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Academic literature on the topic 'CLUSTER VARIANCE'

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Published: 11 September 2023

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Journal articles on the topic "CLUSTER VARIANCE"

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Gregg, Mary, Somnath Datta, and Doug Lorenz. "Variance estimation in tests of clustered categorical data with informative cluster size." Statistical Methods in Medical Research 29, no. 11 (June 8, 2020): 3396–408. http://dx.doi.org/10.1177/0962280220928572.

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In the analysis of clustered data, inverse cluster size weighting has been shown to be resistant to the potentially biasing effects of informative cluster size, where the number of observations within a cluster is associated with the outcome variable of interest. The method of inverse cluster size reweighting has been implemented to establish clustered data analogues of common tests for independent data, but the method has yet to be extended to tests of categorical data. Many variance estimators have been implemented across established cluster-weighted tests, but potential effects of differing methods on test performance has not previously been explored. Here, we develop cluster-weighted estimators of marginal proportions that remain unbiased under informativeness, and derive analogues of three popular tests for clustered categorical data, the one-sample proportion, goodness of fit, and independence chi square tests. We construct these tests using several variance estimators and show substantial differences in the performance of cluster-weighted tests based on variance estimation technique, with variance estimators constructed under the null hypothesis maintaining size closest to nominal. We illustrate the proposed tests through an application to a data set of functional measures from patients with spinal cord injuries participating in a rehabilitation program.
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Sankaran, M., M. R. Dinesh, D. C. S. Gowda, and R. Venugopalan. "Genetic analysis in mango (Mangifera indica L.) based on fruit characteristics of 400 genotypes." Journal of Horticultural Sciences 15, no. 2 (December 31, 2020): 161–72. http://dx.doi.org/10.24154/jhs.2020.v15i02.007.

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The analysis of variance for 6 quantitative traits and 30 qualitative traits showed significant differences among the 400 genotypes of mango which indicates the existence of high heterozygosity. Among the 18 clusters formed, the highest fruit weight of 1404.27 g was recorded in cluster 10 followed by cluster 15 with 1280.67g whereas the lowest fruit weight was recorded in cluster 16 (30.94g). The highest fruit length (22.03 cm) was recorded in cluster 10 followed by 17.80 cm in cluster 14. Similarly, the fruit diameter was highest (12.18 cm) in cluster 10 followed by 12.03 cluster 4. The fruit thickness was highest (10.60 cm) in cluster 15 followed by cluster 4 with 9.96 cm. The pulp recovery was maximum (87.16%) in cluster-14 followed by clusters 4 and 18 with 79.28 and 78.41 %, respectively. Clusters 15 had the varieties meant for pickle making and possessed less TSS whereas the TSS of above 19°B was recorded in cluster 2. The maximum inter-cluster (D2) value was obtained between cluster 10 and cluster 11. These clusters may be used for hybridization programs due to wide variability and the possibility of transgressive sergeants. Estimates of phenotypic variance and genotypic variance had only a narrow difference for all six characters studied indicating that these characters are not much influenced by environmental factors and highly heritable which can be exploited by adopting clonal selection or selection of chance seedlings and selection as parents for breeding purpose.
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Sankaran, M., M. R. Dinesh, D. C. S. Gowda, and R. Venugopalan. "Genetic analysis in mango (Mangifera indica L.) based on fruit characteristics of 400 genotypes." Journal of Horticultural Sciences 15, no. 2 (December 31, 2020): 161–72. http://dx.doi.org/10.24154/jhs.v15i2.944.

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The analysis of variance for 6 quantitative traits and 30 qualitative traits showed significant differences among the 400 genotypes of mango which indicates the existence of high heterozygosity. Among the 18 clusters formed, the highest fruit weight of 1404.27 g was recorded in cluster 10 followed by cluster 15 with 1280.67g whereas the lowest fruit weight was recorded in cluster 16 (30.94g). The highest fruit length (22.03 cm) was recorded in cluster 10 followed by 17.80 cm in cluster 14. Similarly, the fruit diameter was highest (12.18 cm) in cluster 10 followed by 12.03 cluster 4. The fruit thickness was highest (10.60 cm) in cluster 15 followed by cluster 4 with 9.96 cm. The pulp recovery was maximum (87.16%) in cluster-14 followed by clusters 4 and 18 with 79.28 and 78.41 %, respectively. Clusters 15 had the varieties meant for pickle making and possessed less TSS whereas the TSS of above 19°B was recorded in cluster 2. The maximum inter-cluster (D2) value was obtained between cluster 10 and cluster 11. These clusters may be used for hybridization programs due to wide variability and the possibility of transgressive sergeants. Estimates of phenotypic variance and genotypic variance had only a narrow difference for all six characters studied indicating that these characters are not much influenced by environmental factors and highly heritable which can be exploited by adopting clonal selection or selection of chance seedlings and selection as parents for breeding purpose.
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Yasmeen, Uzma, Muhammad Noor-ul-Amin, and Muhammad Hanif. "Variance estimation in stratified adaptive cluster sampling." Statistics in Transition New Series 23, no. 1 (March 1, 2022): 173–84. http://dx.doi.org/10.2478/stattrans-2022-0010.

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Abstract In many sampling surveys, the use of auxiliary information at either the design or estimation stage, or at both these stages is usual practice. Auxiliary information is commonly used to obtain improved designs and to achieve a high level of precision in the estimation of population density. Adaptive cluster sampling (ACS) was proposed to observe rare units with the purpose of obtaining highly precise estimations of rare and specially clustered populations in terms of least variances of the estimators. This sampling design proved to be more precise than its more conventional counterparts, including simple random sampling (SRS), stratified sampling, etc. In this paper, a generalised estimator is anticipated for a finite population variance with the use of information of an auxiliary variable under stratified adaptive cluster sampling (SACS). The bias and mean square error expressions of the recommended estimators are derived up to the first degree of approximation. A simulation study showed that the proposed estimators have the least estimated mean square error under the SACS technique in comparison to variance estimators in stratified sampling.
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Veenman, C. J., M. J. T. Reinders, and E. Backer. "A maximum variance cluster algorithm." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 9 (September 2002): 1273–80. http://dx.doi.org/10.1109/tpami.2002.1033218.

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A.K, et al., Shukla. "Variance Function in Cluster Sampling." International Journal of Computational and Theoretical Statistics 2, no. 1 (May 1, 2015): 25–30. http://dx.doi.org/10.12785/ijcts/020103.

