室内植物表型平台及性状鉴定研究进展和展望

Plant phenomics is under rapid development in recent years, a research field that is progressing towards integration, scalability, multi-perceptivity and high-throughput analysis. Through combining remote sensing, Internet of Things (IoT), robotics, computer vision, and artificial intelligence techniques such as machine learning and deep learning, relevant research methodologies, biological applications and theoretical foundation of this research domain have been advancing speedily in recent years. This article first introduces the current trends of plant phenomics and its related progress in China and worldwide. Then, it focuses on discussing the characteristics of indoor phenotyping and phenotypic traits that are suitable for indoor experiments, including yield, quality, and stress related traits such as drought, cold and heat resistance, salt stress, heavy metals, and pests. By connecting key phenotypic traits with important biological questions in yield production, crop quality and Stress-related tolerance, we associated indoor phenotyping hardware with relevant biological applications and their plant model systems, for which a range of indoor phenotyping devices and platforms are listed and categorised according to their throughput, sensor integration, platform size, and applications. Additionally, this article introduces existing data management solutions and analysis software packages that are representative for phenotypic analysis. For example, ISA-Tab and MIAPPE ontology standards for capturing metadata in plant phenotyping experiments, PHIS and CropSight for managing complicated datasets, and Python or MATLAB programming languages for automated image analysis based on libraries such as OpenCV, Scikit-Image, MATLAB Image Processing Toolbox. Finally, due to the importance of extracting meaningful information from big phenotyping datasets, this article pays extra attention to the future development of plant phenomics in China, with suggestions and recommendations for the integration of multi-scale phenotyping data to increase confidence in research outcomes, the cultivation of cross-disciplinary researchers to lead the next-generation plant research, as well as the collaboration between academia and industry to enable world-leading research activities in the near future

Amylopectin chain length distribution in grains of japonica rice as affected by nitrogen fertilizer and genotype

This study investigated the chain length distribution (CLD) of two japonica rice cultivars under six nitrogen (N) treatments by high performance size exclusion chromatography, with the aims to elucidate the effect of N on rice quality and its biological mechanism. Results showed significant influence of N on CLD. In comparison with low N rate, high N lowered the percentage of short amylopectin branches. Fitting with the CLD model of Wu-Gilbert, it suggested that relative activity of SBE to SS was lower at high N rate, thus producing fewer short amylopectin branches. Comparison of CLD between N rates and between cultivars revealed that decrease in short amylopectin branches or the relative ratio of short to long amylopcetin branches correlated with increase in flour gelatinization temperatures (T, T, and T) and decrease in pasting values (except PaT) and amylose-lipid gelatinization temperatures. In addition, quality traits of Wuyujing3, a cultivar with premium eating quality, expressed stably across N treatments compared with the high-yielding cultivar Wuyunjing7

基于Scopus的植物表型组学研究进展分析

Bibliometric analyses are capable of demonstrating the history and the tendency of scientific and technological development. This article aims to use big scientific data to explore the present status of plant phenomics, based on which sound recommendations could be provided for the development of this emerging research domain. [Methods] Based on academic outputs such as research publications, citations, collaborations, research areas, academic organizations, and authors retrieved from the Scopus database between 2013 and September 2018, statistical analysis tools such as SciVal and CiteSpace 5.0 were applied to quantitatively visualize the development and tendency of plant phenotyping, plant phenomics, and related research areas. [Results] This Scopus-based research has retrieved 20 953 articles that are related to plant phenotyping, plant phenomics, and related applications in plant research, with a total citation of 217 105 and 2.0% of them are TOP1% highly cited papers. According to total citations, the TOP10 countries are the United States, China, Germany, the United Kingdom, France, Japan, Australia, Spain, Canada, and the Netherlands. The TOP10 research organizations based on total citations are Chinese Academy of Sciences (CAS), Institut National de la Recherche Agronomique (INRA), the US Department of Agriculture, Centre National de la Recherche Scientifique (CNRS), Chinese Academy of Agricultural Sciences, Cornell University, Spanish National Research Council, University of California at Davis, Universite Paris-Sacly, and Wageningen University & Research. The scholar with the most academic outputs is Alisdair Robert Fernie at the Koch Planck Institute of Molecular Plant Physiology, Germany. He has published 58 papers using plant cellular phenotypes and was cited 1 246 times. At present, plant phenomics research has focused on a number of plant species, including Arabidopsis, rice, wheat, corn, tomato and soybean. [Conclusion] As an emerging research domain, plant phenomics requires interdisciplinary efforts to integrate agriculture, cultivation, breeding, and other plant biological research with computing sciences. In particular, high-throughput image analysis and related data analysis has become an important research theme at the present stage, with the topical saliency index reaches 98.8%, a very high relevance score

