文昌鱼秦皇岛、青岛和厦门地理种群形态特征的分化

把分别采自秦皇岛、青岛和厦门(各25尾)的文昌鱼种群的形态特征分为计数性状8个和计量性状10个,然后对18个形态特征进行ANOVA、聚类和主成分分析。结果表明:3地种群全长、体高和背腹鳍条数等18个形态特征的平均值均存在显著差异(P<0·05),且种群间的变异大于种群内。进一步比较秦皇岛和青岛种群,除在口笠触须、背鳍条数、腹鳍条数和肌节总数4个特征较为相似外,其余14个特征均存在显著差异(P<0·05);而青岛和厦门种群在全长、体高、腹孔肛门间肌节数、生殖腺数、吻鳍高、背鳍高、上尾鳍高等7个特征上更为相似(P>0·05);此外,秦皇岛和厦门种群在腹鳍高、上尾鳍长和高、下尾鳍长和高等5个特征上存在相似性(P>0·05)。聚类分析结果表明,75个样本明显分成两个类群:第二类群主要由厦门样本构成,而第一类群则主要由秦皇岛和青岛样本构成。由前3个主成分分析结果表明,所有样本可大致分为3个类群:秦皇岛、青岛和厦门类群。其中来自厦门的个体聚成一个明显的独立类群,而来自秦皇岛和青岛的样本在主成分分析图上则存在一定的交叉和重叠。总之,厦门地理种群分化最为明显,而秦皇岛和青岛种群在总体分化的趋势下,个体间形态特征仍存在相似性。     

基于多变量AR模型的脑电相干性分析及其在脑区协作机制研究中的应用

研究脑皮不同区域之间功能协作的机制一直是认知和神经科学关注的重要问题之一。基于多变量自回归模型的动态相干性分析方法,被用于观察不同脑区在认知过程中的同步化,结果显示,人在感知和识别物体时,相关的脑区自动地发生了同步化的神经活动,这种同步化被证实是不受干扰刺激影响的,但被干扰的流产力确定影响了与判断和主动运动有关的脑区的同步化,而且这种影响的趋势与反应时间的分析结果一致。     

Metabolomic profile in hyperthyroid patients before and after antithyroid drug treatment: Correlation with thyroid hormone and TSH concentration

Hyperthyroidism (HT) is characterized by an intense metabolic impact which affects the lipid, carbohydrate and amino acids metabolism, with increased resting energy expenditure and thermogenesis. Metabolomics is a new comprehensive technique that allows to capture an instant metabolic picture of an organism, reflecting peculiar molecular and pathophysiological states. The aim of the present prospective study was to identify a distinct metabolomic profile in HT patients using ¹H NMR spectroscopy before and after antithyroid drug treatment. This prospective study included 15 patients (10 female, 5 male) who were newly diagnosed hyperthyroidism. A nuclear magnetic resonance (¹H NMR) based analysis was performed on plasma samples from the same patients at diagnosis (HypT₀) and when they achieved euthyroidism (HypT₁). The case groups were compared with a control group of 26 healthy volunteers (C). Multivariate statistical analysis was performed with Partial Least Squares-Discriminant Analysis (PLS-DA). PLS-DA identified a distinct metabolic profile between C and untreated hyperthyroid patients (R²X 0.638, R²Y 0.932, Q² 0.783). Interestingly, a significant difference was also found between C and euthyroid patients after treatment (R²X 0.510, R²Y 0.838, Q² 0.607), while similar cluster emerged comparing HypT₀ vs HypT₁ patients. This study shows that metabolomic profile is deeply influenced by hyperthyroidism and this alteration persists after normalization of thyrotropin (TSH) and free thyroid hormone (FT3, FT4) concentration. This suggests that TSH, FT3 and FT4 assays may not be insufficient to detect long lasting peripheral effects of the thyroid hormones action. Further studies are needed to clarify whether and to what extent the evaluation of metabolomics profile may provide relevant information in the clinical management of hyperthyroidism.     

