Ministry of Agriculture and Rural Affairs

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

The stationary life of plants has led to the evolution of a complex gridded antioxidant defence system constituting numerous enzymatic components, playing a crucial role in overcoming various stress conditions. Mainly, these plant enzymes are superoxide dismutase (SOD), catalase (CAT), peroxidase (POX), glutathione peroxidase (GPX), glutathione reductase (GR), glutathione S-transferases (GST), ascorbate peroxidase (APX), monodehydroascorbate reductase (MDHAR), and dehydroascorbate reductase (DHAR), which work as part of the antioxidant defence system. These enzymes together form a complex set of mechanisms to minimise, buffer, and scavenge the reactive oxygen species (ROS) efficiently. The present review is aimed at articulating the current understanding of each of these enzymatic components, with special attention on the role of each enzyme in response to the various environmental, especially abiotic stresses, their molecular characterisation, and reaction mechanisms. The role of the enzymatic defence system for plant health and development, their significance, and cross-talk mechanisms are discussed in detail. Additionally, the application of antioxidant enzymes in developing stress-tolerant transgenic plants are also discussed.

Alternaria leaf spot, Brown spot, Mosaic, Grey spot, and Rust are five common types of apple leaf diseases that severely affect apple yield. However, the existing research lacks an accurate and fast detector of apple diseases for ensuring the healthy development of the apple industry. This paper proposes a deep learning approach that is based on improved convolutional neural networks (CNNs) for the real-time detection of apple leaf diseases. In this paper, the apple leaf disease dataset (ALDD), which is composed of laboratory images and complex images under real field conditions, is first constructed via data augmentation and image annotation technologies. Based on this, a new apple leaf disease detection model that uses deep-CNNs is proposed by introducing the GoogLeNet Inception structure and Rainbow concatenation. Finally, under the hold-out testing dataset, using a dataset of 26,377 images of diseased apple leaves, the proposed INAR-SSD (SSD with Inception module and Rainbow concatenation) model is trained to detect these five common apple leaf diseases. The experimental results show that the INAR-SSD model realizes a detection performance of 78.80% mAP on ALDD, with a high-detection speed of 23.13 FPS. The results demonstrate that the novel INAR-SSD model provides a high-performance solution for the early diagnosis of apple leaf diseases that can perform real-time detection of these diseases with higher accuracy and faster detection speed than previous methods.

Like many other crops, the cultivated peanut (Arachis hypogaea L.) is of hybrid origin and has a polyploid genome that contains essentially complete sets of chromosomes from two ancestral species. Here we report the genome sequence of peanut and show that after its polyploid origin, the genome has evolved through mobile-element activity, deletions and by the flow of genetic information between corresponding ancestral chromosomes (that is, homeologous recombination). Uniformity of patterns of homeologous recombination at the ends of chromosomes favors a single origin for cultivated peanut and its wild counterpart A. monticola. However, through much of the genome, homeologous recombination has created diversity. Using new polyploid hybrids made from the ancestral species, we show how this can generate phenotypic changes such as spontaneous changes in the color of the flowers. We suggest that diversity generated by these genetic mechanisms helped to favor the domestication of the polyploid A. hypogaea over other diploid Arachis species cultivated by humans.

Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.

Akkermansia muciniphila (A. muciniphila), an intestinal symbiont colonizing in the mucosal layer, is considered to be a promising candidate as probiotics. A. muciniphila is known to have an important value in improving the host metabolic functions and immune responses. Moreover, A. muciniphila may have a value in modifying cancer treatment. However, most of the current researches focus on the correlation between A. muciniphila and diseases, and little is known about the causal relationship between them. Few intervention studies on A. muciniphila are limited to animal experiments, and limited studies have explored its safety and efficacy in humans. Therefore, a critical analysis of the current knowledge in A. muciniphila will play an important foundation for it to be defined as a new beneficial microbe. This article will review the bacteriological characteristics and safety of A. muciniphila, as well as its causal relationship with metabolic disorders, immune diseases and cancer therapy.