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Terry, L. Irene, and Gloria DeGrandi-Hoffman. "MONITORING WESTERN FLOWER THRIPS (THYSANOPTERA: THRIPIDAE) IN “GRANNY SMITH” APPLE BLOSSOM CLUSTERS." Canadian Entomologist 120, no. 11 (November 1988): 1003–16. http://dx.doi.org/10.4039/ent1201003-11.

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AbstractThe efficiency and accuracy of sampling western flower thrips (Frankliniella occidentalis [Pergande]) from “Granny Smith” apple blossom clusters were analyzed during 1986–1987 to develop a sampling plan for research purposes. The accuracy of the “shake” method was compared with an “extraction” process of each of three blossom cluster types: pink, open, and petalless (petal fall). Thrip extractions from combined clusters revealed that a 9-s and 6-s “shake” removed 84 and 74%, of the thrips, respectively, but a 3-s “shake” removed 53%, and was more variable. Open blossom clusters always had higher thrips densities than either pink or petal fall clusters, regardless of the bloom state. The effects of cardinal position within trees were not consistent over time. Clusters from the top of the canopy had more thrips than lower canopy clusters, and apical clusters had more thrips than basal clusters during peak bloom. Variance component analyses indicated that thrips counts from clusters within tree were more variable than counts among trees, even when cluster types were analyzed separately. Two sets of indices (Iwao’s regression of mean crowding on mean density and Taylor’s regression of log variance on log mean density) for each cluster type indicated aggregated spatial patterns. Precision level sampling plans were developed using Iwao’s regression coefficients.
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Vostrá Vydrová, Hana, and Zuzana Novotná. "Evaluation of disparities in living standards of regions of the Czech Republic." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 60, no. 4 (2012): 407–14. http://dx.doi.org/10.11118/actaun201260040407.

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This paper focuses on regional differences between the regions of the Czech Republic. We will focus on observation of inequalities between indicators of living in different regions of the Czech Republic. The indicators are evaluated at NUTS 3 (regions), using multivariate statistical techniques - factor analysis and cluster analysis. We have identified the twelve indicators of living standards. Base data was reduced using factor analysis on the three emerging factors: 1) basic characteristics, 2) risk groups, 3) environmental variable. Cluster analysis was compiled groups of regions with similar characteristics. Cluster analysis of the breakdown of the county into three clusters based on selected indicators of living standards. They can be described as a group with higher average and lower standard of living. In the first cluster are only two regions (Liberec Region and Karlovy Vary), the third cluster is composed of Prague and the second cluster includes all other regions of the Czech Republic. To verify the evidence of differences between clusters were calculated by multivariate analysis of variance for the various indicators of living standards. An analysis of variance indicates that significant differences between clusters are caused by the standard of living indicators: GDP (regional), the average wage of women, medical equipment, culture entertainment and recreation, higher education, the disabled handicapped and older people. The data were processed in the program STATISTICA 10th.
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Scott, JoAnna M., Allan deCamp, Michal Juraska, Michael P. Fay, and Peter B. Gilbert. "Finite-sample corrected generalized estimating equation of population average treatment effects in stepped wedge cluster randomized trials." Statistical Methods in Medical Research 26, no. 2 (September 29, 2014): 583–97. http://dx.doi.org/10.1177/0962280214552092.

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Stepped wedge designs are increasingly commonplace and advantageous for cluster randomized trials when it is both unethical to assign placebo, and it is logistically difficult to allocate an intervention simultaneously to many clusters. We study marginal mean models fit with generalized estimating equations for assessing treatment effectiveness in stepped wedge cluster randomized trials. This approach has advantages over the more commonly used mixed models that (1) the population-average parameters have an important interpretation for public health applications and (2) they avoid untestable assumptions on latent variable distributions and avoid parametric assumptions about error distributions, therefore, providing more robust evidence on treatment effects. However, cluster randomized trials typically have a small number of clusters, rendering the standard generalized estimating equation sandwich variance estimator biased and highly variable and hence yielding incorrect inferences. We study the usual asymptotic generalized estimating equation inferences (i.e., using sandwich variance estimators and asymptotic normality) and four small-sample corrections to generalized estimating equation for stepped wedge cluster randomized trials and for parallel cluster randomized trials as a comparison. We show by simulation that the small-sample corrections provide improvement, with one correction appearing to provide at least nominal coverage even with only 10 clusters per group. These results demonstrate the viability of the marginal mean approach for both stepped wedge and parallel cluster randomized trials. We also study the comparative performance of the corrected methods for stepped wedge and parallel designs, and describe how the methods can accommodate interval censoring of individual failure times and incorporate semiparametric efficient estimators.
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Grilli, Leonardo, and Carla Rampichini. "The Role of Sample Cluster Means in Multilevel Models." Methodology 7, no. 4 (August 1, 2011): 121–33. http://dx.doi.org/10.1027/1614-2241/a000030.

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The paper explores some issues related to endogeneity in multilevel models, focusing on the case where the random effects are correlated with a level 1 covariate in a linear random intercept model. We consider two basic specifications, without and with the sample cluster mean. It is generally acknowledged that the omission of the cluster mean may cause omitted-variable bias. However, it is often neglected that the inclusion of the sample cluster mean in place of the population cluster mean entails a measurement error that yields biased estimators for both the slopes and the variance components. In particular, the contextual effect is attenuated, while the level 2 variance is inflated. We derive explicit formulae for measurement error biases that allow us to implement simple post-estimation corrections based on the reliability of the covariate. In the first part of the paper, the issue is tackled in a standard framework where the population cluster mean is treated as a latent variable. Later we consider a different framework arising when sampling from clusters of finite size, where the latent variable methods may have a poor performance, and we show how to effectively modify the measurement error correction. The theoretical analysis is supplemented with a simulation study and a discussion of the implications for effectiveness evaluation.
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Dissertations / Theses on the topic "CLUSTER VARIANCE"

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Akdemir, Deniz. "Components Of Response Variance For Cluster Samples." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1206044/index.pdf.

