Proteomic analysis of a spring wheat cultivar in response to prolonged cold stress

Cold represents one of the major abiotic factors influencing plant growth and development worldwide. We analysed the long-term responsiveness of an Iranian spring wheat (cv. Kohdasht) to cold from a proteomic point of view, in order to unravel the molecular mechanisms helping a cold-sensitive cultivar to survive exposure to suboptimal temperatures. Plants were grown at 20 or 4°C until entering the reproductive stage and a cross-comparison on the leaf proteomes was performed. Quantitative analyses on protein alterations occurring upon low-temperature exposure showed a reinforcement in ascorbate recycling (dehydroascorbate reductase, ascorbate peroxidase) and protein processing (proteasome subunit, cysteine proteinase), as well as the accumulation of the enzyme devoted to tetrapyrrole resynthesis (glutamate semialdehyde aminomutase). In contrast, among proteins down-regulated after cold stress, we could identify some key Krebs cycle enzymes (isocitrate dehydrogenase, malate dehydrogenase), together with many photosynthesis-related proteins (oxygen-evolving complex proteins, ATP synthase subunits, ferredoxin NADPH oxidoreductase and some Calvin cycle enzymes). Physiological and biochemical parameters (such as shoot apex dissection, chlorophyll, proline and sugar content determination) sustained proteomics findings allowing the present research to contribute to the current knowledge on these long-term responses, which may be crucial to stress adaptation under field conditions.     

改进的QGA-ELM算法水稻叶面积指数反演模型

为了通过植被指数（VI）准确、可靠的获取不同施肥梯度、不同品种的水稻叶面积指数（LAI）,提出了一种基于改进的QGA-ELM算法应用于水稻LAI反演。首先通过8折交叉验证确定极限学习机(ELM)最佳的隐含层神经元个数与隐含层激活函数类型,再通过引入组合动态旋转角策略、单点混沌交叉操作、混沌变异操作、确定性选择策略、量子灾变操作对量子遗传算法（QGA）进行改进,最后使用改进后的QGA算法优化ELM神经网络输入层到隐含层的连接权值和隐含层的阈值。为了验证该模型普适性和有效性,依次建立多元线性回归、BP、ELM、QGA-ELM、改进的QGA-ELM算法5种模型,并在不同数据集上进行反演效果比较,结果表明：（1）对比QGA-ELM算法和改进的QGA-ELM算法进化过程,改进的算法能有效提升模型寻优能力,避免算法早熟,且能寻得更优结果。（2）对比五种算法在不同数据集上的反演效果,验证了NDVI,RVI与LAI之间主要为非线性关系,且ELM神经网络模型反演效果要优于BP神经网络模型和多元线性回归模型。（3）对比五种算法在不同数据集上的反演效果,改进的QGA-ELM算法绝大部分情况下拥有最高的反演精度和最低的误差,改进后的算法反演精度得到了明显提升,泛化性能也得到了增强。（4）改进的QGA-ELM算法在各种施肥梯度上均具有最高反演精度和最低误差,且精度较高,能为不同生长状况水稻LAI反演提供依据。（5）五种模型对庆和香LAI反演精度均要高于龙稻18,而改进的QGA-ELM算法在不同水稻品种上依然具有较高的反演精度,且在不同水稻品种上反演精度相差极小,远低于其他四种模型,能很好适应不同水稻品种LAI反演要求,极大提升模型的稳定性性,为不同水稻品种反演提供参考意义。     

低温胁迫下香蕉叶片Chla荧光动力学参量的变化及其品种差异性

低温胁迫下,广东2号香蕉品种和巴西香蕉品种Williams的叶绿素(Chla)荧光动力学参量F_Ⅴ/F_m、ΔF_Ⅴ/F_T以及williams品种的F_IV/F_Ⅴ均呈下降趋势,两个品种的荧光上升半时间T_1/2及广东2号的F_IV/F_Ⅴ呈上升趋势.其中,Williams的F_Ⅴ/F_m、ΔF_Ⅴ/F_T和T_1/2的变化幅度小于广东2号,表明低温胁迫通过抑制光合电子传递速度和PSⅡ无活性中心含量的提高使香蕉叶片PSⅡ原初光能转换效率下降,相同低温对广东2号的影响大于对Williams的影响.F_Ⅴ/F_m和ΔF_Ⅴ/F_T可以作为评价香蕉幼苗抗冷性的物理指标.     

