A Gram-stain-negative, non-motile, non-spore-forming bacterium, designated MLS-26-JM13-11, was isolated from potato stems, collected in Guyuan County, Hebei Province, China. Strain MLS-26-JM13-11 could grow at 10-39 °C (optimum, 30 °C), pH 6.0-9.0 (optimum, pH 7.2) and in the presence of 0-4.0 % (w/v) NaCl (optimum, 1.0 % w/v). Phylogenetic analysis, based on 16S rRNA gene sequences, revealed that strain MLS-26-JM13-11 formed a stable clade with Sphingobacterium bambusae IBFC2009 and Sphingobacterium griseoflavum SCU-B140, with the 16S rRNA gene sequence similarities ranging from 95.9 % to 97.0 %. The major cellular fatty acids comprised iso-C15 : 0 (36.9 %), summed feature 3 (C16 : 1ω7c and/or C16 : 1ω6c, 34.0 %), C16 : 0 (3.0 %) and iso-C17 : 0 3-OH (13.4 %). Strain MLS-26-JM13-11 contained sphingoglycolipid, phosphatidyl ethanolamine, six unknown lipids, one unknown aminolipid, four unknown polarlipids and two unknown aminophospholipids. The isoprenoid quinone was MK-7. The DNA G+C content was 42.6 mol%. Furthermore, the average nucleotide identity and in silico estimated DNA-DNA reassociation values among MLS-26-JM13-11 and S. bambusae KCTC 22814 were in all cases below the respective threshold for species differentiation. On the basis of phenotypic, genotypic and phylogenetic evidence, strain MLS-26-JM13-11 (=ACCC 60057=JCM 32274) represents a novel species within the genus Sphingobacterium, for which the name Sphingobacterium solani sp. nov. is proposed.

Morels (Morchella spp.) are highly prized and popular edible mushrooms. The outdoor cultivation of morels in China first developed at the beginning of the 21st century. Several species, such as Morchella sextelata, M. eximia, and M. importuna, have been commercially cultivated in greenhouses. However, the detriments and obstacles associated with continuous cropping have become increasingly serious, reducing yields and even leading to a complete lack of fructification. It has been reported that the obstacles encountered with continuous morel cropping may be related to changes in the soil microbial community. To study the effect of dazomet treatment on the cultivation of morel under continuous cropping, soil was fumigated with dazomet before morel sowing. Alpha diversity and beta diversity analysis results showed that dazomet treatment altered the microbial communities in continuous cropping soil, which decreased the relative abundance of soil-borne fungal pathogens, including Paecilomyces, Trichoderma, Fusarium, Penicillium, and Acremonium, increased the relative abundance of beneficial soil bacteria, including Bacillius and Pseudomonas. In addition, the dazomet treatment significantly increased the relative abundance of morel mycelia in the soil and significantly improved morel yield under continuous cropping. These results verified the relationship between the obstacles associated with continuous cropping in morels and the soil microbial community and elucidated the mechanism by which the obstacle is alleviated when using dazomet treatment.

Intention estimation has been widely studied in lane change scenarios, which explains a vehicle's behaviour and implies its future motion. However, in dense traffic, lane‐changing is more tactical and interactive. Due to the conflict between merging vehicles and adjacent vehicles, driving intentions become interdependent which fuses passing and yielding. In addition, lane change occurs without a fixed location. Drivers should be aware of each other's intentions along conflict process, and take instant responses. To address these challenges, this paper proposes semantic‐based interactive intention estimation (SIIE), to understand driving intentions during lane change conflict. The problem is addressed by combining driving semantics with probability inference model based on dynamic Bayesian network (DBN). Firstly, the DBN is modelled for the interaction process with Condition‐Intention‐Behaviour relationships. Secondly, the semantics are extracted from the lane change conflict and are inferred with observation methods. Thirdly, SIIE is trained and verified with real‐world driving data. The intention estimation results are demonstrated, and then utilized for multi‐modal motion identification and trajectory prediction. Lane change in dense traffic requires interactive cognition of driving intentions, the findings of this research shall inspire future studies into related scenarios, and promote interactive driving technologies.