In the whole life process, many factors including external and internal factors affect plant growth and development. The morphogenesis, growth, and development of plants are controlled by genetic elements and are influenced by environmental stress. Transcription factors contain one or more specific DNA-binding domains, which are essential in the whole life cycle of higher plants. The AP2/ERF (APETALA2/ethylene-responsive element binding factors) transcription factors are a large group of factors that are mainly found in plants. The transcription factors of this family serve as important regulators in many biological and physiological processes, such as plant morphogenesis, responsive mechanisms to various stresses, hormone signal transduction, and metabolite regulation. In this review, we summarized the advances in identification, classification, function, regulatory mechanisms, and the evolution of AP2/ERF transcription factors in plants. AP2/ERF family factors are mainly classified into four major subfamilies: DREB (Dehydration Responsive Element-Binding), ERF (Ethylene-Responsive-Element-Binding protein), AP2 (APETALA2) and RAV (Related to ABI3/VP), and Soloists (few unclassified factors). The review summarized the reports about multiple regulatory functions of AP2/ERF transcription factors in plants. In addition to growth regulation and stress responses, the regulatory functions of AP2/ERF in plant metabolite biosynthesis have been described. We also discussed the roles of AP2/ERF transcription factors in different phytohormone-mediated signaling pathways in plants. Genomic-wide analysis indicated that AP2/ERF transcription factors were highly conserved during plant evolution. Some public databases containing the information of AP2/ERF have been introduced. The studies of AP2/ERF factors will provide important bases for plant regulatory mechanisms and molecular breeding.

Abstract Human Footprint, the pressure imposed on the eco-environment by changing ecological processes and natural landscapes, is raising worldwide concerns on biodiversity and ecological conservation. Due to the lack of spatiotemporally consistent datasets of Human Footprint over a long temporal span, many relevant studies on this topic have been limited. Here, we mapped the annual dynamics of the global Human Footprint from 2000 to 2018 using eight variables that reflect different aspects of human pressures. The accuracy assessment revealed a good agreement between our mapped results and the previously developed datasets in different years. We found more than two million km 2 of wilderness (i.e., regions with Human Footprint values below one) were lost over the past two decades. The biome dominated by mangroves experienced the most significant loss (i.e., above 5%) of wilderness, likely attributed to intensified human activities in coastal areas. The derived annual and spatiotemporally consistent global Human Footprint can be a fundamental dataset for many relevant studies about human activities and natural resources.

Consequences of drought stress in crop production systems are perhaps more deleterious than other abiotic stresses under changing climatic scenarios. Regulations of physio-biochemical responses of plants under drought stress can be used as markers for drought stress tolerance in selection and breeding. The present study was conducted to appraise the performance of three different maize hybrids (Dong Dan 80, Wan Dan 13, and Run Nong 35) under well-watered, low, moderate and SD conditions maintained at 100, 80, 60, and 40% of field capacity, respectively. Compared with well-watered conditions, drought stress caused oxidative stress by excessive production of reactive oxygen species (ROS) which led to reduced growth and yield formation in all maize hybrids; nevertheless, negative effects of drought stress were more prominent in Run Nong 35. Drought-induced osmolyte accumulation and strong enzymatic and non-enzymatic defense systems prevented the severe damage in Dong Dan 80. Overall performance of all maize hybrids under drought stress was recorded as: Dong Dan 80 > Wan Dan 13 > Run Nong 35 with 6.39, 7.35, and 16.55% yield reductions. Consequently, these biochemical traits and differential physiological responses might be helpful to develop drought tolerance genotypes that can withstand water-deficit conditions with minimum yield losses.

The Brassica genus encompasses three diploid and three allopolyploid genomes, but a clear understanding of the evolution of agriculturally important traits via polyploidy is lacking. We assembled an allopolyploid Brassica juncea genome by shotgun and single-molecule reads integrated to genomic and genetic maps. We discovered that the A subgenomes of B. juncea and Brassica napus each had independent origins. Results suggested that A subgenomes of B. juncea were of monophyletic origin and evolved into vegetable-use and oil-use subvarieties. Homoeolog expression dominance occurs between subgenomes of allopolyploid B. juncea, in which differentially expressed genes display more selection potential than neutral genes. Homoeolog expression dominance in B. juncea has facilitated selection of glucosinolate and lipid metabolism genes in subvarieties used as vegetables and for oil production. These homoeolog expression dominance relationships among Brassicaceae genomes have contributed to selection response, predicting the directional effects of selection in a polyploid crop genome.

