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The genus Paecilomyces is known for its potential application in the control of pests and diseases; however, its use in agriculture is limited to few species. Research interest in new formulations based on microorganisms for the control of pathogens is growing exponentially; therefore, it is necessary to study new isolates, which may help control diseases effectively, and to examine their compatibility with established agricultural control methods. We analysed in vitro and in vivo the antagonistic capacity of Paecilomyces variotii against seven phytopathogens with a high incidence in different crops, and we examined its compatibility with 24 commercial fungicides. P. variotii was applied in the following pathosystems: B. cinereal-melon, Sclerotinia sclerotiorum-pepper, R. solani-tomato, F. solani-zucchini, P. aphanidermatum-melon, M. melonis-melon, and P. xanthii-zucchini. The results showed strong control effects on M. melonis and P. xanthii, reducing the disease severity index by 78% and 76%, respectively. The reduction in disease severity in the other pathosystems ranged from 29% to 44%. However, application of metabolites alone did not cause any significant effect on mycelial growth of phytopathogens, apart from F. solani, in which up to 12% inhibition was observed in vitro when the extract was applied at a concentration of 15% in the medium. P. variotii was compatible with most of the tested fungicides, and of the 24 fungicides tested at the maximum authorised dose, 6 acted as fungicides, 4 as fungistatics, and the remaining showed inhibition rates ranging from 18.2% to 95.8%. These results indicate that P. variotii is a potential biological control agent to be used against several aerial and soil diseases, thus it should be integrated into modern pest management strategies.
Phytopathogenic fungi secrete chitin deacetylase (CDA) to escape the host's immunological defense during infection. Here, we showed that the deacetylation activity of CDA toward chitin is essential for fungal virulence. Five crystal structures of two representative and phylogenetically distant phytopathogenic fungal CDAs, VdPDA1 from Verticillium dahliae and Pst_13661 from Puccinia striiformis f. sp. tritici, were obtained in ligand-free and inhibitor-bound forms. These structures suggested that both CDAs have an identical substrate-binding pocket and an Asp-His-His triad for coordinating a transition metal ion. Based on the structural identities, four compounds with a benzohydroxamic acid (BHA) moiety were obtained as phytopathogenic fungal CDA inhibitors. BHA exhibited high effectiveness in attenuating fungal diseases in wheat, soybean, and cotton. Our findings revealed that phytopathogenic fungal CDAs share common structural features, and provided BHA as a lead compound for the design of CDA inhibitors aimed at attenuating crop fungal diseases.
A total of 62 bacterial isolates were obtained from Gomsohang mud flat, Mohang mud flat, and Jeju Island, Republic of Korea. Among them, the isolate CNU114001 showed significant antagonistic activity against pathogenic fungi by dual culture method. The isolate CNU114001 was identified as Bacillus amyloliquefaciens by morphological observation and molecular data analysis, including 16SrDNA and gyraseA (gyrA) gene sequences. Antifungal substances of the isolate were extracted and purified by silica gel column chromatography, thin layer chromatography, and high performance liquid chromatography. The heat and UV ray stable compound was identified as iturin, a lipopeptide (LP). The isolate CNU114001 showed broad spectrum activity against 12 phytopathogenic fungi by dual culture method. The semi purified compound significantly inhibits the mycelial growth of pathogenic fungi (Alternaria panax, Botrytis cinera, Colletotrichum orbiculare, Penicillium digitatum, Pyricularia grisea and Sclerotinia sclerotiorum) at 200 ppm concentration. Spore germ tube elongation of Botrytis cinerea was inhibited by culture filtrate of the isolate. Crude antifungal substance showed antagonistic activity against cucumber scleotiorum rot in laboratory, and showed antagonistic activity against tomato gray mold, cucumber, and pumpkin powdery mildew in greenhouse condition.
Plant viruses are natural, self-assembling nanostructures with versatile and genetically programmable shells, making them useful in diverse applications ranging from the development of new materials to diagnostics and therapeutics. Here, we describe the design and synthesis of plant virus nanoparticles displaying peptides associated with two different autoimmune diseases. Using animal models, we show that the recombinant nanoparticles can prevent autoimmune diabetes and ameliorate rheumatoid arthritis. In both cases, this effect is based on a strictly peptide-related mechanism in which the virus nanoparticle acts both as a peptide scaffold and as an adjuvant, showing an overlapping mechanism of action. This successful preclinical testing could pave the way for the development of plant viruses for the clinical treatment of human autoimmune diseases.
Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.
The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.
Respiratory inflammation is caused by an air-mediated disease induced by polluted air, smoke, bacteria, and viruses. The COVID-19 pandemic is also a kind of respiratory disease, induced by a virus causing a serious effect on the lungs, bronchioles, and pharynges that results in oxygen deficiency. Extensive research has been conducted to find out the potent natural products that help to prevent, treat, and manage respiratory diseases. Traditionally, wider floras were reported to be used, such as Morus alba, Artemisia indica, Azadirachta indica, Calotropis gigantea, but only some of the potent compounds from some of the plants have been scientifically validated. Plant-derived natural products such as colchicine, zingerone, forsythiaside A, mangiferin, glycyrrhizin, curcumin, and many other compounds are found to have a promising effect on treating and managing respiratory inflammation. In this review, current clinically approved drugs along with the efficacy and side effects have been studied. The study also focuses on the traditional uses of medicinal plants on reducing respiratory complications and their bioactive phytoconstituents. The pharmacological evidence of lowering respiratory complications by plant-derived natural products has been critically studied with detailed mechanism and action. However, the scientific validation of such compounds requires clinical study and evidence on animal and human models to replace modern commercial medicine.
Automated identification of plant diseases is very important for crop protection. Most automated approaches aim to build classification models based on leaf or fruit images. These approaches usually require the collection and annotation of many images, which is difficult and costly process especially in the case of new or rare diseases. Therefore, in this study, we developed and evaluated several methods for identifying plant diseases with little data. Convolutional Neural Networks (CNNs) are used due to their superior ability to transfer learning. Three CNN architectures (ResNet18, ResNet34, and ResNet50) were used to build two baseline models, a Triplet network and a deep adversarial Metric Learning (DAML) approach. These approaches were trained from a large source domain dataset and then tuned to identify new diseases from few images, ranging from 5 to 50 images per disease. The proposed approaches were also evaluated in the case of identifying the disease and plant species together or only if the disease was identified, regardless of the affected plant. The evaluation results demonstrated that a baseline model trained with a large set of source field images can be adapted to classify new diseases from a small number of images. It can also take advantage of the availability of a larger number of images. In addition, by comparing it with metric learning methods, we found that baseline model has better transferability when the source domain images differ from the target domain images significantly or are captured in different conditions. It achieved an accuracy of 99% when the shift from source domain to target domain was small and 81% when that shift was large and outperformed all other competitive approaches.
Infectious diseases, also known as transmissible or communicable diseases, are caused by pathogens or parasites that spread in communities by direct contact with infected individuals or contaminated materials, through droplets and aerosols, or via vectors such as insects. Such diseases cause ˜17% of all human deaths and their management and control places an immense burden on healthcare systems worldwide. Traditional approaches for the prevention and control of infectious diseases include vaccination programmes, hygiene measures and drugs that suppress the pathogen, treat the disease symptoms or attenuate aggressive reactions of the host immune system. The provision of vaccines and biologic drugs such as antibodies is hampered by the high cost and limited scalability of traditional manufacturing platforms based on microbial and animal cells, particularly in developing countries where infectious diseases are prevalent and poorly controlled. Molecular farming, which uses plants for protein expression, is a promising strategy to address the drawbacks of current manufacturing platforms. In this review article, we consider the potential of molecular farming to address healthcare demands for the most prevalent and important epidemic and pandemic diseases, focussing on recent outbreaks of high-mortality coronavirus infections and diseases that disproportionately affect the developing world.
Most plant pathogens exhibit host specificity but when former barriers to infection break down, new diseases can rapidly emerge. For a number of fungal diseases, there is increasing evidence that hybridization plays a major role in driving host jumps. However, the relative contributions of existing variation versus new mutations in adapting to new host(s) is unclear. Here we reconstruct the evolutionary history of two recently emerged populations of the fungus Pyricularia oryzae that are responsible for two new plant diseases: wheat blast and grey leaf spot of ryegrasses. We provide evidence that wheat blast/grey leaf spot evolved through two distinct mating episodes: the first occurred ~60 years ago, when a fungal individual adapted to Eleusine mated with another individual from Urochloa. Then, about 10 years later, a single progeny from this cross underwent a series of matings with a small number of individuals from three additional host-specialized populations. These matings introduced non-functional alleles of two key host-specificity factors, whose recombination in a multi-hybrid swarm probably facilitated the host jump. We show that very few mutations have arisen since the founding event and a majority are private to individual isolates. Thus, adaptation to the wheat or Lolium hosts appears to have been instantaneous, and driven entirely by selection on repartitioned standing variation, with no obvious role for newly formed mutations.
