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Background: Patients with Parkinson's disease (PD) are at a lower risk of suffering cardiovascular events, but the underlying factors for this decreased risk remain unclear. Serum triglycerides (TG) and total cholesterol (TC), and their expression relative to high-density lipoprotein cholesterol (TG/HDL-C and TC/HDL-C), are independent predictors of cardiovascular events. This study aimed to determine if PD patients have decreased lipid levels and lipid ratios, which may underlie the decreased risk of coronary heart disease (CHD). Methods: This retrospective study included 92 PD patients (PD group), 450 control subjects with no CHD (OD group), and 450 CHD patients (CHD group). We analyzed serum lipid levels and lipid ratios in each group. Results: There were significant differences in TC (F = 10.459, p < 0.0001), TG (F = 46.856, p < 0.0001), low density lipoprotein cholesterol (LDL-C) (F = 6.910, p = 0.001), high density lipoprotein cholesterol HDL-C (F = 30.694, p < 0.0001), TC/HDL-C (F = 32.675, p < 0.0001), and TG/HDL-C (F = 45.554, p < 0.0001) between all three groups; TC/LDL-C (F = 2.518, p = 0.081) was not significantly different between groups. Compared to the CHD group, PD patients had lower TC (p < 0.0001), TG (p < 0.0001), LDL-C (p = 0.001), TG/HDL-C (p < 0.0001), and TC/HDL-C (p < 0.0001); TC/LDL-C (p = 0.563) and HDL-C (p = 0.196) were not significantly different. TC and LDL-C levels were positively correlated within individual groups (all p < 0.0001). In addition, TG and HDL-C were negatively correlated in the OD and CHD groups (p < 0.0001); no significant negative association was observed in the PD group (p = 0.077). TG/HDL and LDL-C levels were inversely correlated in the CHD group (p < 0.0001) and weakly positively correlated in the PD (p = 0.159) and OD (p = 0.199) groups. Conclusions: TC/HDL and TG/HDL ratios were significantly lower in PD patients compared to CHD patients, suggesting there is a strong correlation between lipid ratios and incidence of CHD in PD patients.

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The marine environment is complex and changeable, and the interference of noise and reverberation seriously affects the classification performance of active sonar equipment. In particular, when the targets to be measured have similar characteristics, underwater classification becomes more complex. Therefore, a strong, recognizable algorithm needs to be developed that can handle similar feature targets in a reverberation environment. This paper combines Fisher’s discriminant criterion and a dictionary-learning-based sparse representation classification algorithm, and proposes an active sonar target classification method based on Fisher discriminant dictionary learning (FDDL). Based on the learning dictionaries, the proposed method introduces the Fisher restriction criterion to limit the sparse coefficients, thereby obtaining a more discriminating dictionary; finally, it distinguishes the category according to the reconstruction errors of the reconstructed signal and the signal to be measured. The classification performance is compared with the existing methods, such as SVM (Support Vector Machine), SRC (Sparse Representation Based Classification), D-KSVD (Discriminative K-Singular Value Decomposition), and LC-KSVD (label-consistent K-SVD), and the experimental results show that FDDL has a better classification performance than the existing classification methods.

Quantified constraint satisfaction problems (QCSPs) are an extension to constraint satisfaction problems (CSPs) with both universal quantifiers and existential quantifiers. In this paper we apply variable ordering heuristics and integrate standard dependency schemes in QCSP solvers. The technique can help to decide the next variable to be assigned in QCSP solving. We also introduce a new factor into the variable ordering heuristics: a variable's dep is the number of variables depending on it. This factor represents the probability of getting more candidates for the next variable to be assigned. Experimental results show that variable ordering heuristics with standard dependency schemes and the new factor dep can improve the performance of QCSP solvers.