Search

BACKGROUND: The purity of sorghum varieties is an important indicator of the quality of raw materials used in the distillation of liquors. Different varieties of sorghum may be mixed during the acquisition process, which will affect the flavor and quality of liquor. To facilitate the rapid identification of sorghum varieties, this study proposes a sorghum variety identification model using hyperspectral imaging (HSI) technology combined with convolutional neural network (AlexNet). RESULTS: First, the watershed algorithm, which was modified with the extended‐maxim transform, was used to segment the hyperspectral images of a single sorghum grain. The isolated forest algorithm was used to eliminate abnormal spectral data from the complete spectral data. Secondly, the AlexNet model of sorghum variety identification was established based on the two‐dimensional gray image data of sorghum grain in group 1. The effects of different preprocessing methods and different convolution kernel sizes on the performance of the AlexNet model were discussed. The eigenvalues of the last layer of the AlexNet model were visualized using the t‐distributed random neighborhood embedding method, which is used to evaluate the separability of features extracted by the AlexNet model. The performance differences between the optimal AlexNet model and traditional machine learning models for sorghum variety identification were compared. Finally, the varieties of sorghum grains in groups 2 and 3 were identified based on the optimal AlexNet model, and the average accuracy values of the test set reached 95.62% and 95.91% respectively. CONCLUSION: The results in this study demonstrated that HSI combined with the AlexNet model could provide a feasible technical approach for the detection of sorghum varieties. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The analysis of the surface chemical behavior of pyrite is highly crucial in the fields of environmental conservation, metal extraction, and flotation separation. In this paper, the mechanism of atomic reconstruction on the pyrite surface and the adsorption behavior of O2 on a reconstructed surface are calculated by density functional theory (DFT). Different reconstruction surfaces were constructed by deleting S and Fe atoms on the (100) surface of pyrite. In addition, the geometric configuration, formation energy, binding energy, cohesion energy, and surface electronic properties of the reconstruction surface were calculated. The adsorption energies and geometric configurations of O2 on different reconstructed surfaces were also determined. The results show that under Fe-poor conditions, the charge of Fe atoms increases, and S atoms form Sn on the reconstructed surface. The binding energy between the Sn and the substrate (ideal surface) is lower, which is similar to the Sn adsorption on the substrate surface with the Fe atom as the site. Sn has high cohesive energy and is resistant to being attacked by oxidants, which leads to structural collapse, and a low affinity for O2. Under S-poor conditions, the –[Fe–S]n– plane structure formed on the reconstructed surface. The –[Fe–S]n– structure stably bonds to the substrate by an Fe–S bond, and exhibits strong binding energy. However, the –[Fe–S]n– structure has low cohesive energy and exhibits thermodynamic instability. In contrast, O2 shows a strong affinity for the –[Fe–S]n– structure, indicating that the deficiency of the S atom promotes the surface oxidation reaction. The mechanism of atomic reconstruction on the surface of pyrite is of utmost importance for understanding its surface chemical behavior. [ABSTRACT FROM AUTHOR]

Polyacrylonitrile (PAN) fiber is soft and comfortable, but its poor strength compared to other synthetic fibers has limited it wide range of applications. This study effectively improved the strength of PAN fibers by adding graphene oxide (GO) and polyvinyl alcohol (PVA) during PAN spinning. The composite fibers were prepared via gel spinning and subsequent hot drawing process. The results show that the PVA molecular chains embedded into the PAN molecular chain significantly improved the mechanical properties of the hybrid fiber. At the same time, the defect reduced the UV resistance and thermal stability of the hybrid fibers only when the PVA molecular was introduced in the PAN. Surprisingly, after the recomposition of GO in the above mixed polymer system, the interaction between the GO and matrix not only improved the mechanical properties of the fiber, but also enhanced the UV resistance and thermal stability. In addition, when the amount of GO was 0.3 wt%, the crystallinity of the GO/PVA/PAN composite fiber reached the maximum and the tensile strength was the highest. This strategic approach suggests an effective method to prepare graphene-based ternary composites fibers with high strength and novel functional characteristics. [ABSTRACT FROM AUTHOR]

