Freeze-thaw stability of starches from different botanical sources: Correlation with structural features

Native starches from twenty-six botanical sources were determined for their structural features and stability against freeze-thaw treatments. Starch gels (5%, w/w) were prepared and repeatedly freeze-thawed up to five cycles by storing at −18 °C for 21 h and then at 30 °C for 3 h. Water release (syneresis) from the thawed gel after the 1st, 3rd and 5th cycle was measured gravimetrically, and evaluated in relation to apparent amylose content (AAC) and distribution of amylopectin branch chains with degree of polymerization 6-12 (APC ratio). Syneresis was not observed for starch gels of cassava, normal and waxy japonica rice up to the 1st, 3rd and 5th cycle, respectively. On the other hand, syneresis rapidly occurred for starch gels of elephant yam, new cocoyam, potato, edible canna, and water yam. Optimal multiple linear regression models were generated to predict individual effect of AAC and APC ratio on syneresis of starch gels. The prediction models illustrated the positive unit-contribution of AAC and negative unit-contribution of APC ratio to syneresis (P < 0.001).     

Allometric Models for Predicting Aboveground Biomass in Two Widespread Woody Plants in Hawaii

Allometric models are important for quantifying biomass and carbon storage in terrestrial ecosystems. Generalized allometry exists for tropical trees, but species‐ and site‐specific models are more accurate. We developed species‐specific models to predict aboveground biomass in two of the most ubiquitous natives in Hawaiian forests and shrublands, Metrosideros polymorpha and Dodonaea viscosa. The utility of the M. polymorpha allometry for predicting biomass across a range of sites was explored by comparing size structure (diameter at breast height vs. tree height) of the trees used to develop the models against trees from four M. polymorpha‐dominated forests along a precipitation gradient (1630–2380 mm). We also compared individual tree biomass estimated with the M. polymorpha model against existing generalized equations, and the D. viscosa model with an existing species‐specific model. Our models were highly significant and displayed minimal bias. Metrosideros polymorpha size structures from the three highest precipitation sites fell well within the 95% confidence intervals for the harvested trees, indicating that the models are applicable at these sites. However, size structure in the area with the lowest precipitation differed from those in the higher rainfall sites, emphasizing that care should be taken in applying the models too widely. Existing generalized allometry differed from the M. polymorpha model by up to 88 percent, particularly at the extremes of the data range examined, underestimating biomass in small trees and overestimating in large trees. The existing D. viscosa model underestimated biomass across all sizes by a mean of 43 percent compared to our model. The species‐specific models presented here should enable more accurate estimates of biomass and carbon sequestration in Hawaiian forests and shrublands.     

氚水在模拟水稻－水－土壤生态系统中的行为

采用模拟污染物的同位素示踪技术研究HTO(流水)在水稻-水-土壤模拟生态系统中的迁移、消长行为,并应用三库室开系统模型和非线性回归方法确定了水稻、水和土壤分室的拟合方程．结果表明,田表水中的HTO不仅在系统各分室间转移和分配,而且迅速向系统外散逸;HTO中的流以自由水氚和结合态流形式存在于水稻中,以吸湿性水流和结晶水氚存在于土壤,其中自由水氚(或吸湿性水氚)的比活度大于结合态流(或结晶水氚);水稻植株和土壤中HTO比活度随时间增加至最大值后又趋于下降,而结合态氚则呈缓慢增加;水稻茎秆中的总氚比活度高于其它各部位,而后逐渐趋于动态平衡．对实验数据进行回归分析得：田表水、土壤和水稻植株中的总氚比活度分别为Cw(t)=32.19c^-0.0353t 99.94c^-0.330t、Cs(t)=20.42(e^-0.0353t-e^-0.330t)和Cr(t)=38.49c^-0.0353t-10.13e^-0.330t-28.36c^-2.5744t。方差分析结果表明,各回归方程较好地反映了HTO在水稻-水-土壤生态系统中的行为．     

Estimates of subtropical forest biomass based on airborne LiDAR and Landsat 8 OLI data

