辽宁地区产6种乌头的细胞分类学研究

对我国辽宁地区毛莨科乌头属６个种的染色体的数目和形态进行了研究,并进行了核型分析。其染色体基数为Ｘ＝８,核型公式为：两色乌头：２ｎ＝２ｘ＝２ｍ＋１０ｓｍ＋４ｓｔ;蛇岛乌头为：２ｎ＝４ｘ＝１０ｍ＋２０ｓｍ（ＳＡＴ）＋２ｓｔ＋２Ｂ;黄花乌头为：２ｎ＝４ｘ＝４ｍ＋１２ｓｍ（ＳＡＴ）＋８ｓｔ＋１Ｂ;北乌头三倍体为：２ｎ＝３ｘ＝２Ｍ＋４ｍ＋１８ｓｍ;北乌头４倍体为２ｎ＝４ｘ＝４ｍ＋２８ｓｍ。同时,对乌头属下     

九种二变种山茶属植物的核型报道

本文报道了９种２变山茶属植物的核型．结果如下：Ｃａｍｅｌｉａｈｅｎｒｙａｎａ：２ｎ＝２ｘ＝３０＝２１ｍ＋８ｓｍ＋１ｓｔ;Ｃ．ｆｕｒｆｕｒａｃｅａ：２ｎ＝２ｘ＝３０＝２０ｍ＋１０ｓｍ;Ｃ．ｗａｒｄｉ：２ｎ＝２ｘ＝３０＝１８ｍ＋１１ｍ＋１ｓｔ;Ｃ．ａｎｌｕｎｇｅｎｓｉｓ：２ｎ＝２ｘ＝３０＝１９ｍ＋９ｓｍ＋２ｓｔ;Ｃ．ａｎｌｕｎｇｅｎｓｉｓｖａｒ．ａｃｕｔｉｐｅｒｕｌａｔａ：２ｎ＝２ｘ＝３０＝１９ｍ＋９ｓｍ＋２ｓｔ;Ｃ．ｐｙｘｉｄｉａｃｅａ：２ｎ＝２ｘ＝３０＝２０ｍ＋８ｓｍ＋２ｓｔ;Ｃ．ｐｙｘｉｄｉａｃｅａｖａｒ．ｒｕｂｉｔｕｂｅｒｃｕｌａｔａ：２ｎ＝２ｘ＝３０＝２１ｍ＋８ｓｍ＋１ｓｔ;Ｃ．ｂｒｅｖｉｓｔｙｌａ：２ｎ＝２ｘ＝３０＝１８ｍ＋１０ｓｍ＋２ｓｔ;Ｃ．ｌｅｐｔｏｐｈｙｌａ：２ｎ＝２ｘ＝３０＝２４ｍ（１ｓａｔ）＋４ｓｍ（１ｓａｔ）＋２ｓｔ;Ｃ．ｙｕｎｎａｎｅｎｓｉｓ：２ｎ＝２ｘ＝３０＝１８ｍ＋１０ｓｍ＋２ｓｔ;Ｃ．ｐｉｔａｒｄｉ：２ｎ＝２ｘ＝３０＝１８ｍ＋１２ｓｍ．其中,前７种２变种的核型为首次报道,比较前人的有关研究可以看出上述核型在种间较相似,以组为单位进行比较比种间比较具有更大的意义。     

东北蒿属莳萝蒿组6种植物核型研究

报道东北蒿属莳萝蒿组６种植物的染色体数,５种为首次报道,对其中的５种进行了核型分析。核型公式分别为：大籽蒿２ｎ＝２ｘ＝１８＝１４ｍ＋２ｓｍ（２ＳＡＴ）＋２ｓｔ（ＳＡＴ）;矮滨蒿（Ａ．ｎａｋａｉ Ｐａｍｐ）２ｎ＝２ｘ＝１６＝１２ｍ＋４ｓｍ;碱蒿（Ａ．ａｎｅｔｈｉｆｏｌｉａ Ｗｅｂ．ｅｘ Ｓｔｅｃｈｍ．）２ｎ＝２ｘ＝１６＝１２ｍ＋２ｓｍ＋２ｓｔ;莳萝蒿（Ａ．ａｎｅｔｈｏｉｄｅｓ Ｍａｔｔｆ）２ｎ＝２ｘ     