Commercial biocontrol of microbial plant diseases and plant pests, such as nematodes, requires field-effective formulations. The isolate Pseudomonas chlororaphis O6 is a Gram-negative bacterium that controls microbial plant pathogens both directly and indirectly. This bacterium also has nematocidal activity. In this study, we report on the efficacy of a wettable powder-type formulation of P. chlororaphis O6. Culturable bacteria in the formulated product were retained at above 1 × 108 colony forming units/g after storage of the powder at 25 °C for six months. Foliar application of the diluted formulated product controlled leaf blight and gray mold in tomato. The product also displayed preventative and curative controls for root-knot nematode (Meloidogyne spp.) in tomato. Under laboratory conditions and for commercially grown melon, the control was at levels comparable to that of a standard commercial chemical nematicide. The results indicated that the wettable powder formulation product of P. chlororaphis O6 can be used for control of plant microbial pathogens and root-knot nematodes.
Pestalotiopsis and related genera, including Neopestalotiopsis and Pseudopestalotiopsis have damaged many plants for many decades; however, there is little available information about the fungi on tropical fruit in Thailand. This study isolated and characterized pestalotioid fungi on tropical fruit, investigated host specificity, and screened whether plant extracts could control the fungi. In total, 53 diseased fruit samples were sampled from eight types of fruit trees (jackfruit, rose apple, mangosteen, plum, snake fruit, rambutan, strawberry, and avocado). Based on morphological characteristics, 44 isolates were classified as belonging to pestalotioid taxa. Of these isolates, seven with distinct characteristics were selected for identification using molecular analysis, and six isolates were identified as Neopestalotiopsis and one as Pseudopestalotiopsis. In the cross-inoculation experiment, the isolates exhibited nonhost specificity and could infect at least two host plants. The isolates were used to screen for a potential biocontrol resource using six crude plant extracts (clove, ginger, lemongrass, mangosteen, roselle, and turmeric). All crude extracts except mangosteen could inhibit the growth of Neopestalotiopsis and Pseudopestalotiopsis. Application of crude plant extracts could be a potential treatment to control these diseases on tropical fruit.
Due to the emergence of antibiotic-resistant bacteria in agricultural sector, controlling bacterial plant diseases using antibiotics has become challenging. Researchers have turned to alternative methods, such as using bacteriophages as a biocide for plants instead of antibiotics, to control pathogenic bacterial plant diseases. However, the application of bacteriophages as a biocide in agriculture faces several challenges that may impede its success. In this review article, we discuss the various issues that could lead to the failure of its application. We also propose solutions to address each problem to increase awareness and familiarity before implementing the method to better ensure its success.
The use of plant-derived natural products for the treatment of tropical parasitic diseases often has ethnopharmacological origins. As such, plants grown in temperate regions remain largely untested for novel anti-parasitic activities. We describe here a screen of the PhytoQuest Phytopure library, a novel source comprising over 600 purified compounds from temperate zone plants, against in vitro culture systems for Plasmodium falciparum, Leishmania mexicana, Trypanosoma evansi and T. brucei. Initial screen revealed 6, 65, 15 and 18 compounds, respectively, that decreased each parasite's growth by at least 50% at 1-2 µM concentration. These initial hits were validated in concentration-response assays against the parasite and the human HepG2 cell line, identifying hits with EC50 < 1 μM and a selectivity index of >10. Two sesquiterpene glycosides were identified against P. falciparum, four sterols against L. mexicana, and five compounds of various scaffolds against T. brucei and T. evansi. An L. mexicana resistant line was generated for the sterol 700022, which was found to have cross-resistance to the anti-leishmanial drug miltefosine as well as to the other leishmanicidal sterols. This study highlights the potential of a temperate plant secondary metabolites as a novel source of natural products against tropical parasitic diseases.
Severe acute respiratory syndrome (SARS) is a dangerous infection with pandemic potential. It emerged in 2002 and its aetiological agent, the SARS Coronavirus (SARS-CoV), crossed the species barrier to infect humans, showing high morbidity and mortality rates. No vaccines are currently licensed for SARS-CoV and important efforts have been performed during the first outbreak to develop diagnostic tools. Here we demonstrate the transient expression in Nicotiana benthamiana of two important antigenic determinants of the SARS-CoV, the nucleocapsid protein (N) and the membrane protein (M) using a virus-derived vector or agro-infiltration, respectively. For the M protein, this is the first description of production in plants, while for plant-derived N protein we demonstrate that it is recognized by sera of patients from the SARS outbreak in Hong Kong in 2003. The availability of recombinant N and M proteins from plants opens the way to further evaluation of their potential utility for the development of diagnostic and protection/therapy tools to be quickly manufactured, at low cost and with minimal risk, to face potential new highly infectious SARS-CoV outbreaks.
