Unveiling the Occurrence and Non-Negligible Role of Amino Sugars in Waste Activated Sludge Fermentation by an Enriched Chitin-Degradation Consortium.

Polysaccharides in extracellular polymeric substances (EPS) can form a hybrid matrix network with proteins, impeding waste-activated sludge (WAS) fermentation. Amino sugars, such as N-acetyl-d-glucosamine (GlcNAc) polymers and sialic acid, are the non-negligible components in the EPS of aerobic granules or biofilm. However, the occurrence of amino sugars in WAS and their degradation remains unclear. Thus, amino sugars (∼6.0%) in WAS were revealed, and the genera of Lactococcus and Zoogloea were identified for the first time. Chitin was used as the substrate to enrich a chitin-degrading consortium (CDC). The COD balances for methane production ranged from 83.3 and 95.1%. Chitin was gradually converted to oligosaccharides and GlcNAc after dosing with the extracellular enzyme. After doing enriched CDC in WAS, the final methane production markedly increased to 60.4 ± 0.6 mL, reflecting an increase of ∼62%. Four model substrates of amino sugars (GlcNAc and sialic acid) and polysaccharides (cellulose and dextran) could be used by CDC. Treponema (34.3%) was identified as the core bacterium via excreting chitinases (EC 3.2.1.14) and N-acetyl-glucosaminidases (EC 3.2.1.52), especially the genetic abundance of chitinases in CDC was 2.5 times higher than that of WAS. Thus, this study provides an elegant method for the utilization of amino sugar-enriched organics.

Identification and degradation of structural extracellular polymeric substances in waste activated sludge via a polygalacturonate-degrading consortium.

By maintaining the cell integrity of waste activated sludge (WAS), structural extracellular polymeric substances (St-EPS) resist WAS anaerobic fermentation. This study investigates the occurrence of polygalacturonate in WAS St-EPS by combining chemical and metagenomic analyses that identify ∼22% of the bacteria, including Ferruginibacter and Zoogloea, that are associated with polygalacturonate production using the key enzyme EC 5.1.3.6. A highly active polygalacturonate-degrading consortium (GDC) was enriched and the potential of this GDC for degrading St-EPS and promoting methane production from WAS was investigated. The percentage of St-EPS degradation increased from 47.6% to 85.2% after inoculation with the GDC. Methane production was also increased by up to 2.3 times over a control group, with WAS destruction increasing from 11.5% to 28.4%. Zeta potential and rheological behavior confirmed the positive effect which GDC has on WAS fermentation. The major genus in the GDC was identified as Clostridium (17.1%). Extracellular pectate lyases (EC 4.2.2.2 and 4.2.2.9), excluding polygalacturonase (EC 3.2.1.15), were observed in the metagenome of the GDC and most likely play a core role in St-EPS hydrolysis. Dosing with GDC provides a good biological method for St-EPS degradation and thereby enhances the conversion of WAS to methane.

Caproate production from xylose via the fatty acid biosynthesis pathway by genus Caproiciproducens dominated mixed culture fermentation.

Caproate production from organic wastes is deemed as a novel strategy in mixed culture fermtation (MCF). However, producing caproate from natural sugar of xylose by MCF is seldom reported and the metabolic pathway is still unclear. Thus, the caproate production from xylose was investigated in this study by mesophilic MCF. The results showed that the caproate concentration from xylose (10 g/L) was 1.2 ± 0.17 g/L (equal to 2.7 gCOD/L) under pH 5.0. Dosing extra ethanol of 5 g/L could slightly increase the caproate production by âˆ¼ 30% (i.e., 1.6 g/L). While dosing extra acetate of 5 g/L negatively affected the caproate production, which was just 0.2 g/L. The microbial analysis illustrated that genus Caproiciproducens was a main identified caproate producer, occupying over 80% of enriched mixed culture. The fatty acid biosynthesis pathway was identified via metagenomic analysis. These unexpected differences extended the understanding of caproate production from organic wastes.

SF-CNN: Signal Filtering Convolutional Neural Network for Precipitation Intensity Estimation.

Precipitation intensity estimation is a critical issue in the analysis of weather conditions. Most existing approaches focus on building complex models to extract rain streaks. However, an efficient approach to estimate the precipitation intensity from surveillance cameras is still challenging. This study proposes a convolutional neural network known as the signal filtering convolutional neural network (SF-CNN) to handle precipitation intensity using surveillance-based images. The SF-CNN has two main blocks, the signal filtering block (SF block) and the gradually decreasing dimension block (GDD block), to extract features for the precipitation intensity estimation. The SF block with the filtering operation is constructed in different parts of the SF-CNN to remove the noise from the features containing rain streak information. The GDD block continuously takes the pair of the convolutional operation with the activation function to reduce the dimension of features. Our main contributions are (1) an SF block considering the signal filtering process and effectively removing the useless signals and (2) a procedure of gradually decreasing the dimension of the feature able to learn and reserve the information of features. Experiments on the self-collected dataset, consisting of 9394 raining images with six precipitation intensity levels, demonstrate the proposed approach's effectiveness against the popular convolutional neural networks. To the best of our knowledge, the self-collected dataset is the largest dataset for monitoring infrared images of precipitation intensity.