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Microalgae and cyanobacteria are diverse groups of organisms with great potential to benefit societies across the world. These organisms are currently used in food, feed, pharmaceutical and cosmetic industries. In addition, a variety of novel compounds are being isolated. Commercial production of photosynthetic microalgae and cyanobacteria requires cultivation on a large scale with high throughput. However, scaling up production from lab-based systems to large-scale systems is a complex and potentially costly endeavor. In this review, we summarise all aspects of large-scale cultivation, including aims of cultivation, species selection, types of cultivation (ponds, photobioreactors, and biofilms), water and nutrient sources, temperature, light and mixing, monitoring, contamination, harvesting strategies, and potential environmental risks. Importantly, we also present practical recommendations and discuss challenges of profitable large-scale systems associated with economical design, effective operation and maintenance, automation, and shortage of experienced phycologists.

Rice is food consumed regularly and is vital for the food security of over half the world’s population. Rice production on a global scale is predicted to rise by 58 to 567 million tonnes (Mt) by 2030. Rice contains a significant number of calories and a wide variety of essential vitamins, minerals, and other nutritional values. Its nutrients are superior to those found in maize, wheat, and potatoes. It is also recognised as a great source of vitamin E and B5 as well as carbohydrates, thiamine, calcium, folate, and iron. Phytic acid and phenols are among the phenolic compounds found in rice, alongside sterols, flavonoids, terpenoids, anthocyanins, tocopherols, tocotrienols, and oryzanol. These compounds have been positively linked to antioxidant properties and have been shown to help prevent cardiovascular disease and diabetes. This review examines recent global rice production, selected varieties, consumption, ending stocks, and the composition of rice grains and their nutritional values. This review also includes a new method of paddy storage, drying, and grading of rice. Finally, the environmental impacts concerning rice cultivation are discussed, along with the obstacles that must be overcome and the current policy directions of rice-producing countries.

Summary Salt stress dramatically impedes plant growth and development as well as crop yield. The apple production regions are reduced every year, because of the secondary salt damage by improper fertilization and irrigation. To expand the cultivation area of apple (Malus domestica) and select salt‐resistant varieties, the mechanism of salt tolerance in apple is necessary to be clarified. The miR156/SPL regulatory module plays key roles in embryogenesis, morphogenesis, life cycle stage transformation, flower formation and other processes. However, its roles in the mechanisms of salt tolerance are unknown. In order to elucidate the mechanism of 156/SPL regulating salt stress in apple, we performed RLM‐5’ RACE and stable genetic transformation technology to verify that both mdm‐MIR156a and MdSPL13 responded to salt stress in apple and that the latter was the target of the former. MIR156a overexpression weakened salt resistance in apple whereas MdSPL13 overexpression strengthened it. A total of 6094 differentially expressed genes relative to nontransgenic apple plants were found by RNA‐Seq analysis of MdSPL13OE. Further verification indicated that MdSPL13 targeted the MdWRKY100 gene promoter. Moreover, MdWRKY100 overexpression enhanced salt tolerance in apple. Our results revealed that the miR156/SPL module regulates salt tolerance by up‐regulating MdWRKY100 in apple. This study is the first to elucidate the mechanism underlying the miRNA network response to salt stress in apple and provides theoretical and empirical bases and genetic resources for the molecular breeding of salt tolerance in apple.