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Measures of data quality are important for the evaluation and improvement of survey design and procedures. A detailed investigation of the sources, magnitude and impact of errors is necessary to identify how survey design and procedures may be improved and how resources allocated more efficiently among various aspects of the survey operation. A major part of this thesis is devoted to the overview of statistical theory and methods for measuring the contribution of response variability to the overall error of a survey. A very common practice in surveys is to select groups (clusters) of elements together instead of independent selection of elements. In practice cluster samples tend to produce higher sampling variance for statistics than element samples of the same size. Their frequent use stems from the desirable cost features that they have. Most data collection and sample designs involve some overlapping between interviewer workload and the sampling units (clusters). For those cases, a proportion of the measurement variance, which is due to interviewers, is reflected to some degree in the sampling variance calculations. The prime purpose in this thesis is to determine a variance formula that decomposes the total variance into sampling and measurement variance components for two commonly used data collection and sample designs. Once such a decomposition is obtained, determining an optimum allocation in existence of measurement errors would be possible.
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You, Zhiying. "Power and sample size of cluster randomized trials." Thesis, Birmingham, Ala. : University of Alabama at Birmingham, 2008. https://www.mhsl.uab.edu/dt/2009r/you.pdf.

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Park, Misook. "Design and Analysis Methods for Cluster Randomized Trials with Pair-Matching on Baseline Outcome: Reduction of Treatment Effect Variance." VCU Scholars Compass, 2006. http://hdl.handle.net/10156/2195.

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Dunning, Allison. "Comparing Bootstrap and Jackknife Variance Estimation Methods for Area Under the ROC Curve Using One-Stage Cluster Survey Data." VCU Scholars Compass, 2009. http://scholarscompass.vcu.edu/etd/1849.

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The purpose of this research is to examine the bootstrap and jackknife as methods for estimating the variance of the AUC from a study using a complex sampling design and to determine which characteristics of the sampling design effects this estimation. Data from a one-stage cluster sampling design of 10 clusters was examined. Factors included three true AUCs (.60, .75, and .90), three prevalence levels (50/50, 70/30, 90/10) (non-disease/disease), and finally three number of clusters sampled (2, 5, or 7). A simulated sample was constructed for each of the 27 combinations of AUC, prevalence and number of clusters. Estimates of the AUC obtained from both the bootstrap and jackknife methods provide unbiased estimates for the AUC. In general it was found that bootstrap variance estimation methods provided smaller variance estimates. For both the bootstrap and jackknife variance estimates, the rarer the disease in the population the higher the variance estimate. As the true area increased the variance estimate decreased for both the bootstrap and jackknife methods. For both the bootstrap and jackknife variance estimates, as number of clusters sampled increased the variance decreased, however the trend for the jackknife may be effected by outliers. The National Health and Nutrition Examination Survey (NHANES) conducted by the CDC is a complex survey which implements the use of the one-stage cluster sampling design. A subset of the 2001-2002 NHANES data was created looking only at adult women. A separate logistic regression analysis was conducted to determine if exposure to certain furans in the environment have an effect on abnormal levels of four hormones (FSH, LH, TSH, and T4) in women. Bootstrap and jackknife variance estimation techniques were applied to estimate the AUC and variances for the four logistic regressions. The AUC estimates provided by both the bootstrap and jackknife methods were similar, with the exception of LH. Unlike in the simulated study, the jackknife variance estimation method provided consistently smaller variance estimates than bootstrap. AUC estimates for all four hormones suggested that exposure to furans effects abnormal levels of hormones more than expected by chance. The bootstrap variance estimation technique provided better variance estimates for AUC when sampling many clusters. When only sampling a few clusters or as in the NHANES study where the entire population was treated as a single cluster, the jackknife variance estimation method provides smaller variance estimates for the AUC.
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Randriatsiferana, Rivo Sitraka A. "Optimisation énergétique des protocoles de communication des réseaux de capteurs sans fil." Thesis, La Réunion, 2014. http://www.theses.fr/2014LARE0019/document.

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Pour augmenter la durée de vie des réseaux de capteurs sans fil, une solution est d'améliorer l'efficacité énergétique des protocoles de communication. Le regroupement des nœuds du réseau de capteurs sans fil en cluster est l'une des meilleures méthodes. Cette thèse présente propose plusieurs améliorations en modifiant les paramètres du protocole de référence LEACH. Pour améliorer la distribution énergétique des "cluster-heads", nous proposons deux protocoles de clustering centralisés k-LEACH et sa version optimisée k-LEACH-VAR. Un algorithme distribué, appelé e-LEACH, est également proposé pour réduire l'échange d'information périodique entre les nœuds et la station de base lors de l'élection des "cluster-heads". Par ailleurs, le concept l'équilibrage énergétique est introduit dans les métriques d'élection pour éviter les surcharges des nœuds. Ensuite, nous présentons une version décentralisée de k-LEACH qui, en plus des objectifs précédents, intègre la consommation d'énergie globale du réseau. Ce protocole, appelé, k-LEACH-C2D, vise également à favoriser la scalabilité du réseau. Pour renforcer ce dernier et l'autonomie des réseaux, les deux protocoles de routage "multi-hop" probabiliste, dénotés FRSM et CB-RSM construisent des chemins élémentaires entre les "cluster-heads" et la station de base. Le protocole CB-RSM forme une hiérarchie des "cluster-heads" pendant la phase de formation des clusters, en mettant un accent sur l'auto-ordonnancement et l'auto-organisation entre les "cluster-heads" pour rendre les réseaux le plus "scalable". Ces différents protocoles reposent sur l'idée de base que les nœuds ayant l'énergie résiduelle la plus élevée et la plus faible variance de consommation de l'énergie deviennent "cluster-head". Nous constantans le rôle central de la consommation du nœud dans nos différentes propositions. Ce point fera l'objet de la dernière partie de cette thèse. Nous proposons une méthodologie pour caractériser expérimentalement la consommation d'un nœud. Les objectifs visent à mieux appréhender la consommation pour différentes séquences d'état du nœud. Enfin, nous proposons un modèle global de la consommation du nœud
To increase the lifetime of wireless sensor networks, a solution is to improve the energy efficiency of the communication's protocol. The grouping of nodes in the wireless sensor network clustering is one of the best methods. This thesis proposes several improvements by changing the settings of the reference protocol LEACH. To improve the energy distribution of "cluster-heads", we propose two centralized clustering protocols LEACH and k-optimized version k-LEACH-VAR. A distributed algorithm, called e-LEACH, is proposed to reduce the periodic exchange of information between the nodes and the base station during the election of "cluster-heads". Moreover, the concept of energy balance is introduced in metric election to avoid overloading nodes. Then we presented a decentralized version of k-LEACH, which in addition to the previous objectives, integrates the overall energy consumption of the network. This protocol, called k-LEACH-C2D, also aims to promote the scalability of the network. To reinforce the autonomy and networks, both routing protocols "multi-hop" probability, denoted CB-RSM and FRSM build elementary paths between the "cluster-heads" and elected the base station. The protocol, CB-RSM, forms a hierarchy of "cluster-heads" during the training phase clusters, with an emphasis on self-scheduling and self-organization between "cluster-heads" to make the networks more scalable. These protocols are based on the basic idea that the nodes have the highest residual energy and lower variance of energy consumption become "cluster-head". We see the central role of consumption of the node in our proposals. This point will be the last part of this thesis. We propose a methodology to characterize experimentally the consumption of a node. The objectives are to better understand the consumption for different sequences of the node status. In the end, we propose a global model of the consumption of the node
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Diaz, Acosta Beatriz. "Experiments in Image Segmentation for Automatic US License Plate Recognition." Thesis, Virginia Tech, 2004. http://hdl.handle.net/10919/9988.

