Axenic in vitro cultivation and genome diploidization of the moss Vesicularia montagnei for horticulture utilization

Mosses are widely used in the establishment of greenery. However, little research has been conducted to choose a suitable species or improve their performance for this application. In our study, we examined Vesicularia montagnei (V. montagnei), a robust moss that is widely distributed in temperate, subtropical, and tropical Asia with varying environmental conditions. Axenic cultivation system of V. montagnei was developed on modified BCD medium, which enabled its propagation and multiplication in vitro. In this axenic cultivation environment, several diploid V. montagnei lines with enhancement of rhizoid system were generated through artificial induction of diploidization. Transcriptomic analysis revealed that several genes responsible for jasmonic acid (JA) biosynthesis and signaling showed significant higher expression levels in the diploid lines compared to the wild type. These results are consistent with the increasement of JA content in the diploid lines. Our establishment of the axenic cultivation method may provide useful information for further study of other Vesicularia species. The diploid V. montagnei lines with improved rhizoid system may hold promising potential for greenery applications. Additionally, our study sheds light on the biosynthesis and functions of JA in the early landed plants

A Synthetic Plasmid Toolkit for Shewanella oneidensis MR-1

Shewanella oneidensis MR-1 is a platform microorganism for understanding extracellular electron transfer (EET) with a fully sequenced and annotated genome. In comparison to other model microorganisms such as Escherichia coli, the available plasmid parts (such as promoters and replicons) are not sufficient to conveniently and quickly fine-tune the expression of multiple genes in S. oneidensis MR-1. Here, we constructed and characterized a plasmid toolkit that contains a set of expression vectors with a combination of promoters, replicons, antibiotic resistance genes, and an RK2 origin of transfer (oriT) cassette, in which each element can be easily changed by fixed restriction enzyme sites. The expression cassette is also compatible with BioBrick synthetic biology standards. Using green fluorescent protein (GFP) as a reporter, we tested and quantified the strength of promoters. The copy number of different replicons was also measured by real-time quantitative PCR. We further transformed two compatible plasmids with different antibiotic resistance genes into the recombinant S. oneidensis MR-1, enabling control over the expression of two different fluorescent proteins. This plasmid toolkit was further used for overexpression of the MtrCAB porin-c-type cytochrome complex in the S. oneidensis ΔmtrA strain. Tungsten trioxide (WO3) reduction and microbial fuel cell (MFC) assays revealed that the EET efficiency was improved most significantly when MtrCAB was expressed at a moderate level, thus demonstrating the utility of the plasmid toolkit in the EET regulation in S. oneidensis. The plasmid toolkit developed in this study is useful for rapid and convenient fine-tuning of gene expression and enhances the ability to genetically manipulate S. oneidensis MR-1

Engineering Microbial Consortia for High-Performance Cellulosic Hydrolyzates-Fed Microbial Fuel Cells

Microbial fuel cells (MFCs) are eco-friendly bio-electrochemical reactors that use exoelectrogens as biocatalyst for electricity harvest from organic biomass, which could also be used as biosensors for long-term environmental monitoring. Glucose and xylose, as the primary ingredients from cellulose hydrolyzates, is an appealing substrate for MFC. Nevertheless, neither xylose nor glucose can be utilized as carbon source by well-studied exoelectrogens such as Shewanella oneidensis. In this study, to harvest the electricity by rapidly harnessing xylose and glucose from corn stalk hydrolysate, we herein firstly designed glucose and xylose co-fed engineered Klebsiella pneumoniae-S. oneidensis microbial consortium, in which K. pneumoniae as the fermenter converted glucose and xylose into lactate to feed the exoelectrogens (S. oneidensis). To produce more lactate in K. pneumoniae, we eliminated the ethanol and acetate pathway via deleting pta (phosphotransacetylase gene) and adhE (alcohol dehydrogenase gene) and further constructed a synthesis and delivery system through expressing ldhD (lactate dehydrogenase gene) and lldP (lactate transporter gene). To facilitate extracellular electron transfer (EET) of S. oneidensis, a biosynthetic flavins pathway from Bacillus subtilis was expressed in a highly hydrophobic S. oneidensis CP-S1, which not only improved direct-contacted EET via enhancing S. oneidensis adhesion to the carbon electrode but also accelerated the flavins-mediated EET via increasing flavins synthesis. Furthermore, we optimized the ratio of glucose and xylose concentration to provide a stable carbon source supply in MFCs for higher power density. The glucose and xylose co-fed MFC inoculated with the recombinant consortium generated a maximum power density of 104.7 ± 10.0 mW/m2, which was 7.2-folds higher than that of the wild-type consortium (12.7 ± 8.0 mW/m2). Lastly, we used this synthetic microbial consortium in the corn straw hydrolyzates-fed MFC, obtaining a power density 23.5 ± 6.0 mW/m2

