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The accumulation of high amounts of plastic waste in the environment has raised ecological and health concerns, particularly in croplands, and biological degradation presents a promising approach for the sustainable treatment of this issue. In this study, a polyvinyl chloride (PVC)-degrading bacterium was isolated from farmland soil samples attached to waste plastic, utilizing PVC as the sole carbon source. The circular chromosome of the strain Cbmb3, with a length of 5,768,926 bp, was subsequently sequenced. The average GC content was determined to be 35.45%, and a total of 5835 open reading frames were identified. The strain Cbmb3 was designated as Bacillus toyonensis based on phylogenomic analyses and genomic characteristics. The bioinformatic analysis of the Cbmb3 genome revealed putative genes encoding essential enzymes involved in PVC degradation. Additionally, the potential genomic characteristics associated with phytoprobiotic effects, such as the synthesis of indole acetic acid and secondary metabolite synthesis, were also revealed. Overall, the present study provides the first complete genome of Bacillus toyonensis with PVC-degrading properties, suggesting that Cbmb3 is a potential strain for PVC bioremediation and application.

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Nowadays, the detection of environmental microorganism indicators is essential for us to assess the degree of pollution, but the traditional detection methods consume a lot of manpower and material resources. Therefore, it is necessary for us to make microbial data sets to be used in artificial intelligence. The Environmental Microorganism Image Dataset Seventh Version (EMDS-7) is a microscopic image data set that is applied in the field of multi-object detection of artificial intelligence. This method reduces the chemicals, manpower and equipment used in the process of detecting microorganisms. EMDS-7 including the original Environmental Microorganism (EM) images and the corresponding object labeling files in ".XML" format file. The EMDS-7 data set consists of 41 types of EMs, which has a total of 2,65 images and 13,216 labeled objects. The EMDS-7 database mainly focuses on the object detection. In order to prove the effectiveness of EMDS-7, we select the most commonly used deep learning methods (Faster-Region Convolutional Neural Network (Faster-RCNN), YOLOv3, YOLOv4, SSD, and RetinaNet) and evaluation indices for testing and evaluation. EMDS-7 is freely published for non-commercial purpose at: https://figshare.com/articles/dataset/EMDS-7_DataSet/16869571.

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Discovery of microorganisms and their relevant surface peptides that specifically bind to target materials of interest can be achieved through iterative biopanning-based screening of cellular libraries having high diversity. Recently, microfluidics-based biopanning methods have been developed and exploited to overcome the limitations of conventional methods where controlling the shear stress applied to remove cells that do not bind or only weakly bind to target surfaces is difficult and the overall experimental procedure is labor-intensive. Despite the advantages of such microfluidic methods and successful demonstration of their utility, these methods still require several rounds of iterative biopanning. In this work, a magnetophoretic microfluidic biopanning platform was developed to isolate microorganisms that bind to target materials of interest, which is gold in this case. To achieve this, gold-coated magnetic nanobeads, which only attached to microorganisms that exhibit high affinity to gold, were used. The platform was first utilized to screen a bacterial peptide display library, where only the cells with surface peptides that specifically bind to gold could be isolated by the high-gradient magnetic field generated within the microchannel, resulting in enrichment and isolation of many isolates with high affinity and high specificity toward gold even after only a single round of separation. The amino acid profile of the resulting isolates was analyzed to provide a better understanding of the distinctive attributes of peptides that contribute to their specific material-binding capabilities. Next, the microfluidic system was utilized to screen soil microbes, a rich source of extremely diverse microorganisms, successfully isolating many naturally occurring microorganisms that show strong and specific binding to gold. The results show that the developed microfluidic platform is a powerful screening tool for identifying microorganisms that specifically bind to a target material surface of interest, which can greatly accelerate the development of new peptide-driven biological materials and hybrid organic-inorganic materials.

