模拟增雨对荒漠植物幼苗生长和根系形态的影响

降雨是荒漠生态系统过程和功能的最重要限制因子,荒漠植物幼苗对生长季降雨的变化极端敏感。为探讨荒漠植物对未来降雨格局变化的响应,选取乌兰布和沙漠两种典型荒漠植物幼苗(白刺和油蒿)为研究对象,根据生长季内(6—9月)每次降雨量,进行不同梯度的人工模拟增雨试验(CK:自然降雨、A:增雨25%、B:增雨50%、C:增雨75%、D:增雨100%),研究两种植物幼苗生长和根系形态特征对降雨量变化的响应。结果表明:(1)不同增雨处理对白刺和油蒿幼苗的地上部生长有显著影响(P<0.05),增雨处理的白刺和油蒿幼苗的株高、平均冠幅和基径显著高于CK,并随着增雨量的增大而增大(白刺基径除外);(2)增雨处理之间、白刺和油蒿之间在总根长、总表面积、平均直径、总体积、根尖数和分叉数均有显著差异(P<0.05)。对白刺幼苗而言,B处理和C处理的根系参数均显著大于CK、A和D处理,且B和C处理之间没有显著差异(平均直径除外);对油蒿幼苗而言,随着增雨量的增加,油蒿总根长、总表面积、总体积、根尖数和分叉数呈现逐渐增加的趋势,而平均直径呈现先增加后降低的趋势,且在B处理下达到最大值。(3)增雨处理显著降低...     

模拟增温对青藏高原高寒草甸根系生物量的影响

在青藏高原高寒草甸布设模拟增温实验样地,采用土钻法于2012—2013年植被生长季获取5个土层的根系生物量,探讨增温处理下根系生物量在生长季不同月份、不同土壤深度的变化趋势及其与相应土层土壤水分、温度的关系。结果表明:(1)根系生物量在2012年随月份呈增加趋势,其中7—9月较大,其平均值在对照、增温处理下分别为3810.88 g/m~2和4468.08 g/m~2;在2013年随月份呈减小趋势,其中5—6月较大,其平均值在对照、增温处理下分别为4175.39 g/m~2和4141.6 g/m~2。增温处理下的总根系生物量高出对照处理293.97 g/m~2,而各月份总根系生物量在处理间的差值均未达到显著水平。表明在增温处理下根系生物量略有增加,但在生长季不同月份其增加的程度不同,致使年际间的增幅出现差异。(2)根系生物量主要分布在0—10 cm深度,所占百分比为50.61%。在增温处理下,0—10 cm深度的根系生物量减少,减幅为8.38%;10—50 cm深度的根系生物量增加,增幅为2.1%。相对于对照处理,增温处理下0—30 cm深度的根系生物量向深层增加,30—50 cm深度的根系生物量增加趋势略有减缓。可见,在增温处理下根系生物量的增幅趋向于土壤深层。(3)根系生物量与土壤水分呈极显著的递减关系,在增温处理下线性关系减弱;与土壤温度呈极显著的递增关系,在增温处理下线性关系增强。表明土壤水分、温度都可极显著影响根系生物量,但在增温处理下土壤温度对根系生物量的影响较土壤水分更为敏感而迅速。     

模拟酸雨对七种森林植物生物量的影响

马尾松(pinus massonina Lamb.)、杉木(Cunninghamia lanceolata(Lamh.) hook)、青冈(Cyclobananopsis glauca Thumb. Oeret)、油茶(Camelia oleifera Abel)、木荷(Schima superba Gardn,et Ghamp. )、黄樟(Cinnamomum porrectum(Roxb)Kosterm)、火力楠(Michelia macclurei)属亚热带森林植物,在我国南方林业生产中占居十分重要的地位。为了探讨酸雨对植物生长和森     

基于遗传算法的人工神经网络模型在冬小麦根系分布预报中的应用

In this study, a controlled experiment of winter wheat under water stress at the seedling stage was conducted in soil columns in greenhouse. Based on the data gotten from the experiment, a model to estimate root length density distribution was developed through optimizing the weights of neural network by genetic algorithm. The neural network model was constructed by using forward neural network framework, by applying the strategy of the roulette wheel selection and reserving the most optimizing series of weights, which were composed by real codes.This model was applied to predict the root length density distribution of winter wheat, and the predicted root length density had good agreement with experiment data. The way could save a lot of manpower and material resources for determining the root length density distribution of winter wheat.     

