Early blight (EB), caused by the pathogen Alternaria solani, is a major threat to global potato and tomato production. Early and accurate diagnosis of this disease is therefore important. In this study, we conducted a loop-mediated isothermal amplification (LAMP) assay, as well as conventional polymerase chain reaction (PCR), nested PCR, and quantitative real-time PCR (RT-qPCR) assays to determine which of these techniques was less time consuming, more sensitive, and more accurate. We based our assays on sequence-characterized amplified regions of the histidine kinase gene with an accession number (FJ424058). The LAMP assay provided more rapid and accurate results, amplifying the target pathogen in less than 60 min at 63°C, with 10-fold greater sensitivity than conventional PCR. Nested PCR was 100-fold more sensitive than the LAMP assay and 1000-fold more sensitive than conventional PCR. qPCR was the most sensitive among the assays evaluated, being 10-fold more sensitive than nested PCR for the least detectable genomic DNA concentration (100 fg). The LAMP assay was more sensitive than conventional PCR, but less sensitive than nested PCR and qPCR; however, it was simpler and faster than the other assays evaluated. Despite of the sensitivity, LAMP assay provided higher specificity than qPCR. The LAMP assay amplified A. solani artificially, allowing us to detect naturally infect young potato leaves, which produced early symptoms of EB. The LAMP assay also achieved positive amplification using diluted pure A. solani culture instead of genomic DNA. Hence, this technique has greater potential for developing quick and sensitive visual detection methods than do other conventional PCR strategies for detecting A. solani in infected plants and culture, permitting early prediction of disease and reducing the risk of epidemics.

Machine metaphor understanding is one of the major topics in NLP. Most of the recent attempts consider it as classification or sequence tagging task. However, few types of research introduce the rich linguistic information into the field of computational metaphor by leveraging powerful pre-training language models. We focus a novel reading comprehension paradigm for solving the token-level metaphor detection task which provides an innovative type of solution for this task. We propose an end-to-end deep metaphor detection model named DeepMet based on this paradigm. The proposed approach encodes the global text context (whole sentence), local text context (sentence fragments), and question (query word) information as well as incorporating two types of part-of-speech (POS) features by making use of the advanced pretraining language model. The experimental results by using several metaphor datasets show that our model achieves competitive results in the second shared task on metaphor detection.

PurposeThe outbreak of the novel COVID-19 virus has spread throughout the world, causing unprecedented disruption to not only China's agricultural trade but also the world's agricultural trade at large. This paper attempts to provide a preliminary analysis of the impact of the COVID-19 pandemic on China's agricultural importing and exporting from both short- and long-term perspectives.Design/methodology/approachThis study seeks to analyze how the outbreak of COVID-19 could potentially impact China's agricultural trade. With respect to exports, the authors have pinpointed major disruptive factors arising from the pandemic which have affected China's agricultural exports in both the short and long term; in doing so, we employ scenario analysis which simulates potential long-term effects. With regard to imports, possible impacts of the pandemic regarding the prospects of food availability in the world market are investigated. Using scenario analysis, the authors estimate the potential change in China's food market—especially meat import growth—in light of the implementation of the newly signed Sino-US Economic and Trade Agreement (SUETA).FindingsThe results show that China's agricultural exports have been negatively impacted in the short-term, mostly due to the disruption of the supply chain. In the long term, dampened external demand and potential imposition of non-tariff trade barriers (NTBs) will exert more profound and lasting negative effects on China's agricultural export trade. On the other hand, despite panic buying and embargoing policies from some exporting and importing countries, the world food availability and China's food import demand are still optimistic. The simulation results indicate that China's import of pork products, in light of COVID-19 and the implementation of SUETA, would most likely see a sizable climb in quantity, but a lesser climb in terms of value.Originality/valueAgricultural trade in China has been a focal-point of attention in recent years, with new challenges slowing exports and increasing dependence on imports for food security. The outbreak of the COVID-19 pandemic adds significant uncertainty to agricultural trade, giving rise to serious concerns regarding its potential impact. By exploring the impact of the unprecedented pandemic on China's agricultural trade, this study should contribute to a better understanding of the still-evolving pandemic and shed light on pertinent policy implications.

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