Three-dimensional petrographical investigations on borehole rock samples: a comparison between X-ray computed- and neutron tomography

Technical difficulties associated with excavation works in tectonized geological settings are frequent. They comprise instantaneous and/or delayed convergence, sudden collapse of gallery roof and/or walls, outpouring of fault-filling materials and water inflows. These phenomena have a negative impact on construction sites and their safety. In order to optimize project success, preliminary studies on the reliability of rock material found on site are needed. This implies in situ investigations (surface mapping, prospective drilling, waterflow survey, etc.) as well as laboratory investigations on rock samples (permeability determination, moisture and water content, mineralogy, petrography, geochemistry, mechanical deformation tests, etc.). A set of multiple parameters are then recorded which permit better insight on site conditions and probable behavior during excavation. Because rock formations are by nature heterogeneous, many uncertainties remain when extrapolating large-scale behavior of the rock mass from analyses of samples order of magnitudes smaller. Indirect large-scale field investigations (e.g. geophysical prospecting) could help to better constrain the relationships between lithologies at depth. At a much smaller scale, indirect analytical methods are becoming more widely used for material investigations. We discuss in this paper X-ray computed tomography (XRCT) and neutron tomography (NT), showing promising results for 3D petrographical investigations of the internal structure of opaque materials. Both techniques record contrasts inside a sample, which can be interpreted and quantified in terms of heterogeneity. This approach has the advantage of combining genetic parameters (physico-chemical rock composition) with geometric parameters resulting from alteration or deformation processes (texture and structure). A critical analysis of such 3D analyses together with the results of mechanical tests could improve predictions of short- and long-term behavior of a rock unit. Indirect methods have the advantage of being non-destructive. However, as it is the case with large-scale geophysical surveying, XRCT and NT are affected by several error factors inherent to the interaction of a radiation modality (X-ray or neutron beam) with the atomic structure of the investigated materials. Recorded signals are therefore in particular cases not artifact-free and need to be corrected in a subsequent stage of data processin

Human Urinary Epithelial Cells as a Source of Engraftable Hepatocyte-Like Cells Using Stem Cell Technology

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4,4′-Dibromo-7,7′-dimeth­oxy-1,1′-spiro­biindane

In the title compound, C19H18Br2O2, the dihedral angle between the two benzene rings of the spiro­biindane molecule is 70.44 (8)°. In the crystal, mol­ecules are inter­connected along the c axis by C—H⋯O hydrogen bonds and π–π stacking [centroid–centroid distance = 3.893 (2) Å] inter­actions, forming an infinite chain structure. The chains are further inter­connected through another set of C—H⋯O hydrogen bonds, forming layers approximately parallel to the bc plane

Mapping Water, Energy and Carbon Footprints Along Urban Agglomeration Supply Chains

China's urban population will increase by 268 million from 2010 to 2030, with the consumption of a large number of resource-intensive products. Quantitative analysis of the environmental impacts (water, energy and carbon) of urban agglomerations can make trade-offs among water conservation, energy use, climate change mitigation, and urban development. In this study, a multi-layer water-energy-carbon production path analysis (MWPPA) model is developed for identifying the key final demands, sectors and supply chain paths of the Pearl River Delta urban agglomeration (PUA). Results show that, water, energy and carbon-emission intensities respectively reduced by 27.3%, 35.6% and 27.6% in 2015, compared to the levels in 2012. More than half of the water-energy-carbon (WEC) footprints are export-driven, where Guangzhou, Shenzhen and Foshan dominate the WEC footprints of PUA. Results also disclose that Shenzhen is the main recipient of water-energy, while Jiangmen and Huizhou are the main providers of water and energy, respectively. Policy makers are suggested that each industry actively integrate into global value chains in order to leverage its comparative advantage, and Huizhou should take full advantage of its fossil base to form a complete industry chain from the R&D end to the production end around the energy industry.publishedVersio