研究植物根系分泌物的方法

文章对植物根系分泌物的定义、组成、分类、影响因素和分泌部位,根系分泌物的收集、分离及鉴定方法进行了概述,并对此问题的今后研究方向作了展望。     

三种湿地植物的生长及根系溶解性有机碳分泌物研究

研究了美人蕉(Canna indica Linn.)、风车草(Cyperus flabelliformis Rottb.)和水鬼蕉(Hymenocallis littoralis (Jack) Salisb.)3种湿地植物在人工气候室水培条件下的根系溶解性有机碳分泌物分泌量及其与生长的关系.结果表明,风车草和美人蕉的植...     

植物茎叶燃烧产生的烟雾水溶液对水稻生长的效应

根据农村稻田中稻草燃烧后栽种的水稻可增加产量的现象,我们以稻草、大豆杆、玉米茎叶、白茅（俗称茅草）、马尾松针叶等燃烧产生的气体导入水中所成的溶液对水稻生长的影响进行了实验。以10g或20g燃料置于IL三角瓶中,用带有2支玻管的皮塞塞住。1支玻管与空气接触,l支与带有2支管子的蒸馏水瓶相连。蒸馏水瓶的1支管接烟雾通道,另1管接水力泵或真空泵。燃料瓶底加热,同时开动泵机,烟雾随空气流动而溶于蒸馏水瓶中。此烟雾水溶液呈黄色,置于室温或4℃冰箱中备用。水稻（Oryzasativa）品种以窄叶青8号为主,其他尚有特青、湖农大的GE…     

外来植物紫茎泽兰18个种群的茎叶形态结构比较研究

利用石蜡切片法结合光学显微镜观察了紫茎泽兰18个种群的茎叶形态结构。结果表明：各种群间茎和叶的形态结构均表现出一定的变化,其中茎的维管束束数、叶表皮的部分特征变化较明显。应用SPSS统计软件对叶表皮的特征分析后,发现种群间的气孔器密度、气孔器指数、气孔器长度、气孔器宽度、上下表皮细胞数目均随地理条件的变化而表现出明显差异。相关分析表明气孔器密度、气孔器指数与海拔高度呈正相关。但紫茎泽兰各种群间的叶表皮细胞形状无明显变化,均为无规则型,垂周壁式样均为浅波状深波状;气孔器类型均为无规则型。     

模拟酸雨对蒙古栎幼苗生长和根系伤流量的影响

选择中国北方落叶阔叶林的主要组成树种蒙古栎(Quercus mongolica Fisch.ex Ledeb.)的1年生幼苗为研究对象,设置4个酸雨酸度(重度、中度、轻度和对照)和3个降雨量(自然雨量和增、减雨量30％),以期阐明酸雨对蒙古栎幼苗形态生长、生物量和根系伤流量的影响,探讨中国北方日趋严重的酸雨是否会影响蒙古栎幼苗的生长,为酸雨区森林恢复植物的选择提供依据.研究结果显示:1)在本实验的酸雨酸度下,酸雨降雨量的增加对蒙古栎幼苗各生理生态指标均有一定的促进作用;2)酸雨酸度对蒙古栎幼苗形态和生物量的影响不显著,但酸度增加降低了幼苗的根系伤流量;3)增雨的重度酸雨处理促进了蒙古栎幼苗形态生长和生物量累积;4)两因素对蒙古栎幼苗的影响没有交互作用.说明蒙古栎对酸雨具有一定的抗性,可考虑选择为酸雨区植被构建的物种.     

