Effect comparisons of different conditioners and microbial agents on the degradation of estrogens during dairy manure composting.

To investigate the degradation efficiency of conditioners and commercial microbial agents on estrogens (E1, 17α-E2, 17ß-E2, E3, EE2, and DES) in the composting process of dairy manure, seven different treatments (RHB-BF, OSP-BF, SD-BF, MR-BF, MR-FS, MR-EM, and MR-CK) under forced ventilation conditions were composted and monitored regularly for 30 days. The results indicated that the removal rates of estrogens in seven treatments ranged from 95.35% to 99.63%, meanwhile the degradation effect of the composting process on 17ß-Estradiol equivalent (EEQ) was evaluated, and the removal rate of ΣEEQ ranged from 96.42% to 99.72%. With the combined addition of rice husk biochar (RHB) or oyster shell powder (OSP) and bio-bacterial fertilizer starter cultures (BF), namely RHB-BF and OSP-BF obviously promoted the rapid degradation of estrogens. 17ß-E2 was completely degraded on the fifth day of composting in OSP-BF. Microbial agents have some promotional effect and enhances the microbial degradation of synthetic estrogen (EE2, DES). According to the results of RDA, pH and EC were the main environmental factors affecting on the composition and succession of estrogen-related degrading bacteria in composting system. As predominant estrogens-degrading genera, Acinetobacter, Bacillus, and Pseudomonas effected obviously on the change of estrogens contents. The research results provide a practical reference for effective composting of dairy manure to enhancing estrogens removal and decreasing ecological risk.

The influence of cognitive activity on subsequent daytime nap: A deep neural network model based on sleep spindles.

OBJECTIVES: Understanding the influence of cognitive activity on subsequent sleep has both theoretical and applied implications. This study aims to investigate the effect of pre-sleep cognitive activity, in the context of avoiding emotional interference, on macro-sleep and sleep spindles. METHODS: In a within-subjects design, participants' sleep electroencephalography was collected in both the with and without pre-sleep cognitive activity conditions. Subsequent macro-sleep (i.e., sleep stage distribution and sleep parameters) and spindle characteristics (i.e., density, amplitude, duration, and frequency) were analyzed. In addition, a novel machine learning framework (i.e., deep neural network, DNN) was used to discriminate between cognitive activity and control conditions. RESULTS: There were no significant differences in macro-sleep and sleep spindles between the cognitive activity and control conditions. Spindles-based DNN models achieved over 96% accuracy in differentiating between the two conditions, with fast spindles performing better than full-range and slow spindles. CONCLUSIONS: These results suggest a weak but positive effect of pre-sleep cognitive activity on subsequent sleep. It sheds light on a possible low-cost and easily accessible sleep intervention strategy for clinical and educational purposes.