Gene Expression Profile of Mesenchymal Stem Cells from Paired Umbilical Cord Units: Cord is Different from Blood

Mesenchymal stem cells (MSC) are multipotent cells which can be obtained from several adult and fetal tissues including human umbilical cord units. We have recently shown that umbilical cord tissue (UC) is richer in MSC than umbilical cord blood (UCB) but their origin and characteristics in blood as compared to the cord remains unknown. Here we compared, for the first time, the exonic protein-coding and intronic noncoding RNA (ncRNA) expression profiles of MSC from match-paired UC and UCB samples, harvested from the same donors, processed simultaneously and under the same culture conditions. The patterns of intronic ncRNA expression in MSC from UC and UCB paired units were highly similar, indicative of their common donor origin. The respective exonic protein-coding transcript expression profiles, however, were significantly different. Hierarchical clustering based on protein-coding expression similarities grouped MSC according to their tissue location rather than original donor. Genes related to systems development, osteogenesis and immune system were expressed at higher levels in UCB, whereas genes related to cell adhesion, morphogenesis, secretion, angiogenesis and neurogenesis were more expressed in UC cells. These molecular differences verified in tissue-specific MSC gene expression may reflect functional activities influenced by distinct niches and should be considered when developing clinical protocols involving MSC from different sources. In addition, these findings reinforce our previous suggestion on the importance of banking the whole umbilical cord unit for research or future therapeutic use.

The use of fluorescein for labeling genomic probes in the checkerboard DNA-DNA hybridization method

Molecular methods that permit the simultaneous detection and quantification of a large number of microbial species are currently employed in the evaluation of complex ecosystems. The checkerboard DNA-DNA hybridization technique enables the simultaneous identification of distinct bacterial. species in a large number of dental samples. The original technique employed digoxigenin-labeled whole genomic DNA probes which were detected by chemiluminescence. In this study, we present an alternative protocol for labeling and detecting whole genomic DNA probes in the Checkerboard DNA-DNA hybridization method. Whole genomic DNA was extracted from five bacterial species and labeled with fluorescein. The fluorescein labeled whole genomic DNA probes were hybridized against whole genomic DNA or subgingival plaque samples in a checkerboard hybridization format, followed by chemiluminescent detection. Our results reveal that fluorescein is a viable and adequate alternative labeling reagent to be employed in the checkerboard DNA-DNA hybridization technique. (c) 2007 Elsevier GmbH. All rights reserved.

Characterization of spliced leader genes of Trypanosoma (Megatrypanum) theileri: phylogeographical analysis of Brazilian isolates from cattle supports spatial clustering of genotypes and parity with ribosomal markers

Trypanosoma (Megatrypanum) theileri from cattle and trypanosomes of other artiodactyls form a clade of closely related species in analyses using ribosomal sequences. Analysis of polymorphic sequences of a larger number of trypanosomes from broader geographical origins is required to evaluate the Clustering of isolates as suggested by previous studies. Here, we determined the sequences of the spliced leader (SL) genes of 21 isolates from cattle and 2 from water buffalo from distant regions of Brazil. Analysis of SL gene repeats revealed that the 5S rRNA gene is inserted within the intergenic region. Phylogeographical patterns inferred using SL sequences showed at least 5 major genotypes of T. theileri distributed in 2 strongly divergent lineages. Lineage TthI comprises genotypes IA and IB from buffalo and cattle, respectively, from the Southeast and Central regions, whereas genotype IC is restricted to cattle from the Southern region. Lineage Tth II includes cattle genotypes IIA, which is restricted to the North and Northeast, and IIB, found in the Centre, West, North and Northeast. PCR-RFLP of SL genes revealed valuable markers for genotyping T. theileri. The results of this study emphasize the genetic complexity and corroborate the geographical structuring of T. theileri genotypes found in cattle.