Airborne fungi and their ecological functions have been largely ignored in plant invasions. In this study, high-throughput sequencing technology was used to characterize the airborne fungi in the canopy air of the invasive weed Ageratina adenophora. Then, representative phytopathogenic strains were isolated from A. adenophora leaf spots and their virulence to A.adenophora as well as common native plants in the invaded range was tested. The fungal alpha diversities were not different between the sampling sites or between the high/low part of the canopy air, but fungal co-occurrences were less common in the high than in the low part of the canopy air. Interestingly, we found that the phytopathogenic Didymellaceae fungi co-occurred more frequently with themselves than with other fungi. Disease experiments indicated that all 5 Didymellaceae strains could infect A. adenophora as well as the 16 tested native plants and that there was large variation in the virulence and host range. Our data suggested that the diverse pathogens in the canopy air might be a disease infection source that weakens the competition of invasive weeds, a novel phenomenon that remains to be explored in other invasive plants.
Proteomics has become one of the most relevant high-throughput technologies. Several approaches have been used for studying, for example, tumor development, biomarker discovery, or microbiology. In this "post-genomic" era, the relevance of these studies has been highlighted as the phenotypes determined by the proteins and not by the genotypes encoding them that is responsible for the final phenotypes. One of the most interesting outcomes of these technologies is the design of new drugs, due to the discovery of new disease factors that may be candidates for new therapeutic targets. To our knowledge, no commercial fungicides have been developed from targeted molecular research, this review will shed some light on future prospects. We will summarize previous research efforts and discuss future innovations, focused on the fight against one of the main agents causing a devastating crops disease, fungal phytopathogens.
Plant Diseases and Pests are a major challenge in the agriculture sector. An accurate and a faster detection of diseases and pests in plants could help to develop an early treatment technique while substantially reducing economic losses. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. In this paper, we present a deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions. Our goal is to find the more suitable deep-learning architecture for our task. Therefore, we consider three main families of detectors: Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN), and Single Shot Multibox Detector (SSD), which for the purpose of this work are called "deep learning meta-architectures". We combine each of these meta-architectures with "deep feature extractors" such as VGG net and Residual Network (ResNet). We demonstrate the performance of deep meta-architectures and feature extractors, and additionally propose a method for local and global class annotation and data augmentation to increase the accuracy and reduce the number of false positives during training. We train and test our systems end-to-end on our large Tomato Diseases and Pests Dataset, which contains challenging images with diseases and pests, including several inter- and extra-class variations, such as infection status and location in the plant. Experimental results show that our proposed system can effectively recognize nine different types of diseases and pests, with the ability to deal with complex scenarios from a plant's surrounding area.
Polyphenols have long been recognized as health-promoting entities, including beneficial effects on cardiovascular disease, but their reputation has been boosted recently following a number of encouraging clinical studies in multiple chronic pathologies, that seem to validate efficacy. Health benefits of polyphenols have been linked to their well-established powerful antioxidant activity. This review aims to provide comprehensive and up-to-date knowledge on the current therapeutic status of polyphenols having sufficient heed towards the treatment of cardiovascular diseases. Furthermore, data about the safety profile of highly efficacious polyphenols has also been investigated to further enhance their role in cardiac abnormalities. Evidence is presented to support the action of phenolic derivatives against cardiovascular pathologies by following receptors and signaling pathways which ultimately cause changes in endogenous antioxidant, antiplatelet, vasodilatory, and anti-inflammatory activities. In addition, in vitro antioxidant and pre-clinical and clinical experiments on anti-inflammatory as well as immunomodulatory attributes of polyphenols have revealed their role as cardioprotective agents. However, an obvious shortage of in vivo studies related to dose selection and toxicity of polyphenols makes these compounds a suitable target for clinical investigations. Further studies are needed for the development of safe and potent herbal products against cardiovascular diseases. The novelty of this review is to provide comprehensive knowledge on polyphenols safety and their health claims. It will help researchers to identify those moieties which likely exert protective and therapeutic effects towards cardiovascular diseases.
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