Abstract Land surface temperature (LST) is a crucial parameter that reflects land–atmosphere interaction and has thus attracted wide interest from geoscientists. Owing to the rapid development of Earth observation technologies, remotely sensed LST is playing an increasingly essential role in various fields. This review aims to summarize the progress in LST estimation algorithms and accelerate its further applications. Thus, we briefly review the most‐used thermal infrared (TIR) LST estimation algorithms. More importantly, this review provides a comprehensive collection of the widely used TIR‐based LST products and offers important insights into the uncertainties in these products with respect to different land cover conditions via a systematic intercomparison analysis of several representative products. In addition to the discussion on product accuracy, we address problems related to the spatial discontinuity, spatiotemporal incomparability, and short time span of current LST products by introducing the most effective methods. With the aim of overcoming these challenges in available LST products, much progress has been made in developing spatiotemporal seamless LST data, which significantly promotes the successful applications of these products in the field of surface evapotranspiration and soil moisture estimation, agriculture drought monitoring, thermal environment monitoring, thermal anomaly monitoring, and climate change. Overall, this review encompasses the most recent advances in TIR‐based LST and the state‐of‐the‐art of applications of LST products at various spatial and temporal scales, identifies critical further research needs and directions to advance and optimize retrieval methods, and promotes the application of LST to improve the understanding of surface thermal dynamics and exchanges.

Abstract Recently, research on antioxidants has become increasingly active in various fields. Antioxidants react through free radical or molecular oxygen quenching, being capable to either delay or inhibit the oxidation processes that occur under the influence of molecular oxygen or reactive oxygen species. Accordingly, assays developed to evaluate the antioxidant activity of food constituents vary. Therefore, to investigate the antioxidant activity of chemical(s), choosing an adequate assay based on the properties of chemical(s) is critical. Antioxidant assays may be broadly classified as electron transfer (ET)‐based assays and hydrogen atom transfer (HAT)‐based assays. ET‐based assays include ABTS assay, DPPH assay, ferrous oxidation‐xylenol orange assay, ferric thiocyanate assay, ferric reducing/antioxidant power assay, potassium ferricyanide reducing power assay, and cupric reducing antioxidant power assay. HAT‐based assays include oxygen radical absorbance capacity assay, total peroxyl radical‐trapping antioxidant parameter assay, thiobarbituric acid assay, β‐carotene bleaching assay, and cellular antioxidant activity assay. In this guideline, assays used recently were selected for extended discussion, including the mechanisms underlying each assay as well as the practice of antioxidant capacity assessment.

The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.

Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food science and engineering. To our knowledge, this review is the first in the food domain. In this paper, we provided a brief introduction of deep learning and detailedly described the structure of some popular architectures of deep neural networks and the approaches for training a model. We surveyed dozens of articles that used deep learning as the data analysis tool to solve the problems and challenges in food domain, including food recognition, calories estimation, quality detection of fruits, vegetables, meat and aquatic products, food supply chain, and food contamination. The specific problems, the datasets, the preprocessing methods, the networks and frameworks used, the performance achieved, and the comparison with other popular solutions of each research were investigated. We also analyzed the potential of deep learning to be used as an advanced data mining tool in food sensory and consume researches. The result of our survey indicates that deep learning outperforms other methods such as manual feature extractors, conventional machine learning algorithms, and deep learning as a promising tool in food quality and safety inspection. The encouraging results in classification and regression problems achieved by deep learning will attract more research efforts to apply deep learning into the field of food in the future.

Abstract In a healthy body, reactive oxygen species (ROS) and antioxidants remain balanced. When the balance is broken toward an overabundance of ROS, oxidative stress appears and may lead to oocyte aging. Oocyte aging is mainly reflected as the gradual decrease of oocyte quantity and quality. Here, we aim to review the relationship between oxidative stress and oocyte aging. First, we introduced that the defective mitochondria, the age‐related ovarian aging, the repeated ovulation, and the high‐oxygen environment were the ovarian sources of ROS in vivo and in vitro. And we also introduced other sources of ROS accumulation in ovaries, such as overweight and unhealthy lifestyles. Then, we figured that oxidative stress may act as the “initiator” for oocyte aging and reproductive pathology, which specifically causes follicular abnormally atresia, abnormal meiosis, lower fertilization rate, delayed embryonic development, and reproductive disease, including polycystic ovary syndrome and ovary endometriosis cyst. Finally, we discussed current strategies for delaying oocyte aging. We introduced three autophagy antioxidant pathways like Beclin‐VPS34‐Atg14, adenosine 5‘‐monophosphate (AMP)‐activated protein kinase/mammalian target of rapamycin (AMPK/mTOR), and p62‐Keap1‐Nrf2. And we also describe the different antioxidants used to combat oocyte aging. In addition, the hypoxic (5% O 2 ) culture environment for oocytes avoiding oxidative stress in vitro. So, this review not only contribute to our general understanding of oxidative stress and oocyte aging but also lay the foundations for the therapies to treat premature ovarian failure and oocyte aging in women.

