Microbial assemblages in water hyacinth silages with different initial moistures.

Making silage is a green process to use the fast-growing water hyacinth (Eichhornia crassipes) biomass. However, the high moisture (∼95%) of the water hyacinth is the biggest challenge to making silage while its effects on fermentation processes are less studied. In this study, water hyacinths silage with different initial moistures were conducted to investigate the fermentation microbial communities and their roles on the silage qualities. Results show that both silages with 70% (S70) and 90% (S90) of initial moistures achieved the target of silage fermentation, however, their microbial processes were significantly different. Their succession directions of microbial communities were different: Plant cells in S70 were destroyed by the air-dry treatment, thus there were more soluble carbohydrates, which helped the inoculated fermentative bacteria become dominant (Lactobacillus spp. > 69%) and produce abundant lactic acid; In contrast, stochastic succession became dominant over time in S90 (NST = 0.79), in which Lactobacillus spp. and Clostridium spp. produced butyric that also obviously decreased the pH and promoted the fermentation process. Different microbial succession led to different metabolic patterns: S70 had stronger starch and sucrose metabolisms while S90 had stronger amino acid and nitrogen metabolisms. Consequently, S70 had higher lactic acid, crude protein and lower ammonia nitrogen and S90 had higher in vitro digestibility of dry matter and higher relative feeding value. Moreover, the variance partitioning analysis indicated that moisture could only explain less information (5.9%) of the microbial assemblage than pH value (41.4%). Therefore, the colonization of acid-producing bacteria and establishment of acidic environment were suggested as the key on the silage fermentation no matter how much is the initial moisture. This work can provide a basis for the future preparation of high-moisture raw biomasses for silage.

Application of comprehensive score of risk factors to determine the best time for surgical intervention in neonatal necrotizing enterocolitis.

AIM: Necrotizing enterocolitis (NEC) is a severe digestive systemic disease. Surgery is critical treatment for NEC patients. However, it is still hard to predict the optimal timing of surgery. Our study aimed to explore the indicative effect of comprehensive risk factors method in surgical intervention in NEC patients. METHODS: Retrospective analysis was made on newborns with NEC, who were divided into group I (patients with NEC conservative therapeutic approach or no necrosis or perforation found in NEC operation) and group II (patients with necrosis or perforation found in NEC operation or failure of NEC conservative approach). Clinical manifestations and complications were recorded and the risk factors scores were calculated. The receiver operating characteristic curve and Youden index were calculated. RESULTS: One hundred and six NEC patients in Huizhou Central People's hospital were analysed. Significant difference was observed in the comprehensive scores of the two groups to judge the indications of NEC surgery. Among the risk factors of NEC surgery, the incidence specificity of abdominal induration, abdominal tenderness, hypotension and pneumoperitoneum was 100% in group II. The area under the curve of the comprehensive score of risk factors was 0.981 (95% confidence interval 0.958-1.000). The best cutoff value obtained by the maximum Youden index is 3.5 (sensitivity 97.2%; specificity 52.8%). When the specificity is 100%, the best interception value is 7.5 (sensitivity 91.4%; specificity 100%). Complications such as surgical intervention, mortality, stenosis after conservative treatment, perforation or necrosis during surgery were significantly different in different comprehensive scores. CONCLUSIONS: Abdominal induration, abdominal tenderness, hypotension and pneumoperitoneum are independent predictors of NEC surgery. Comprehensive risk factors score may be a potentially useful clinical decision-making tool for NEC surgery.

Deep Learning Enabled Diagnosis of Children's ADHD Based on the Big Data of Video Screen Long-Range EEG.

Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children. At the same time, ADHD is prone to coexist with other mental disorders, so the diagnosis of ADHD in children is very important. Electroencephalogram (EEG) is the sum of the electrical activity of local neurons recorded from the extracranial scalp or intracranial. At present, there are two main methods of long-range EEG monitoring commonly used in clinical practice: one is ambulatory EEG monitoring, and the other is long-range video EEG monitoring. The purpose of this study is to summarize the brain electrical activity and clinical characteristics of children with ADHD through the video long-range computer graphics data of children with ADHD and to explore the clinical significance of video long-range EEG in the diagnosis of children with ADHD. For a more effective analysis, this study further processed the video data of long-range computer graphics of children with ADHD and constructed several neural network algorithm models based on deep learning, mainly including fully connected neural network models and two-dimensional convolutional neural networks. Model and long- and short-term memory neural network model. By comparing the recognition effects of these several algorithms, find the appropriate recognition algorithm to improve the accuracy and then establish a recognition method for the diagnosis of children's ADHD based on deep learning long-range EEG big data. Finally, it is concluded that long-term video EEG can analyze the EEG relationship of children with ADHD and provide a diagnostic basis for the diagnosis of ADHD.