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License plate recognition/identification (LPR/I) applies image processing and character recognition technology to identify vehicles by automatically reading their license plates. In the United States, however, each state has its own standard-issue plates, plus several optional styles, which are referred to as special license plates or varieties. There is a clear absence of standardization and multi-colored, complex backgrounds are becoming more frequent in license plates. Commercially available optical character recognition (OCR) systems generally fail when confronted with textured or poorly contrasted backgrounds, therefore creating the need for proper image segmentation prior to classification. The image segmentation problem in LPR is examined in two stages: license plate region detection and license plate character extraction from background. Three different approaches for license plate detection in a scene are presented: region distance from eigenspace, border location by edge detection and the Hough transform, and text detection by spectral analysis. The experiments for character segmentation involve the RGB, HSV/HSI and 1976 CIE L*a*b* color spaces as well as their Karhunen-Loéve transforms. The segmentation techniques applied include multivariate hierarchical agglomerative clustering and minimum-variance color quantization. The trade-off between accuracy and computational expense is used to select a final reliable algorithm for license plate detection and character segmentation. The spectral analysis approach together with the K-L L*a*b* transformed color quantization are found experimentally as the best alternatives for the two identified image segmentation stages for US license plate recognition.
Master of Science
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Piana, Clause Fátima de Brum. "Regionalização para o cultivo do feijão no Rio Grande do Sul com base na interação genótipo x ambiente." Universidade Federal de Pelotas, 2009. http://repositorio.ufpel.edu.br/handle/ri/2081.

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Made available in DSpace on 2014-08-20T14:06:15Z (GMT). No. of bitstreams: 1 Tese_ Clause_Fatima_ Piana.pdf: 1906168 bytes, checksum: 6bfd8bec615dee869166bbd0c0269e0d (MD5) Previous issue date: 2009-03-13
In Brazil, common bean (Phaseolus vulgaris L.) is cultivated in a range of ecologically differentiated environments. For being a culture highly influenced by the environment variation, its average productivity in the Country is unstable and low. An origin of this variation of productivity is the genotype x environment interaction, which has been one of the largest impediments for obtaining genotypes that maintain consistently high yield in the growing environments. The methods proposed for the exploration of the genotype x environment interaction are directed to the stability of the yield of the genotypes or to the regionalization of the growing locations. Most of the common bean genotypes registered for cultivation in Rio Grande do Sul evidences yield instability. The present research explored data from Rio Grande do Sul Common Bean State Trial ("Ensaio Estadual de Feijão" - EEF), executed at 24 locations in the period from 1987/88 to 1994/95, with considerable variation of genotypes and locations among those years. This research had two main objectives: (1) to evaluate the magnitude and the nature of the genotype x environment interaction and (2) to identify possible stratification of the growing region of common bean in the State in sub-regions inside of which the genotypes have stable relative performance. The inferences about the components of the interaction genotype x environment were proceeded by the joint analysis of each one of the eight years and the analyses of two subsets of four years and of the set of eight years. Because of the intent of obtaining a long time regionalization, general for the growing location of the Rio Grande do Sul and for any collection of beans genotypes, the factors year, location and genotype were considered random. The maximum likelihood and the generalized minimum squares methods were used. This approach allowed taking into account the incomplete and unbalanced structure of the data and the heterogeneity of variance of the experimental error. The results of the annual analyses revealed high significance of the component of the interaction genotype x location in all of the years, indicating that the relative performance of the genotypes varies among locations. This interaction was also revealed significant in the analysis of the eight years, but was not significant in the analyses of the two subsets four years. In these three joint analyses of years the triple interaction genotype x location x year was highly significant. The indication of heterogeneous performance of the genotypes among the locations and the possibility that the pattern of performance have some consistence along the years justified the attempt to the grouping of the locations. Cluster analyses were performed for each one of the eight years and for the set of eight years by the method of Sokal and Michener, that uses the Euclidean distance as similarity measure. The cluster analysis of the set of eight years constituted subregions that are generally incoherent with the sub-regions formed by the annual analyses that, by they turn, were inconsistent amongst themselves. This incoherence and inconsistency of groupings disabled the characterization of a division of the State for the regionalization of the indication of cultivars. It should be observed, however, that these evidences might have been influenced by the considerable alterations of the genotypes and of the locations of execution of the EEF among the years of the period from 1987/88 to 1994/95 in whose data they are based. They can also have resulted, partly, of flaws of the experimental techniques adopted in that period of execution of EEF, particularly of the accentuated variations of the sowing date and of the stand by plot.
No Brasil, o feijão (Phaseolus vulgaris L.) é cultivado em uma gama de ambientes ecologicamente diferenciados. Por ser uma cultura altamente influenciada pela variação de ambiente, sua produtividade média no país é instável e baixa. Uma origem da oscilação da produtividade é a interação genótipo x ambiente, a qual tem sido um dos maiores entraves para a obtenção de genótipos que mantenham rendimentos consistentemente elevados nos diversos ambientes de cultivo. Os métodos propostos para a exploração da interação genótipo x ambiente são direcionados para a estabilidade do rendimento dos genótipos ou para a regionalização dos locais de cultivo. A maioria dos genótipos de feijão registrados para cultivo no Rio Grande do Sul evidencia instabilidade de rendimento. A presente pesquisa explorou dados do Ensaio Estadual de Feijão (EEF) do Rio Grande do Sul, conduzido em 24 locais no período de 1987/88 a 1994/95, com variação considerável de genótipos e de locais entre esses anos. Essa pesquisa teve dois objetivos principais: (1) avaliar a magnitude e a natureza da interação genótipo x ambiente e (2) identificar possível estratificação da região de cultivo do feijão no Estado em sub-regiões dentro das quais os genótipos tenham desempenho relativo estável. As inferências sobre os componentes da interação genótipo x ambiente foram procedidas pela análise conjunta de cada um dos oito anos e as análises de dois subconjuntos de quatro anos e do conjunto dos oito anos. Em razão de se pretender lograr uma regionalização de longo prazo, geral para os locais de cultivo do Rio Grande do Sul e para qualquer coleção de genótipos de feijão, os fatores ano, local e genótipo foram considerados aleatórios. Foram utilizadas as metodologias de máxima verossimilhança e quadrados mínimos generalizados. Essa abordagem permitiu levar em conta a estrutura incompleta e não balanceada dos dados e a heterogeneidade da variância do erro experimental. Os resultados das análises anuais revelaram alta significância do componente da interação genótipo x local em todos os anos, indicando que o desempenho relativo dos genótipos se altera entre os locais. Essa interação também se revelou significativa na análise dos oito anos, mas não significativa nas análises dos dois subconjuntos de quatro anos. Nessas três análises conjuntas de anos a interação tripla genótipo x local x ano foi altamente significativa. A indicação de desempenho heterogêneo dos genótipos entre os locais e a possibilidade do padrão desse desempenho ter alguma consistência ao longo dos anos justificou a tentativa de agrupamento desses locais. Foram efetuadas análises de agrupamento para cada um dos oito anos e para o conjunto dos oito anos, pelo método de Sokal e Michener, que utiliza a distância euclidiana como medida de similaridade. A análise de agrupamento do conjunto dos oito anos constituiu sub-regiões incoerentes com as sub-regiões formadas pelas análises anuais que, por sua vez, foram inconsistentes entre si. Essa incoerência e inconsistência de agrupamentos impossibilitaram a caracterização de uma divisão do Estado para a regionalização da indicação de cultivares. Observe-se, entretanto, que essas evidências podem ter sido influenciadas pelas consideráveis alterações dos genótipos e dos locais de condução do EEF no período de 1987/88 a 1994/95 em cujos dados elas se baseiam. Também podem ter decorrido, em parte, de falhas das técnicas experimentais adotadas nesse período de execução do EEF, particularmente das acentuadas variações da data de semeadura e do estande por parcela.
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Sartorio, Simone Daniela. "Aplicações de técnicas de análise multivariada em experimentos agropecuários usando o software R." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-06082008-172655/.