Comparative phylogeography of Trypanosoma cruzi TCIIc: New hosts, association with terrestrial ecotopes, and spatial clustering

We characterized 28 new isolates of Trypanosoma cruzi IIc (TCIIc) of mammals and triatomines from Northern to Southern Brazil, confirming the widespread distribution of this lineage. Phylogenetic analyses using cytochrome b and SSU rDNA sequences clearly separated TCIIc from TCIIa according to terrestrial and arboreal ecotopes of their preferential mammalian hosts and vectors. TCIIc was more closely related to TCIId/e, followed by TCIIa, and separated by large distances from TCIIb and TCI. Despite being indistinguishable by traditional genotyping and generally being assigned to Z3, we provide evidence that TCIIa from South America and TCIIa from North America correspond to independent lineages that circulate in distinct hosts and ecological niches. Armadillos, terrestrial didelphids and rodents, and domestic dogs were found infected by TCIIc in Brazil. We believe that, in Brazil, this is the first description of TCIIc from rodents and domestic dogs. Terrestrial triatomines of genera Panstrongylus and Triatoma were confirmed as vectors of TCIIc. Together, habitat, mammalian host and vector association corroborated the link between TCIIc and terrestrial transmission cycles/ecological niches. Analysis of ITS1 rDNA sequences disclosed clusters of TCIIc isolates in accordance with their geographic origin, independent of their host species. (C) 2009 Elsevier B.V. All rights reserved.

Evolutionary fuzzy clustering of relational data

This paper is concerned with the computational efficiency of fuzzy clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. A fuzzy variant of an evolutionary algorithm for relational clustering is derived and compared against two systematic (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of fuzzy clusters in relational data. An extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed. (C) 2011 Elsevier B.V. All rights reserved.

Partitions selection strategy for set of clustering solutions

Clustering is a difficult task: there is no single cluster definition and the data can have more than one underlying structure. Pareto-based multi-objective genetic algorithms (e.g., MOCK Multi-Objective Clustering with automatic K-determination and MOCLE-Multi-Objective Clustering Ensemble) were proposed to tackle these problems. However, the output of such algorithms can often contains a high number of partitions, becoming difficult for an expert to manually analyze all of them. In order to deal with this problem, we present two selection strategies, which are based on the corrected Rand, to choose a subset of solutions. To test them, they are applied to the set of solutions produced by MOCK and MOCLE in the context of several datasets. The study was also extended to select a reduced set of partitions from the initial population of MOCLE. These analysis show that both versions of selection strategy proposed are very effective. They can significantly reduce the number of solutions and, at the same time, keep the quality and the diversity of the partitions in the original set of solutions. (C) 2010 Elsevier B.V. All rights reserved.

Investigation of a new GRASP-based clustering algorithm applied to biological data

A large amount of biological data has been produced in the last years. Important knowledge can be extracted from these data by the use of data analysis techniques. Clustering plays an important role in data analysis, by organizing similar objects from a dataset into meaningful groups. Several clustering algorithms have been proposed in the literature. However, each algorithm has its bias, being more adequate for particular datasets. This paper presents a mathematical formulation to support the creation of consistent clusters for biological data. Moreover. it shows a clustering algorithm to solve this formulation that uses GRASP (Greedy Randomized Adaptive Search Procedure). We compared the proposed algorithm with three known other algorithms. The proposed algorithm presented the best clustering results confirmed statistically. (C) 2009 Elsevier Ltd. All rights reserved.

Generalized external indexes for comparing data partitions with overlapping categories

There is a family of well-known external clustering validity indexes to measure the degree of compatibility or similarity between two hard partitions of a given data set, including partitions with different numbers of categories. A unified, fully equivalent set-theoretic formulation for an important class of such indexes was derived and extended to the fuzzy domain in a previous work by the author [Campello, R.J.G.B., 2007. A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment. Pattern Recognition Lett., 28, 833-841]. However, the proposed fuzzy set-theoretic formulation is not valid as a general approach for comparing two fuzzy partitions of data. Instead, it is an approach for comparing a fuzzy partition against a hard referential partition of the data into mutually disjoint categories. In this paper, generalized external indexes for comparing two data partitions with overlapping categories are introduced. These indexes can be used as general measures for comparing two partitions of the same data set into overlapping categories. An important issue that is seldom touched in the literature is also addressed in the paper, namely, how to compare two partitions of different subsamples of data. A number of pedagogical examples and three simulation experiments are presented and analyzed in details. A review of recent related work compiled from the literature is also provided. (c) 2010 Elsevier B.V. All rights reserved.

Adapting Web content for low-literacy readers by using lexical elaboration and named entities labeling

This paper presents an approach for assisting low-literacy readers in accessing Web online information. The oEducational FACILITAo tool is a Web content adaptation tool that provides innovative features and follows more intuitive interaction models regarding accessibility concerns. Especially, we propose an interaction model and a Web application that explore the natural language processing tasks of lexical elaboration and named entity labeling for improving Web accessibility. We report on the results obtained from a pilot study on usability analysis carried out with low-literacy users. The preliminary results show that oEducational FACILITAo improves the comprehension of text elements, although the assistance mechanisms might also confuse users when word sense ambiguity is introduced, by gathering, for a complex word, a list of synonyms with multiple meanings. This fact evokes a future solution in which the correct sense for a complex word in a sentence is identified, solving this pervasive characteristic of natural languages. The pilot study also identified that experienced computer users find the tool to be more useful than novice computer users do.

