Accelerated testing of mold growth on traditional and recycled book paper

The growth of molds on paper containing cellulose is a frequent occurrence when the level of relative air humidity is high or when books become wet due to water leaks in libraries. The aim of this study is to differentiate the bioreceptivity of different types of book paper for different fungi. Laboratory tests were performed with strains of Aspergillus niger, Cladosporium sp., Chaetomium globosum and Trichoderma harzianum isolated from books. Four paper types were evaluated: couche Men (offset), recycled and a reference paper containing only cellulose. The tests were carried out in chambers with relative air humidity of 95% and 100%. Mold growth was greatest in the tests at 100% relative humidity. Results of stereoscopic microscopy observation showed that Cladosporium sp. grew in 74% of these samples, A. niger in 75%, T. harzianum in 72% and C. globosum in 60%. In the chambers with 95% air humidity Cladosporium sp. grew in only 9% of the samples, A. niger in 1%, T harzianum in 3% and C globosum did not grow in any sample. The most bioreceptive paper was couche and the least receptive was recycled paper. The composition of the recycled paper, however, varies depending on the types of waste materials used to make it. (C) 2011 Elsevier Ltd. 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.

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.

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.

Clustering and diffusion in a symplectic map lattice with non-local coupling

We study a symplectic chain with a non-local form of coupling by means of a standard map lattice where the interaction strength decreases with the lattice distance as a power-law, in Such a way that one can pass continuously from a local (nearest-neighbor) to a global (mean-field) type of coupling. We investigate the formation of map clusters, or spatially coherent structures generated by the system dynamics. Such clusters are found to be related to stickiness of chaotic phase-space trajectories near periodic island remnants, and also to the behavior of the diffusion coefficient. An approximate two-dimensional map is derived to explain some of the features of this connection. (C) 2008 Elsevier Ltd. All rights reserved.

Design of a Modern Liposome and Bee Venom Formulation for the Traditional VIT-Venom Immunotherapy

Traditional venom immunotherapy uses injections of whole bee venom in buffer or adsorbed in Al (OH)(3) in an expensive, time-consuming way. New strategies to improve the safety and efficacy of this treatment with a reduction of injections would, therefore, be of general interest. It would improve patient compliance and provide socio-economic benefits. Liposomes have a long tradition in drug delivery because they increase the therapeutic index and avoid drug degradation and secondary effects. However, bee venom melittin (Mel) and phospholipase (PLA(2)) destroy the phospholipid membranes. Our central idea was to inhibit the PLA(2) and Mel activities through histidine alkylation and or tryptophan oxidation (with pbb, para-bromo-phenacyl bromide, and/or NBSN-bromosuccinimide, respectively) to make their encapsulations possible within stabilized liposomes. We strongly believe that this formulation will be nontoxic but immunogenic. In this paper, we present the whole bee venom conformation characterization during and after chemical modification and after interaction with liposome by ultraviolet, circular dichroism, and fluorescence spectroscopies. The PLA(2) and Mel activities were, measured indirectly by changes in turbidity at 400(nm), rhodamine leak-out, and hemolysis. The native whole bee venom (BV) presented 78.06% of alpha-helical content. The alkylation (A-BV) and succynilation (S-BV) of BV increased 0.44 and 0.20% of its alpha-helical content. The double-modified venom (S-A-BV) had a 0.74% increase of alpha-helical content. The BV chemical modification induced another change on protein conformations observed by Trp that became buried with respect to the native whole BV. It was demonstrated that the liposomal membranes must contain pbb (SPC:Cho:pbb, 26:7:1) as a component to protect them from aggregation and/or fusion. The membranes containing pbb maintained the same turbidity (100%) after incubation with modified venom, in contrast with pbb-free membranes that showed a 15% size decrease. This size decrease was interpreted as membrane degradation and was corroborated by a 50% rhodamine leak-out. Another fact that confirmed our interpretation was the observed 100% inhibition of the hemolytic activity after venom modification with pbb and NBS (S-A-BV). When S-A-BV interacted with liposomes, other protein conformational changes were observed and characterized by the increase of 1.93% on S-A-BV alpha-helical content and the presence of tryptophan residues in a more hydrophobic environment. In other words, the S-A-BV interacted with liposomal membranes, but this interaction was not effective to cause aggregation, leak-out, or fusion. A stable formulation composed by S-A-BV encapsulated within liposomes composed by SPC:Cho:pbb, at a ratio of 26:7:1, was devised. Large unilamellar vesicles of 202.5 nm with a negative surface charge (-24.29 mV) encapsulated 95% of S-A-BV. This formulation can, now, be assayed on VIT.

Clustering Lab value working with medical data

Data mining is a relatively new field of research that its objective is to acquire knowledge from large amounts of data. In medical and health care areas, due to regulations and due to the availability of computers, a large amount of data is becoming available [27]. On the one hand, practitioners are expected to use all this data in their work but, at the same time, such a large amount of data cannot be processed by humans in a short time to make diagnosis, prognosis and treatment schedules. A major objective of this thesis is to evaluate data mining tools in medical and health care applications to develop a tool that can help make rather accurate decisions. In this thesis, the goal is finding a pattern among patients who got pneumonia by clustering of lab data values which have been recorded every day. By this pattern we can generalize it to the patients who did not have been diagnosed by this disease whose lab values shows the same trend as pneumonia patients does. There are 10 tables which have been extracted from a big data base of a hospital in Jena for my work .In ICU (intensive care unit), COPRA system which is a patient management system has been used. All the tables and data stored in German Language database.