Bacillus aciditolerans sp. nov., isolated from paddy soil.

A Gram-stain-positive, motile, rod-shaped bacterial strain, YN-1T, was isolated from a rice field in the town of Jietou, Yunnan Province, PR China. Colonies were circular, 1-2 mm in diameter, creamy white, with slightly irregular margins. The isolate grew optimally at 37 °C, pH 7.0 and with 1.0 % (w/v) NaCl. On the basis of the results of 16S rRNA gene sequence similarity comparisons, YN-1T clustered together with other species of the genus Bacillus and showed highest similarities with Bacillus onubensis 0911MAR22V3T (98.0 %), Bacillus humi LMG22167T (97.5 %), 'Bacillus timonensis' 10403023 (97.4 %) and 'Bacillussinesaloumensis' P3516 (97.1 %). However, the DNA-DNA hybridization values between YN-1T and closely related strains of species of the genus Bacillus were well below 47 %, indicating that they represent different taxa. The average nucleotide identity and the Genome-to-Genome Distance Calculator also revealed low relatedness (below 95 and 70 %, respectively) between YN-1T and type strains of closely related species of the genus Bacillus. The DNA G+C content of the strain was 40 mol%. The major cellular fatty acids were iso-C15 : 0, anteiso-C15 : 0, and C16 : 0. The polar lipids were diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, two unidentified phospholipids, three unidentified aminophospholipids and two other unidentified lipids. Physiological and biochemical test results were also different from those of the most closely related species. On the basis of the phenotypic, genetic and chemotaxonomic data, strain YN-1T is considered to represent a novel species of the genus Bacillus, for which the name Bacillusaciditolerans sp. nov. is proposed, with strain YN-1T (=CCTCC AB 2017280T=JCM 32973T) as the type strain.

Biodegradation of malachite green by an endophytic bacterium Klebsiella aerogenes S27 involving a novel oxidoreductase.

Endophytic microorganisms can metabolize organic contaminants and assist in plant growth, thus facilitating the phytoremediation of polluted environments. An endophytic bacterium capable of decoloring malachite green (MG) was isolated from the leaves of the wetland plant Suaeda salsa and was identified as Klebsiella aerogenes S27. Complete decolorization of MG (100 mg/l) was achieved in 8 h at 30 °C and pH 7.0. Ultraviolet-visible spectroscopy and Fourier-transform infrared spectroscopy analyses indicated the degradation of MG by the isolate. The enzymic assays of the strain showed the triphenylmethane reductase (TMR) activity. A gene encoding putative TMR-like protein (named as KaTMR) was cloned and heterologously expressed in Escherichia coli. KaTMR showed only 42.6-43.3% identities in amino acids compared with well-studied TMRs, and it phylogenetically formed a new branch in the family of TMRs. The degraded metabolites by recombinant KaTMR were detected by liquid chromatography-mass spectrometry, showing differences from the products of reported TMRs. The biotransformation pathway of MG was proposed. Phytotoxicity studies revealed the less-toxic nature of the degraded metabolites compared to the dye. This study presented the first report of an endophyte on the degradation and detoxification of triphenylmethane dye via a novel oxidoreductase, thus facilitating the study of the plant-endophyte symbiosis in the bioremediation processes.

Methoxycarbonylation of diisobutylene into methyl isononanoate catalyzed by cobalt complexes dispersed by poly(ionic liquids).

The catalytic performance of cobalt complex catalysts coordinated with various poly(ionic liquids) for the methoxycarbonylation of diisobutene into methyl isononanoate was investigated. The poly(ionic liquids) were synthesized via a solvothermal polymerization method and were characterized using Fourier transform infrared spectroscopy, thermogravimetric analysis, scanning electron microscopy, transmission electron microscopy, N2 adsorption-desorption and elemental analyses. A diisobutene conversion of 88.0% and a methyl isononanoate selectivity of 91.4% were achieved using HVIMI-VPy-DVB (1 : 1)@Co2(CO)8 as catalysts at the optimized reaction conditions of 8.0 MPa CO and 150 °C. Furthermore, the catalyst system can be suitable for the methoxycarbonylation of various terminal olefins and exhibits high recoverability and thermostability.

Continuous cropping system altered soil microbial communities and nutrient cycles.

Understanding the response of microbial communities and their potential functions is essential for sustainability of agroecosystems under long-term continuous cropping. However, limited research has focused on investigating the interaction between soil physicochemical factors and microbial community dynamics in agroecosystems under long-term continuous cropping. This study probed into the physicochemical properties, metabolites, and microbial diversity of tobacco rhizosphere soils cropped continuously for 0, 5, and 20 years. The relative abundance of bacterial genera associated with nutrient cycling (e.g., Sphingomonas) increased while potential plant pathogenic fungi and beneficial microorganisms showed synergistic increases with the duration of continuous cropping. Variations in soil pH, alkeline nitrogen (AN) content, and soil organic carbon (SOC) content drove the shifts in soil microbial composition. Metabolites such as palmitic acid, 3-hydroxypropionic acid, stearic acid, and hippuric acid may play a key role in soil acidification. Those results enhance our ability to predict shifts in soil microbial community structure associated with anthropogenic continuous cropping, which can have long-term implications for crop production.

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning.

The maturity affects the yield, quality, and economic value of tobacco leaves. Leaf maturity level discrimination is an important step in manual harvesting. However, the maturity judgment of fresh tobacco leaves by grower visual evaluation is subjective, which may lead to quality loss and low prices. Therefore, an objective and reliable discriminant technique for tobacco leaf maturity level based on near-infrared (NIR) spectroscopy combined with a deep learning approach of convolutional neural networks (CNNs) is proposed in this study. To assess the performance of the proposed maturity discriminant model, four conventional multiclass classification approaches-K-nearest neighbor (KNN), backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM)-were employed for a comparative analysis of three categories (upper, middle, and lower position) of tobacco leaves. Experimental results showed that the CNN discriminant models were able to precisely classify the maturity level of tobacco leaves for the above three data sets with accuracies of 96.18%, 95.2%, and 97.31%, respectively. Moreover, the CNN models with strong feature extraction and learning ability were superior to the KNN, BPNN, SVM, and ELM models. Thus, NIR spectroscopy combined with CNN is a promising alternative to overcome the limitations of sensory assessment for tobacco leaf maturity level recognition. The development of a maturity-distinguishing model can provide an accurate, reliable, and scientific auxiliary means for tobacco leaf harvesting.