Project description:Pesticides are commonly used in food crop production systems to control crop pests and diseases and ensure maximum yield with high market value. However, the accumulation of these chemical inputs in crop fields increases risks to biodiversity and human health. In addition, people are increasingly seeking foods in which pesticide residues are low or absent and that have been produced in a sustainable fashion. More than half of the world's human population is dependent on rice as a staple food and chemical pesticides to control pests is the dominant paradigm in rice production. In contrast, the use of natural enemies to suppress crop pests has the potential to reduce chemical pesticide inputs in rice production systems. Currently, predators and parasitoids often do not persist in rice production landscapes due to the absence of shelter or nutritional sources. In this study, we modified the existing rice landscape through an eco-engineering technique that aims to increase natural biocontrol agents for crop protection. In this system, planting nectar-rich flowering plants on rice bunds provides food and shelter to enhance biocontrol agent activity and reduce pest numbers, while maintaining grain yield. The abundance of predators and parasitoids and parasitism rates increased significantly in the eco-engineering plots compared to the insecticide-treated and control plots. Moreover, a significantly lower number of principal insect pests and damage symptoms were found in treatments where flowering plants were grown on bunds than in plots where such plants were not grown. This study indicates that manipulating habitat for natural enemies in rice landscapes enhances pest suppression and maintains equal yields while reducing the need for insecticide use in crop fields.
Project description:Lepidopteran insect pests are the main class of pests causing significant damage to crop plant yields. Insecticidal scorpion peptides exhibit toxicity specific for insects. Here, we report that a peptide LMX, optimized from the insect-specific scorpion neurotoxin LqhIT2, showed high levels of activity against rice leaf folder in vitro and in planta. Oral ingestion of LMX protein led to a significant decrease in feeding on rice leaves, repression of larval growth and development, delay in molting, and increase in larval lethality. Compared with LqhIT2 protein, the stability and insecticidal efficacy of LMX was better. Meanwhile, biochemical analysis showed that LMX protein ingestion dramatically decreased ecdysone content in rice leaf folder larvae, and down-regulated enzymatic activities of the detoxification system (α-naphthyl acetate esterase and glutathione S-transferase), the digestive system (tryptase and chymotrypsin), and the antioxidant system (catalase). These changes were tightly correlated with the dosage of LMX protein. Transgene analysis showed that the rate of leaf damage, and the number of damaged tillers and leaves in the transgenic line were greatly reduced relative to wild type plants and empty vector plants. Based on these observations, we propose that the insect-specific scorpion neurotoxin peptide LMX is an attractive and effective alternative molecule for the protection of rice from rice leaf folder.
Project description:Controlled formation of catalytically-relevant states within crystals of complex metalloenzymes represents a significant challenge to structure-function studies. Here we show how electrochemical control over single crystals of [NiFe] hydrogenase 1 (Hyd1) from Escherichia coli makes it possible to navigate through the full array of active site states previously observed in solution. Electrochemical control is combined with synchrotron infrared microspectroscopy, which enables us to measure high signal-to-noise IR spectra in situ from a small area of crystal. The output reports on active site speciation via the vibrational stretching band positions of the endogenous CO and CN- ligands at the hydrogenase active site. Variation of pH further demonstrates how equilibria between catalytically-relevant protonation states can be deliberately perturbed in the crystals, generating a map of electrochemical potential and pH conditions which lead to enrichment of specific states. Comparison of in crystallo redox titrations with measurements in solution or of electrode-immobilised Hyd1 confirms the integrity of the proton transfer and redox environment around the active site of the enzyme in crystals. Slowed proton-transfer equilibria in the hydrogenase in crystallo reveals transitions which are only usually observable by ultrafast methods in solution. This study therefore demonstrates the possibilities of electrochemical control over single metalloenzyme crystals in stabilising specific states for further study, and extends mechanistic understanding of proton transfer during the [NiFe] hydrogenase catalytic cycle.