Small Target Detection Algorithm for UAV Aerial Images Based on Improved YOLOv7-tiny

ObjectiveUAVs provide advantages such as easy control, low cost, and good performance, and efficiently perform tasks in diverse sites and complex environments. UAV aerial image target detection is widely applied in practical scenarios, including urban transportation, military reconnaissance, and smart agriculture. This study proposes a small target detection algorithm for UAV aerial images using a ConvMixer detection head based on the improved YOLOv7-tiny to address the problems of missed detection and false detection caused by significant variations in target scale, densely distributed small-sized targets and complex backgrounds in UAV aerial images.MethodsFirst, the activation function LeakyReLU is replaced with SiLU to compensate for the limited nonlinear expression of LeakyReLU and to enhance convergence speed and model generalization during training. Second, to strengthen the feature extraction capability for multi-scale targets and improve the detection of small targets, a small-target detection layer is designed, leading to a tiny-target detection head that increases the model receptive field and better addresses the scale variance problem caused by drastic target size changes. In addition, the ConvMixer layer is integrated into the prediction head; the depthwise and pointwise convolutions in ConvMixer capture the spatial and channel relationships in the feature information, improving the processing capability for small targets. Finally, the coupled detection head of YOLOv7-tiny is replaced with a more efficient decoupled head, which separates feature channels for localization and classification tasks and enhances both classification and localization accuracy. Regarding experiments, ablation experiments are designed from two directions to comprehensively verify the effectiveness of each improvement. Comparative experiments are also conducted to assess and analyze the detection performance of the improved algorithm against other algorithms.This study mainly addresses the following aspectsConclusionsThis study mainly addresses and improves the issues of missed and false detections caused by large-scale variations, dense small target distributions, and complex backgrounds in UAV aerial images. Key contributions include enhancing the model’s feature extraction capabilities, providing more accurate localization and classification, and improving small target detection. However, limitations remain: 1) Some missed detections still occur for small targets with minimal pixel information and insufficient features to distinguish them from the background. 2) A balance between detection accuracy and real-time performance has not yet been achieved. The model’s parameter count and computational complexity require reduction. Future research will focus on further improving the detection of very small targets and optimizing the model for lightweight applications.;Results and Discussions1) The network structure of the improved algorithm is proposed, and the principles and components of each improvement are introduced. Based on the YOLOv7-tiny network, the LeakyReLU activation function in the convolution block CBL is replaced by the SiLU activation function. A small target detection layer is introduced at the neck of the network, and a prediction head is incorporated. Several ConvMixer layers are also integrated into the end of the backbone network and the detection head. Finally, the efficient decoupled head structure is adopted for target prediction. All these enhancements to the baseline form the improved YOLOv7-tiny algorithm network structure. 2) Ablation experiments are designed to verify the effectiveness of each modification. This includes, firstly, adding a single improved module to the original YOLOv7-tiny algorithm to observe its impact and, secondly, removing individual modules from the final improved YOLOv7-tiny-SFCE model to evaluate their effect. Ten sets of ablation experiments are conducted under identical conditions. Results indicate that introducing the efficient decoupled head leads to the most significant accuracy improvement, increasing mAP by 1.1%. Removing the fourth small target detection head results in the most obvious performance degradation, reducing detection accuracy by 2.4%. 3) Comparative experiments are conducted to verify the comprehensive performance of the improved algorithm. More than ten recently proposed advanced algorithms are selected for comparison in terms of AP and mAP values across ten target categories. Results show that the proposed algorithm achieves the highest mAP value of 40.9% and performs best in detecting the categories pedestrian, people, car, and motor. Among these, the pedestrian, people, and motor categories show especially strong detection performance. 4) The detection performance of the improved algorithm is verified in real-world scenarios through comparative analysis. Detection results are demonstrated in various conditions, including sparse and dense distributions and day and night scenarios. Five images featuring dense targets, minimal targets, dark scenes, occluded targets, and complex backgrounds are randomly selected from the Visdrone2021 test challenge set to evaluate detection performance in UAV aerial images. Comparative visual detection results with the baseline YOLOv7-tiny show that the proposed algorithm significantly improves the identification of multi-scale small targets and reduces both missed and false detections