 快速、定量、精确地估算区域森林生物量一直是森林生态功能评价以及碳储量研究的重要问题。该研究基于机载激光雷达(LiDAR)点云与Landsat 8 OLI多光谱数据, 借助江苏省常熟市虞山地区55块调查样地数据, 首先提取并分析了87个特征变量(53个OLI特征变量, 34个LiDAR特征变量)与森林地上、地下生物量的Pearson’s相关系数以进行变量优选, 然后利用多元逐步回归法建立森林生物量估算模型(OLI生物量估算模型和LiDAR生物量估算模型), 并与基于两种数据建立的综合生物量估算模型的结果进行比较, 讨论预测结果及其精确性。结果表明: 3种模型(OLI模型、LiDAR模型和综合模型)在所有样地无区分分析时, 地上和地下生物量的估算精度均达到0.4以上, 基于不同森林类型(针叶林、阔叶林、混交林)分析时地上和地下生物量的估算精度均有明显提高, 达到0.67及以上。利用分森林类型模型估算生物量, 综合生物量估算模型精度(地上生物量: R²为0.88; 地下生物量: R²为0.92)优于OLI生物量估算模型(地上生物量: R²为0.73; 地下生物量: R²为0.81)和LiDAR生物量估算模型(地上生物量: R²为0.86; 地下生物量: R²为0.83)。     

Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway

This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U−¹³C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown.We acknowledge the support of NIH Grant DK58533 and the DuPont-MIT Alliance.     

酚酸类物质的抑草效应分析

运用正交旋转回归试验设计分析5种常见的化感物质替代物水饧酸、对羟基苯甲酸、肉桂酸、香草酸和阿魏酸对田间伴生杂草稗草的抑制效应．结果表明,肉桂酸对稗草根长抑制率的影响最显著。其关系函数的二次项系数为-6．18,达极显著水平,水杨酸、对羟基苯甲酸和阿魏酸对稗草根长的抑制效应趋势与肉桂酸相同,效应曲线均为“n”形抛物线;而香草酸的效应曲线则为“U”形抛物线．当水饧酸、对羟基苯甲酸、肉桂酸、香草酸和阿魏酸浓度水平分别为0．06、0．60、0．24、0．02和0．02mmol·L^-1时,混合物对稗草根长的抑制率最大,达到78．65％。     

陕西吴起县生态恢复区直翅目昆虫的多样性

应用多样性指数、主成分分析及多元逐步回归等方法,对陕西吴起县生态恢复区直翅目昆虫的群落结构,植被因素与直翅目昆虫群落特征的相关性进行研究。共获得标本1 155号,隶属于10科17属,共计20种。黄胫小车蝗Oedaleus infernalis Saussure,宽翅曲背蝗Pararcyptera microptera meridionalis,短星翅蝗Calliptamus abbreviatusIkonn分别占总数量的17.58%,15.15%和14.89%,共同构成该地区直翅目昆虫的优势类群。多样性的变化趋势是县城>白豹>周湾>王洼。对各生境植被因素和直翅目昆虫群落所进行的主成分分析,两维主成分的累计方差贡献率分别达到了95.299%和86.304%。影响直翅目昆虫种类和数量变化的植被因素有草本植物高度多样性、草本植物种数、菊科优势度、禾本科优势度、其他科优势度。     

温度对褐飞虱发育、存活和产卵影响的关系模型

设置系列恒温19、22、25、28、31、34℃,测定了温度对褐飞虱发育、存活和产卵的影响,建立了温度对褐飞虱生物学参数影响的关系模型。试验结果表明:(1)褐飞虱卵和若虫的发育历期在28℃下最短。除22℃以外,短翅型雌若虫的发育历期长于雄若虫的发育历期。(2)34℃下,褐飞虱卵未成功孵化,推测34℃是褐飞虱发育的限制性高温。不同温度下若虫的存活率为:25℃22℃28℃19℃31℃;低温主要导致低龄若虫死亡,而高温则主要导致高龄若虫死亡。(3)不同温度下产卵前期为19℃31℃22℃25℃28℃。产卵期22℃时最长。短翅型雌虫寿命随着温度降低而延长,产卵量在28℃时最大(256粒),19℃时最低(108粒)。(4)采用温度(x)与褐飞虱各生物学参数(y)间进行回归分析,获得一系列关系模型;其中,卵历期(y=0.079 x2-4.462 x+70.536),若虫历期(y=0.233 x2-12.886 x+189.878),产卵前期(y=0.068 x2-3.614 x+49.88)和短翅型雌成虫寿命(y=-0.622 x+35.03)与温度间具有显著的回归关系。