五种珍珠菜的核型研究

本文对５种珍珠菜亚属植物进行了染色体数目及核型研究。其中瓣珍珠菜２ｎ＝２４＝１２ｍ＋８ｓｍ＋４ｓｔ（２ＳＡＴ）、黑腺珍珠菜２ｎ＝２２＝２ｍ＋４ｓｍ＋６ｓｔ＋１０ｔ、泽珍珠菜２ｎ＝２４＝１４ｍ＋６ｓｍ（２ＳＡＴ）＋４ｓｔ和小叶珍珠菜２ｎ＝４８＝３４ｍ＋１０ｓｍ＋４ｓｔ的染色体数目及核型为首次报道。中国九江产的红根草核型２ｎ＝２４＝２０ｍ＋４ｓｍ（２ＳＡＴ）与日本产的核型２ｎ＝２４＝１８ｍ（１ＳＡＴ）     

四种中药柴胡植物的核型分析

报道了４种中药柴胡植物的染色体核型,其核型如下：北柴胡２ｎ＝１２＝６ｍ＋４ｓｍ＋２ｓｔ,核型类型为２Ａ型;红柴胡２ｎ＝１２＝１０ｍ＋２ｓｍ,２Ａ型;线叶柴胡２ｎ＝１２＝４ｍ＋２ｍ＾ｓａｔ＋６ｓｍ,２Ａ型;双鸭山居群２ｎ＝２６,为二型性核型。北柴胡和线叶柴胡的核型为首次报道。结合有关文献对４种柴胡的核型变异进行了简略的讨论,发现黑龙江双鸭山１个居群的核型与姜传明等报道的肇东五里木居群一样,具有与日本     

蒙古苍耳核型分析

首次对蒙古苍耳染色体数目及核型进行了研究,结果表明,染色体数目２ｎ＝３６,核型公式为Ｋ（２ｎ）＝３６＝２６ｍ＋２ｍ（ｓａｔ）＋８ｓｍ．与苍耳的核型进行比较,认为蒙古苍耳的核型比苍耳的原始。     

安徽黄精属的细胞分类学研究

本文首次报道黄精属ＰｏｌｙｇｏｎａｔｕｍＭｉｌｌ我国三种特有植物的染色体数目和核型,结果如下：安徽黄精Ｐ．ａｎｈｕｉｅｎｓｅ发现两个细胞型：（１）２ｎ＝２４＝４ｍ＋６ｓｍ＋１４ｓｔ;（２）２ｎ＝２０＝４ｍ十６ｓｍ＋１０ｓｔ;　　黄精Ｐ．ｌａｎｇｙａｅｎｓｙ２ｎ＝１８＝６ｍ＋８ｓｍ＋４ｔ;距药黄精Ｐ．ｆｒａｎｃｈｅｔｉｉ有三个细胞型：（１）２ｎ＝２２＝８ｍ＋８ｓｍ（２ｓｃ）＋６ｓｔ;（２）２ｎ＝２０＝２ｍ＋１４ｓｍ＋４ｓｔ;（３）２ｎ＝１８＝４ｍ＋８ｓｍ＋４ｓｔ＋２Ｔ,全部属３Ｂ核型。黄精属植物安徽共有１０种,本文对９种黄精的染色体数目、核型进行了比较研究,发现它们可划分成三个类群,与中国植物志（第十五卷）的形态分类基本相符。     

国产蜘蛛抱蛋属的细胞分类学研究I.四个广西特有种的核型

研究了广西特有的４种蜘蛛抱蛋属植物的核型,结果如下：巨型蜘蛛抱蛋,２ｎ＝３８＝２４ｍ（２ｓａｔ）＋２ｓｍ＋１２ｓｔ;长药蜘蛛抱蛋,２ｎ＝３６＝１８ｍ＋２ｓｍ（２ｓａｔ）＋１６ｓｔ;广西蜘蛛抱蛋,２ｎ＝３６＝１８ｍ＋４ｓｍ（２ｓａｔ）＋１４ｓｔ,隆安蜘蛛抱蛋,２ｎ＝３８＝２０ｍ＋６ｓｍ（２ｓａｔ）＋１２ｓｔ。以上核型都属２Ｃ,具有明显的二型性．     

仲彬草属5种植物的核型研究

本文对我国西部高原仲彬草属Ｋｅｎｇｙｉｌｉａ５种植物的核型进行了分析。它们的染色体数目均为２ｎ＝４２,六倍体。核型是：糙毛鹅观草Ｋ．ｈｉｒｓｕｔａ,２ｎ＝６ｘ＝４２＝３６６＋６ｓｍ;青海鹅观草Ｋ．ｋｏｋｏｎｏｒｉｃａ,２ｎ＝６ｘ＝４２＝３６ｍ＋６ｓｍ：黑药鹅观草Ｋ．ｍｅｌａｎｔｈｅｒａ,２ｎ＝６ｘ＝４２＝３８ｍ＋４ｓｍ;硬秆鹅观草Ｋ．ｒｉｇｉｄｕｌａ,２ｎ＝６ｘ＝４２＝３８ｍ＋４ｓｍ;窄颖鹅观草Ｋ．ｓｔｅｎａｃｈｙｒａ,２ｎ＝６ｘ＝４２＝３８ｍ＋４ｓｍ。它们的核型属于１Ｂ或２Ｂ型。染色体中均未发现随体。     

