The next day, the brains were dissected out, removed, and cryoprotected with 30% sucrose at 4C. Frozen transverse (horizontal) sections were made of 50 μm thickness on a sliding microtome and collected in 0.1 M PBS. Sections were mounted on glass slides and mounted with Vectashield

mounting medium with DAPI (Burlingame, CA, buy LY2109761 USA) for visualization of nuclei. Sections were imaged in the NIS-Elements software (Nikon Instruments, Inc., Melville, NY, USA) using a Nikon DS-Fil color digital camera on a Nikon E400 microscope equipped with TRITC, FITC, and DAPI fluorescence cubes. RESULTS HISTOLOGIC VALIDATION OF CHANNEL EXPRESSION AND ELECTRODE PLACEMENT Channelrhodopsin-2 expression in the MS (Figure ​Figure2A2A, green) and hippocampus (Figure ​Figure2B2B, red) was robust upon histologic evaluation. From the MS, axonal projections to the hippocampus (Figures 2A, C) were readily apparent, coinciding with the passage of the electrodes (Figure ​Figure2C2C, red and white arrows) and the hippocampal pyramidal cell

layer (yellow arrow). The NeuroNexus array also passed alongside the expressing pyramidal cell layer of the hippocampus (Figure ​Figure2B2B). Consequently, we would expect our recordings to appropriately reflect the influence of optogenetic stimulation on these respective neuron populations. FIGURE 2 Robust expression of ChR2 on transverse section histology and verification of electrode placement. (A) AAV5-hSyn-ChR2-EYFP injection into the medial septum (MS) produced robust ChR2-EYFP expression (green). Axons from the MS express along the septohippocampal … VALIDATION OF HIPPOCAMPAL RESPONSE TO PULSATILE STIMULATION PATTERNS IN THE MEDIAL SEPTUM To

validate the effectiveness of the platform, we first explored the LFP response in the dorsal hippocampus to square-wave pulsatile stimulation of the MS (Figure ​Figure33). The MS has been stimulated electrically previously, producing a stimulus-frequency specific response (McNaughton et al., 2006) that we hypothesized we would recapitulate. At 50 mW/mm2, stimulation of the MS produced readily visible delayed pulsatile responses in the hippocampal Brefeldin_A LFP in both the CA1 and CA3 layers during the stimulation epoch (Figures ​Figures1B1B and ​and3A3A). These responses did not persist into the post-stimulation epoch, but instead were highly time-locked to the stimulus onset and offset. In order to examine the waveform of the LFP response, a peristimulus average was constructed by determining the mean LFP signal between 5 ms preceding and 40 ms following onset of each stimulus pulse. These were calculated across every stimulation parameter to produce the mean (solid line) and SD (shaded area; Figure ​Figure3B3B). As expected, the stimulation parameter specifications had a large impact on response waveform amplitude, shape, and timing. Increasing the amplitude of the stimulus pulse tended to generate a quicker time to peak response.

We surveyed 450 respondents in central business districts, outlets, transportation hubs, office buildings, and large enterprises in Tangshan. Out of the total of 424 questionnaires received, 419 are qualified. The calculation is executed by SPSS chemical compound library software. 4.2. Utility Function The MNL model is used to model the individual travel mode choice. It is assumed that all the factors are independent from each other and obey the Gumbel distribution with zero mean. Equation (4) is the utility function: Vin=θiXin=θi0+∑k=1KθikXink, i∈An.

