茄子嫁接苗与自根苗光合特性比较

对茄子嫁接苗与自根苗的光合生理特性进行了比较研究。结果表明,不同时期嫁接苗功能叶片的净光合速率（Pn）均显著高于自根苗;茄子嫁接苗与自根苗功能叶片的Pn日变化均呈“双峰”曲线,但嫁接苗的“午休”程度较自根苗轻;嫁接苗比自根苗有较低的光补偿点（LCP）和CO2补偿点（CCP）,较高的光饱和点（LSP）和CO2饱和点（CSP）;有较低的光合冷限温度和较高的光合热限温度,光合最适温度两者之间没有明显的差别;有较高的光饱和、CO2饱和及最适温度时的Pn;嫁接苗Pn高是因为嫁接苗胞间的光合反应的底物浓度大（Ci高）,表观量子效率（AQY）、羧化效率（CE）和光合能力（A350）高的缘故。     

水稻剑叶取向对其光合功能的影响

水稻的水平剑叶净光合速率 (Pn)和羧化效率(CE)显著高于直立剑叶 ,其胞间CO2 浓度 (Ci)显著低于直立剑叶 ,但两者的气孔导度 (Gs)没有明显差别。这表明剑叶取向对水稻叶片的光合能力有重要影响。水平剑叶的高Pn可能同其RuBP羧化酶含量和活性高有关。这可能是水平叶生长期间吸收光量较多的结果。     

CO2阶跃变化对茄子动态光合特征参数的影响

针对CO2阶跃变化下茄子光合的振荡动态现象,采用CO2阶跃起点(Cal)和阶点(Ca2)处理,以及不周光照水平(PAR)下的CO2阶跃处理,结合光合系统反馈控制动态生化模型中传递函数的振荡单元,分析了茄子动态光合的振荡特征参数.通过稳态光合参数,将茄子光合速率(Pn)转化为羧化速率(Vc)的动态进程,发现在较低的CO2...     

生态沟渠铜钱草光合日变化的研究

以生态沟渠铜钱草为材料,采用便携式CID-340光合仪对铜钱草成熟叶片净光合速率(Pn)以及胞间CO2浓度(Ci)、光合有效辐射(PAR)、气孔导度(Gs)、气温(Gs)、叶温(Tl))和蒸腾速率(Tr)等影响因子进行测定,以探讨其光合生理生态特性,旨在为修复沟渠湿地提供一定的理论依据。结果表明:(1)铜钱草叶片净光合速率(Pn)日变化曲线呈双峰型,主峰(19.32μmol.m-2.s-1)出现在15:00左右,次峰(16.21μmol.m-2.s-1)出现在11:00,中午出现光合"午休"现象。(2)用逐步多元回归方法得到净光合速率日变化与主要生理生态因子的回归方程为:Pn=-5.45613+0.006797PAR+0.050099Gs(复相关系数0.868)。逐步回归结果表明Pn受PAR和Gs的影响较大。偏相关分析和通径分析的结果表明PAR、Gs对铜钱草Pn日变化有重要影响,是影响铜钱草Pn的主要因子,影响大小的顺序为:Gs>PAR。     

当归叶片光合参数日变化及其与环境因子的关系

在当归根茎膨大期,利用CI-310便携式光合仪,田间活体测定了当归的净光合速率(Pn)、蒸腾速率(Tr)、气孔导度(Gs)、胞间CO2浓度(Ci)等光合生理生态因子,以及光合有效辐射(PAR)、田间CO2浓度(Ca)、相对湿度(RH)、大气温度(Ta)、叶温(TL)等环境因子的日变化。结果表明,当归Pn日变化曲线为"双峰"型,最高峰出现在10:00左右,次高峰出现在16:00左右,午间有明显的"午休"现象,气孔因素是导致其"午休"的主要原因。Tr、Gs和Ci的日变化曲线均为"双峰"型,最高峰出现在10:00,次高峰出现在17:00。当归叶片净光合速率随环境因子日变化最优逐步多元回归方程为yPn=14.3108 0.0076xPAR-0.5271xTL(R2=0.6601,P<0.05),TL和PAR是对光合速率直接影响最大的生态因子,而Ca、RH和Ta主要是通过TL而间接影响光合速率的变化。     

