Research and prospects of environmental DNA (eDNA) for detection of invasive aquatic species in East Asia

The issue of biological invasions in aquatic ecosystems is becoming increasingly severe in the contemporary world. Due to the lack of monitoring and management systems for aquatic invasive species, the difficulty in identifying aquatic invasive species, and the limited effectiveness of conventional control methods in aquatic environments, biological control in water bodies is comparatively more challenging than other types of interventions. In recent years, environmental DNA (eDNA) survey methods have rapidly developed in various fields, such as biological monitoring, community ecology, paleoenvironmental research, conservation biology, and invasion ecology, due to their unique advantages of being rapid, sensitive, efficient, and non-invasive. Because of these characteristics, this innovative molecular approach has gained wider acceptance and is being increasingly utilized for the detection of biological diversity in aquatic environments. Furthermore, it has emerged as a novel technology to address the pressing and significant issue of aquatic invasive species in the vast freshwater and marine resources of the East Asian region. This paper summarizes a variety of literature sources to summarize the major aquatic invasive species in East Asian countries and the current application status of eDNA technology in their survey processes. Using China as a case study, it expounds on the prospective incorporation of the 4E strategy with eDNA technology for the surveillance of biological invasions. Furthermore, it explores the potential prospects of eDNA technology in species diversity management and policy formulation, offering theoretical guidance for establishing aquatic invasive species monitoring systems. From a technological standpoint, the integration of eDNA technology with the 4E strategy holds significant potential for application, thereby offering a promising reference for the formulation of policies related to the management of aquatic biological invasions and biodiversity

Metabolomics and Transcriptomics Reveal the Response Mechanisms of Mikania micrantha to Puccinia spegazzinii Infection

Mikania micrantha is one of the worst invasive species globally and can cause significant negative impacts on agricultural and forestry economics, particularly in Asia and the Pacific region. The rust Puccinia spegazzinii has been used successfully as a biological control agent in several countries to help manage M. micrantha. However, the response mechanisms of M. micrantha to P. spegazzinii infection have never been studied. To investigate the response of M. micrantha to infection by P. spegazzinii, an integrated analysis of metabolomics and transcriptomics was performed. The levels of 74 metabolites, including organic acids, amino acids, and secondary metabolites in M. micrantha infected with P. spegazzinii, were significantly different compared to those in plants that were not infected. After P. spegazzinii infection, the expression of the TCA cycle gene was significantly induced to participate in energy biosynthesis and produce more ATP. The content of most amino acids, such as L-isoleucine, L-tryptophan and L-citrulline, increased. In addition, phytoalexins, such as maackiain, nobiletin, vasicin, arachidonic acid, and JA-Ile, accumulated in M. micrantha. A total of 4978 differentially expressed genes were identified in M. micrantha infected by P. spegazzinii. Many key genes of M. micrantha in the PTI (pattern-triggered immunity) and ETI (effector-triggered immunity) pathways showed significantly higher expression under P. spegazzinii infection. Through these reactions, M. micrantha is able to resist the infection of P. spegazzinii and maintain its growth. These results are helpful for us to understand the changes in metabolites and gene expression in M. micrantha after being infected by P. spegazzinii. Our results can provide a theoretical basis for weakening the defense response of M. micrantha to P. spegazzinii, and for P. spegazzinii as a long-term biological control agent of M. micrantha

Genetic structure and insecticide resistance characteristics of fall armyworm populations invading China

The rapid wide-scale spread of fall armyworm (Spodoptera frugiperda) has caused serious crop losses34 globally. However, differences in the genetic background of subpopulations and the mechanisms of rapid adaptation behind the invasion are still not well understood. Here we report the assembly of a 390.38-M chromosome-level genome of fall armyworm using Pacific Bioscience (PacBio) and Hi-C sequencing technologies with scaffold N50 of 12.7 M consisting of 22260 annotated protein-coding genes. Genome-wide resequencing of 103 samples from 16 provinces in China revealed that the fall armyworm population comprises a complex inter-strain hybrid, mainly with the corn-strain genetic background and less of the rice-strain, which highlights the inaccuracy of strain identification using mitochondrial or Triosephosphate isomerase (Tpi) genes. Analysis of genes related to pesticide- and Bt-resistance showed that the risk of fall armyworm developing resistance to conventional pesticides is very high. Laboratory bioassay results showed that insects invading China carry resistance to organophosphate and pyrethroid pesticides, but are sensitive to genetically modified maize expressing Bacillus thuringiensis (Bt) toxins Cry1Ab in field experiments. Additionally, two mitochondrial fragments are inserted into the nuclear genome, and the insertion event occurred after the differentiation of the two strains. This study represents a valuable advance toward improving management strategies for fall armyworm

Water Chlorophyll a Estimation Using UAV-Based Multispectral Data and Machine Learning