The moisture content and distribution of Daqu significantly influences the quality of Daqu products. This work presents the visualization of the moisture content in Daqu using a combination of spectral and spatial information from hyperspectral imaging. The least squares support vector machine (LS-SVM) and partial least squares regression (PLSR) methods were adopted to establish the predictive models based on the full wavelengths and the 29 feature wavelengths combined with color features, respectively. The best prediction model was PLSR ( R p 2 = 0.9823 , RMSEP = 0.0109) based on feature wavelengths. The results showed that the combination of spectral and spatial information of hyperspectral imaging can accurately predict the moisture content in Daqu during different fermentation processes, and the visualization of the distribution map of moisture content in Daqu provided a more convenient and understandable assessment of moisture content. This work presents a novel, rapid, and nondestructive approach for moisture content detection in Daqu, and provides theoretical support and basis for intelligent adjustment of temperature, humidity and other environmental parameters of Daqu fermentation. [ABSTRACT FROM AUTHOR]

Liver cirrhosis occurs as a consequence of many chronic liver diseases that are prevalent worldwide. Here we characterize the gut microbiome in liver cirrhosis by comparing 98 patients and 83 healthy control individuals. We build a reference gene set for the cohort containing 2.69 million genes, 36.1% of which are novel. Quantitative metagenomics reveals 75,245 genes that differ in abundance between the patients and healthy individuals (false discovery rate < 0.0001) and can be grouped into 66 clusters representing cognate bacterial species; 28 are enriched inpatients and 38 in control individuals. Most (54%) of the patient-enriched, taxonomically assigned species are of buccal origin, suggesting an invasion of the gut from the mouth in liver cirrhosis. Biomarkers specific to liver cirrhosis at gene and function levels are revealed by a comparison with those for type 2 diabetes and inflammatory bowel disease. On the basis of only 15 biomarkers, a highly accurate patient discrimination index is created and validated on an independent cohort. Thus microbiota-targeted biomarkers may be a powerful tool for diagnosis of different diseases., Cirrhosis is an advanced liver disease resulting from acute or chronic liver injury, including alcohol abuse, obesity and hepatitis virus infection. The prognosis for patients with decompensated liver cirrhosis is [...]

N -Acyl- N , O -acetals are key components in a variety of bioactive natural products. Furthermore, they are synthetic equivalents of unstable N -acylimines and building blocks in organic synthesis. Tremendous efforts have been made in the synthesis of such acetals, these methods can be broadly classified into two categories: electrochemical oxidation and chemical methods. Herein, we will summarize progress in the preparation of these subunits, which may aid the development of new synthetic methods for N -acyl- N , O -acetals. 1 Introduction 2 Synthetic Methods for Preparing N -Acyl- N , O -acetals 2.1 Electrochemical Oxidation 2.2 Chemical Methods 2.3 Other Methods 3 Summary and Outlook [ABSTRACT FROM AUTHOR]