Background Tomato gray leaf spot is a worldwide disease, especially in warm and humid areas. The continuous expansion of greenhouse tomato cultivation area and the frequent introduction of foreign varieties in recent years have increased the severity of the epidemic hazards of this disease in some tomato planting bases annually. This disease is a newly developed one. Thus, farmers generally lack prevention and control experience and measures in production; the disease is often misdiagnosed or not prevented and controlled timely; this condition results in tomato production reduction or crop failure, which causes severe economic losses to farmers. Therefore, tomato gray leaf spot disease should be identified in the early stage, which will be important in avoiding or reducing the economic loss caused by the disease. The advent of the era of big data has facilitated the use of machine learning method in disease identification. Therefore, deep learning method is proposed to realise the early recognition of tomato gray leaf spot. Tomato growers need to develop the app of image detection mobile terminal of tomato gray leaf spot disease to realise real-time detection of this disease. Results This study proposes an early recognition method of tomato leaf spot based on MobileNetv2-YOLOv3 model to achieve a good balance between the accuracy and real-time detection of tomato gray leaf spot. This method improves the accuracy of the regression box of tomato gray leaf spot recognition by introducing the GIoU bounding box regression loss function. A MobileNetv2-YOLOv3 lightweight network model, which uses MobileNetv2 as the backbone network of the model, is proposed to facilitate the migration to the mobile terminal. The pre-training method combining mixup training and transfer learning is used to improve the generalisation ability of the model. The images captured under four different conditions are statistically analysed. The recognition effect of the models is evaluated by the F1 score and the AP value, and the experiment is compared with Faster-RCNN and SSD models. Experimental results show that the recognition effect of the proposed model is significantly improved. In the test dataset of images captured under the background of sufficient light without leaf shelter, the F1 score and AP value are 94.13% and 92.53%, and the average IOU value is 89.92%. In all the test sets, the F1 score and AP value are 93.24% and 91.32%, and the average IOU value is 86.98%. The object detection speed can reach 246 frames/s on GPU, the extrapolation speed for a single 416 × 416 picture is 16.9 ms, the detection speed on CPU can reach 22 frames/s, the extrapolation speed is 80.9 ms and the memory occupied by the model is 28 MB. Conclusions The proposed recognition method has the advantages of low memory consumption, high recognition accuracy and fast recognition speed. This method is a new solution for the early prediction of tomato leaf spot and a new idea for the intelligent diagnosis of tomato leaf spot.

Jujube ( Ziziphus jujuba Mill.), or Chinese date, is the most important species of Rhamnaceae, a large cosmopolitan family, and is one of the oldest cultivated fruit trees in the world. It originates from the middle and lower reaches of the Yellow River, the ‘mother river’ of the Chinese people. It is distributed in at least 48 countries on all continents except Antarctica and is becoming increasingly important, especially in arid and semiarid marginal lands. Based on a systematic analysis of the unique characteristics of jujube, we suggest that it deserves to be recognized as a superfruit. We summarized historical research achievements from the past 3000 years and reviewed recent research advances since 1949 in seven fields, including genome sequencing and application, germplasm resources and systematic taxonomy, breeding and genetics, cultivation theory and techniques, pest control, postharvest physiology and techniques, and nutrition and processing. Based on the challenges facing the jujube industry, we discuss eight research aspects to be focused on in the future.

Summary To expand the cultivation area of apple (Malus×domestica Borkh.) and select resistant varieties by genetic engineering, it is necessary to clarify the mechanism of salt and osmotic stress tolerance in apple. The MdMYB46 transcription factor was identified, and the stress treatment test of MdMYB46‐overexpressing and MdMYB46‐RNAi apple lines indicated that MdMYB46 could enhance the salt and osmotic stress tolerance in apple. In transgenic Arabidopsis and apple, MdMYB46 promoted the biosynthesis of secondary cell wall and deposition of lignin by directly binding to the promoter of lignin biosynthesis‐related genes. To explore whether MdMYB46 could coordinate stress signal transduction pathways to cooperate with the formation of secondary walls to enhance the stress tolerance of plants, MdABRE1A, MdDREB2A and dehydration‐responsive genes MdRD22 and MdRD29A were screened out for their positive correlation with osmotic stress, salt stress and the transcriptional level of MdMYB46. The further verification test demonstrated that MdMYB46 could activate their transcription by directly binding to the promoters of these genes. The above results indicate that MdMYB46 could enhance the salt and osmotic stress tolerance in apple not only by activating secondary cell wall biosynthesis pathways, but also by directly activating stress‐responsive signals.