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Additional file 1: Figure S1. Mouse breeding and experimental protocol. a Breeding schematic to obtain VPS35f/f:CX3CR1Cre���ER:5xFAD mice. b Schematic illustrating TMX injection and behavioral testing timeline. Tamoxifen (100 mg/kg) was administered (i.p.) into VPS35f/f:CX3CR1Cre���ER:5xFAD mice [as mutants (VPS35CX3CR1:5XFAD)]. In addition, tamoxifen was injected to VPS35f/f:5xFAD or vehicle (Corn oil) was injected to VPS35f/f:CX3CR1Cre���ER:5xFAD mice [as controls (Ctrl:5XFAD)]. c Double immunostaining analysis for Iba1 (green) and VPS35 (Red) immunostaining. Scale bar = 10 ��m. d Quantitative analysis of relative microglial VPS35 intensity compared with Ctrl: 5xFAD mice. (n = 3 per group, mean �� SD, **, P 0.05, Student���s t test). Figure S3. Increased reactive astrocytes in microglial VPS35 deficient 5XFAD brain. a Co-immunostaining of GFAP (green), ApoE (red) and Thio-S (gray) in the cortex of Ctrl:5XFAD and VPS35CX3CR1:5XFAD mice. b���e Quantification of GFAP+ cell density, ApoE+ cell density, average ApoE fluorescence intensity per astrocyte and relative ApoE+Thio-S+ density in the cortex of Ctrl:5XFAD and VPS35CX3CR1:5XFAD mice (n = 5 per group, mean �� SD, **, P

In this paper, trajectory control of arbitrary shape mandrel in three-dimensional circular braiding is studied. To obtain accurate trajectory, offset of mandrel is predicted and compensated for trajectory of mandrel. Firstly, the equation of the force of all yarns on three-dimensional mandrel is given. Then offset of mandrel in single layer braiding machine is analyzed via finite element software. Learning these data via back propagation neural network algorithm, offset of mandrel at each moment is derived. The trajectory generation of three-dimensional mandrel based on offset compensation by roll pitch yaw transformation is given. Lastly, braiding angle for the mandrel is analyzed theoretically. In the practical engineering, this method is proven to effectively reduce the error of braiding angle and helpful for the precise control of the trajectory of arbitrary shape mandrel.

Photovoltaic (PV) power prediction is an effective way to ensure the safe operation of the grid connected photovoltaic power station, and the prediction accuracy is very important as well as difficult. In order to improve the prediction accuracy, a combination prediction model based on entropy weight and traditional prediction methods is demonstrated. The combination prediction model used entropy weight to combine three traditional prediction methods which are persistence method, support vector machine (SVM) and prediction method based on similar data. And the entropy weight of each single prediction method is got by evaluating the amount of information of each method objectively. The prediction accuracy of entropy weight combination prediction model and these three single prediction methods are compared by simulation. Case study results show that the combination model based on entropy weight can get proper combination weights for these traditional methods. As a result, the prediction accuracy of entropy weight combination prediction model is much higher than that of single traditional prediction method for all weather types.

Even though recent hybrid methods have helped to avoid certain limitations of Content-based (CB) filtering and collaborative filtering (CF), scalability and sparsity are still major problems in large-scale recommendation systems. This paper presents a novel collaborative recommendation method based on ontology for personalized TV program application. In the proposed method, we give a new formula to calculate user ratings explicitly according to four TV-watching related behaviors' preferences. More specifically, in order to overcome sparsity problems of CF, we build the TV programs ontology to take semantic relationship to estimate contents' concept similarity. Experiments show that Ontology can explicitly reveal the similarity of programs. In our WEB-TV prototype system, Ontology-based collaborative recommendation method illustrate the ability improving the precision and recall in a large degree.