To evaluate the quality of Daqu, the change of moisture content during the fermentation process is an important indicator. To improve the accuracy of measuring the moisture content, we studied a measuring method that used a near‐infrared hyperspectral imaging technique. In the process of using multigranularity cascade forest algorithm to do deep learning on the detection task, two improvements were made to improve detection accuracy. One was to establish a multigranularity cascade forest model with various scanning window combinations to solve the problem of distribution offset in the prediction results. The other was to do continuum removal processing to obtain enhanced spectral data from the sample spectral data. The results showed that the multigranularity cascade forest was markedly better than a support vector machine and a backpropagation neural network. The coefficient of determination of the prediction set (R2) and root mean square error of the Prediction set reached 0.9977 and 0.0021, respectively. The study results indicated that the hyperspectral imaging technique could achieve highly accurate detection and distribution visualization of the moisture content of Daqu during the fermentation process. Practical applications: Daqu, a flavoring agent and fermentation starter, plays a vital role in the solid‐state liquor brewing process. During the fermentation process, moisture content is an important indicator for quality evaluation of Daqu. Therefore, it is highly important to detect the moisture content in Daqu quickly and accurately. The hyperspectral imaging technique, as an emerging detection technology, has advantages that traditional detection methods do not have, so that the disadvantages of traditional detection can be overcome. In our study, the hyperspectral imaging technique could achieve high‐precision detection of the moisture content of Daqu during the fermentation process, indicating that hyperspectral technology could be used to detect Daqu moisture content. Moreover, this method has great potential for real‐time indicators detection in winery for future work. [ABSTRACT FROM AUTHOR]

Daqu, a Chinese liquor fermentation starter, contains all kinds of microorganisms and enzymes for Chinese liquor fermentation. The moisture content of Daqu significantly influence on the reproduction of microorganisms in Daqu. This work presents for the first time that determination of moisture content of Daqu with hyperspectral imaging. The characteristic spectrum of water is extracted based on comparative experiments with varying moisture content. The molds based on the full bands and feature bands were established by the support vector regression (SVR) method, which is used to predict moisture content of Daqu during fermentation process. The performance of the model based on the feature bands (R2 = 0.9870, root mean square error (RMSE) = 0.0091) is comparable to the full bands and the dimensions of the spectral information were significantly reduced. This work presents a novel, rapid and nondestructive approach for detecting the moisture content in Daqu and lays a foundation for the application of hyperspectral imaging. [ABSTRACT FROM AUTHOR]

Background The relationship between Klebsiella pneumoniae and nosocomial and community-acquired infections is well known, and K. pneumoniae resistance to most antibiotics is increasing worldwide. In contrast, tigecycline remains active against many bacterial strains, and serves as a last resort for treating multi-drug resistant bacterial infections. That tigecycline nonsusceptibility among K. pneumoniae isolates has been reported worldwide is worrying. However, the mechanisms of tigecycline resistance in K. pneumoniae are less well known. We report the genome sequence and genomic characterization of tigecycline-resistant K. pneumoniae strain 5422 isolated from the bile samples of a patient with cholangiocarcinoma. Results We sequenced the K. pneumoniae strain 5422 genome using next-generation sequencing technologies. Sequence data assembly revealed a 5,432,440-bp draft genome and 57.1% G + C content, which contained 5397 coding sequences. The genome has extensive similarity to other sequenced K. pneumoniae genomes, but also has several resistance-nodulation-cell division (RND) efflux pump genes that may be related to tigecycline resistance. Conclusions K. pneumoniae strain 5422 is resistant to multiple antibiotics. The genome sequence of the isolate and comparative analysis with other K. pneumoniae strains presented in this paper are important for better understanding of K. pneumoniae multi-drug resistance. The RND efflux pump genes identified in the genome indicate the presence of an antibiotic resistance mechanism prior to antibiotics overuse. The availability of the genome sequence forms the basis for further comparative analyses and studies addressing the evolution of the K. pneumoniae drug resistance mechanism and the K. pneumoniae transcriptome. Keywords: Tigecycline-resistant, Klebsiella pneumoniae, Cholangiocarcinoma, Next-generation sequencing, Comparative genomics, Author(s): Beiwen Zheng[sup.1,2] , Ang Li[sup.1,2] , Xiawei Jiang[sup.1,2] , Xinjun Hu[sup.1,2] , Jian Yao[sup.1,2] , Lina Zhao[sup.1,2] , Jinru Ji[sup.1,2] , Min Ye[sup.1,2] , Yonghong Xiao[sup.1,2] and Lanjuan Li[sup.1,2] [...]