Chlorophyll a (chl-a) concentration is an important parameter for evaluating the degree of water eutrophication. Monitoring it accurately through remote sensing is thus of great significance for early warnings of water eutrophication, and the inversion of water quality from UAV images has attracted more and more attention. In this study, a regression method to estimate chl-a was proposed; it used a small multispectral UAV to collect data and took the vegetation indices as intermediate variables. For this purpose, ten monitoring points were selected in Erhai Lake, China, and two months of monitoring and data collection were conducted during a cyanobacterial bloom period. Finally, 155 sets of valid data were obtained. The imaging data were obtained using a multispectral UAV, water samples were collected from the lake, and the chl-a concentration was obtained in the laboratory. Then, the images were preprocessed to extract the information from different wavebands. The univariate regression of each vegetation index and the regression using band information were used for comparative analysis. Four machine learning algorithms were used to build the model: support vector machine (SVM), random forest (RF), extreme learning machine (ELM), and convolutional neural network (CNN). The results showed that the effect of estimating the chl-a concentration via multiple regression using vegetation indices was generally better than that via regression with a single vegetation index and original band information. The CNN model obtained the best results (R2 = 0.7917, RMSE = 8.7660, and MRE = 0.2461). This study showed the reliability of using multiple regression based on vegetation indices to estimate the chl-a of surface water

Piezoelectric and ferroelectric properties of Na0.5Bi4.5Ti4O15–BaTiO3 composite ceramics with Mg doping

As one of the representatives of lead-free NBT ceramics, Na0.5Bi4.5Ti4O15 has still attracted much attention due to its excellent dielectric properties and has become the focus of research. However, its piezoelectric properties are far from satisfactory. In order to improve the piezoelectric properties of Na0.5Bi4.5Ti4O15, Na0.5Bi4.5Ti4−xMgxOy–BaTiO3 (NBTM–BT) composite ceramics were synthesized by a conventional mixed oxide route and sintered at 1040∘C through two-step method. We optimized the electrical properties of NBTM–BT by changing the stoichiometric ratio of Mg content and studied its microscopic mechanism. The piezoelectric coefficient (d33) is stable at about 20 pC/N. Moreover, the maximum remanent polarization (2Pr) of the ceramic is 3.08μC/cm2 with the coercive field of 18.01kV/cm. The dielectric constant and dielectric loss for Na0.5Bi4.5Ti3.96Mg0.04–BT composite ceramic were found to be 486 and 0.17 at 10kHz, respectively. The characteristic peaks of BT and Na0.5Bi4.5Ti4O15 can be observed clearly from the X-ray diffraction analysis. SEM analysis showed that all samples were well crystallized. Consequently, the piezoelectric and ferroelectric properties of Na0.5Bi4.5Ti4O15–BT composite ceramic will be enhanced much by Mg doping, which means it has a wider range of applications in electronic devices such as piezoelectric devices

Crystal Structure and Computational Study on Methyl-3-Aminothiophene-2-Carboxylate

Methyl-3-aminothiophene-2-carboxylate (matc) is a key intermediate in organic synthesis, medicine, dyes, and pesticides. Single crystal X-ray diffraction analysis reveals that matc crystallizes in the monoclinic crystal system P21/c space group. Three matc molecules in the symmetric unit are crystallographically different and further linked through the N–H⋯O and N–H⋯N hydrogen bond interactions along with weak C–H⋯S and C–H⋯Cg interactions, which is verified by the three-dimensional Hirshfeld surface, two-dimensional fingerprint plot, and reduced density gradient (RDG) analysis. The interaction energies within crystal packing are visualized through dispersion, electrostatic, and total energies using three-dimensional energy-framework analyses. The dispersion energy dominates in crystal packing. To better understand the properties of matc, electrostatic potential (ESP) and frontier molecular orbitals (FMO) were also calculated and discussed. Experimental and calculation results suggested that amino and carboxyl groups can participate in various inter- and intra-interactions

IPMCNet: A Lightweight Algorithm for Invasive Plant Multiclassification

Invasive plant species pose significant biodiversity and ecosystem threats. Real-time identification of invasive plants is a crucial prerequisite for early and timely prevention. While deep learning has shown promising results in plant recognition, the use of deep learning models often involve a large number of parameters and high data requirements for training. Unfortunately, the available data for various invasive plant species are often limited. To address this challenge, this study proposes a lightweight deep learning model called IPMCNet for the identification of multiple invasive plant species. IPMCNet attains high recognition accuracy even with limited data and exhibits strong generalizability. Simultaneously, by employing depth-wise separable convolutional kernels, splitting channels, and eliminating fully connected layer, the model’s parameter count is lower than that of some existing lightweight models. Additionally, the study explores the impact of different loss functions, and the insertion of various attention modules on the model’s accuracy. The experimental results reveal that, compared with eight other existing neural network models, IPMCNet achieves the highest classification accuracy of 94.52%. Furthermore, the findings suggest that focal loss is the most effective loss function. The performance of the six attention modules is suboptimal, and their insertion leads to a decrease in model accuracy