FAAS测定嫩、老荷叶中7种营养元素含量

采用火焰原子吸收光谱法对嫩、老(成熟)荷叶中K、Ca、Mg、Mn、Fe、Zn、Cu7种营养元素的含量进行测定。结果表明,嫩、老荷叶中7种营养元素含量高低顺序一致,依次为KCaMgMnFeZnCu,但成熟荷叶中钙、锰含量明显高于嫩荷叶,约为嫩荷叶的两倍,铁含量稍高于嫩荷叶,钾、镁、锌、铜含量略低于嫩荷叶。其中成熟荷叶中锰的含量为738.04μg.g-1,比一般中药高出近20倍,荷叶的多种药用价值可能与富含微量元素锰有密切关系。     

Near-infrared reflectance spectroscopy as a fast and non-destructive tool to predict foliar organic constituents of several woody species

Near-infrared reflectance spectroscopy (NIRS) was used to estimate N, neutral detergent fibre (NDF), acid detergent fibre (ADF), lignin and cellulose contents in leaves of a heterogeneous group of 17 woody species from the Central Western region of the Iberian Peninsula. The sample set consisted of 182 samples of leaves of deciduous and evergreen species, showing a wide range of concentrations determined by reference methods: 6.60–35.2 g kg⁻¹ (N), 15.5–66.0% (NDF), 10.2–57.3% (ADF), 3.45–27.4% (lignin) and 5.79–31.3% (cellulose). Reflectance spectra, obtained for samples of dried and ground leaves, were recorded as log1/R (R=reflectance) from 1,100 to 2,500 nm. NIRS calibrations were developed using multiple linear (MLR) and partial least-squares (PLSR) regressions, and tested by external validation. Spectral data were transformed to the first and second derivative (1D, 2D). The PLSR method and derivative transformations provided the best statistics and showed lower standard errors of calibration (SEC) and higher coefficients of multiple determination (R ²). In the external validation the standard errors of prediction (SEP) were 0.76 g kg⁻¹ (N), 2.11% (NDF), 1.47% (ADF), 0.85% (lignin) and 0.86% (cellulose). The results obtained show that NIRS is very effective for the estimation of these organic constituents in leaf tissue of woody species. This technique can be used in ecological or ecophysiological studies as an alternative to the more time-consuming standard methods.     

Polynomial kernels for 3-leaf power graph modification problems

A graph G=(V,E) is a 3-leaf power iff there exists a tree T the leaf set of which is V and such that uv∈E iff u and v are at distance at most 3 in T. The 3-leaf power graph edge modification problems, i.e. edition (also known as the closest 3-leaf power), completion and edge-deletion are FPT when parameterized by the size of the edge set modification. However, polynomial kernels were known for none of these three problems. For each of them, we provide kernels with O(k³) vertices that can be computed in linear time. We thereby answer an open problem first mentioned by Dom et al. (2004) [8].     

Protein extraction from Phoenix dactylifera L. leaves, a recalcitrant material, for two-dimensional electrophoresis

This work was aimed at optimizing a protein extraction procedure for date palm (Phoenix dactylifera L.) leaves, a highly recalcitrant plant tissue for 2-DE. Five protein extraction protocols based on different protein precipitation agents (TCA/acetone vs. phenol (Ph) methods) and protein resolubilization methods (physical treatments, e.g., sonication, shaking and/or heating) were tested. Ph/SDS extraction with methanol/ammonium acetate precipitation, followed by DOC preincubation and TCA/acetone precipitation and, finally, solubilization by shaking in rehydration solution was found to be the best protein extraction method. We conclude that DOC with TCA/acetone precipitation step eliminates interfering compounds, thus allowing efficient resolubilization of date palm leaf proteins. This method could be appropriate for proteomic studies such as date palm colonization by entomopathogenic fungi.     

