Volatile Compounds Fingerprints for White Duck down and White Goose down Determined by Gas Chromatography-Ion Mobility Spectrometry

This work first describes a simple approach for the untargeted profiling of volatile compounds for distinguishing between white duck down (WDD) and white goose down (WGD) based on resolution-optimized GC-IMS combined with optimized chemometric techniques, namely PCA. The detection method for down samples was established by using GC-IMS. Meanwhile, the reason of unpleasant odors caused by WDD was explained on the basis of the characteristic volatile compounds identification. GC-IMS fingerprinting can be considered a revolutionary approach for a truly fully automatable, cost-efficient, and in particular highly sensitive method. A total of 22 compounds were successfully separated and identified through GC-IMS method, and the significant differences in volatile compounds were observed in three parts of WDD and WGD samples. The most characteristic volatile compounds of WGD belong to aldehydes, whereas carboxylic acids from WDD were detected generated by autoxidation reaction. Meanwhile, the main reason of unpleasant odor generation was possibly attributed to the high concentration of volatile carboxylic acids of WDD. Therefore, the constructed model presents a simple and efficient method of analysis and serves as a basis for down processing and quality control.

Anti-hyperglycemic effects of dihydromyricetin in streptozotocin-induced diabetic rats

Dihydromyricetin(DHM),as a bioactive flavanonol compound,is mainly found in“Tengcha”(Ampelopsis grossedentata)cultivated in south of China.This study aimed to investigate the anti-hyperglycemic and antidyslipidemic activities of DHM using type 2 diabetes mellitus(T2D)rats,which was induced by feeding with high fat and fructose diet for 42 days and intraperitoneal administration of streptozocin.Forty-eight freshlyweaned rats were randomly assigned into the negative control(Blank),low dose(100 mg/kg),medium dose(200 mg/kg),high dose(400 mg/kg),and positive(40 mg/kg,met)groups.Fasting blood glucose and body weight were measured at weekly interval.Oral glucose tolerance tests were performed on days 42.The results revealed that DHM possessed significant antihyperglycaemic and antihyperinsulinemic effects.Moreover,after the DHM treatment,p-Akt and p-AMPK expression was upregulated,and glycogen synthase kinase-3β(GSK-3β)expression was downregulated,indicating that the potential anti-diabetic mechanism of DHM might be due to the regulation of the AMPK/Akt/GSK-3βsignaling pathway.

Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts

The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault detection and diagnosis.In practice,the potentials of advanced supervised learning algorithms have not been fully realized due to the lack of sufficient labeled data.To tackle such data challenges,this study proposes a graph neural network-based approach to effectively utilizing both labeled and unlabeled operational data for optimum decision-makings.More specifically,a graph generation method is proposed to transform tabular building operational data into association graphs,based on which graph convolutions are performed to derive useful insights for fault classifications.Data experiments have been designed to evaluate the values of the methods proposed.Three datasets on HVAC air-side operations have been used to ensure the generalizability of results obtained.Different data scenarios,which vary in training data amounts and imbalance ratios,have been created to comprehensively quantify behavioral patterns of representative graph convolution networks and their architectures.The research results indicate that graph neural networks can effectively leverage associations among labeled and unlabeled data samples to achieve an increase of 2.86%–7.30%in fault classification accuracies,providing a novel and promising solution for smart building management.

Changes of microbial composition during wastewater reclamation and distribution systems revealed by high- throughput sequencing analyses

This study employed 454-pyrosequencing to investigate microbial and pathogenic communities in two wastewater reclamation and distribution systems. A total of 11972 effective 16S rRNA sequences were acquired from these two reclamation systems, and then designated to relevant taxonomic ranks by using RDP classifier. The Chao index and Shannon diversity index showed that the diversities of microbial communities decreased along wastewater reclamation processes, eroteobacteria was the most dominant phylum in reclaimed water after disinfection, which accounted for 83% and 88% in two systems, respectively. Human opportunistic pathogens, including Clostridium, Escherichia, Shigella, Pseudomonas and Mycobacterium, were selected and enriched by disinfection processes. The total chlorine and nutrients （TOC, NH3-N and NO3-N） significantly affected the microbial and pathogenic communities during reclaimed water storage and distribution processes. Our results indicated that the disinfectant-resistant pathogens should be controlled in reclaimed water, since the increases in relative abundances of pathogenic bacteria after disinfec- tion implicate the potential public health associated with reclaimed water.

Quantification of viable bacteria in wastewater treatment plants by using propidium monoazide combined with quantitative PCR(PMA-qPCR)

The detection of viable bacteria in wastewater treatment plants （WWTPs） is very important for public health, as WWTPs are a medium with a high potential for waterborne disease transmission. The aim of this study was to use propidium monoazide （PMA） combined with the quantitative polymerase chain reaction （PMA-qPCR） to selectively detect and quantify viable bacteria cells in full-scale WWTPs in China. PMA was added to the concentrated WWTP samples at a final concentration of 100 μmol/L and the samples were incubated in the dark for 5 min, and then lighted for 4 min prior to DNA extraction and qPCR with specific primers for Escherichia coli and Enterococci, respectively. The results showed that PMA treatment removed more than 99% of DNA from non-viable cells in all the WWTP samples, while matrices in sludge samples markedly reduced the effectiveness of PMA treatment. Compared to qPCR, PMA-qPCR results were similar and highly linearly correlated to those obtained by culture assay, indicating that DNA from non-viable cells present in WWTP samples can be eliminated by PMA treatment, and that PMA-qPCR is a reliable method for detection of viable bacteria in environmental samples. This study demonstrated that PMA-qPCR is a rapid and selective detection method for viable bacteria in WWTP samples, and that WWTPs have an obvious function in removing both viable and non-viable bacteria. The results proved that PMA-qPCR is a promising detection method that has a high potential for application as a complementary method to the standard culture-based method in the future.

Determination of Random Periodic Structures in Transverse Magnetic Polarization

Consider an inverse problem that aims to identify key statistical pro-perties of the profile for the unknown random perfectly conducting grating structure by boundary measurements of the diffracted fields in transverse mag-netic polarization.The method proposed in this paper is based on a novel combination of the Monte Carlo technique,a continuation method and the Karhunen-Loève expansion for the uncertainty quantification of the random structure.Numerical results are presented to demonstrate the effectiveness of the proposed method.