(4) In (5), Pin is the probability of traveler n selecting travel mode i: Pin=exp⁡Vin∑j∈Anexp⁡Vjn=exp⁡θiXin∑j∈Anexp⁡θXjn, i∈An, (5) where Vin is the utility function when traveler n chooses travel mode i; Xin = [Xin0, Xin1,…, Xink,…, XinK] is an eigenvector of traveler n choosing travel mode i; the component Xink is the value of variable k when traveler n chooses mode i, Xin0 = 1; θi = [θi0, θi1,…, θik,…, θiK] is the vector

of utility coefficients; and θik is the impact coefficient of variable k on travel mode i. 4.3. Results and Model Validation SPSS17.0 is used to process the data. The results of the MNL model are shown in Table 2. Table 2 The calculated parameters of the MNL model. The calculated parameters in Table 2 and the variable values in Table 1 are put into (4) and (5) to calculate the utility value and choice probability. Therefore, it is possible to forecast the sample individuals’ choice. The observed and forecasted choices are presented in Table 3. Table 3 Comparison of predicted and observed selection. There are different tests for model validation, the main ones of which are the goodness-of-fit test, F-test, and t-test. These three methods are fitted to test the linear model. Because the MNL model is a nonlinear

exponential model and the unbiased estimate of the error variance cannot be obtained from the estimated residuals, the t-test or F-test cannot be used here to test the significance either for the individual or for the population [27]. Furthermore, the model residuals do not necessarily sum to zero and ESS and RSS do not necessarily add up to TSS; therefore, R2 = ESS/TSS may not be a meaningful descriptive statistic for this model. Consequently, an alternative to pseudo R-square is proposed to estimate the goodness Drug_discovery of fit. It can be seen as a rough approximation of model prediction accuracy [28]. Three pseudo R-squares calculated by SPSS are shown in Table 4. Generally, the pseudo R-squared value falls in [0, 1]. When the independent variable is completely unrelated to the dependent variable, the pseudo R-squared value will be close to zero; otherwise, it will be close to 1, which indicates that the model perfectly predicts the objective. The results listed in Table 4 show that the model is acceptable. Table 4 Pseudo R-square. 5. Analysis and Implication 5.1.

2.1. FCM We define X = x1,…, xN as the universe of a clustering data set, B = β1,…, βC as the prototypes of C clusters, and U = [uij]N×C as a fuzzy partition matrix, where uij ∈ [0,1] is the membership of xi in a cluster with prototype βj; xi, βj ∈ RP, where P is the data dimensionality, 1 ≤ i ≤ N, and 1 ≤ j ≤ C. The FCM algorithm is derived by minimizing the objective Foretinib molecular weight function [22] JFCMU,B,X=∑j=1C∑i=1Nuijmdij2xi,βj, (1) where m > 1.0 is the weighting exponent

on each fuzzy membership and dij is the Euclidian distance from data vectors xi to cluster center βj. And ∑j=1Cuij=1 ∀i=1,2,…,N,0<∑i=1Nuij

through the problem space by following the current best particles. Each particle keeps track of its coordinates in the problem space which are associated with the best solution that has been achieved so far. The solution is evaluated by the fitness value, which is also stored. This value is called pbest. Another best value that is tracked by the PSO is the best value, obtained so far by any particle in the swarm. The best value is a global best and is called gbest. The search for the better positions follows the rule as Vt+1=wVt+c1r1pbestt−Pt+c2r2gbestt−Pt,Pt+1=Pt+Vt+1, (5) where P and V are position and velocity vector of particle, respectively, w is inertia weight, c1 and c2 are positive constants,

called acceleration coefficients which control the influence of pbest and gbest in search process, and r1 and r2 are random values in the range [0,1]. The fitness value of Anacetrapib each particle’s position is determined by a fitness function, and PSO is usually executed with repeated application of (5) until a specified number of iterations have been exceeded or the velocity updates are close to zero over a number of iterations. 2.3. PSO-Based FCM In this algorithm [26], each particle Partl represents a cluster center vector, which is constructed as Partl=Pl1,…,Plj,…,PlC, (6) where l represents the lth particle, l = 1,2,…L, L is the number of particles, and L < N. Plj is the jth cluster center of particle Partl. Therefore, a swarm represents a number of candidates cluster center for the data vector. Each data vector belongs to a cluster according to its membership function and thus a fuzzy membership is assigned to each data vector.