Abstract Missing heritability in genome-wide association studies defines a major problem in genetic analyses of complex biological traits 1,2 . The solution to this problem is to identify all causal genetic variants and to measure their individual contributions 3,4 . Here we report a graph pangenome of tomato constructed by precisely cataloguing more than 19 million variants from 838 genomes, including 32 new reference-level genome assemblies. This graph pangenome was used for genome-wide association study analyses and heritability estimation of 20,323 gene-expression and metabolite traits. The average estimated trait heritability is 0.41 compared with 0.33 when using the single linear reference genome. This 24% increase in estimated heritability is largely due to resolving incomplete linkage disequilibrium through the inclusion of additional causal structural variants identified using the graph pangenome. Moreover, by resolving allelic and locus heterogeneity, structural variants improve the power to identify genetic factors underlying agronomically important traits leading to, for example, the identification of two new genes potentially contributing to soluble solid content. The newly identified structural variants will facilitate genetic improvement of tomato through both marker-assisted selection and genomic selection. Our study advances the understanding of the heritability of complex traits and demonstrates the power of the graph pangenome in crop breeding.

Anthocyanins are one of the most widespread families of natural pigments in the plant kingdom. Their health beneficial effects have been documented in many in vivo and in vitro studies. This review summarizes the most recent literature regarding the health benefits of anthocyanins and their molecular mechanisms. It appears that several signaling pathways, including mitogen-activated protein kinase, nuclear factor κB, AMP-activated protein kinase, and Wnt/β-catenin, as well as some crucial cellular processes, such as cell cycle, apoptosis, autophagy, and biochemical metabolism, are involved in these beneficial effects and may provide potential therapeutic targets and strategies for the improvement of a wide range of diseases in future. In addition, specific anthocyanin metabolites contributing to the observed in vivo biological activities, structure-activity relationships as well as additive and synergistic efficacy of anthocyanins are also discussed.

Timely monitoring of crop lands is important in order to make agricultural activities more sustainable, as well as ensuring food security. The use of Earth Observation (EO) data allows crop monitoring at a range of spatial scales, but can be hampered by limitations in the data. Crop growth modelling, on the other hand, can be used to simulate the physiological processes that result in crop development. Data assimilation (DA) provides a way of blending the monitoring properties of EO data with the predictive and explanatory abilities of crop growth models. In this paper, we first provide a critique of both the advantages and disadvantages of both EO data and crop growth models. We use this to introduce a solid and robust framework for DA, where different DA methods are shown to be derived from taking different assumptions in solving for the a posteriori probability density function (pdf) using Bayes’ rule. This treatment allows us to provide some recommendation on the choice of DA method for particular applications. We comment on current computational challenges in scaling DA applications to large spatial scales. Future areas of research are sketched, with an emphasis on DA as an enabler for blending different observations, as well as facilitating different approaches to crop growth models. We have illustrated this review with a large number of examples from the literature.

Abstract Water lilies belong to the angiosperm order Nymphaeales. Amborellales, Nymphaeales and Austrobaileyales together form the so-called ANA-grade of angiosperms, which are extant representatives of lineages that diverged the earliest from the lineage leading to the extant mesangiosperms 1–3 . Here we report the 409-megabase genome sequence of the blue-petal water lily ( Nymphaea colorata ). Our phylogenomic analyses support Amborellales and Nymphaeales as successive sister lineages to all other extant angiosperms. The N. colorata genome and 19 other water lily transcriptomes reveal a Nymphaealean whole-genome duplication event, which is shared by Nymphaeaceae and possibly Cabombaceae. Among the genes retained from this whole-genome duplication are homologues of genes that regulate flowering transition and flower development. The broad expression of homologues of floral ABCE genes in N. colorata might support a similarly broadly active ancestral ABCE model of floral organ determination in early angiosperms. Water lilies have evolved attractive floral scents and colours, which are features shared with mesangiosperms, and we identified their putative biosynthetic genes in N. colorata . The chemical compounds and biosynthetic genes behind floral scents suggest that they have evolved in parallel to those in mesangiosperms. Because of its unique phylogenetic position, the N. colorata genome sheds light on the early evolution of angiosperms.

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.