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O uso das técnicas de análise multivariada está reservado aos grandes centros de pesquisa, µas grandes empresas e ao ambiente acad^emico. Essas técnicas s~ao muito interessantes porque utilizam simultaneamente todas as variáveis respostas na interpretação teórica do conjunto de dados, levando em conta as correlações existentes entre elas. Uma das principais barreiras para a utilização dessas técnicas é o seu desconhecimento pelos pesquisadores interessados na pesquisa quantitativa. A outra dificuldade é que a grande maioria de softwares que permitem esse tipo de análise (SAS, MINITAB, BMDP, STATISTICA, S-PLUS, SYSTAT, etc.) não são de domínio público. A disseminação do uso das técnicas multivariadas pode melhorar a qualidade das pesquisas, proporcionar uma economia relativa de tempo e de custo, e facilitar a interpretação das estruturas dos dados, diminuindo a perda de informação. Neste trabalho, foram confirmadas algumas vantagens das técnicas multivariadas sobre as univariadas na análise de dados de expe- rimentos agropecuários. As análises foram realizadas com o auxílio do software R, um software aberto, \"amigável\" e gratuito, com inúmeros recursos disponíveis.
The use of the techniques of multivariate analysis is restricted to large centers of research, the higher companies and the academic environment. These techniques are very inte- resting because of the use of all answers variables simultaneously in theoretical interpretation of the data set, considering the correlations between them. One of the main obstacle to the usage of these techniques is that researchers interested in the quantitative research do not know them. The other di±culty is that most of the software that allow this type of analysis (SAS, MINITAB, BMDP, STATISTICA, S-PLUS, SYSTAT etc.) are not in public domain. Publishing the use of Multivariate techniques can improve the quality of the research, decrease the time spend and the cost, and make easy the interpretation of the structures of the data without cause damage of the information. In this report, were con¯rmed some advantages of the multivariate techniques in a univariate analysis for data of agricultural experiments. The analysis were taken with R software, a open software, \"friendly\" and free, with many statistical resources available.
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Dimitrakopoulou, Vasiliki. "Bayesian variable selection in cluster analysis." Thesis, University of Kent, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.594195.

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Statistical analysis of data sets of high-dimensionality has met great interest over the past years, with great applications on disciplines such as medicine, nellascience, pattern recognition, image analysis and many others. The vast number of available variables though, contrary to the limited sample size, often mask the cluster structure of the data. It is often that some variables do not help in distinguishing the different clusters in the data; patterns over the samp•.l ed observations are, thus, usually confined to a small subset of variables. We are therefore interested in identifying the variables that best discriminate the sample, simultaneously to recovering the actual cluster structure of the objects under study. With the Markov Chain Monte Carlo methodology being widely established, we investigate the performance of the combined tasks of variable selection and clustering procedure within the Bayesian framework. Motivated by the work of Tadesse et al. (2005), we identify the set of discriminating variables with the use of a latent vector and form the clustering procedure within the finite mixture models methodology. Using Markov chains we draw inference on, not just the set of selected variables and the cluster allocations, but also on the actual number of components: using the f:teversible Jump MCMC sampler (Green, 1995) and a variation of t he SAMS sampler of Dahl (2005). However, sensitivity t o the hyperparameters settings of the covariance structure of the suggested model motivated our interest in an Empirical Bayes procedure to pre-specify the crucial hyper parameters. Further on addressing the problem of II ~----. -- 1 hyperparameters' sensitivity, we suggest several different covariance structures for the mixture components. Developing MATLAB codes for all models introduced in this thesis, we apply and compare the various models suggested on a set of simulated data, as well as on three real data sets; the iris, the crabs and the arthritis data sets.
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Rastelli, Riccardo, and Nial Friel. "Optimal Bayesian estimators for latent variable cluster models." Springer Nature, 2018. http://dx.doi.org/10.1007/s11222-017-9786-y.