山东10种植物的核型分析

对山东１０ 种植物进行了核型分析。茴茴蒜（ Ｒａｎｕｎｃｕｌｕｓｃｈｉｎｅｎｓｉｓ Ｂｇｅ－） 染色体数目２ｎ ＝１６ , 核型公式Ｋ（２ｎ） ＝ ２ｘ ＝ １６ ＝ ２ Ｍ ＋ ２ｍ ＋ ２ｓｍ ＋ １０ｓｔ, “３Ａ”类型; 五脉地椒（ Ｔｈｙｍｕｓｑｕｉｎｑｕｅｃｏｓｔａｔｕｓ Ｃｅｌａｋ－） 染色体数目２ｎ＝ ２６ , 核型公式Ｋ （２ｎ） ＝ ２ｘ＝ ２６ ＝ ８ Ｍ ＋ １８ｍ , “１Ａ”类型; 蛇床（ Ｃｎｉｄｉｕｍ ｍｏｎｎｉｅｒｉ（Ｌ－） Ｃｕｓｓ－） 染色体数目２ｎ＝ ２０ , 核型公式Ｋ （２ｎ） ＝ ２ｘ＝ ２０ ＝ ２Ｍ＋ １６ｍ ＋ ２ｓｍ , “２Ｂ”类型; 波斯菊（ Ｃｏｓｍｏｓ ｂｉｐｉｎｎａｔｕｓ Ｃａｖ－） 染色体数目２ｎ ＝ ２４ , 核型公式Ｋ（２ｎ） ＝ ２ｘ ＝ ２４ ＝ １６ｍ ＋ ２ｍ （ｓａｔ） ＋ ６ｓｍ , “２Ａ”类型; 白车轴草（ Ｔｒｉｆｏｌｉｕｍ ｒｅｐｅｎｓ Ｌ－） 染色体数目２ｎ＝ ３２ , 核型公式Ｋ （２ｎ） ＝ ４ｘ ＝ ３２ ＝ ３２ｍ , “１Ａ”类型; 铁苋菜（ Ａｃａｌｙｐｈａ ａｕｓｔｒａｌｉｓ Ｌ－）染色体数目２ｎ ＝ ３２ , 核型公式Ｋ （２ｎ） ＝ ２ｘ＝ ３２ ＝ ３２ｍ , “１Ｂ”类型; 地构叶（ Ｓｐｅｒａｎｓｋｉａ ｔ?     

The log multinomial regression model for nominal outcomes with more than two attributes

An estimate of the risk or prevalence ratio, adjusted for confounders, can be obtained from a log binomial model (binomial errors, log link) fitted to binary outcome data. We propose a modification of the log binomial model to obtain relative risk estimates for nominal outcomes with more than two attributes (the "log multinomial model"). Extensive data simulations were undertaken to compare the performance of the log multinomial model with that of an expanded data multinomial logistic regression method based on the approach proposed by Schouten et al. (1993) for binary data, and with that of separate fits of a Poisson regression model based on the approach proposed by Zou (2004) and Carter, Lipsitz and Tilley (2005) for binary data. Log multinomial regression resulted in "inadmissable" solutions (out-of-bounds probabilities) exceeding 50% in some data settings. Coefficient estimates by the alternative methods produced out-of-bounds probabilities for the log multinomial model in up to 27% of samples to which a log multinomial model had been successfully fitted. The log multinomial coefficient estimates generally had lesser relative bias and mean squared error than the alternative methods. The practical utility of the log multinomial regression model was demonstrated with a real data example. The log multinomial model offers a practical solution to the problem of obtaining adjusted estimates of the risk ratio in the multinomial setting, but must be used with some care and attention to detail.     

The statistical analysis of the differential blood count

The differential blood count obtained by unbiased sampling mathematically follows a multinomial distribution. The variation between individuals can be formalized by a mixing distribution of the parameters of the multinomial distribution. By DIRICHLET-distributions used as mixing distributions the main phenomena of the observed data can be described, and they are useful in estimating and testing treatment effects. If the correlation between the different types of leucocytes is taken into account in an appropriate manner, univariate test procedures can be applied also.     

