Ciceribacter sichuanensis sp. nov., a plant growth promoting rhizobacterium isolated from root nodules of soybean in Sichuan, China.

The fast-growing rhizobia-like strains S101T and S153, isolated from root nodules of soybean (Glycine max) in Sichuan, People's Republic of China, underwent characterization using a polyphasic taxonomy approach. The strains exhibited growth at 20-40 °C (optimum, 28 °C), pH 4.0-10.0 (optimum, pH 7.0) and up to 2.0% (w/v) NaCl (optimum, 0.01%) on Yeast Mannitol Agar plates. The 16S rRNA gene of strain S101T showed 98.4% sequence similarity to the closest type strain, Ciceribacter daejeonense L61T. Major cellular fatty acids in strain S101T included summed feature 8 (C18:1ω7c and/or C18:1ω6c) and C19:0 cyclo ω8c. The predominant quinone was ubiquinone-10. The polar lipids of strain S101T included diphosphatidylglycerol, phosphatidylglycerol, phosphatidylmethyl ethanolamine, phosphatidyl ethanolamine, amino phospholipid, unidentified phosphoglycolipid and unidentified amino-containing lipids. The DNA G + C contents of S101T and S153 were 61.1 and 61.3 mol%, respectively. Digital DNA-DNA hybridization relatedness and average nucleotide identity values between S101T and C. daejeonense L61T were 46.2% and 91.4-92.2%, respectively. In addition, strain S101T promoted the growth of soybean and carried nitrogen fixation genes in its genome, hinting at potential applications in sustainable agriculture. We propose that strains S101T and S153 represent a novel species, named Ciceribacter sichuanensis sp. nov., with strain S101T as the type strain (= CGMCC 1.61309 T = JCM 35649 T).

How drivers perform under different scenarios: Ability-related driving style extraction for large-scale dataset.

The extraction and analysis of driving style are essential for a comprehensive understanding of human driving behaviours. Most existing studies rely on subjective questionnaires and specific experiments, posing challenges in accurately capturing authentic characteristics of group drivers in naturalistic driving scenarios. As scenario-oriented naturalistic driving data collected by advanced sensors becomes increasingly available, the application of data-driven methods allows for a exhaustive analysis of driving styles across multiple drivers. Following a theoretical differentiation of driving ability, driving performance, and driving style with essential clarifications, this paper proposes a quantitative determination method grounded in large-scale naturalistic driving data. Initially, this paper defines and derives driving ability and driving performance through trajectory optimisation modelling considering various cost indicators. Subsequently, this paper proposes an objective driving style extraction method grounded in the Gaussian mixture model. In the experimental phase, this study employs the proposed framework to extract both driving abilities and performances from the Waymo motion dataset, subsequently determining driving styles. This determination is accomplished through the establishment of quantifiable statistical distributions designed to mirror data characteristics. Furthermore, the paper investigates the distinctions between driving styles in different scenarios, utilising the Jensen-Shannon divergence and the Wilcoxon rank-sum test. The empirical findings substantiate correlations between driving styles and specific scenarios, encompassing both congestion and non-congestion as well as intersection and non-intersection scenarios.