The Morris water maze (MWM) task and novel object recognition (NOR) task were used to test mouse memory. Nissl staining, Western blot (WB), immunohistochemistry and immunofluorescence staining of ß-amyloid (Aß42), glial fibrillary acidic protein (GFAP) and NF200 were conducted for pathological analysis. The ADT group and the CTL group had a shorter escape latency and more platform-region crossings than the AD group and ADST group in the MWM. There was no significant difference in the discrimination index among the groups in the NOR task. Pathological analysis showed visible differences between the AD group and ADT group. This study revealed that early-stage APP/PS1 transgenic mice did not show recognition memory impairment. AtDCS effectively improved spatial learning and memory in the early-stage APP/PS1 transgenic mouse model of AD, alleviating Aß burden and having a protective effect on neurons. AtDCS could improve AD-related symptoms by activating many glial cells to promote the degradation and clearance of Aß or directly affecting production and degradation of Aß to reduce glial activation. AtDCS is an effective means of early intervention in the early stage of AD.

A Novel Quantum-Behaved Bat Algorithm with Mean Best Position Directed for Numerical Optimization.

This paper proposes a novel quantum-behaved bat algorithm with the direction of mean best position (QMBA). In QMBA, the position of each bat is mainly updated by the current optimal solution in the early stage of searching and in the late search it also depends on the mean best position which can enhance the convergence speed of the algorithm. During the process of searching, quantum behavior of bats is introduced which is beneficial to jump out of local optimal solution and make the quantum-behaved bats not easily fall into local optimal solution, and it has better ability to adapt complex environment. Meanwhile, QMBA makes good use of statistical information of best position which bats had experienced to generate better quality solutions. This approach not only inherits the characteristic of quick convergence, simplicity, and easy implementation of original bat algorithm, but also increases the diversity of population and improves the accuracy of solution. Twenty-four benchmark test functions are tested and compared with other variant bat algorithms for numerical optimization the simulation results show that this approach is simple and efficient and can achieve a more accurate solution.

[Exercise ECG signal de-noising using unbiased risk estimate and wavelet transform].

In this paper a filtering method for EECG (Exercise ECG) signal is proposed which is based on wavelet transform (WT) and Stein's unbiased risk estimate (SURE). This algorithm was used to decompose original EECG signals into detail signals on different frequency bands by using WT and get different thresholds with SURE. According to EECG signal features and by using the above thresholds, the method amended several detail signals so that the main interferences in EECG signal can be removed efficiently. The authors also put forward two indexes to estimate the validity of such algorithms. Our experimental results demonstrate that this is an efficient de-noising method for EECG.