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Environmental microorganisms (EMs) are ubiquitous around us and have an important impact on the survival and development of human society. However, the high standards and strict requirements for the preparation of environmental microorganism (EM) data have led to the insufficient of existing related datasets, not to mention the datasets with ground truth (GT) images. This problem seriously affects the progress of related experiments. Therefore, This study develops the Environmental Microorganism Dataset Sixth Version (EMDS-6), which contains 21 types of EMs. Each type of EM contains 40 original and 40 GT images, in total 1680 EM images. In this study, in order to test the effectiveness of EMDS-6. We choose the classic algorithms of image processing methods such as image denoising, image segmentation and object detection. The experimental result shows that EMDS-6 can be used to evaluate the performance of image denoising, image segmentation, image feature extraction, image classification, and object detection methods. EMDS-6 is available at the https://figshare.com/articles/dataset/EMDS6/17125025/1.

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In recent years, deep learning has made brilliant achievements in Environmental Microorganism (EM) image classification. However, image classification of small EM datasets has still not obtained good research results. Therefore, researchers need to spend a lot of time searching for models with good classification performance and suitable for the current equipment working environment. To provide reliable references for researchers, we conduct a series of comparison experiments on 21 deep learning models. The experiment includes direct classification, imbalanced training, and hyper-parameters tuning experiments. During the experiments, we find complementarities among the 21 models, which is the basis for feature fusion related experiments. We also find that the data augmentation method of geometric deformation is difficult to improve the performance of VTs (ViT, DeiT, BotNet, and T2T-ViT) series models. In terms of model performance, Xception has the best classification performance, the vision transformer (ViT) model consumes the least time for training, and the ShuffleNet-V2 model has the least number of parameters.

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To not only optimize the hyper-parameters of the classification layer of dense convolutional network with 201 convolutional layers (DenseNet-201) but also use data augmentation processes could enhance the performance of DenseNet-201, and DenseNet-201 is rarely applied to the identifications of the environmental microorganism (EM) images. Hence, this study was to propose the optimally fine-tuned DenseNet-201 (OFTD) with data augmentation to better classify the EM images on Environmental Microorganism Dataset (EMDS). The training dataset was composed of 70% Environmental Microorganism Dataset (EMDS) images and so was mainly used to fit the parameters of convolutional layers of optimally fine-tuned DenseNet-201 (OFTD). Meanwhile, the other EMDS images were considered as the testing dataset and used to qualify the performance of the OFTD. Also, gradient-weighted class activation mapping method (Grad-CAM) was adopted to visually illustrate the dominant features of the EM images. Based on the results, the OFTD model with data augmentation achieved the highest classification accuracy of 98.4%. In this case, so its stability and accuracy were guaranteed. Besides, the optimally fine-tuned classification layer is considered a more efficient method than the data augmentation technique adopted in this study when it comes to the improvement of the performance in DenseNet-201 implemented on EMDS. Grad-CAM highlighted the coarse EM features identified effectively by the OFTD; for example, foot and stalk were considered as the dominated features of Rotifera and Vorticella, respectively. In summary, the proposed OFTD with data augmentation could provide an efficient solution for the EM detection in digital microscope.

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Rapid environmental microorganism (EM) classification under microscopic images would help considerably identify water quality. Because of the development of artificial intelligence, a deep convolutional neural network (CNN) has become a major solution for image classification. Three popular CNNs, referred to as ResNet50, Vgg16, and Inception-v3, were transferred to identify the EM images present on the Environmental Microorganism Dataset (EMDS), and EMAD was the small dataset, which only has 294 EM images with 21 EM classes. Besides data augmentation, optimizing the fully connected layer of CNN, i.e., both optimally fine-tuned neuron number and dropout rate, was adopted to enhance the performance produced by CNN. The discussions on the causes of the accuracy improved by optimization are also provided. The results showed that the Inception-v3 model obtained 84.9% of the accuracy and performed better than the other two famous CNNs. Also, the implement of data augmentation enhanced the performance of Inception-v3 on EMDS. To add to that, the optimized Inception-v3 model archived 90.5% of the accuracy, and this result demonstrated the improvement effect obtained by using genetic algorithm (GA) to optimize the fully connected layer of the Inception-v3. Therefore, the optimize Inception-v3 with data augmentation process obtained the accuracy of 92.9% and improved almost 21% higher than that obtained from the famous Vgg16. In addition, the optimized Inception-v3 would need less neurons, when compared with that of the optimized Vgg16 possibly. This optimized Inception-v3 could provide a solution to the EM classification in microscope with a digital camera system.