植物根系吸水过程中根系水流阻力的变化特征

以植物根系吸水的人工模拟试验所测得的数据为依据,运用水流的电模拟原理,定理分析了不同土壤水分水平处理下植物根系吸水过程中根系水流阻力各主要分量的大小、变化规律及其相对重要性．结果表明,在同一水分水平处理中,植物根内木质部传导阻力（Ｒｃ）随生长时间的推移而减小,随土层深度的加深而增大,土根接触阻力（Ｒｓｒ）、植物根系吸收阻力（Ｒｒ）随生长时间表现出先下降后上升阶段的动态变化特征;在不同水分水平处理中,Ｒｃ、Ｒｓｒ、Ｒｒ均随土壤湿度减小而大幅度增大;在植物根系水流阻力各分量中,Ｒｒ占根系水流阻力的比例为５５％～９６％,Ｒｓｒ约占根系水流阻力的４％～４５％,而Ｒｃ仅占根系水流阻力的７×１０－６,故Ｒｒ是决定植物根系吸水速率的重要因素     

Predicting siRNA activity based on back-propagation neural network

RNA interference (RNAi) is a phenomenon of gene silence induced by a double-stranded RNA (dsRNA) homologous to a target gene. RNAi can be used to identify the function of genes or to knock down the targeted genes. In RNAi technology, 19 bp double-stranded short interfering RNAs (siRNA) with characteristic 39 overhangs are usually used. The effects of siRNAs are quite varied due to the different choices in the sites of target mRNA. Moreover, there are many factors influencing siRNA activity and these factors are usually nonlinear. To find the motif features and the effect on siRNA activity, we carried out a feature extraction on some published experimental data and used these features to train a back-propagation neural network (BP NN). Then, we used the trained BP NN to predict siRNA activity. __________ Translated from Acta Biophysica Sinica, 2006, 22(6): 429–434 [译自: 生物物理学报]     

Root primordia and endogenous auxin in maize roots cultured in vitro

Quantitative analyses of indol-3yl-acetic acid (I aa ) in Zea mays L. (cv. LG 11) root segments cultured in vitro were performed by gas chromatography-mass spectrometry with selected ion monitoring. The root extracts were first purified by highperformance liquid chromatography. Root primordia initiation in intact and decapitated roots showed different patterns: decapitation strongly enhanced primordia initiation in their first 10 mm. During the culture (5 days), I aa content decreased in both intact and decapitated roots. No correlation was found between the level of endogenous auxin and the numher of root primordia initiated from either intact or decapitated maize root segments.     

基于大脑皮层互信息理论的睡眠分级研究

睡眠的分级研究是睡眠状况分析和睡眠质量评价的前提和基本内容。目前国际通用的睡眠分级方法,是利用脑电信号另加脑功能信号（如肌电图、眼动电流图）,且必须由人工来判别分析的。大脑皮层互信息理论是研究脑功能变化的有力工具。通过动态计算睡眠脑电四个导联之间的互信息时间序列的复杂度,并利用一个三层的人工神经网络进行六个级别的分类,6例720个不同时期的睡眠片段的测试表明,系统睡眠分级与人工分级的总相符率达到90.83％,且实现了睡眠动态自动分级。神经网络的学习功能,可使系统的准确率进一步提高,逐渐接近或达到人工分级的水平。     

What we have learned from the study of learning in the leech

The use of invertebrate preparations has contributed greatly to our understanding of the neural basis of learning. The leech is especially useful for studying behavioral changes and their underlying neuronal mechanisms. Learning in the leech is essentially identical to that found in other animals, both vertebrate and invertebrate. Using anatomical and physiological techniques on leeches as they learn, we have begun to characterize the properties of individual neurons and neuronal networks that play a role in learning. We have been able to show two neuronal mechanisms that have not been previously associated with associative conditioning. The first has to do with the importance of contingency: one stimulus [the conditional stimulus (CS)] becomes associated with a second stimulus [the unconditional stimulus, (US)] in proportion to the ability of the CS to predict the US. We have found that important properties for encoding predictability, such as circuit reconfiguration, may lie in the US pathway. The firing of the serotonergic Retzius cells is taken as the US; consistent CS prediction of a US prevents “dropout” of a critical component of one US pathway. Throughout training, predicted USs continue to elicit a barrage of action potentials in these cells. Recurring unpredicted USs degrade both the learning and the response of the Retzius cell to the US. A second insight is that at least two US pathways contribute to learning, the Retzius cell pathway and the nociceptive (N) cell pathway. This second pathway persists after the elimination of the Retzius cell pathway. The observation of multiple US pathways raises a host of issues concerning CS–US convergence and the functional significance of distinct US pathways, and our results are discussed in terms of implications to current models of learning. © 1995 John Wiley & Sons, Inc.     

