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In response to the low recall rate of detection-based methods and the problem of missing target location information in density-based methods, which are the two mainstream dense-counting methods, a detection and counting method based on density map regression is proposed by combining the two tasks, achieving the counting and positioning of target objects in dense scenes. Complementing the advantages of two methods not only improves recall rate but also calibrates all targets. To extract richer feature information to deal with complex data scenarios, a feature pyramid optimization module is proposed, which vertically fuses low-level high-resolution features with top-level abstract semantic features and horizontally fuses same-size features to enrich the semantic expression of target objects. To address the issue of low pixel proportions occupied by target objects in dense counting scenarios, an attention mechanism for small targets is proposed to improve the network’s detection sensitivity, which can enhance the attention of the network to target objects by constructing a mask on the input image. Experimental results demonstrate that the proposed method significantly improves recall rate and accurately locates targets while maintaining accuracy, effectively providing counting and positioning information of input image, which has a wide range of application prospects in various fields such as industry and ecology.

Objective To explore whether type 2 diabetes mellitue (DM) aggravates the spatial learning and memory impairment in rats with chronic cerebral hypoperfusion (CCH) and the possible mechanism. Methods Twenty-four Sprague-Dawley (SD) rats were randomly divided into 4 groups: control group (normal diet + sham); DM group [high-fat diet + streptozocin (STZ) injection]; CCH group [normal diet + two vessel occlussion (2-VO)] and DM + CCH group (high-fat diet + STZ injection + 2-VO). All rats were submitted to behavioral testing for spatial learning and memory in Morris water maze test, and immunofluorescence staining to detect hippocampal β-site amyloid precursor protein cleaving enzyme 1 (BACE-1) positive cells. The relative expressions of BACE-1 in hippocampus were detected with Western blotting. Results Morris water maze test showed escape latency of DM + CCH group was significantly longer than control group on 2nd-5th days [(54.60 ± 3.75) s vs (25.99 ± 4.10) s, P = 0.000; (45.39 ± 2.78) s vs (27.50 ± 4.39) s, P = 0.003; (39.71 ± 3.47) s vs (20.34 ± 3.69) s, P = 0.001; (41.43 ± 4.48) s vs (11.35 ± 3.95) s, P = 0.000]. The percentage of target quadrant time in DM + CCH group on the 6th day was significantly reduced compared with the control group [(22.38 ± 3.41)% vs (43.69 ± 3.22)%, P = 0.000]. Compared with control group, BACE-1 positive cells significantly increased in the hippocampus of DM + CCH group, and the relative expression of BACE-1 increased statistically (0.23 ± 0.04 vs 0.06 ± 0.02, P = 0.005). Conclusions Type 2 diabetes mellitus exacerbates spatial learning and memory impairment of rats with chronic cerebral hypoperfusion, which may be related to the abnormal expression of BACE-1 in the hippocampus of rats. doi: 10.3969/j.issn.1672-6731.2014.06.010

Dolittle algorithm is a numerical analysis method with high accuracy and low cost. In the present paper, the Dolittle algorithm is introduced into chemometrics. Dolittle multivariate correction method was put forward for the first time and was applied to solve the problem of overlapped spectra of multi-components. In addition, Dolittle multivariate correction method was used to resolve the problem of spectrum of mixed system of benzene homologs. The result shows that the Dolittle multivariate correction method is superior to the K-matrix method. The relative standard deviation of the Dolittle multivariate correction method is from -6. 35% to 8. 70%, while the relative standard deviation of K-matrix is from -14. 76% to 13. 40%. It was demonstrated that Doolittle algorithm is effective in analytical chemistry.