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目的/意义人工智能（Artificial Intelligence，AI）技术已在学术和工程应用领域掀起了研究高潮，在地球物理参数和农业气象遥感参数反演方面也表现出了强大的应用潜力。目前大部分AI技术在地学和农学的应用还是“黑箱”，没有物理意义或缺乏可解释性及通用性。为了促进AI在地学和农学的应用和培养交叉学科的人才，本研究提出基于AI耦合物理和统计方法的地球物理参数反演范式理论。方法首先基于物理能量平衡方程进行物理逻辑推理，从理论上构造反演方程组，然后基于物理推导构建泛化的统计方法。通过物理模型模拟获得物理方法的代表性解以及利用多源数据获得统计方法代表性的解作为深度学习的训练和测试数据库，最后利用深度学习进行优化求解。［结果和讨论］判定形成具有通用性和物理可解释的范式条件包括：（1）输入与输出变量（参数）之间必须存在因果关系；（2）输入和输出变量（参数）之间理论上可以构建闭合的方程组（未知数个数少于或等于方程组个数），也就是说输出参数可以被输入参数唯一确定。如果输入参数（变量）和输出参数（变量）之间存在很强的因果关系，则可以直接使用深度学习进行反演。如果输入参数和输出参数之间存在弱相关性，则需要添加先验知识来提高输出参数的反演精度。此外，本研究以农业气象遥感中的关键参数地表温度、发射率、近地表空气温度和大气水汽含量联合反演作为案例对理论进行了证明，分析结果表明本理论是可行的，并且可以辅助优化设计卫星传感器波段组合。结论本理论和判定条件的提出在地球物理参数反演史上具有里程碑意义。

Lesion mimic mutants (LMMs) are plants that spontaneously form lesions without pathogen infection or external stimulus and exhibit resistance to pathogens. Here, a rice LMM was created by ethyl methane sulfonate mutagenesis, named as hpil (hydrogen peroxide induced lesion). Diaminobenzidine and trypan blue staining showed that large amounts of H2O2 were produced and cell death was occurred at and around the parts of lesion mimic in the rice leaves. The phenotype of hpil is controlled by a single recessive gene, localized at a 2 Mb interval on chromosome 2. The data suggested that hpil is a novel LMM with enhanced bacterial and fungal disease resistance, and multiple pathogenesis-related proteins (PRs) were up-regulated. The proteomes of leaves at three positions (different degrees of lesion mimic severity) were characterized in hpil compared with its wild type plant. Differentially expressed proteins were detected by two dimensional difference gel electrophoresis and 274 proteins were identified by MALDI TOF/TOF™. These proteins were related to metabolic process, cellular process and response to stimulus, with mostly down-regulated in hpil leaves. Many of these proteins were related to the Calvin cycle, photosynthetic electron transport chain, glycolysis/gluconeogenesis and phosphonates pathways. Some resistance-related proteins including 14-3-3 proteins, OsPR10 and antioxidases such as peroxidase, superoxide dismutase and ascorbate peroxidase were up-regulated in leaves with lesion mimic. These results provide the foundation for cloning of the target gene and shed light on the mechanism involved in autoimmunity of rice.

OsPR10a is one of the well known pathogenesis-related genes in rice, and is induced by multiple plant hormones and pathogens. However, the underlying transcriptional regulation mechanisms in response to different signals and their crosstalks are still largely unknown. In order to find new players participated in the activation of OsPR10a, we systematically analyzed the basal expression patterns as well as the expression responses of a 2.5 kb OsPR10a promoter in rice transgenic plants after phytohormone and pathogen stimulations. In agreement with the native gene expression, the OsPR10a promoter can drive glucuronidase (GUS) gene expressing in spots of leaf cells, leaf trichomes, lemmas and paleae, germinating embryos, calli and root tips. The leaf expression of OsPR10a::GUS was dramatically increased upon jasmonic acid (JA) and cytokinin (CK) treatments, or challenges of the pathogen Magnaporthe grisea and Xanthomonas oryzae pv. oryzae. Thus, the OsPR10a promoter reported here can faithfully reflect its native gene expression. The effects of several JA and CK responsive OsWRKY genes on the regulation of OsPR10a promoter were then inspected by luciferase transient expression assay, and the JA inducible OsWRKY10 transcription factor was found as a new positive regulator of OsPR10a. However, the key transcription factors of JA and CK signaling pathways, OsMYC2 and B-type response regulators, were not responsible for the activation of OsPR10a promoter. Our findings provided new insights into the regulation of OsPR10a expression during plant-hormone/pathogen interactions, and the OsPR10a reporter system can be useful to unravel novel regulators from both pathogen and host.