地毯草的光合特性研究

地毯草是一种典型的暖季型草坪草。用Li 6400便携式光合分析仪测定地毯草的光合特性。地毯草的光合日进程呈单峰型,无"午休"现象且受非气孔限制,最大净光合速率出现在14∶00,变化趋势与光合有效辐射(PAR)、气温(Ta)、蒸腾速率(Tr)等因子相同,与空气相对湿度(RH)、胞间CO2浓度(Ci)等相反;其光饱合点超过2000μmol.m 2s 1,最大净光合速率达22μmol.m 2s 1,暗呼吸速率为1.34μmol.m 2s 1,CO2补偿点为0.97μmol.mol 1,光补偿点为22.2μmol.m 2s 1,羧化效率为0.2539μmol.m 2s 1,表观光量子效率高达0.0592μmol.mol 1;表明地毯草是一种具有较强耐荫性的C4型阳性的暖季型草坪草,除了在园林中可以广泛用作草坪地被外还可以在一些较荫湿的特殊地带作为绿化和防护之用。     

山椒子光合特性日变化与其环境因子的相关性分析

为探明山椒子光合速率变化规律及其与环境因子的关系, 使用LCi-SD便携式光合仪测定三年生山椒子叶片的净光合速率(Pn) 、蒸腾速率(Tr、X1) 、光合有效辐射(PAR、X2)、空气CO2浓度(Ca、X3)、叶片温度(Tl、X4)、空气相对湿度(RH、X5)、胞间CO2浓度 (Ci、X6) 、气孔导度(Gs、X7)的日变化进程。结果表明: 山椒子Pn日变化曲线为“双峰型”并存在明显的“光合午休”现象, 且主要由气孔限制因素引起; 利用多元线性逐步回归求得山椒子Pn的最优回归方程为: Y=-8.474+1.819X1-0.016X6+0.048X5+0.023X3(R2=0.988, 复相关系数R=0.994, 标准估计误差Se=0.203, F值=159.020, P<0.001); 通径分析结果表明, 影响山椒子Pn变化的主要因子是Tr、Ci、RH、Ca, 且各主要因子影响的直接效应顺序为Ci>Tr>Ca>RH, 总效应顺序为Tr>Ci>Ca>RH。     

七子花苗期光合日进程及光响应

对七子花苗期光合日进程及光响应进行了研究,结果表明:(1)净光合速率的日进程曲线呈双峰型,胞间CO2浓度的日进程基本与净光合速率相反,中午胞间CO2浓度增高表明净光合速率午间降低主要受非气孔限制因素的影响。暗呼吸速率日进程呈单峰曲线,中午最高。光能利用效率以上午7∶00为最高,中午最低,以后又逐渐上升。(2)净光合速率最适温度为25～30℃,高光合有效辐射和高温下光抑制加剧。(3)29℃下饱和光强为890μmolm 2s 1,表观量子效率为0.0325,光补偿点为44μmolm 2s 1;随着光合有效辐射的增强,气孔阻力减小,但光合有效辐射过高,气孔阻力却有所上升。(4)在29℃的饱和光强下,羧化效率为0.0351,CO2补偿点为173μmolmol 1,低于或高于饱和光强,羧化效率下降,CO2补偿点升高。     