Novel hybrid method for the evaluation of parameters contributing in determination of protein structural classes

Due to the increasing gap between structure-determined and sequenced proteins, prediction of protein structural classes has been an important problem. It is very important to use efficient sequential parameters for developing class predictors because of the close sequence-structure relationship. The multinomial logistic regression model was used for the first time to evaluate the contribution of sequence parameters in determining the protein structural class. An in-house program generated parameters including single amino acid and all dipeptide composition frequencies. Then, the most effective parameters were selected by a multinomial logistic regression. Selected variables in the multinomial logistic model were Valine among single amino acid composition frequencies and Ala-Gly, Cys-Arg, Asp-Cys, Glu-Tyr, Gly-Glu, His-Tyr, Lys-Lys, Leu-Asp, Leu-Arg, Pro-Cys, Gln-Met, Gln-Thr, Ser-Trp, Val-Asn and Trp-Asn among dipeptide composition frequencies. Also a neural network model was constructed and fed by the parameters selected by multinomial logistic regression to build a hybrid predictor. In this study, self-consistency and jackknife tests on a database constructed by Zhou [1998. An intriguing controversy over protein structural class prediction. J. Protein Chem. 17(8), 729-738] containing 498 proteins are used to verify the performance of this hybrid method, and are compared with some of prior works. The results showed that our two-stage hybrid model approach is very promising and may play a complementary role to the existing powerful approaches.     

Monitoring habitat types by the mixed multinomial logit model using panel data

Habitats in the Wadden Sea, a world heritage area, are affected by land subsidence resulting from natural gas extraction and by sea level rise. Here we describe a method to monitor changes in habitat types by producing sequential maps based on point information followed by mapping using a multinomial logit regression model with abiotic variables of which maps are available as predictors.In a 70 ha study area a total of 904 vegetation samples has been collected in seven sampling rounds with an interval of 2–3 years. Half of the vegetation plots was permanent, violating the assumption of independent data in multinomial logistic regression. This paper shows how this dependency can be accounted for by adding a random effect to the multinomial logit (MLN) model, thus becoming a mixed multinomial logit (MMNL) model. In principle all regression coefficients can be taken as random, but in this study only the intercepts are treated as location-specific random variables (random intercepts model). With six habitat types we have five intercepts, so that the number of extra model parameters becomes 15, 5 variances and 10 covariances.The likelihood ratio test showed that the MMNL model fitted significantly better than the MNL model with the same fixed effects. McFadden-R² for the MMNL model was 0.467, versus 0.395 for the MNL model. The estimated coefficients of the MMNL and MNL model were comparable; those of altitude, the most important predictor, differed most. The MMNL model accounts for pseudo-replication at the permanent plots, which explains the larger standard errors of the MMNL coefficients. The habitat type at a given location-year combination was predicted by the habitat type with the largest predicted probability. The series of maps shows local trends in habitat types most likely driven by sea-level rise, soil subsidence, and a restoration project.We conclude that in environmental modeling of categorical variables using panel data, dependency of repeated observations at permanent plots should be accounted for. This will affect the estimated probabilities of the categories, and even stronger the standard errors of the regression coefficients.     

Scalable estimation and regularization for the logistic normal multinomial model

Clustered multinomial data are prevalent in a variety of applications such as microbiome studies, where metagenomic sequencing data are summarized as multinomial counts for a large number of bacterial taxa per subject. Count normalization with ad hoc zero adjustment tends to result in poor estimates of abundances for taxa with zero or small counts. To account for heterogeneity and overdispersion in such data, we suggest using the logistic normal multinomial (LNM) model with an arbitrary correlation structure to simultaneously estimate the taxa compositions by borrowing information across subjects. We overcome the computational difficulties in high dimensions by developing a stochastic approximation EM algorithm with Hamiltonian Monte Carlo sampling for scalable parameter estimation in the LNM model. The ill‐conditioning problem due to unstructured covariance is further mitigated by a covariance‐regularized estimator with a condition number constraint. The advantages of the proposed methods are illustrated through simulations and an application to human gut microbiome data.     

A Comparison of Regression Models for Small Counts

ABSTRACT Count data with means <2 are often assumed to follow a Poisson distribution. However, in many cases these kinds of data, such as number of young fledged, are more appropriately considered to be multinomial observations due to naturally occurring upper truncation of the distribution. We evaluated the performance of several versions of multinomial regression, plus Poisson and normal regression, for analysis of count data with means <2 through Monte Carlo simulations. Simulated data mimicked observed counts of number of young fledged (0, 1, 2, or 3) by California spotted owls (Strix occidentalis occidentalis). We considered size and power of tests to detect differences among 10 levels of a categorical predictor, as well as tests for trends across 10-year periods. We found regular regression and analysis of variance procedures based on a normal distribution to perform satisfactorily in all cases we considered, whereas failure rate of multinomial procedures was often excessively high, and the Poisson model demonstrated inappropriate test size for data where the variance/mean ratio was <1 or >1.2. Thus, managers can use simple statistical methods with which they are likely already familiar to analyze the kinds of count data we described here.     

