Migration of phosphorus in pig manure during pyrolysis process and slow-release mechanism of biochar in hydroponic application

Pyrolysis is an effective method for treating of livestock and poultry manure developed in recent years. It can completely decompose pathogens and antibiotics, stabilize heavy metals, and enrich phosphorus (P) in biochar. To elucidate the P migration mechanism under different pig manure pyrolysis temperatures, sequential fractionation, solution 31P nuclear magnetic resonance, X-ray photoelectron spectroscopy, X-ray diffraction, and K-edge X-ray absorption near-edge structure techniques were used to analyze the P species in pig manure biochar (PMB). The results indicated that most of the organic P in the pig manure was converted to inorganic P during pyrolysis. Moreover, the transformation to different P groups pathways was clarified. The phase transition from amorphous to crystalline calcium phosphate was promoted when the temperature was above 600 °C. The content of P extracted by hydrochloric acid, which was the long-term available P for plant uptake, increased significantly. PMB pyrolyzed at 600 °C can be used as a highly effective substitute for P source. It provides the necessary P species (e.g. water-soluble P.) and metal elements for the growth of water spinach plants, and which are slow-release comparing with the Hogland nutrient solution.

[A deep learning-based lung nodule density classification and segmentation method and its effectiveness under different CT reconstruction algorithms]

Objective: To evaluate the diagnostic value of the lung nodule classification and segmentation algorithm based on deep learning among different CT reconstruction algorithms. Methods: Chest CT of 363 patients from June 2019 to September 2019 in Radiology Department of Tianjin Medical University Chu Hsien-I Memorial Hospital were retrospectively collected in this study, each of which consisted of images by three different reconstruction methods (lung reconstruction, mediastinal reconstruction, bone reconstruction).These collected data were used as testing set and a total of 4 185 Chest CTs including the public data set and the constructed private data set were used as the training set. A model combines 3D deep convolutional neural network and recurrent neural network under a multi-task joint learning algorithm for lung nodule classification and segmentation were constructed. The well-trained method was tested on 363 test cases using two metrics, i.e., the accuracy of the density classification and the Dice coefficient of nodule segmentation. The performances under three reconstruction methods were statistically analyzed according to the variance analysis among three different reconstruction methods. Results: The average classification accuracies of the nodule under three reconstruction methods were 98.67%±5.70%, 98.38%±6.61% and 97.89%±7.32%. Specifically, the accuracies of the solid nodules under three reconstruction methods were 98.79%±5.58%, 98.49%±6.89% and 97.90%±7.41% and the accuracies of the sub-solid nodules were 97.57%±10.19%, 98.52%±7.77% and 98.52%±7.77%. There was no significant difference in the classification accuracy of pulmonary nodules under three different reconstruction algorithms (all P>0.05). The average Dice coefficients of nodule segmentation was 79.87%±5.78%, 79.02%±6.04% and 79.31%±5.95%. There was no significant difference in the average Dice coefficients of nodule segmentation under three different reconstruction algorithms (all P>0.05). Conclusion: Deep learning algorithm which combined with 3D convolutional neural network and recurrent neural network has demonstrated relatively stable in classification and segmentation of lung nodules under different CT reconstruction method.