楚科奇海和白令海网采浮游植物群聚的多元分析

根据1999年夏季(7~8月)我国首次北极科学考察期间“雪龙”号考察船在楚科奇海(66°0.3'~75°18.6' N, 153°36.5'~174°59.5' W)和白令海(55°59.8'~66°0.3' N,173°21.1' E~175°53.9' W)49个站采集的样品研究了该区网采浮游植物的分布特征。结果显示,两个调查区的浮游植物多样性指数和均匀度平均值几乎相等且都较低,分别变动在1.88~1.90和0.43~0.45之间。然而,两个区存在明显的空间异质性。就丰富度和种类组成而言,楚科奇海的细胞密度和物种总数量远高于白令海,前者为9 122.56×10⁴ cells·m^-3 和103种,而后者为161.78×10⁴ cells·m^-3和71种。上述生态学指标不同站点间的变异也大。用多元分析方法对浮游植物群聚的分布异质性加以讨论。主成分分析(PCA)结果表明,解析调查区浮游植物分布方差的33.0%的两个最主要成分(即主要物种变量)分别是北极-北方浅海群落(泛北极群落)和北方大洋群落的主要种群。前者包括某些圆心硅藻,如旋链角毛藻 (Chaetoceros curvisetus)、柔弱角毛藻(C. debilis)、聚生角毛藻(C. socialis)、叉尖角毛藻(C.furcellatus)等多种寡色体类角毛藻以及冷淡劳德藻 (Lauderia glacialis)、丹麦细柱藻 (Leptocylindrus danicus)、诺登海链藻(Thalassiosira nordenskioldii)和脆杆链藻(Bacteriosira fragilis)等;后者主要有小环毛藻 (Corethron criophilum)、平截鼻状藻 (Proboscia truncata)、无刺鼻状藻 (P. inermis)、西氏细齿状藻(Dentecula seminae)和一些多色暗角毛类角毛藻,如大西洋角毛藻(Chaetoceros atlanticus)等。泛北极群落主要分布于楚科奇海南部和中部,而北方大洋群落的主要分布区在白令海调查区的西南部。通过聚类分析还辨认出7个包含不同群落的主要网采浮游植物分布区,但是由于不同水系的不同程度影响,以及极具不确定性的浮冰的位置及其物理状态(聚集、开裂和消融等)也会在短时间内给调查区生物过程导入变异性,从而使得这些分布区的归类复杂化,尤其在楚科奇海。     

长江中下游地区若干湖泊水质的多元分析与比较

1996年11月-1998年11月,对鄱阳湖等长江中下游若干浅水湖泊进行了水化学分析,并进行了主要水质指标(包括透明度、pH值、总氮、总磷、氨氮和硝酸盐等)的季节变动规律,并对这些指标进行了方差分析。结果表明:不同利用方式的水体间除硬度、碱度和T-N在几个湖区间的差异较小外,其它各项指标的差异均较显着;季节间除硬度、碱度、Ca、Mg和T-P外,其余各项指标在季节间的差异均较显着,说明其季节变动均不稳定。       

笋材两用毛竹林林分结构数量关系研究

作为在闽中尤溪县 6年试验研究结果的一部分 ,采用 PCA方法分析笋材两用毛竹林 (Phyllostachyspubescens)林分结构因素。结果表明 ,新成竹数量、竹林密度、年龄结构和新竹平均胸围 (GBH)是影响笋材两用毛竹林林分结构的主导因素。对各类林分进行系统聚类分析 ,笋材两用毛竹林可分为 3类 ,大致相当于竹林密度 2 5 0 0株· hm- 2 以下、2 80 0～ 40 0 0株· hm- 2 和 40 0 0株· hm- 2 以上 3种情形。用正态分布拟合新成竹胸围 (GBH )分布效果较好。新成竹胸围与竹林密度相关不显著。     

Inference from Stratified Samples: Study Design,Bias and Graphical Model Representations

Stratification is a widely used strategy in empirical research to improve efficiency of the sampling design. One concern of stratification is that ignoring it on analysis may bias the relationship between variables. A weighted analysis can only be carried out when sampling weights are known. When these are unknown, valid inference on the relationship between variables then depends on the ignorability of the design, which may be difficult to establish. Here, graphical representations of multivariate dependencies and independencies are used to find necessary conditions for ignorability of stratified sampling designs for inference on conditional and marginal relationships between variables.     

Exact Multiple Comparisons of Three or More Regression Lines: Pairwise Comparisons and Comparisons with a Control

The problem of finding exact simultaneous confidence bounds for differences in regression models for k groups via the union‐intersection method is considered. The error terms are taken to be iid normal random variables. Under an assumption slightly more general than having identical design matrices for each of the k groups, it is shown that an existing probability point for the multivariate studentized range can be used to find the necessary probability point for pairwise comparisons of regression models. The resulting methods can be used with simple or multiple regression. Under a weaker assumption on the k design matrices that allows more observations to be taken from the control group than from the k‐1 treatment groups, a method is developed for computing exact probability points for comparing the simple linear regression models of the k‐1 groups to that of the control. Within a class of designs, the optimal design for comparisons with a control takes the square root of (k‐1) times as many observations from the control than from each treatment group. The simultaneous confidence bounds for all pairwise differences and for comparisons with a control are much narrower than Spurrier's intervals for all contrasts of k regression lines.