Multinomial-Sampling Models for Random Genetic Drift

Three different derivations of models with multinomial sampling of genotypes in a finite population are presented. The three derivations correspond to the operation of random drift through population regulation, conditioning on the total number of progeny, and culling, respectively. Generations are discrete and nonoverlapping; the diploid population mates at random. Each derivation applies to a single multiallelic locus in a monoecious or dioecious population; in the latter case, the locus may be autosomal or X-linked. Mutation and viability selection are arbitrary; there are no fertility differences. In a monoecious population, the model yields the Wright-Fisher model (i.e., multinomial sampling of genes) if and only if the viabilities are multiplicative. In a dioecious population, the analogous reduction does not occur even for pure random drift. Thus, multinomial sampling of genotypes generally does not lead to multinomial sampling of genes. Although the Wright-Fisher model probably lacks a sound biological basis and may be inaccurate for small populations, it is usually (perhaps always) a good approximation for genotypic multinomial sampling in large populations.     

Comparison of Populations Based on Attribute Data,when Covariance Matrices are Unequal

Mahalanobis—like distance indices B² and G² of BALAKRISHNAN and SANGHVI (1968) are employed to compare multinomial populations of proportions in human data. Recently, BOWERING and MISRA (1982) employed these methods, with modifications, in a different field viz. to compare fish stocks, based on meristic characters. The method of BALAKRISHNAN and SANGHVI (1968), however, assumes equality of covariance matrices, which is doubtful with multinomial populations of proportions. In this paper a statistical method for comparing such populations (where covariance matrices are not equal) is presented. Also, a feature of the G² method of BALAKRISHNAN and SANGHVI (1968) is discussed, which makes its statistical status a little more clear.     

自我重复取样技术与多项式定理在年龄-龄期两性生命表研究中的应用

年龄-龄期两性生命表(age-stage, two-sex life table)简称两性生命表,是种群生态学研究与害虫治理中常用的重要理论与分析工具。根据两性生命表理论而设计的方便用户的软件TWOSEX-MSChart近年来被越来越多国内外学者用于昆虫种群研究的数据分析。两性生命表软件的分析功能是由许多的统计技术与计算机模拟方法作为数据分析的支撑,其中自我重复取样(bootstrap)是其重要技术之一。本文详述了bootstrap技术的基本原理、方法、优缺点及其在两性生命表分析中的应用,并介绍了其理论基础多项式定理(multinomial theorem)在生命表研究中的应用。与常用统计方法相比,bootstrap不需要数据分布假设就可以对数据总体的分布特性进行统计和推断。在两性生命表分析中,bootstrap不仅可以估算种群参数或一般统计值的方差和标准误,同时利用paired bootstrap test还可以比较不同处理间的差异,准确显示种群的变异性。利用相同的自我重复取样样本(same bootstrap samples)可以正确计算昆虫的孵化率与不同繁殖型对种群参数的贡献,并可连接天敌的生命表与捕食率分析,以正确分析昆虫种群的繁殖力或天敌种群的捕食潜能等。此外,本文介绍了bootstrap数学理论基础的多项式定理,其在两性生命表研究中的应用进一步证明了bootstrap重复取样技术能得到较稳定和可靠的总体参数估计,并分析了生命表研究中考虑无效bootstrap样本的重要性。近年来,关于两性生命表及bootstrap的应用相关研究较多,但是关于bootstrap技术的原理及方法很少报道。本文将有助于从事昆虫学和生态学的研究人员理解bootstrap技术和多项式定理的基本理论与原理及其在两性生命表分析中的应用,以更好地将其运用于相关科研工作中。     

CO2激光对茄子幼苗子叶干重及叶面积增长的数学模拟

采用功率密度为825mw/cm^2的CO2激光对茄子干种子进行10s,13s,15s的辐照处理,0s为对照组,然后播种于基质为土的苗盘中,每个处理200粒种子,三次重复,随机排列,出苗10天开始测定不同处理的子叶干重、子叶面积的变化,然后应用多项式回归进行数学模型,探讨了激光处理对子叶生长的影响及子叶生长规律。