倒伏胁迫下的玉米冠层结构特征变化与光谱响应解析

倒伏胁迫是玉米生产中的主要灾害之一,严重影响玉米的产量、品质和机械收获能力。解析不同倒伏胁迫强度下玉米冠层结构变化规律及其光谱响应机理,是玉米倒伏灾情大范围遥感监测的基础。分别在玉米抽雄期、灌浆中期设置茎倒、茎折、根倒3种强度的倒伏处理,基于田间多频次持续观测实验,分析生育期、倒伏类型对玉米冠层结构动态变化及其自我恢复能力的影响;采用传统光谱变换与连续小波变换方法对倒伏玉米冠层高光谱进行处理,选取叶面积密度（LAD）为玉米倒伏冠层结构特征指标,筛选叶面积密度最佳敏感波段和小波系数,基于随机森林法构建叶面积密度高光谱响应模型,利用未参与建模的实测样本验证模型精度,重点探讨小波分解尺度和光谱分辨率对LAD光谱响应能力的影响规律。研究结果表明：叶面积密度作为单位体积内叶面积总量的冠层结构表征指标,与倒伏胁迫强度具有较好的响应关系,灌浆期的倒伏玉米LAD普遍高于抽雄期,抽雄期LAD整体表现为茎折＞根倒＞茎倒＞未倒伏,灌浆期LAD整体表现为根倒＞茎折＞茎倒＞未倒伏;经连续小波变换后,玉米倒伏冠层光谱对玉米倒伏LAD的响应能力普遍优于传统光谱变换,随着小波分解尺度的增加,LAD与敏感波段的相关性越强,其中10尺度相关系数最高,达0.74;连续小波变换所构建的模型精度普遍优于传统光谱变换,其中由原始光谱小波变换后构建的LAD响应模型精度最高,检验样本的R2为0.811,RMSE为1.763,表明连续小波变换技术可凸显和利用冠层光谱中的细微信息。因此,叶面积密度可有效定量表征不同倒伏胁迫程度的玉米冠层结构变化特征,连续小波变换能有效提升冠层光谱对倒伏玉米结构变化的响应能力,基于随机森林法构建的倒伏玉米叶面积密度诊断模型具有较高的精度和稳定性,可为区域尺度的夏玉米倒伏灾情遥感监测提供先验知识。     

Leaf flutter by torsional galloping: Experiments and model

When wind blows on trees, leaves flutter. The induced motion is known to affect biological functions at the tree scale such as photosynthesis. This paper presents an experimental and theoretical study of the aeroelastic instability leading to leaf flutter. Experiments in a wind tunnel are conducted on ficus leaves (Ficus Benjamina) and artificial leaves. We show that stability and flutter domains are separated by a well-defined limit depending on leaf orientation and wind speed. This limit is also theoretically predicted through a stability analysis of the leaf motion.     

不同含水量条件下树种叶片光谱差异分析

叶片含水量是影响树种光谱特征的一个重要因素,探索不同叶片含水量条件下相同树种叶片光谱特征的变化规律及相同叶片含水量条件下不同树种叶片的光谱差异,不仅是高光谱植被遥感信息识别的关键,也为研究植被光谱随着叶片含水量变化产生的差异提供理论支撑。利用地物光谱仪对6个树种叶片进行观测,获取了不同含水量叶片的反射光谱,同时进行一阶微分光谱变换,从而分析不同叶片含水量条件下各树种叶片的光谱变化特征,对比了相同叶片含水量条件下不同树种叶片的光谱差异,探索高光谱遥感识别树种叶片含水量的可能波段。结果表明：各树种叶片光谱都随着叶片含水量的改变而产生较大差异,但变化规律各有不同;相同叶片含水量条件下,不同树种叶片的光谱在部分波长范围差异较大,为实现树种高精度识别提供了可能。本研究旨在为植被叶片光谱库的构建以及植被的高光谱识别提供理论支撑和基础数据。