Optimization of Composite Enzymatic Extraction, Structural Characterization and Biological Activity of Soluble Dietary Fiber from Akebia trifoliata Peel.

In order to reduce the waste of Akebia trifoliata peel and maximize its utilization, in this study, on the basis of a single-factor experiment and the response surface method, the optimum technological conditions for the extraction of soluble dietary fiber from Akebia trifoliata peel with the compound enzyme method were obtained. The chemical composition, physical and chemical properties, structural characterization and biological activity of the purified soluble dietary fiber (AP-SDF) from the Akebia trifoliata peel were analyzed. We discovered that that the optimum yield was 20.87% under the conditions of cellulase addition 600 U/g, enzymolysis time 100 min, solid-liquid ratio 1:24 g/mL and enzymolysis temperature 51 °C. At the same time, AP-SDF was a porous network structure cellulose type I acidic polysaccharose mainly composed of arabinoxylan (36.03%), galacturonic acid (27.40%) and glucose (19.00%), which possessed the structural characteristic peaks of the infrared spectra of polysaccharides and the average molecular weight (Mw) was 95.52 kDa with good uniformity. In addition, the AP-SDF exhibited high oil-holding capacity (15.11 g/g), good water-holding capacity and swelling capacity, a certain antioxidant capacity in vitro, hypoglycemic activity in vitro for α-glucosidase inhibition and hypolipidemic activity in vitro for the binding ability of bile acids and cholesterol. These results will provide a theoretical basis for the development of functional products with antioxidant, hypoglycemic and hypolipidemic effects, which have certain application value in related industries.

Effect of Environmental pH on the Mechanics of Chitin and Chitosan: A Single-Molecule Study.

Chitin and chitosan are important structural macromolecules for most fungi and marine crustaceans. The functions and application areas of the two molecules are also adjacent beyond their similar molecular structure, such as tissue engineering and food safety where solution systems are involved. However, the elasticities of chitin and chitosan in solution lack comparison at the molecular level. In this study, the single-molecule elasticities of chitin and chitosan in different solutions are investigated via atomic force microscope (AFM) based single-molecule spectroscopy (SMFS). The results manifest that the two macromolecules share the similar inherent elasticity in DOSM due to their same chain backbone. However, obvious elastic deviations can be observed in aqueous conditions. Especially, a lower pH value (acid environment) is helpful to increase the elasticity of both chitin and chitosan. On the contrary, the tendency of elastic variation of chitin and chitosan in a larger pH value (alkaline environment) shows obvious diversity, which is mainly determined by the side groups. This basic study may produce enlightenment for the design of intelligent chitin and chitosan food packaging and biomedical materials.

Identification of the ALMT gene family in the potato (Solanum tuberosum L.) and analysis of the function of StALMT6/10 in response to aluminum toxicity.

Introduction: Aluminum (Al)-activated malate transporters (ALMTs) play an important role in the response to Al toxicity, maintenance of ion homeostasis balance, mineral nutrient distribution, and fruit quality development in plants. However, the function of the StALMT gene family in potato remains unknown. Methods and results: In this study, 14 StALMT genes were identified from the potato genome, unevenly distributed on seven different chromosomes. Collinearity and synteny analyses of ALMT genes showed that potatoes had 6 and 22 orthologous gene pairs with Arabidopsis and tomatoes, respectively, and more than one syntenic gene pair was identified for some StALMT gene family members. Real-time quantitative polymerase chain reaction (qPCR) results showed differential expression levels of StALMT gene family members in different tissues of the potato. Interestingly, StALMT1, StALMT6, StALMT8, StALMT10, and StALMT12 had higher expression in the root of the potato cultivar Qingshu No. 9. After being subjected to Al3+ stress for 24 h, the expression of StALMT6 and StALMT10 in roots was evidently increased, displaying their decisive role in Al3+ toxicity. Discussion: In addition, overexpression of StALMT6 and StALMT10 in Arabidopsis enhanced its tolerance to Al toxicity. These results indicate that StALMT6 and StALMT10 impart Al3+ resistance in the potato, establishing the foundation for further studies of the biological functions of these genes.

Elucidation of the effects of autochthonous starter on nitrogen-containing compounds during fermentation of Yujiangsuan by metabolomics.

To understand the role of microorganisms in nitrogen (N)-containing compound changes during the processing of Yujiangsuan by autochthonous starter cultures, the GC-TOF-MS-based metabolomics method was adopted to investigate the effects of Weissella cibaria and Lactobacillus plantarum. The results demonstrated that inoculation of autochthonous strains led to differential metabolites, such as fatty acids, organic oxygen compounds, and carboxylic acids on day 4 to day 12 of fermentation. The N-containing compounds under the inoculated fermentation group showed a faster relative concentration change. Nucleotide metabolism and arginine and proline metabolism exerted an influence on the formation of N-containing compounds. Apart from that, the effect of W. cibaria and L. plantarum on the hydrolysis of macromolecules was the main factor causing differences in major N-containing compounds.

Potential Schizophrenia Disease-Related Genes Prediction Using Metagraph Representations Based on a Protein-Protein Interaction Keyword Network: Framework Development and Validation.

