A multivariate diagnosis approach applied to celery

Celery (Apium graveolens L. var Dulce) is a high value crop affected at different growth stages by a variety of nutrient disorders. Each nutrient concentration can be corrected for its dependence on concentrations of other nutrients by recognizing plant composition as a closed system whose components add up to one. New variables z _i are computed as logratioed values of individual nutrients, where each nutrient concentration is corrected for the geometric mean of all nutrient concentrations. The z _i are used together with principal component analysis (PCA) to relate celery composition to yield, deficiency symptoms and quality parameters. A survey of commercial celery fields suggested that (1) celery growth is most often limited by P and N deficiencies associated with Fe toxicity; (2) K uptake is most likely to become limiting when the crop reaches 15 cm in height; (3) blackheart incidence can be traced to low levels of K and Mg in external petioles, and (4) cracked stem incidence is related to low B when the crop is 30 cm in height.     

日本落叶松引种区林地棕壤肥力的多变量分析

本研究从日本落叶松引种区林地棕壤9个主要理化性状及与之配套的17株优势木材料入手,采用多变量分析方法筛选出影响该地区日本落叶松生长的主要肥力因子为氮及磷,並据此将该林地棕壤划分为9个肥力类型。在进行聚类分析时,本研究的创新在于以任意二样本点在各主成份上的坐标值为变量,以各主成份的贡献率为权重,重新定义欧氏距离公式,对主成份坐标值聚类,使其更为合理、直观。同时采用回归分析拟合出理化性状间的多个回归方程。此研究为该林地科学施肥、速生丰产、永续利用提供了可靠的数量依据。     

骨骼肌M蛋白抗血清的制备及正常人骨骼肌M蛋白的免疫电镜定位

分别制备了兔抗人Ｍ蛋白（Ｂ成分）抗血清和兔抗人Ｃ成分［１］抗血清。用蛋白Ａ－胶体金作标记物,对经ＬｏｗｉｃｒｙｌＫ４Ｍ低温包埋的人骨骼肌超薄切片中Ｍ蛋白和分子量１４００００的Ｃ成分进行免疫电镜定位。发现Ｍ蛋白分布于整个Ｍ线,而Ｃ成分虽然也集中于Ｍ线以内,但主要分布于Ｍ线内的边缘区域。     

Diversity of Xiphinema americanum-group Species and Hierarchical Cluster Analysis of Morphometrics

Of the 39 species composing the Xiphinema americanum group, 14 were described originally from North America and two others have been reported from this region. Many species are very similar morphologically and can be distinguished only by a difficult comparison of various combinations of some morphometric characters. Study of morphometrics of 49 populations, including the type populations of the 39 species attributed to this group, by principal component analysis and hierarchical cluster analysis placed the populations into five subgroups, proposed here as the X. brevicolle subgroup (seven species), the X. americanum subgroup (17 species), the X. taylori subgroup (two species), the X. pachtaicum subgroup (eight species), and the X. lambertii subgroup (five species).     

Principal directions of the evolution of Monimotrochida

Phylogenetic relations among the main groups of Monimotrochida are considered. The principal directions of monimotrochid evolution were defined by comparative investigations of mastax morphology (SEM), basic body structures, and general biology. On the basis of these results we propose a revision of previous rotifer taxonomy. We suggest to place the Monimotrochida in the order Protoramida divided into two suborders Flosculariina and Conochilina.     

Restriction fragment length polymorphism and molecular taxonomy in Vitis vinifera L.

Forty-six accessions of grapevine (V. vinifera L.) were compared by restriction fragment length polmorphism (RFLP) analysis, and 111 informative or unique restriction fragments were found that revealed an important level of polymorphism. RFLP patterns were compared in two ways: by calculating electrophoretic similarity degree values further analyzed by principal component analysis and by studying the distribution of rare restriction fragments. Six taxonomic groups could be defined, which partially confirmed relationships derived from ampelographical data. Our data support the existence of ecogeographical groups.     

Testing different 3D techniques using geometric morphometrics: Implications for cranial fluctuating asymmetry in humans

This study aimed to test the performance of 3D digitizer, CT scanner, and surface scanner in detecting cranial fluctuating asymmetry. Sets of 32 landmarks (6 in the midline and 13 bilateral) were acquired from 14 archeological crania using a 3D digitizer, and from 3D models generated from a CT scanner and surface scanner using Viewbox 4. Levels of shape variation were analyzed in MorphoJ using Procrustes analysis of variance and Principal component analysis. Intra-observer error accounted for 1.7%, 1.8%, and 4.5% of total shape variation for 3D digitizer, CT scanner, and surface scanner respectively. Fluctuating asymmetry accounted for 15%–16% of total shape variation. Variation between techniques accounted for 18% of total shape variation. We found a higher level of missing landmarks in our surface scan data than for both 3D digitizer and CT scanner data, and both 3D model-based techniques sometimes obscured taphonomic damage. All three 3D techniques are appropriate for measuring cranial fluctuating asymmetry. We advise against combining data collected with different techniques.     

Variable selection in nonlinear function-on-scalar regression

We develop a new method for variable selection in a nonlinear additive function-on-scalar regression (FOSR) model. Existing methods for variable selection in FOSR have focused on the linear effects of scalar predictors, which can be a restrictive assumption in the presence of multiple continuously measured covariates. We propose a computationally efficient approach for variable selection in existing linear FOSR using functional principal component scores of the functional response and extend this framework to a nonlinear additive function-on-scalar model. The proposed method provides a unified and flexible framework for variable selection in FOSR, allowing nonlinear effects of the covariates. Numerical analysis using simulation study illustrates the advantages of the proposed method over existing variable selection methods in FOSR even when the underlying covariate effects are all linear. The proposed procedure is demonstrated on accelerometer data from the 2003–2004 cohorts of the National Health and Nutrition Examination Survey (NHANES) in understanding the association between diurnal patterns of physical activity and demographic, lifestyle, and health characteristics of the participants.     

Functional data analysis with covariate-dependent mean and covariance structures

Functional data analysis has emerged as a powerful tool in response to the ever-increasing resources and efforts devoted to collecting information about response curves or anything that varies over a continuum. However, limited progress has been made with regard to linking the covariance structures of response curves to external covariates, as most functional models assume a common covariance structure. We propose a new functional regression model with covariate-dependent mean and covariance structures. Particularly, by allowing variances of random scores to be covariate-dependent, we identify eigenfunctions for each individual from the set of eigenfunctions that govern the variation patterns across all individuals, resulting in high interpretability and prediction power. We further propose a new penalized quasi-likelihood procedure that combines regularization and B-spline smoothing for model selection and estimation and establish the convergence rate and asymptotic normality of the proposed estimators. The utility of the developed method is demonstrated via simulations, as well as an analysis of the Avon Longitudinal Study of Parents and Children concerning parental effects on the growth curves of their offspring, which yields biologically interesting results.