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Evolution of electrocardiographic problems and arrhythmias throughout grownup patients using beta-thalassemia significant after a short-term follow-up.
② To make full use of mountainous areas and barren slopes to carry out wild planta tending or planting imitates wild condition. ③ According to the development law and biological characteristics of medicinal plants, the land resources should be developed and used rationally according to local conditions.This can not only meet the requirements of the specific growth environment of Chinese medicinal materials, realize the sustainable development of the Chinese medicinal materials industry, but also increase the economic income of people in mountainous areas, provide scientific and effective solutions for the land use of Chinese medicinal materials, and also have important significance for the protection of wild Chinese medicinal materials.As an environment-friendly agriculture, ecological agriculture of Chinese materia medica(CMM) is being implemented in all parts of the country. Due to the stronger dependence on natural environmental conditions, ecological agriculture of CMM shows obvious regional differences in production practice. More mature CMM ecological planting patterns representative of each region were collected. It was found that common types of patterns in various regions of the country mainly included intercropping,intercropping,rotation planting mode, undergrowth planting mode, wild tending planting mode and landscape ecological planting mode. Based on the Construction Plan of National Dao-di Herbs Production Base(2018-2025) and Chinese Medicine Division, this paper systematically sorts out the pattern of ecological planting of CMM in the 8-avenue medicinal materials production areas according to the varieties and regions. The specific pattern of ecological planting of CMM included the ginseng undergrowth planting pattern in northeastern China, the bionics wild ecological planting of the Forsythia suspensa in northern China, the Fritillaria thunbergii-rice rotation in eastern China, the imitation wild planting pattern under the Polygonatum cyrtonema in central China, the planting pattern of the Fructus amomi under forest in southern China, the Ligusticum chuanxiong-rice rotation pattern in the Southwest, wild tending of Glycyrrhiza uralensis in the Northwest, and rhubarb imitation wild planting pattern in Qinghai-Tibet area. Finally, it is expected to provide reference for the screening and popularization of ecological planting patterns of other CMMs in various distribution areas.The ecological agriculture of Chinese materia medica(CMM) has become the most dynamic and promising new field in the global ecological agriculture. The development of ecological planting of CMM has become the national strategy of Chinese traditional medicine agriculture. It has been highly valued and has flourished throughout the country, and has formed some more mature ecological planting models of CMM. AGI-24512 mw Based on the system level, this paper sorts out the common ecological cultivation patterns of CMM, and obtains five basic patterns landscape pattern at the ecological landscape level, circulation pattern at the ecosystem level, stereo model at the bio-community level, biodiversity patterns at the level of biological populations and well-established models at the level of biological individuals. On this basis, eight common ecological planting techniques of CMM were obtained, includingwild tending techniques, fine agricultural farming techniques, directional cultivation techniques, soil improvement techniques, soil testing and fertilization techniques, mycorrhizal cultivation techniques, green control technology for pests and diseases and facility cultivation techniques.This paper aims to provide theoretical basis for scientific research and popularization and application of CMM ecological planting.This paper analyzed life form, habitats and environmental stresses of medicinal plants and algal fungi collected in Chinese Pharmacopoeia(2015). ①It was found that only 0.94% of the medicinal plants mainly cultivated in field. The most common habitats of medicinal plants are divided into two types those whose natural habitats are forest margins/undergrowth(about 42.53%) and those whose natural habitats are roadside, hillside, wasteland/sand(about 43.78%). The former mainly faces environmental stresses such as weak light, pests and diseases; the latter often faces the main environmental stresses of drought, strong light, ultraviolet radiation, high temperature, low temperature(day and night or annual temperature difference is large), nutrient deficiency, pests and so on. ②Based on analyzing the strategies of medicinal plants to adapt to environmental stresses, it is pointed out that the synthesis and accumulation of secondary metabolites are the most important strategies of medicinal plants to protect against environmental stresses. In the process of long-term adaptation to specific stress, the accumulation of relevant genetic variation and epigenetic inheritance has become an important condition for the formation of quality of medicinal plants. ③It is proposed that "simulative habitat cultivation" has obvious advantages in balancing growth and secondary metabolism and guaranting the quality of traditional Chinese medicinal materials.Significance The use of optofluidic time-stretch flow cytometry enables extreme-throughput cell imaging but suffers from the difficulties of capturing and processing a large amount of data. As significant amounts of continuous image data are generated, the images require identification with high speed. Aim We present an intelligent cell phenotyping framework for high-throughput optofluidic time-stretch microscopy based on the XGBoost algorithm, which is able to classify obtained cell images rapidly and accurately. The applied image recognition consists of density-based spatial clustering of applications with noise outlier detection, histograms of oriented gradients combining gray histogram fused feature, and XGBoost classification. Approach We tested the ability of this framework against other previously proposed or commonly used algorithms to phenotype two groups of cell images. We quantified their performances with measures of classification ability and computational complexity based on AUC and test runtime.
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