BACKGROUND: Schizophrenia is a serious mental disease. With increased research funding for this disease, schizophrenia has become one of the key areas of focus in the medical field. Searching for associations between diseases and genes is an effective approach to study complex diseases, which may enhance research on schizophrenia pathology and lead to the identification of new treatment targets. OBJECTIVE: The aim of this study was to identify potential schizophrenia risk genes by employing machine learning methods to extract topological characteristics of proteins and their functional roles in a protein-protein interaction (PPI)-keywords (PPIK) network and understand the complex disease-causing property. Consequently, a PPIK-based metagraph representation approach is proposed. METHODS: To enrich the PPI network, we integrated keywords describing protein properties and constructed a PPIK network. We extracted features that describe the topology of this network through metagraphs. We further transformed these metagraphs into vectors and represented proteins with a series of vectors. We then trained and optimized our model using random forest (RF), extreme gradient boosting, light gradient boosting machine, and logistic regression models. RESULTS: Comprehensive experiments demonstrated the good performance of our proposed method with an area under the receiver operating characteristic curve (AUC) value between 0.72 and 0.76. Our model also outperformed baseline methods for overall disease protein prediction, including the random walk with restart, average commute time, and Katz models. Compared with the PPI network constructed from the baseline models, complementation of keywords in the PPIK network improved the performance (AUC) by 0.08 on average, and the metagraph-based method improved the AUC by 0.30 on average compared with that of the baseline methods. According to the comprehensive performance of the four models, RF was selected as the best model for disease protein prediction, with precision, recall, F1-score, and AUC values of 0.76, 0.73, 0.72, and 0.76, respectively. We transformed these proteins to their encoding gene IDs and identified the top 20 genes as the most probable schizophrenia-risk genes, including the EYA3, CNTN4, HSPA8, LRRK2, and AFP genes. We further validated these outcomes against metagraph features and evidence from the literature, performed a features analysis, and exploited evidence from the literature to interpret the correlation between the predicted genes and diseases. CONCLUSIONS: The metagraph representation based on the PPIK network framework was found to be effective for potential schizophrenia risk genes identification. The results are quite reliable as evidence can be found in the literature to support our prediction. Our approach can provide more biological insights into the pathogenesis of schizophrenia.

Multi-genome comprehensive identification of SSR/SV and development of molecular markers database to serve Sorghum bicolor (L.) breeding.

BACKGROUND: As an important food and cash crop, identification of DNA molecular markers is of great significance for molecular marker-assisted breeding of Sorghum (Sorghum bicolor (L.) moench). Although some sorghum-related mutation databases have been published, the special SSR and SV databases still need to be constructed and updated. RESULTS: In this study, the quality of 18 different sorghum genomes was evaluated, and two genomes were assembled at chromosome level. Through the identification and comparative analysis of SSR loci in these genomes, the distribution characteristics of SSR in the above sorghum genomes were initially revealed. At the same time, five representative reference genomes were selected to identify the structural variation of sorghum. Finally, a convenient SSR/SV database of sorghum was constructed by integrating the above results ( http://www.sorghum.top:8079/ ; http://43.154.129.150:8079/ ; http://47.106.184.91:8079/ ). Users can query the information of related sites and primer pairs. CONCLUSIONS: Anyway, our research provides convenience for sorghum researchers and will play an active role in sorghum molecular marker-assisted breeding.

The effectiveness of artificial microbial community selection: a conceptual framework and a meta-analysis.

The potential for artificial selection at the community level to improve ecosystem functions has received much attention in applied microbiology. However, we do not yet understand what conditions in general allow for successful artificial community selection. Here we propose six hypotheses about factors that determine the effectiveness of artificial microbial community selection, based on previous studies in this field and those on multilevel selection. In particular, we emphasize selection strategies that increase the variance among communities. We then report a meta-analysis of published artificial microbial community selection experiments. The reported responses to community selection were highly variable among experiments; and the overall effect size was not significantly different from zero. The effectiveness of artificial community selection was greater when there was no migration among communities, and when the number of replicated communities subjected to selection was larger. The meta-analysis also suggests that the success of artificial community selection may be contingent on multiple necessary conditions. We argue that artificial community selection can be a promising approach, and suggest some strategies for improving the performance of artificial community selection programs.

A Deep Learning Model for the Normalization of Institution Names by Multisource Literature Feature Fusion: Algorithm Development Study.

