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The occurrence and behavior of endocrine disrupting chemicals (EDCs) in sewage treatment plants (STPs), especially estrogens and phenols, have been closely concerned in previous studies. However, the systematical researches about progestogens and androgens were scarce in STPs adopting different treatment technologies. This work investigated the occurrence, removal, and fate of one progestogen, three androgens, four estrogens, and six phenols in six STPs around Dianchi Lake in China, where the influents, effluents of primary treatment, secondary treatment, and advanced treatment, as well as excess sludge samples, were analyzed. All of the above EDCs were detected out in influents of the six STPs. Bisphenol A, nonylphenol-mono-ethoxylate, and nonylphenol-diethoxylate were the dominant EDCs detected in those influent samples with the concentrations that varied from 637.6 to 1,684.0 ng/L, 633.8 to 1,540.0 ng/L, and 648.7 to 2,246.0 ng/L, respectively; E1 and dihydrotestosterone were the major steroids with the mean concentration of 126.8 and 277.4 ng/L. For effluents and sludges, phenols showed higher concentration (366.8-1,233.0 ng/L and 1,478.1-6,948.9 ng/g dry weight (dw)) and detection rate (100 %). The total removal rates were more than 80 % for most compounds in wastewater treatment processes, and high removal efficiency (86-100 %) was found for androgens and progestogens compared with estrogens (75-92 %) and phenols (62-85 %). The secondary treatment processes play significant roles on degrading EDCs, whereas the primary sedimentation has little effects. The treatment capacity of anoxic-anaerobic-anoxic membrane bioreactor and anaerobic/anoxic/oxic technologies was superior to the conventional oxidation ditch in the degradation of EDCs. The advanced treatment process, two units of filter (D-type or V-type), and ultraviolet disinfection were adopted and presented effective to remove these compounds. According to fate analysis, it was obvious that biological degradation was the main pathway on the removal of EDCs in STPs compared with adsorption. Risk quotients were calculated to assess ecological risks of those EDCs. Risk quotients of 54 and 61 % were more than 1 in effluents and sludges, respectively, showing potential hazard of effluents and sludges to the environment.

Intermuscular coupling is essential in the coordination of agonist and antagonist muscles. However, its dynamic characteristics are not fully understood, especially the alterations of intermuscular coupling induced by stroke and aging. This study aimed to investigate the aging-and stroke-related changes in the global and instantaneous intermuscular coupling between agonist and antagonist muscles. In the experiment, 8 patients after stroke, 18 healthy young subjects and 10 healthy middle-aged subjects were recruited and instructed to finish the elbow flexion and extension tasks. Cross-fuzzy entropy (C-FuzzyEn) and instantaneous C-FuzzyEn (iC-FuzzyEn) based on a sliding window were used to analyze the global and instantaneous intermuscular coupling, respectively. Instantaneous FuzzyEn (i-FuzzyEn) based on a sliding window was also applied to investigate the dynamic complexity of the EMG segment. Pearson correlation analysis revealed that i-FuzzyEn values were negatively correlated with iC-FuzzyEn values in most cases, which implied that there was a positive correlation between EMG complexity and intermuscular coupling. The C-FuzzyEn values between agonist and antagonist muscles increased significantly in both tasks of the patients after stroke than those of the healthy subjects (p<0.05), which might be due to the decrease in intermuscular coupling induced by the damage of the corticospinal pathways after stroke. The combined application of C-FuzzyEn, iC-FuzzyEn and i-FuzzyEn provides a more comprehensive understanding of the global and instantaneous intermuscular coupling.

Whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm inspired by humpback whale hunting behavior. WOA has many similarities with other swarm intelligence algorithms (PSO, GWO, etc.). WOA's unique search mechanism enables it to have a strong global search capability while taking into account the strong global search capabilities. In this work, considering the the deficiency of WOA in local search mechanism, combined with the optimization methods of other group intelligent algorithms, perceptual perturbation mechanism is introduced, which makes the agent perform more detailed searches near the local extreme point. At the same time, since the WOA uses a logarithmic spiral curve, the agent cannot fully search all the spaces within its search range, even though the introduction of the perturbation mechanism may still lead to the algorithm falling into a local optimum. Therefore, the equal pitch Archimedes spiral curve is chosen to replace the classic logarithmic spiral curve. In order to fully verify the effect of the search path on the performance of the algorithm, several other spiral curves have been chosen for experimental comparison. By utilizing the 23 benchmark test functions, the simulation results show that WOA (PDWOA) with perceptual perturbation significantly outperforms the standard WOA. Then, based on the PDWOA, the effect of the search path on the performance of the algorithm has been verified. The simulation results show that the equal pitch of the Archimedean spiral curve is best.

The segmentation results of retinal blood vessels have a significant impact on the automatic diagnosis of various ophthalmic diseases. In order to further improve the segmentation accuracy of retinal vessels, we propose an improved algorithm based on multiscale vessel detection, which extracts features through densely connected networks and reuses features. Methods: A parallel fusion and serial embedding multiscale feature dense connection U-Net structure are designed. In the parallel fusion method, features of the input images are extracted for Inception multiscale convolution and dense block convolution, respectively, and then the features are fused and input into the subsequent network. In serial embedding mode, the Inception multiscale convolution structure is embedded in the dense connection network module, and then the dense connection structure is used to replace the classical convolution block in the U-Net network encoder part, so as to achieve multiscale feature extraction and efficient utilization of complex structure vessels and thereby improve the network segmentation performance. Results: The experimental analysis on the standard DRIVE and CHASE_DB1 databases shows that the sensitivity, specificity, accuracy, and AUC of the parallel fusion and serial embedding methods reach 0.