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In cluster analysis interest lies in probabilistically capturing partitions of individuals, items or observations into groups, such that those belonging to the same group share similar attributes or relational profiles. Bayesian posterior samples for the latent allocation variables can be effectively obtained in a wide range of clustering models, including finite mixtures, infinite mixtures, hidden Markov models and block models for networks. However, due to the categorical nature of the clustering variables and the lack of scalable algorithms, summary tools that can interpret such samples are not available. We adopt a Bayesian decision theoretical approach to define an optimality criterion for clusterings and propose a fast and context-independent greedy algorithm to find the best allocations. One important facet of our approach is that the optimal number of groups is automatically selected, thereby solving the clustering and the model-choice problems at the same time. We consider several loss functions to compare partitions and show that our approach can accommodate a wide range of cases. Finally, we illustrate our approach on both artificial and real datasets for three different clustering models: Gaussian mixtures, stochastic block models and latent block models for networks.
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Books on the topic "CLUSTER VARIANCE"

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Ritter, Gunter. Robust Cluster Analysis and Variable Selection. Taylor & Francis Group, 2015.

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Ritter, Gunter. Robust Cluster Analysis and Variable Selection. Taylor & Francis Group, 2014.

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Ritter, Gunter. Robust Cluster Analysis and Variable Selection. Taylor & Francis Group, 2014.

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Welty, Daniel E. A search for giant and asymptotic-giant-branch variable stars in six globular clusters. 1986.

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Eiran, Ehud. Post-Colonial Settlement Strategy. Edinburgh University Press, 2019. http://dx.doi.org/10.3366/edinburgh/9781474437578.001.0001.

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Settlement projects are sustained clusters of policies that allow states to strategically plan, implement and support the permanent transfer of nationals into a territory not under their sovereignty. Once a common feature of the international system, settlement projects are now rare, and contradict international norms. Yet, these modern projects had been an important feature of some of the longest conflicts of our times, such as Israel-Palestine and Morocco-Western Sahara. Moreover, they had a profound effect on conflicts: they led to their prolongations, affected their levels of violence, patterns of resolution, as well as post-conflict stability. With this significance in mind, the book asks why states launched new settlement projects during the era of decolonization, against common practice and against international norms. The book introduces the international environment as an important enabling variable for the launch of these projects. By drawing comparisons between three such major projects--Israel in the West Bank and Gaza, Morocco in Western Sahara and Indonesia in East-Timor—the book classifies post-colonial settlement projects as a distinct cluster of cases that warrant a different analytical approach to traditional colonial studies, including settler-colonialism approaches. Built on a careful synthesis of existing principles in international relations theory and empirical research, the book advances a clearly formulated theoretical position on the launch of post-colonial settlement projects. The result yields a number of fresh insights into the relationship between conflict, territory and international norms.
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MacGregor, Alex, Ana Valdes, and Frances M. K. Williams. Genetics of osteoarthritis. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0044.

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In this chapter we outline the approaches which have been adopted to identify genetic variants predisposing to osteoarthritis (OA), a condition long recognized as having a heritable component. Such routes to their identification include examining mendelian traits in which OA is a feature, candidate gene studies based on knowledge of OA pathobiology, linkage analysis in related individuals, and, more recently, genome-wide association studies in large samples of unrelated individuals. It is increasingly evident that the main symptom deriving from OA—notably joint pain—also has a genetic basis but this is differs from that underlying OA. Variants convincingly shown to predispose to OA lie in the GDF5 and MCF2L genes and in the chr7 cluster mapping to the COG5 gene, in addition to the ASPN gene in Asian populations. Those associated with pain in OA include TRPV1 and PACE4. Epigenetic influences are also being explored in both the pathogenesis of OA and the variation of pain processing.
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Coates, David. The effects of clusters on the utility of factor analysis: The implications of systematic interrater variance for the analysis of students' evaluations of teaching. Loughborough University Business School, 1997.

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Fernie, J. D. Variable Stars in Globular Clusters and in Related Systems: Proceedings of the IAU Colloquium No. 21 Held at the University of Toronto, Toronto. Springer, 2011.

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Euro-Asian Astronomical Society, _., ed. Astronomical and Astrophysical Transactions, Vol. 32, No. 2. Cambridge Scientific Publishers, 2021. http://dx.doi.org/10.17184/eac.9781908106797.

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This issue of Astronomical and Astrophysical Transactions comprises the papers presented at the tenth annual conference on Modern Stellar Astronomy, held in Special Astrophysical Observatory of the Russian Academy of Sciences in October 2019. The “Modern Stellar Astronomy” conferences provide a forum for Russian scientists and scientists from the former Soviet Union concerned with stellar astronomy and related topics. The program consisted of invited talks, contributed oral talks, and poster papers. There were about 110 registered participants at the meeting. The program of the 2019 conference included 84 oral and 26 poster presentations. The key topics for the conference were Binary stars, Variable stars, Stellar clusters, Star formation, Exoplanets, Structure, kinematics and dynamics of the Milky Way Galaxy, Other galaxies. This volume comprises eleven of the papers that were presented at the conference.
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Fernie, J. D. Variable Stars in Globular Clusters and in Related Systems: Proceedings of the IAU Colloquium No. 21 Held at the University of Toronto, Toronto, Canada August 29-31 1972. Springer, 2012.

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Book chapters on the topic "CLUSTER VARIANCE"

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Rzaḑca, Krzysztof, and Francesc J. Ferri. "Incrementally Assessing Cluster Tendencies with a~Maximum Variance Cluster Algorithm." In Pattern Recognition and Image Analysis, 868–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_100.

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Wang, Shuihua, Xingxing Zhou, Guangshuai Zhang, Genlin Ji, Jiquan Yang, Zheng Zhang, Zeyuan Lu, and Yudong Zhang. "Cluster Analysis by Variance Ratio Criterion and Quantum-Behaved PSO." In Cloud Computing and Security, 285–93. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27051-7_24.

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Fan, Jiu-Lun, Xue-Feng Zhang, and Feng Zhao. "Three-Dimension Maximum Between-Cluster Variance Image Segmentation Method Based on Chaotic Optimization." In Interactive Technologies and Sociotechnical Systems, 164–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11890881_19.

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Lord, Jenny, and Kevin Morgan. "Clusterin." In Genetic Variants in Alzheimer's Disease, 25–51. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7309-1_3.

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Stevenson, David. "Variable Stars." In The Complex Lives of Star Clusters, 57–99. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14234-0_3.

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Brown, Kristelle, James Turton, and Kevin Morgan. "Membrane-Spanning 4-Domains Subfamily A, MS4A Cluster." In Genetic Variants in Alzheimer's Disease, 159–79. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7309-1_8.