Multi-objective clustering ensemble for gene expression data analysis

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi-objective genetic algorithm, with a special crossover operator, which uses clustering validation measures as objective functions. The algorithm proposed can deal with data sets presenting different types of clusters, without the need of expertise in cluster analysis. its result is a concise set of partitions representing alternative trade-offs among the objective functions. We compare the results obtained with our algorithm, in the context of gene expression data sets, to those achieved with multi-objective Clustering with automatic K-determination (MOCK). the algorithm most closely related to ours. (C) 2009 Elsevier B.V. All rights reserved.

On comparing two sequences of numbers and its applications to clustering analysis

A conceptual problem that appears in different contexts of clustering analysis is that of measuring the degree of compatibility between two sequences of numbers. This problem is usually addressed by means of numerical indexes referred to as sequence correlation indexes. This paper elaborates on why some specific sequence correlation indexes may not be good choices depending on the application scenario in hand. A variant of the Product-Moment correlation coefficient and a weighted formulation for the Goodman-Kruskal and Kendall`s indexes are derived that may be more appropriate for some particular application scenarios. The proposed and existing indexes are analyzed from different perspectives, such as their sensitivity to the ranks and magnitudes of the sequences under evaluation, among other relevant aspects of the problem. The results help suggesting scenarios within the context of clustering analysis that are possibly more appropriate for the application of each index. (C) 2008 Elsevier Inc. All rights reserved.

On the efficiency of evolutionary fuzzy clustering

This paper tackles the problem of showing that evolutionary algorithms for fuzzy clustering can be more efficient than systematic (i.e. repetitive) approaches when the number of clusters in a data set is unknown. To do so, a fuzzy version of an Evolutionary Algorithm for Clustering (EAC) is introduced. A fuzzy cluster validity criterion and a fuzzy local search algorithm are used instead of their hard counterparts employed by EAC. Theoretical complexity analyses for both the systematic and evolutionary algorithms under interest are provided. Examples with computational experiments and statistical analyses are also presented.

Quality indices for (practical) clustering evaluation

Clustering quality or validation indices allow the evaluation of the quality of clustering in order to support the selection of a specific partition or clustering structure in its natural unsupervised environment, where the real solution is unknown or not available. In this paper, we investigate the use of quality indices mostly based on the concepts of clusters` compactness and separation, for the evaluation of clustering results (partitions in particular). This work intends to offer a general perspective regarding the appropriate use of quality indices for the purpose of clustering evaluation. After presenting some commonly used indices, as well as indices recently proposed in the literature, key issues regarding the practical use of quality indices are addressed. A general methodological approach is presented which considers the identification of appropriate indices thresholds. This general approach is compared with the simple use of quality indices for evaluating a clustering solution.

HiPP: A Novel Hierarchical Point Placement Strategy and its Application to the Exploration of Document Collections

Point placement strategies aim at mapping data points represented in higher dimensions to bi-dimensional spaces and are frequently used to visualize relationships amongst data instances. They have been valuable tools for analysis and exploration of data sets of various kinds. Many conventional techniques, however, do not behave well when the number of dimensions is high, such as in the case of documents collections. Later approaches handle that shortcoming, but may cause too much clutter to allow flexible exploration to take place. In this work we present a novel hierarchical point placement technique that is capable of dealing with these problems. While good grouping and separation of data with high similarity is maintained without increasing computation cost, its hierarchical structure lends itself both to exploration in various levels of detail and to handling data in subsets, improving analysis capability and also allowing manipulation of larger data sets.

HIERARCHICAL DECOMPOSITION OF MULTICLASS PROBLEMS

Several popular Machine Learning techniques are originally designed for the solution of two-class problems. However, several classification problems have more than two classes. One approach to deal with multiclass problems using binary classifiers is to decompose the multiclass problem into multiple binary sub-problems disposed in a binary tree. This approach requires a binary partition of the classes for each node of the tree, which defines the tree structure. This paper presents two algorithms to determine the tree structure taking into account information collected from the used dataset. This approach allows the tree structure to be determined automatically for any multiclass dataset.