环境强光诱导玉簪叶片光抑制的机制

为进一步阐述光抑制的强光诱导和发生机制,该文以喜阴植物玉簪(Hosta spp.)为材料研究其光抑制发生规律及其与环境光强的关系。结果表明,全日照和遮阴条件下玉簪叶片发育分别形成适应强光和弱光的形态特征;与遮阴处理相比,强光下生长的玉簪光合速率和叶绿素含量较低,但两种处理叶片最大光化学效率差异很小,证明强光下植株可以正常生长且光合机构未发生严重的光抑制。将遮阴处生长的植株转移到全日照下,光合速率和最大光化学效率急剧下降;荧光诱导动力学曲线发生明显改变,而且光系统II供体侧和受体侧荧光产量的变化幅度分别达到24.3%和34.2%,表明玉簪由弱光转入强光后光系统II发生不可逆失活,且受体侧受到的伤害较供体侧更严重。因此,作者认为环境光强骤然提高并超过玉簪生长光强时很容易诱导其光合机构发生严重的光抑制。该研究对于理解植物适应光环境的策略以及喜阴植物的优质栽培有重要意义

RAPD分析野生和养殖太湖秀丽白虾的遗传多样性

<正>秀丽白虾(Exopalaemon modestus)广泛分布于我国淡水湖泊及河流中,南北均产。在太湖的虾类组成中,秀丽白虾占50%以上。近年来,由于水质污染和过度捕捞等原因,太湖秀丽白虾的繁衍、生存受到了很大威胁,白虾种质资源保护

Nitrate Measurement in the Ocean Based on Neural Network Model

Nitrate concentration is an important indicator for the marine ecosystem.Compared with laboratory chemical methods such as Cadmium-Reduction method,in-situ nitrate optical sensor is much faster and reagent-free in a long time and continuous monitoring.Partial Least Squares(PLS)method is often used in ultraviolet absorption spectrum modeling,which is difficult to optimize and has low generalization ability.The neural network can compel any no-linear function by any precision,which has high generalization ability in the modeling.A neural network model is established in the in-situ nitrate sensor to measure the nitrate concentration in seawater in which the nitrate concentration range is 30~750mug·L~(-1).Double-hidden layer neural network model is determined to adopt by contrasting performance of single-hidden layer and double-hidden layer to measure nitrate concentration,the input layer is absorption spectrum from 200to 275nm,the output layer is nitrate concentration,and sigmoid function is used as the activation function.Gradient descent method is used to update weighting parameters for the neural network of each layer,after 55 000times iteration,network training is conducted based on the learning rate of 0.26. After validation for the blind test of the model through 8-group randomized validation data,the nitrate concentration using double-hidden layer neural network model is higher in linear correlation to its actual concentration(R~2=0.997)in which the Root Mean Squared Error is 10.864,average absolute error is 8.442mug·L~(-1),average the relative error is 2.8%.Compared with single-hidden layer neural network model,the double-hidden layer neural network model has higher accuracy in which the average relative error is reduced by 4.92%,the Root Mean Squared Error of PLS is 4.58%using the same spectral data,while the mean relative error is 11.470.The result shows that the neural network model is much better than the Partial Least Squares model under certain conditions.It verifies the superiority of the neural network model applied to the nitrate concentration measurement by ultraviolet absorption spectrometry.The application test was carried out on theEnvironmental Monitoring 01 monitoring vessel of the Ministry of Natural Resources,the measurement results are basically identical with the laboratory method in 11stations,which is further proved from the reliability and practicality

臭氧对玉簪叶片近轴侧和远轴侧伤害的比较

为探讨臭氧(O3)是否伤害叶片远轴侧以及对与近轴侧和远轴侧伤害的异同,以玉簪为材料通过显微观察、叶绿素荧光诱导动力学和气体交换等技术研究了该问题。叶片形态和显微观察表明,200μg·kg-1 O3处理10 d后玉簪叶片近轴侧表面和叶肉组织呈现明显伤害症状,而远轴侧在300μg·kg-1 O3条件下也发生明显伤害。O3处理显著改变了叶片近轴侧和远轴侧荧光诱导动力学曲线的形状;尽管两侧荧光诱导动力学曲线的K、J和I点相对荧光产量均增加,但仅近轴侧K点的相对荧光上升幅度更加明显。此外,O3处理下玉簪叶片的光合速率和叶绿素含量均下降,相对电导率和膜脂过氧化程度大幅增加。鉴于显微观察和荧光诱导动力学的测定结果,我们认为O3能够同时伤害玉簪叶片近轴侧和远轴侧叶肉组织,并就臭氧对玉簪叶片两侧的伤害机制进行了比较和讨论

study on zno nanorods as well as mirorods formed in the chemical bath deposition combined with sol-gel processes

采用化学浴沉积法在氧化锌种子层上制备了整齐有序且具有c轴取向的氧化锌纳米棒,同时还出现了自由分布的微米棒,其生长速度高于纳米棒,且生长模式符合扩散控制"Ostwald熟化"机制,但纳米棒生长过程的影响因素除扩散过程外还有形核密度、生长界面的反应动力学等.并研究了氧化锌纳米棒的微观结构和光学性质