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Toso, Rodrigo F., Evgeny V. Bauman, Casimir A. Kulikowski, and Ilya B. Muchnik. "Experiments with a Non-convex Variance-Based Clustering Criterion." In Clusters, Orders, and Trees: Methods and Applications, 51–62. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0742-7_3.

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Csirik, János, Leah Epstein, Csanád Imreh, and Asaf Levin. "Online Clustering with Variable Sized Clusters." In Mathematical Foundations of Computer Science 2010, 282–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15155-2_26.

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Cain, Nicole M., Emily B. Ansell, and Anthony Pinto. "Cluster C Personality Disorders and Anxiety Disorders." In Handbook of Treating Variants and Complications in Anxiety Disorders, 349–62. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6458-7_22.

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Divya Saini, Manoj Singh, and Iti Sharma. "Variance-Based Clustering for Balanced Clusters in Growing Datasets." In Proceedings of the International Congress on Information and Communication Technology, 559–65. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0767-5_58.

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Conference papers on the topic "CLUSTER VARIANCE"

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Zhang, A., M. Chung, B. Lee, R. Cho, S. Kazadi, and R. Vishwanath. "Variance in converging puck cluster sizes." In the first international joint conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/544741.544791.

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Yang, Shuhong, and Tong Zhang. "Images thresholding via within-cluster weighting variance." In EITCE 2021: 2021 5th International Conference on Electronic Information Technology and Computer Engineering. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3501409.3501495.

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Dey, Sayak, Swagatam Das, and Rammohan Mallipeddi. "The Sparse MinMax k-Means Algorithm for High-Dimensional Clustering." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/291.

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Classical clustering methods usually face tough challenges when we have a larger set of features compared to the number of items to be partitioned. We propose a Sparse MinMax k-Means Clustering approach by reformulating the objective of the MinMax k-Means algorithm (a variation of classical k-Means that minimizes the maximum intra-cluster variance instead of the sum of intra-cluster variances), into a new weighted between-cluster sum of squares (BCSS) form. We impose sparse regularization on these weights to make it suitable for high-dimensional clustering. We seek to use the advantages of the MinMax k-Means algorithm in the high-dimensional space to generate good quality clusters. The efficacy of the proposal is showcased through comparison against a few representative clustering methods over several real world datasets.
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Candogan, Ozan, Chen Chen, and Rad Niazadeh. "Correlated Cluster-Based Randomized Experiments: Robust Variance Minimization." In EC '23: 24th ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3580507.3597820.

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Yuan, Kun, Bicheng Ying, and Ali H. Sayed. "COVER: A Cluster-based Variance Reduced Method for Online Learning." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682527.

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Gullo, Francesco, Giovanni Ponti, and Andrea Tagarelli. "Minimizing the Variance of Cluster Mixture Models for Clustering Uncertain Objects." In 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 2010. http://dx.doi.org/10.1109/icdm.2010.134.

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Yong Liu, Sha Chen, and Ying Lin. "Grain bags detection based on improved maximum between-cluster variance algorithm." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5622758.

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Fu, Zeng, Jianfeng He, Yan Xiang, Rui Cui, and Sanli Yi. "Image segmentation based on gray-level spatial correlation maximum between-cluster variance." In 2015 International Symposium on Computers and Informatics. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/isci-15.2015.28.

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ABDULLAH, Mohammed, Rizgar AHMED, and Yener ALTUN. "Comparison Between Factor Analysis and Cluster Analysis to Determine the Most Important Affecting Factors for Students' Admission and Their Interests in The Specializations: A Sample of Salahaddin University-Erbil." In 3rd International Conference of Mathematics and its Applications. Salahaddin University-Erbil, 2020. http://dx.doi.org/10.31972/ticma22.03.

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The main goal of this thesis is to determine the most important effective factors for student admission and his/her interests in the specialization by using multivariate methods. Therefore, it focused on using factor analysis by identifying a number of the obtained factors and cluster analysis by classifying them into five clusters. Furthermore, the factor analysis and cluster analysis results will be compared to each other. Moreover, this study depends on the analysis of 350 questionnaire forms, distributed by random stratified sample method on students in the first stage of three different colleges, including Scientific colleges and Humanity colleges of Salahaddin University in Northern Iraq for the academic year 2018-2019. Thus, the IBM SPSS Statistics V: 25 software programs have been used in data analysis. Additionally, the results have demonstrated that Reliability is accepted, and also in factor analysis, the rate of the total variance interpretation is %62.157. Moreover, the most common variables between the factor analysis and cluster analysis can be considered the most important and influential variables for student admission and their interests in choosing a specialization. Consequently, the first factor and the first cluster have five significant variables in common; they are V1, V2, V3, V4 and V5 (the system is helpful for student admission to colleges to get their desired professions). The second factor and the second cluster have four influential variables in common they are V24, V32, V35 and V37 (the new system may help master's and PhD students to be admitted to colleges and get competitive results by utilizing their accounts). In the fourth factor and the fourth cluster, there is one variable in common, which is V18 (decreasing the number of students admitted in the parallel system by using the graduated students who must not be able to refill admission forms). Ultimately, the conclusion has shown a kind of approach and similarity between factor analysis and cluster analysis.
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Na, Wang. "The application of the improved maximum between-cluster variance method in special images." In ICNSER2020: The 2nd International Conference On Industrial Control Network And System Engineering Research. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3411016.3411021.

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Reports on the topic "CLUSTER VARIANCE"

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Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk, and Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4470.

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Cluster analysis of the efficiency of the recreational forest use of the region by separate components of the recreational forest use potential is provided in the article. The main stages of the cluster analysis of the recreational forest use level based on the predetermined components were determined. Among the agglomerative methods of cluster analysis, intended for grouping and combining the objects of study, it is common to distinguish the three most common types: the hierarchical method or the method of tree clustering; the K-means Clustering Method and the two-step aggregation method. For the correct selection of clusters, a comparative analysis of several methods was performed: arithmetic mean ranks, hierarchical methods followed by dendrogram construction, K- means method, which refers to reference methods, in which the number of groups is specified by the user. The cluster analysis of forestries by twenty analytical grounds was not proved by analysis of variance, so the re-clustering of certain objects was carried out according to the nine most significant analytical features. As a result, the forestry was clustered into four clusters. The conducted cluster analysis with the use of different methods allows us to state that their combination helps to select reasonable groupings, clearly illustrate the clustering procedure and rank the obtained forestry clusters.
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Kott, Phillip S. The Degrees of Freedom of a Variance Estimator in a Probability Sample. RTI Press, August 2020. http://dx.doi.org/10.3768/rtipress.2020.mr.0043.2008.

