Authors:M.Z. Islam, M.A. Siddique, N. Akter, M.F.R.K. Prince, M.R. Islam, M. Anisuzzaman, and M.A.K. Mian
Genetic diversity of 28 restorer lines of rice was studied under irrigated condition through Mahalanobis D2 statistics and simple sequence repeat (SSR) banding patterns. The cluster analysis grouped the lines into five clusters. The principal component analysis showed that the first four components with vector values > 1 contributed 76.32% of the total variations. The intra- and inter-cluster distances were the maximum in cluster V (0.86) and between clusters II and V (15.14), respectively. Flag leaf area, plant height, panicle length, five panicle weight, primary branches per panicle and secondary branches per panicle had maximum contribution towards genetic divergence. A total of 24 alleles varied from 2 to 5 with an average of 2.67 alleles per locus were detected for the nine microsatellite markers across 28 lines. The polymorphism information content (PIC) values ranged from 0.12 to 0.71 with an average of 0.29. RM229 was considered as the best markers on the basis of the highest PIC value. Phylogenetic cluster analysis of the SSR data based on distance divided all lines into three groups (A, B and C), whereas the cluster analysis divided these lines into five groups (I, II, III, IV and V). Besides, evaluation of yield contributing traits demonstrated that the restorer lines under the current study possessed a considerable genetic diversity. Potential lines such as BU1IR, China2R, China1R, BHD3R, IR509R and Heera5R can be used as pollen parent in developing new commercial hybrid varieties. Therefore, potential restorer lines need to be conserved in Genebank for future hybrid rice breeding programs.
A new method for the analysis of leadership and subdisciplinary structure of a scientific discipline is discussed. The database consists of lists of participants in international scientific meetings. Disciplinary leaders are identified by means of their frequency of participation. The subdisciplinary structure is mapped by means of cluster analysis of meetings with respect to degree of similarily. The method possesses strengths not shared by citation analysis: in addition to scientists frequently cited in the literature for their contribution to cognitive research programs, it also identifies administrative discipline builders. The method may also represent better the cognitive interests of scientists.
In this paper, co-word analysis is used to analyze the evolvement in stem cell field. Articles in the stem cell journals are downloaded from PubMed for analysis. Terms selection is one of the most important steps in co-word analysis, so the useless and the general subject headings are removed firstly, and then the major subject headings and minor subject headings are weighted respectively. Then, improved information entropy is exploited to select the subject headings with the experts consulting. Hierarchical cluster analysis is used to cluster the subject headings and the strategic diagram is formed to analyze the evolutionary trends in the stem cell field.
Authors:J. Kučera, J. Novák, K. Kranda, J. Poncar, I. Krausová, L. Soukal, O. Cunin, and M. Lang
We determined 35 major, minor and trace elements in sandstone samples taken from building blocks of 19 Angkor temples and
from an old and a new quarry using INAA. We also characterized the sandstone samples with conventional microscopy and electron
microprobe analysis. Using cluster analysis, we found no straightforward correlation between the chemical/petrological properties
of the sandstones and a presumed period of individual temples construction. The poor correlation may result either from the
inherent inhomogeneity of sandstone or just reflect the diversity of quarries that supplied building blocks for the construction
of any particular temple.
Thirteen brain regions were dissected from both hemispheres of fifteen ‘normal’ ageing subjects (8 females, 7 males) of mean
age 79±7 years. Elemental compositions were determined by simultaneous application of particle induced X-ray emissions (PIXE)
and Rutherford backscattering (RBS) analyses using a 2 MeV, 4nA proton beam scaned over 4 mm2 of the sample surface. Elemental concentrations were found to be dependent upon the brain region and hemisphere studied.
Hierarchical cluster analysis was applied to group the brain regions according to the sample concentrations of eight elements.
The resulting dendrogram is preseted and its clusters related to the sample compositions of grey and white matter.
This research uses descriptive multivariate data-analytic techniques—in particular, multidimensional scaling and hierarchical
cluster analysis—to explore and visualize the structure of the pharmacy literature as refracted through the editorial policies
of theInternational Pharmaceutical Abstracts (IPA) database. Specifically, the co-occurrence of the section headings/codes, used to exhaustively categorize publications
in the IPA database, are clustered and mapped to evaluate the usefulness of two methods of section heading assignment. A secondary
purpose of this research is to evaluate the use of descriptive multivariate data-analytic techniques and co-classification
analysis to explore and depict the structure of an inherently heterogeneous and multidisciplinary professional literature,
such as pharmacy.
A cocitation analysis for thirty-six journals and other publications in neural networks research and related disciplines was
conducted over three consecutive time periods spanning the years 1990-early 1997. Cluster analysis and MDS maps identified
groupings representing foundation research areas (physics/optics, computer engineering, neuroscience, expert systems & cognition,
and perception) along with neural networks and mathematical modeling of neural systems. Principal components analysis demonstrated
a similar structure, with several journals and books loading on a majority of the factors. An INDSCAL analysis showed an increasing
separation between natural sciences/psychology and engineering/neural networks research from the first time period to the
Authors:Sanja Ilić, Maja Natić, Dragana Dabić, Dušanka Milojković-Opsenica, and Živoslav Tešić
Two-dimensional (2D) TLC of eleven phenols has been performed on octadecyl-silica. The most efficient systems were selected by analysis of retention data obtained by one-dimensional chromatography using aqueous and non-aqueous mobile phases. Correlation graphs were plotted to illustrate the separation in the chromatographic systems examined. Complete separation of the phenols was achieved by using an aqueous mobile phase in the first dimension and a non-aqueous mobile phase in the second dimension. Statistical methods (principal-components analysis and cluster analysis) were used for better characterization of the TLC systems.
Authors:A. Oluwole, O. Asubiojo, J. Nwachukwu, J. Ojo, O. Ogunsola, J. Adejumo, R. Filby, S. Fitzgerald, and C. Grimm
A total of 40 crude oils from 10 different oil fields in Nigeria were analysed for 39 elements by Instrumental Neutron Activation Analysis (INAA). Significant correlations were found between Ni and V concentrations and Ni versus Se concentrations. The American Petroleum Institute (API) gravities are inversely correlated with total transition metal concentration of the oils but there is no obvious correlation of the V/Ni ratio with the age of the oil fields. The oils are very similar to North Alaska Type B oils in key transition metal parameters and cluster analysis results using the transition metals as variables indicate that the oils might have been formed from two closely related sources.
This paper describes exploratory research on the application of computerized text analysis techniques to all U.S. engineering
doctoral dissertation abstracts dated 1981, 1986, and 1991. Experts were utilized to categorize abstracts by industrial relevance,
and to identify appropriate non-technology-specific word indicators within the abstracts. Word frequency and cluster analysis
techniques were also explored for their potential utility in identifying technology-related work indicators of industrial
relevance. The results of this work suggest that text analysis of engineering dissertation abstracts holds potential utility
for identifying industrially relevant university-based engineering research, when used in conjunction with expert input and