Project description:This article explores the dissipative control for a class of nonlinear DP-CPS (distributed parameter cyber physical system) within a finite-time interval. By utilizing a Takagi-Sugeno (T-S) fuzzy model to represent the system's nonlinear aspects, the studied system is formulated as a class of fuzzy parabolic partial differential equation (PDE). In order to optimize network resources, both the system state and input signal are subjected to quantization using dynamic quantizers. Subsequently, a dynamic state control strategy is proposed, taking into account potential DoS attack. The finite-time boundedness of the fuzzy parabolic PDE is analyzed, with respect to the influence of quantization, through the construction of an appropriate Lyapunov functional. The article then presents the conditions for finite-time dissipative control design, alongside the adjustment parameters for the dynamic quantizers within the fuzzy closed-loop system. Furthermore, the decoupling of interlinked nonlinear terms in the control design conditions is achieved by using an arbitrary matrix. Finally, an example is provided and the simulation results indicate the effectiveness of the dissipative control method proposed.
Project description:Ecological decision problems, such as those encountered in agriculture, often require managing conflicts between short-term costs and long-term benefits. Dynamic programming is an ideal method for optimally solving such problems but agricultural problems are often subject to additional complexities that produce state spaces intractable to exact solutions. In contrast, look-ahead policies, a class of approximate dynamic programming (ADP) algorithm, may attempt to solve problems of arbitrary magnitude. However, these algorithms focus on a temporally truncated caricature of the full decision problem over a defined planning horizon and as such are not guaranteed to suggest optimal actions. Thus, look-ahead policies may offer promising means of addressing detail-rich ecological decision problems but may not be capable of fully utilizing the information available to them, especially in scenarios where the best short- and long-term solutions may differ. We constructed and applied look-ahead policies to the management of a hypothetical, stage-structured, continually reproducing, agricultural insect pest. The management objective was to minimize the combined costs of management actions and crop damage over a 16-week growing season. The manager could elect to utilize insecticidal sprays or one of six release ratios of male-selecting transgenic insects where the release ratio determines the number of transgenic insects to be released for each wild-type male insect in the population. Complicating matters was the expression of insecticide resistance at non-trivial frequencies in the pest population. We assessed the extent to which look-ahead policies were able to recognize the potential threat of insecticide resistance and successfully integrate insecticides and transgenic releases to capitalize upon their respective benefits. Look-ahead policies were competent at anticipating and responding to ecological and economic information. Policies with longer planning horizons made fewer, better-timed insecticidal sprays and made more frequent transgenic releases, which consequently facilitated lower resistance allele frequencies. However, look-ahead policies were ultimately inefficient resistance managers, and directly responded to resistance only when it was dominant and prevalent. Effective long-term agricultural management requires the capacity to anticipate and respond to the evolution of resistance. Look-ahead policies can accommodate all the information pertinent to making the best long-term decision but may lack the perspective to actually do so.
Project description:MicroRNAs (miRNAs) are a class of endogenous regulatory RNA molecules 21-24 nucleotides in length that act as functional regulators of post-transcriptional repression of messenger RNA. We report the identification and characterization of a conserved miRNA and 171 novel miRNAs in the migratory rice pest Sogatella furcifera by deep sequencing, which were observed to be biased towards female adults of the insect, modulating the functionality and targets of the miRNAs in sex differentiation. A switch in arm usage was also observed in 9 miRNA when compared to the insect ancestor during insect evolution. The miRNA loci showed high 5' fidelity in both miRNA and star species and about 93.4% of WBPH miRNAs conserved within non-planthopper species were homologous with planthopper species. The novel miRNAs identified in this study provide a better understanding of the sRNA and the regulatory role of miRNA in sexual dimorphism and alteration in the expression or function of miRNAs in the rice pest.