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Inferences from probability-sampling theory (more commonly called “design-based sampling theory”) often rely on the asymptotic normality of nearly unbiased estimators. When constructing a two-sided confidence interval for a mean, the ad hoc practice of determining the degrees of freedom of a probability-sampling variance estimator by subtracting the number of its variance strata from the number of variance primary sampling units (PSUs) can be justified by making usually untenable assumptions about the PSUs. We will investigate the effectiveness of this conventional and an alternative method for determining the effective degrees of freedom of a probability-sampling variance estimator under a stratified cluster sample.
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Petre, Melinda. Comparison of Outputs for Variable Combinations Used in Cluster Analysis on Polarmetric Imagery. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada476766.

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Thompson, William L. Comparison of Three Plot Selection Methods for Estimating Change in Temporally Variable, Spatially Clustered Populations. Office of Scientific and Technical Information (OSTI), July 2001. http://dx.doi.org/10.2172/785591.

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Levisohn, Sharon, Maricarmen Garcia, David Yogev, and Stanley Kleven. Targeted Molecular Typing of Pathogenic Avian Mycoplasmas. United States Department of Agriculture, January 2006. http://dx.doi.org/10.32747/2006.7695853.bard.

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Intraspecies identification (DNA "fingerprinting") of pathogenic avian mycoplasmas is a powerful tool for epidemiological studies and monitoring strain identity. However the only widely method available for Mycoplasma gallisepticum (MG) and M. synoviae (MS)wasrandom amplified polymorphic DNA (RAPD). This project aimed to develop alternative and supplementary typing methods that will overcome the major constraints of RAPD, such as the need for isolation of the organism in pure culture and the lack of reproducibility intrinsic in the method. Our strategy focussed on recognition of molecular markers enabling identification of MG and MS vaccine strains and, by extension, pathogenic potential of field isolates. Our first aim was to develop PCR-based systems which will allow amplification of specific targeted genes directly from clinical material. For this purpose we evaluated the degree of intraspecies heterogeneity in genes encoding variable surface antigens uniquely found in MG all of which are putative pathogenicity factors. Phylogenic analysis of targeted sequences of selected genes (pvpA, gapA, mgc2, and lp) was employed to determine the relationship among MG strains.. This method, designated gene targeted sequencing (GTS), was successfully employed to identify strains and to establish epidemiologically-linked strain clusters. Diagnostic PCR tests were designed and validated for each of the target genes, allowing amplification of specific nucleotide sequences from clinical samples. An mgc2-PCR-RFLP test was designed for rapid differential diagnosis of MG vaccine strains in Israel. Addressing other project goals, we used transposon mutagenesis and in vivo and in vitro models for pathogenicity to correlated specific changes in target genes with biological properties that may impact the course of infection. An innovative method for specific detection and typing of MS strains was based on the hemagglutinin-encoding gene vlhA, uniquely found in this species. In parallel, we evaluated the application of amplified fragment length polymorphism (AFLP) in avian mycoplasmas. AFLP is a highly discriminatory method that scans the entire genome using infrequent restriction site PCR. As a first step the method was found to be highly correlated with other DNA typing methods for MG species and strain differentiation. The method is highly reproducible and relatively rapid, although it is necessary to isolate the strain to be tested. Both AFLP and GTS are readily to amenable to computer-assisted analysis of similarity and construction of a data-base resource. The availability of improved and diverse tools will help realize the full potential of molecular typing of avian mycoplasmas as an integral and essential part of mycoplasma control programs.
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Or, Dani, Shmulik Friedman, and Jeanette Norton. Physical processes affecting microbial habitats and activity in unsaturated agricultural soils. United States Department of Agriculture, October 2002. http://dx.doi.org/10.32747/2002.7587239.bard.

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experimental methods for quantifying effects of water content and other dynamic environmental factors on bacterial growth in partially-saturated soils. Towards this end we reviewed critically the relevant scientific literature and performed theoretical and experimental studies of bacterial growth and activity in modeled, idealized and real unsaturated soils. The natural wetting-drying cycles common to agricultural soils affect water content and liquid organization resulting in fragmentation of aquatic habitats and limit hydraulic connections. Consequently, substrate diffusion pathways to soil microbial communities become limiting and reduce nutrient fluxes, microbial growth, and mobility. Key elements that govern the extent and manifestation of such ubiquitous interactions include characteristics of diffusion pathways and pore space, the timing, duration, and extent of environmental perturbations, the nature of microbiological adjustments (short-term and longterm), and spatial distribution and properties of EPS clusters (microcolonies). Of these key elements we have chosen to focus on a manageable subset namely on modeling microbial growth and coexistence on simple rough surfaces, and experiments on bacterial growth in variably saturated sand samples and columns. Our extensive review paper providing a definitive “snap-shot” of present scientific understanding of microbial behavior in unsaturated soils revealed a lack of modeling tools that are essential for enhanced predictability of microbial processes in soils. We therefore embarked on two pronged approach of development of simple microbial growth models based on diffusion-reaction principles to incorporate key controls for microbial activity in soils such as diffusion coefficients and temporal variations in soil water content (and related substrate diffusion rates), and development of new methodologies in support of experiments on microbial growth in simple and observable porous media under controlled water status conditions. Experimental efforts led to a series of microbial growth experiments in granular media under variable saturation and ambient conditions, and introduction of atomic force microscopy (AFM) and confocal scanning laser microscopy (CSLM) to study cell size, morphology and multi-cell arrangement at a high resolution from growth experiments in various porous media. The modeling efforts elucidated important links between unsaturated conditions and microbial coexistence which is believed to support the unparallel diversity found in soils. We examined the role of spatial and temporal variation in hydration conditions (such as exist in agricultural soils) on local growth rates and on interactions between two competing microbial species. Interestingly, the complexity of soil spaces and aquatic niches are necessary for supporting a rich microbial diversity and the wide array of microbial functions in unsaturated soils. This project supported collaboration between soil physicists and soil microbiologist that is absolutely essential for making progress in both disciplines. It provided a few basic tools (models, parameterization) for guiding future experiments and for gathering key information necessary for prediction of biological processes in agricultural soils. The project sparked a series of ongoing studies (at DTU and EPFL and in the ARO) into effects of soil hydration dynamics on microbial survival strategy under short term and prolonged desiccation (important for general scientific and agricultural applications).
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