We present PandaX-II constraints on candidate WIMP-nucleon effective interactions involving the nucleon or WIMP spin, including, in addition to standard axial spin-dependent (SD) scattering, various couplings among vector and axial currents, magnetic and electric dipole moments, and tensor interactions. The data set corresponding to a total exposure of 54-ton-days is reanalyzed to determine constraints as a function of the WIMP mass and isospin coupling. We obtain WIMP-nucleon cross section bounds of 1.6×10 −41 cm 2 and 9.0×10 −42 cm 2 (90% c.l.) for neutron-only SD and tensor coupling, respectively, for a mass M WIMP ∼40GeV/c 2 . The SD limits are the best currently available for M WIMP >40GeV/c 2 . We show that PandaX-II has reached a sensitivity sufficient to probe a variety of other candidate spin-dependent interactions at the weak scale.

The response of mesophyll conductance (gm) to CO2 plays a key role in photosynthesis and ecosystem carbon cycles under climate change. Despite numerous studies, there is still debate about how gm responds to short‐term CO2 variations. Here we used multiple methods and looked at the relationship between stomatal conductance to CO2 (gsc) and gm to address this aspect. We measured chlorophyll fluorescence parameters and online carbon isotope discrimination (Δ) at different CO2 mole fractions in sunflower (Helianthus annuus L.), cowpea (Vigna unguiculata L.), and wheat (Triticum aestivum L.) leaves. The variable J and Δ based methods showed that gm decreased with an increase in CO2 mole fraction, and so did stomatal conductance. There were linear relationships between gm and gsc across CO2 mole fractions. gm obtained from A‐Ci curve fitting method was higher than that from the variable J method and was not representative of gm under the growth CO2 concentration. gm could be estimated by empirical models analogous to the Ball‐Berry model and the USO model for stomatal conductance. Our results suggest that gm and gsc respond in a coordinated manner to short‐term variations in CO2, providing new insight into the role of gm in photosynthesis modelling. Summary statement: For photosynthesis modelling, the response of gm to short‐term CO2 changes is essential. Using variable J and carbon isotope discrimination methods, we showed that gm decreased with an increase in CO2 mole fraction, which contradicts the assumption of a constant gm in photosynthesis models. [ABSTRACT FROM AUTHOR]

Differential evolution algorithm is a very powerful and recently proposed evolutionary algorithm. Generally, only a mutation operator and predefined parameter values of differential evolution algorithm are utilized to solve various optimization problems, which limits the performance of the algorithm. In this paper, six commonly used mutation operators are divided into three categories according to their own features. A mutation pool is established based on the three categories. A parameter pool with three predefined values is designed. During evolution, three mutation operators are randomly chosen from the three categories, and three parameter values are also randomly selected from the parameter pool. The three groups of mutation operators and parameter values are employed to produce trial vectors. The proposed algorithm makes good use of different mutation operators. Three recently proposed differential evolution variants and three non-differential evolution algorithms are used to make comparisons on the 29 testing functions from CEC. The experimental results have demonstrated that the proposed algorithm is very competitive. The proposed algorithm is utilized to solve three real applications, and the results are superior. [ABSTRACT FROM AUTHOR]