Project description:Resistance to toxins in insects is generally thought of as their own genetic trait, but recent studies have revealed that gut microorganisms could mediate resistance by detoxifying phytotoxins and man-made insecticides. By laboratory experiments, we here discovered a striking example of gut symbiont-mediated insecticide resistance in a serious rice pest, Cletus punctiger. The rice bug horizontally acquired fenitrothion-degrading Burkholderia through oral infection and housed it in midgut crypts. Fenitrothion-degradation test revealed that the gut-colonizing Burkholderia retains a high degrading activity of the organophosphate compound in the insect gut. This gut symbiosis remarkably increased resistance against fenitrothion treatment in the host rice bug. Considering that many stinkbug pests are associated with soil-derived Burkholderia, our finding strongly supports that a number of stinkbug species could gain resistance against insecticide simply by acquiring insecticide-degrading gut bacteria.
Project description:Species distribution models (SDMs) are often built upon the "niche conservatism" assumption, such that they ignore the possibility of "evolutionary rescue" and may underestimate species' future range limits under climate change. We select aphids and ladybirds as model species and develop an eco-evolutionary model to explore evolutionary rescue in a predator-prey system under climate change. We model the adaptive change of species' thermal performances, accounting for biotic interactions. Our study suggests that, without considering evolutionary adaptation, the warming climate will result in a reduction in aphid populations and the extinction of ladybirds in large parts of the United States. However, when incorporating evolutionary adaptation into the model, aphids can adapt to climate change, whereas ladybirds demonstrate geographic variation in their evolutionary rescue potential. Specifically, ladybirds in southern regions are more likely to be rescued than those in the north. In certain northern regions, ladybirds do not avoid extinction due to severe warming trends and seasonality of the climate. While higher warming trends do prompt stronger evolutionary changes in phenotype, they also lead to reduced aphid population abundance such that ecology constrains ladybird population growth. Higher seasonality induces an ecological effect by limiting the length of reproductive season, thereby reducing the capacity for evolutionary rescue. Together, these findings reveal the complex interplay between ecological and evolutionary dynamics in the context of evolutionary adaptation to climate change.
Project description:With the increasing pressure on global food security, the effective detection and management of rice pests have become crucial. Traditional pest detection methods are not only time-consuming and labor-intensive but also often fail to achieve real-time monitoring and rapid response. This study aims to address the issue of rice pest detection through deep learning techniques to enhance agricultural productivity and sustainability. The research utilizes the IP102 large-scale rice pest benchmark dataset, publicly released by CVPR in 2019, which includes 9,663 images of eight types of pests, with a training-to-testing ratio of 8:2. By optimizing the YOLOv8 model, incorporating the CBAM (Convolutional Block Attention Module) attention mechanism, and the BiFPN (Bidirectional Feature Pyramid Network) for feature fusion, the detection accuracy in complex agricultural environments was significantly improved. Experimental results show that the improved YOLOv8 model achieved mAP@0.5 and mAP@0.5:0.95 scores of 98.8% and 78.6%, respectively, representing increases of 2.8% and 2.35% over the original model. This study confirms the potential of deep learning technology in the field of pest detection, providing a new technological approach for future agricultural pest management.
Project description:A variety of adverse conditions including drought stress severely affect rice production. Root system plays a critical role in drought avoidance, which is one of the major mechanisms of drought resistance. In this study, we adopted genome-wide association study (GWAS) to dissect the genetic basis controlling various root traits by using a natural population consisting of 529 representative rice accessions. A total of 413 suggestive associations, containing 143 significant associations, were identified for 21 root traits, such as maximum root length, root volume, and root dry weight under normal and drought stress conditions at the maturation stage. More than 80 percent of the suggestive loci were located in the region of reported QTLs for root traits, while about 20 percent of suggestive loci were novel loci detected in this study. Besides, 11 reported root-related genes, including DRO1, WOX11, and OsPID, were found to co-locate with the association loci. We further proved that the association results can facilitate the efficient identification of causal genes for root traits by the two case studies of Nal1 and OsJAZ1. These loci and their candidate causal genes provide an important basis for the genetic improvement of root traits and drought resistance.