暗褐网柄牛肝菌半人工模拟栽培及“宿主树”根系上菌丝生长的持久性

用暗褐网柄牛肝菌纯培养菌种, 接种盆栽及袋栽的小粒咖啡(Coffea arabica L.)苗、田 间小粒咖啡树的根系, 结果表明：接种 30 d~90 d , 子实体幼蕾紧靠苗(树)的茎基或于茎基四周土壤中生长子实体并发育成熟。子实体单生或丛生, 出菇至成熟3 d~4 d, 单个子实体重20.0 g~62.0 g; 小粒咖啡苗(树)的根茎、主根及侧根被茶褐色的菌索和菌膜包裹, 而根尖及靠近根尖的侧根上没有菌丝和菌索生长或生长很少; 盆栽小粒咖啡苗, 在接种90 d后其根系表面的菌索死亡。     

Evaluation of a Learning-based Deformable Registration Method on Abdominal CT Images

BackgroundReliable image comparisons, based on fast and accurate deformable registration methods, are recognized as key steps in the diagnosis and follow-up of cancer as well as for radiation therapy planning or surgery. In the particular case of abdominal images, the images to compare often differ widely from each other due to organ deformation, patient motion, movements of gastrointestinal tract or breathing. As a consequence, there is a need for registration methods that can cope with both local and global large and highly non-linear deformations.MethodDeformable registration of medical images traditionally relies on the iterative minimization of a cost function involving a large number of parameters. For complex deformations and large datasets, this process is computationally very demanding, leading to processing times that are incompatible with the clinical routine workflow. Moreover, the highly non-convex nature of these optimization problems leads to a high risk of convergence toward local minima. Recently, deep learning approaches using Convolutional Neural Networks (CNN) have led to major breakthroughs by providing computationally fast unsupervised methods for the registration of 2D and 3D images within seconds. Among all the proposed approaches, the VoxelMorph learning-based framework pioneered to learn in an unsupervised way the complex mapping, parameterized using a CNN, between every couple of 2D or 3D pairs of images and the corresponding deformation field by minimizing a standard intensity-based similarity metrics over the whole learning database. Voxelmorph has so far only been evaluated on brain images. The present study proposes to evaluate this method in the context of inter-subject registration of abdominal CT images, which present a greater challenge in terms of registration than brain images, due to greater anatomical variability and significant organ deformations.ResultsThe performances of VoxelMorph were compared with the current top-performing non-learning-based deformable registration method “Symmetric Normalization” (SyN), implemented in ANTs, on two representative databases: LiTS and 3D-IRCADb-01. Three different experiments were carried out on 2D or 3D data, the atlas-based or pairwise registration, using two different similarity metrics, namely (MSE and CC). Accuracy of the registration was measured by the Dice score, which quantifies the volume overlap for the selected anatomical region.All the three experiments exhibit that the two deformable registration methods significantly outperform the affine registration and that VoxelMorph accuracy is comparable or even better than the reference non-learning based registration method ANTs (SyN), with a drastically reduced computation time.ConclusionBy substituting a time consuming optimization problem, VoxelMorph has made an outstanding achievement in learning-based registration algorithm, where a registration function is trained and thus, able to perform deformable registration almost accurately on abdominal images, while reducing the computation time from minutes to seconds and from seconds to milliseconds in comparison to ANTs (SyN) on a CPU.