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Objectives: The non-O1/non-O139 Vibrio cholerae caused outbreaks or sporadic cases of gastroenteritis that was rarely seen in good sanitary condition. It was described a case of systemic multiple organ lesions that worsened because of non-O1/non-O139 V. cholerae, suggesting that serogroups have a potential virulence in enhancing pathogenicity with patients with underlying diseases compared with a healthy population. Design or methods: Samples are identified by strain culture, polymerase chain reaction (PCR) virulence identification, and whole genome sequencing. Results: A middle-aged man was diagnosed with cytotoxin-producing and nontoxin V. cholerae non-O1/non-O139 serogroups. Although lacking the CT toxin encoded by ctxAB gene, the pathogenesis of cholera relies on the synergistic action of many other genes, especially virulence genes. Conclusions: This case suggested that the laborers engaging in agricultural production are at potential risk of V. cholerae infection by exposure of open wounds to contaminated water . However, epidemiological investigation should focus on the objective cause of the change of working environment. Furthermore, common diseases can possibly enhance the virulence of non-O1/non-O139 serogroups by attacking the tight junction of small intestinal epithelial cells, further triggering bacteremia, a process that may lead to death within 48–72 hours, which requires great attention.

Accurate wall-to-wall estimation of forest crown cover is critical for a wide range of ecological studies. Notwithstanding the increasing use of UAVs in forest canopy mapping, the ultrahigh-resolution UAV imagery requires an appropriate procedure to separate the contribution of understorey from overstorey vegetation, which is complicated by the spectral similarity between the two forest components and the illumination environment. In this study, we investigated the integration of deep learning and the combined data of imagery and photogrammetric point clouds for boreal forest canopy mapping. The procedure enables the automatic creation of training sets of tree crown (overstorey) and background (understorey) data via the combination of UAV images and their associated photogrammetric point clouds and expands the applicability of deep learning models with self-supervision.Based on the UAV images with different overlap levels of 12 conifer forest plots that are categorized into “I”, “II” and “III” complexity levels according to illumination environment, we compared the self-supervised deep learning-predicted canopy maps from original images with manual delineation data and found an average intersection of union (IoU) larger than 0.9 for “complexity I” and “complexity II” plots and larger than 0.75 for “complexity III” plots. The proposed method was then compared with three classical image segmentation methods (i.e., maximum likelihood, Kmeans, and Otsu) in the plot-level crown cover estimation, showing outperformance in overstorey canopy extraction against other methods. The proposed method was also validated against wall-to-wall and pointwise crown cover estimates using UAV LiDAR and in situ digital cover photography (DCP) benchmarking methods. The results showed that the model-predicted crown cover was in line with the UAV LiDAR method (RMSE of 0.06) and deviate from the DCP method (RMSE of 0.18). We subsequently compared the new method and the commonly used UAV structure-from-motion (SfM) method at varying forward and lateral overlaps over all plots and a rugged terrain region, yielding results showing that the method-predicted crown cover was relatively insensitive to varying overlap (largest bias of less than 0.15), whereas the UAV SfM-estimated crown cover was seriously affected by overlap and decreased with decreasing overlap. In addition, canopy mapping over rugged terrain verified the merits of the new method, with no need for a detailed digital terrain model (DTM). The new method is recommended to be used in various image overlaps, illuminations, and terrains due to its robustness and high accuracy. This study offers opportunities to promote forest ecological applications (e.g., leaf area index estimation) and sustainable management (e.g., deforestation).

XPS, GPC, FT-IR, and GC-MS analyses were conducted on corn straw tar and 70# petroleum asphalt. The results indicate that the sulfur content in corn straw tar is lower than that in petroleum asphalt, potentially mitigating the volatilization of harmful substances upon substituting petroleum asphalt. This finding serves as evidence for the substantial presence of phenolic substances in corn straw tar. Upon employing the BOX-Behnken response surface analysis and utilizing resin yield as the evaluation index, the significance of three factors was established as follows: reaction time > phenol molar ratio > straw tar content. Based on the secondary multiple regression model, the optimal conditions for synthetic resin production are a phenolic mole ratio of 0.8, a reaction time of 125 min, and a straw tar dosage of 10 %. An assessment of resin viscosity at different VI temperatures reveals that corn stover tar can partially replace phenol and formaldehyde in the condensation reaction. Additionally, viscosity improvement is observed at elevated temperatures. Thermal gravimetric(TG) spectroscopy indicates lower mass loss in B-PF resin at high temperatures compared to PF resin or corn stover tar. In the evaluation of biological bitumen performance, it is discerned that the mixing amount of the prepared biological bitumen should be controlled at approximately 10 % of its performance. This ensures optimal efficacy without adversely affecting the performance of petroleum bitumen., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Authors declare that they have no competing financial interests or personal relationships that could influent the work reported in this paper. Author(s) agree that the copyright for their article is transferred to their publisher if and when the article is accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints, photographic reproductions, microform or any other reproductions of similar nature and translations. No part of this publication may be reproduced, stored in retrieval system or transmitted in any form or by any means, photocopying, electronic, recording, etc., (Copyright © 2024 Elsevier B.V. All rights reserved.)