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Soil antibiotic resistome and the nitrogen cycle are affected by florfenicol addition to manured soils but their interactions have not been fully described. In the present study, antibiotic resistance genes (ARGs) and nitrogen cycle genes possessed by soil bacteria were characterized using real-time fluorescence quantification PCR (qPCR) and metagenomic sequencing in a short-term (30 d) soil model experiment. Florfenicol significantly changed in the abundance of genes conferring resistance to aminoglycosides, ß-lactams, tetracyclines and macrolides. And the abundance of Sphingomonadaceae, the protein metabolic and nitrogen metabolic functions, as well as NO reductase, nitrate reductase, nitrite reductase and N2O reductase can also be affected by florfenicol. In this way, ARG types of genes conferring resistance to aminoglycosides, ß-lactamases, tetracyclines, colistin, fosfomycin, phenicols and trimethoprim were closely associated with multiple nitrogen cycle genes. Actinobacteria, Chlorobi, Firmicutes, Gemmatimonadetes, Nitrospirae, Proteobacteria and Verrucomicrobia played an important role in spreading of ARGs. Moreover, soil physicochemical properties were important factors affecting the distribution of soil flora. This study provides a theoretical basis for further exploration of the transmission regularity and interference mechanism of ARGs in soil bacteria responsible for nitrogen cycle.

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Polyvinyl alcohol (PVA) is used widely in industry, and associated environmental pollution is a serious problem. Herein, we report a novel, efficient PVA degrader, Stenotrophomonas rhizophila QL-P4, isolated from fallen leaves from a virgin forest in the Qinling Mountains. The complete genome was obtained using single-molecule real-time (SMRT) technology and corrected using Illumina sequencing. Bioinformatics analysis revealed eight PVA/vinyl alcohol oligomer (OVA)-degrading genes. Of these, seven genes were predicted to be involved in the classic intracellular PVA/OVA degradation pathway, and one (BAY15_3292) was identified as a novel PVA oxidase. Five PVA/OVA-degrading enzymes were purified and characterized. One of these, BAY15_1712, a PVA dehydrogenase (PVADH), displayed high catalytic efficiency toward PVA and OVA substrate. All reported PVADHs only have PVA-degrading ability. Most importantly, we discovered a novel PVA oxidase (BAY15_3292) that exhibited higher PVA-degrading efficiency than the reported PVADHs. Further investigation indicated that BAY15_3292 plays a crucial role in PVA degradation in S. rhizophila QL-P4. Knocking out BAY15_3292 resulted in a significant decline in PVA-degrading activity in S. rhizophila QL-P4. Interestingly, we found that BAY15_3292 possesses exocrine activity, which distinguishes it from classic PVADHs. Transparent circle experiments further proved that BAY15_3292 greatly affects extracellular PVA degradation in S. rhizophila QL-P4. The exocrine characteristics of BAY15_3292 facilitate its potential application to PVA bioremediation. In addition, we report three new efficient secondary alcohol dehydrogenases (SADHs) with OVA-degrading ability in S. rhizophila QL-P4; in contrast, only one OVA-degrading SADH was reported previously.IMPORTANCE With the widespread application of PVA in industry, PVA-related environmental pollution is an increasingly serious issue. Because PVA is difficult to degrade, it accumulates in aquatic environments and causes chronic toxicity to aquatic organisms. Biodegradation of PVA, as an economical and environment-friendly method, has attracted much interest. To date, effective and applicable PVA-degrading bacteria/enzymes have not been reported. Herein, we report a new efficient PVA degrader (S. rhizophila QL-P4) that has five PVA/OVA-degrading enzymes with high catalytic efficiency, among which BAY15_1712 is the only reported PVADH with both PVA- and OVA-degrading abilities. Importantly, we discovered a novel PVA oxidase (BAY15_3292) that is not only more efficient than other reported PVA-degrading PVADHs but also has exocrine activity. Overall, our findings provide new insight into PVA-degrading pathways in microorganisms and suggest S. rhizophila QL-P4 and its enzymes have the potential for application to PVA bioremediation to reduce or eliminate PVA-related environmental pollution.

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