两种高质牧草不同生育期光合生理日变化及光响应特征

于2010年5月25日(拔节、分枝期)、7月6日(抽穗、结荚期)、8月25日(开花期)晴朗天气,采用LI-6400便携式光合作用测定系统对新引1号东方山羊豆和柳枝稷各项光合生理生态指标进行了测定。结果表明:(1)新引1号东方山羊豆净光合速率(Pn)日变化呈双峰曲线,存在"光合午休"现象;分枝期Pn、光能利用效率(LUE)、水分利用效率(WUE)日均值高于结荚期,而结荚期的叶温(Tl)、蒸腾速率(Tr)、气孔导度(Gs)、胞间CO2浓度(Ci)日均值高于分枝期。(2)柳枝稷的Pn日变化呈单峰曲线,拔节期Pn、Tr日均值高于抽穗期,开花期最低;WUE与Ci日均值在开花期最高,拔节期次之,抽穗期最小;Tl、LUE日均值在抽穗期最高,拔节期次之,开花期最小。(3)Pn与Tr、Gs、叶片气孔限制值(Ls)、光合有效辐射(PAR)具有极显著正相关关系,与Ci呈极显著负相关;Tr与Gs、叶温下蒸汽压亏缺(Vpdl)、气温(Ta)、Tl、PAR均呈极显著正相关关系,与大气CO2浓度(Ca)呈显著负相关;Tl、Gs与PAR呈极显著正相关。(4)通过光响应曲线的绘制及相关生理指标的计算,结合相关评价标准,得出柳枝稷为阳生植物,而新引1号东方山羊豆为耐荫植物,且柳枝稷比新引1号东方山羊豆对光环境的适应性强。     

无患子光合生理日变化及其与生理生态因子的关系

以8年生无患子植株为试验材料,采用便携式Li-6400光合测定仪,分别于花期和果期测定了其净光合速率(Pn)、蒸腾速率(Tr)、气孔导度(Gs)、胞间CO2浓度(Ci)、空气温度(Ta)、叶片光合有效辐射(PAR)、空气相对湿度(RH)和空气CO2浓度(Ca)等参数的日变化,以揭示无患子光合特征及其与主要环境因子的关系。结果显示:(1)无患子Pn和Tr在花期和果期日变化均呈"M"型双峰曲线,并存在"光合午休"现象,第1和第2峰值分别出现在10:00和14:00左右。(2)无患子花期Pn、Tr、Gs、水分利用效率(WUE)日均值高于果期,而果期的Ci日均值高于花期。(3)在8:00~14:00,无患子花期光能利用效率(LUE)大于果期,并在10:00左右出现峰值,而在14:00~18:00其果期LUE大于花期。(4)无患子叶片Pn与Tr、Gs、PAR具有极显著正相关关系,且与Gs关系最为密切,与Ci呈极显著负相关关系,与Ca则表现为显著负相关关系。(5)影响无患子叶片Pn变化的决定生理因子是Gs和Tr,主要限制生理因子是Ci;主要决定生态因子是RH,主要限制生态因子是Ta,且Gs是影响无患子Pn的最重要的生理生态因子。研究表明,无患子对光强有较强的适应能力,是喜光植物,这为其人工林定向培育,以及花期和果期的管理提供了一定理论依据。     

To model or not to model

Summary Self-improving control systems may belong to either of two categories, according to whether or not they embody an explicit model of the part of their environment with which they interact. The two forms of operation are discussed and compared, and it is shown that the two may be mathematically equivalent. The treatment also gives theoretical justification for a particular mode of operation for nonmodel-forming controllers.     

A future of the model organism model

Changes in technology are fundamentally reframing our concept of what constitutes a model organism. Nevertheless, research advances in the more traditional model organisms have enabled fresh and exciting opportunities for young scientists to establish new careers and offer the hope of comprehensive understanding of fundamental processes in life. New advances in translational research can be expected to heighten the importance of basic research in model organisms and expand opportunities. However, researchers must take special care and implement new resources to enable the newest members of the community to engage fully with the remarkable legacy of information in these fields.     