BACKGROUND: The normalization of institution names is of great importance for literature retrieval, statistics of academic achievements, and evaluation of the competitiveness of research institutions. Differences in authors' writing habits and spelling mistakes lead to various names of institutions, which affects the analysis of publication data. With the development of deep learning models and the increasing maturity of natural language processing methods, training a deep learning-based institution name normalization model can increase the accuracy of institution name normalization at the semantic level. OBJECTIVE: This study aimed to train a deep learning-based model for institution name normalization based on the feature fusion of affiliation data from multisource literature, which would realize the normalization of institution name variants with the help of authority files and achieve a high specification accuracy after several rounds of training and optimization. METHODS: In this study, an institution name normalization-oriented model was trained based on bidirectional encoder representations from transformers (BERT) and other deep learning models, including the institution classification model, institutional hierarchical relation extraction model, and institution matching and merging model. The model was then trained to automatically learn institutional features by pretraining and fine-tuning, and institution names were extracted from the affiliation data of 3 databases to complete the normalization process: Dimensions, Web of Science, and Scopus. RESULTS: It was found that the trained model could achieve at least 3 functions. First, the model could identify the institution name that is consistent with the authority files and associate the name with the files through the unique institution ID. Second, it could identify the nonstandard institution name variants, such as singular forms, plural changes, and abbreviations, and update the authority files. Third, it could identify the unregistered institutions and add them to the authority files, so that when the institution appeared again, the model could identify and regard it as a registered institution. Moreover, the test results showed that the accuracy of the normalization model reached 93.79%, indicating the promising performance of the model for the normalization of institution names. CONCLUSIONS: The deep learning-based institution name normalization model trained in this study exhibited high accuracy. Therefore, it could be widely applied in the evaluation of the competitiveness of research institutions, analysis of research fields of institutions, and construction of interinstitutional cooperation networks, among others, showing high application value.

Genome-wide identification of the PYL gene family of tea plants (Camellia sinensis) revealed its expression profiles under different stress and tissues.

BACKGROUND: PYL (Pyrabactin resistance 1-like) protein is a receptor of abscisic acid (ABA), which plays an important role in ABA signaling and influences plant growth and development and stress response. However, studies on PYL gene family in tea plants have not been reported. RESULTS: In this study, we identified 20 PYL genes from the reference genome of tea plant ('Shuchazao'). Phylogeny analysis indicated that PYLs from tea and other plant species were clustered into seven groups. The promoter region of PYL genes contains a large number of cis-elements related to hormones and stresses. A large number of PYL genes responding to stress were found by analyzing the expression levels of abiotic stress and biotic stress transcriptome data. For example, CSS0047272.1 were up-regulated by drought stress, and CSS0027597.1 could respond to both anthracnose disease and geometrid feeding treatments. In addition, 10 PYL genes related to growth and development were verified by RT-qPCR and their tissue expression characteristics were revealed. CONCLUSIONS: Our results provided a comprehensive characteristic of the PYL gene family in tea plants and provided an important clue for further exploring its functions in the growth and development, and resistance to stress of tea plants.

Review on supported metal catalysts with partial/porous overlayers for stabilization.

Heterogeneous catalysts of supported metals are important for both liquid-phase and gas-phase chemical transformations which underpin the petrochemical sector and manufacture of bulk or fine chemicals and pharmaceuticals. Conventional supported metal catalysts (SMC) suffer from deactivation resulting from sintering, leaching, coking and so on. Besides the choice of active species (e.g. atoms, clusters, nanoparticles) to maximize catalytic performances, strategies to stabilize active species are imperative for rational design of catalysts, particularly for those catalysts that work under heated and corrosive reaction conditions. The complete encapsulation of metal active species within a matrix (e.g. zeolites, MOFs, carbon, etc.) or core-shell arrangements is popular. However, the use of partial/porous overlayers (PO) to preserve metals, which simultaneously ensures the accessibility of active sites through controlling the size/shape of diffusing reactants and products, has not been systematically reviewed. The present review identifies the key design principles for fabricating supported metal catalysts with partial/porous overlayers (SMCPO) and demonstrates their advantages versus conventional supported metals in catalytic reactions.

Modification of C-segment of Cfa DnaE split intein for improving clean-in-place in chromatography process.

This research focuses on the construction of an affinity purification system based on Cfa DnaE split intein. Cfa DnaE intein is an artificially constructed intein with the advantages of a fast cleavage reaction and good stability. In a previous study, a purification system that uses Cfa intein as a tag was constructed, the separation of the target protein and the tag during the purification process was completed, and the purity of the purified target protein reached 98.21%. Guided by molecular docking results, we identified flexible regions in the split intein and inserted several glycines into the protein to decrease the stability of the Cfa IC , thereby improving the regenerability of the IN media. Inserting 6 glycines between amino acids 14 and 15 of IC improved the regeneration rate of IC -GFP on the column to approximately 96%.

[Nonlinear optical absorption characteristics of a novel organic material ZnTBP/CA/PhR].

The measurement of optical absorption spectrum in visible light range of a novel organic material Zn-tetrabenzoporphincrotonic acid/phenoxy resin (ZnTBP/CA/PhR) is reported in this paper. The nonlinear absorption property of the sample illuminated by lasers with different wavelengths was studied. And the absorption alteration and kinetic process of the major spectrum bands were observed. The saturation absorption (SA) and reverse saturation absorption (RSA) appeared as the input fluence increased, and re-reverse saturation absorption process of the material was demonstrated in the experiment. These properties occurred in a broad range of visible spectrum. The paper qualitatively analyses the physical mechanism during the whole course. Based on the nonlinear absorption characteristics, we showed some applications in photonics field of ZnTBP/CA/PhR.