The influence of model parameters on model validation

The study examined the sensitivity of two musculoskeletal models to the parameters describing each model. Two different models were examined: a phenomenological model of human jumping with parameters based on live subject data, and the second a model of the First Dorsal Interosseous with parameters based on cadaveric measurements. Both models were sensitive to the model parameters, with the use of mean group data not producing model outputs reflective of either the performance of any group member or the mean group performance. These results highlight the value of subject specific model parameters, and the problems associated with model validation.     

Infinite-allele model and infinite-site model in population genetics

Both the infinite-allele model and infinite-site model have contributed to development of population genetics. Although the former is a model mainly for protein polymorphism and the latter is mainly for DNA polymorphism, these two models are related: the expected heterozygosity and homozygosity can be obtained from the infinite-site model, and the expectation of the amount of DNA polymorphism can be obtained from the infinite-allele model.     

Antibodies as a model system for comparative model refinement

Predicting the conformations of loops is a critical aspect of protein comparative (homology) modeling. Despite considerable advances in developing loop prediction algorithms, refining loops in homology models remains challenging. In this work, we use antibodies as a model system to investigate strategies for more robustly predicting loop conformations when the protein model contains errors in the conformations of side chains and protein backbone surrounding the loop in question. Specifically, our test system consists of partial models of antibodies in which the “scaffold” (i.e., the portion other than the complementarity determining region, CDR, loops) retains native backbone conformation, whereas the CDR loops are predicted using a combination of knowledge‐based modeling (H1, H2, L1, L2, and L3) and ab initio loop prediction (H3). H3 is the most variable of the CDRs. Using a previously published method, a test set of 10 shorter H3 loops (5–7 residues) are predicted to an average backbone (N? Cα? C? O) RMSD of 2.7 Å while 11 longer loops (8–9 residues) are predicted to 5.1 Å, thus recapitulating the difficulties in refining loops in models. By contrast, in control calculations predicting the same loops in crystal structures, the same method reconstructs the loops to an average of 0.5 and 1.4 Å for the shorter and longer loops, respectively. We modify the loop prediction method to improve the ability to sample near‐native loop conformations in the models, primarily by reducing the sensitivity of the sampling to the loop surroundings, and allowing the other CDR loops to optimize with the H3 loop. The new method improves the average accuracy significantly to 1.3 Å RMSD and 3.1 Å RMSD for the shorter and longer loops, respectively. Finally, we present results predicting 8–10 residue loops within complete comparative models of five nonantibody proteins. While anecdotal, these mixed, full‐model results suggest our approach is a promising step toward more accurately predicting loops in homology models. Furthermore, while significant challenges remain, our method is a potentially useful tool for predicting antibody structures based on a known Fv scaffold. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Timeline: Neurospora: a model of model microbes

In the 1940s, studies with Neurospora pioneered the use of microorganisms in genetic analysis and provided the foundations for biochemical genetics and molecular biology. What has happened since this orange mould was used to show that genes control metabolic reactions? How did it come to be the fungal counterpart of Drosophila? We describe its continued use during the heyday of research with Escherichia coli and yeast, and its emergence as a biological model for higher fungi.     

Predicting climate-induced range shifts: model differences and model reliability

Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common data set, we investigated the potential implications of alternative modeling approaches for conclusions about future range shifts and extinctions. Our common data set entailed the current ranges of 100 randomly selected mammal species found in the western hemisphere. Using these range maps, we compared six methods for modeling predicted future ranges. Predicted future distributions differed markedly across the alternative modeling approaches, which in turn resulted in estimates of extinction rates that ranged between 0% and 7%, depending on which model was used. Random forest predictors, a model‐averaging approach, consistently outperformed the other techniques (correctly predicting >99% of current absences and 86% of current presences). We conclude that the types of models used in a study can have dramatic effects on predicted range shifts and extinction rates; and that model‐averaging approaches appear to have the greatest potential for predicting range shifts in the face of climate change.