14-3-3: Defense and regulatory proteins coding by Eucalyptus genome

The data mining of Eucalyptus ESTs genome finds four clusters (EGCEST2257E11.g, EGBGRT3213F11.g, and EGCCFB1223H11.g) from highly conservative 14-3-3 protein family which modulates a wide variety of cellular processes. Multiple alignments were built from twenty four sequences of 14-3-3 proteins searched into the GenBank databases and into the four pools of Eucalyptus genome programs. The alignment has shown two regions highly conservative on the sequences corresponding to the motifs of protein phosphorylation and nine highly conservative regions on the sequence corresponding to the linkage regions of alpha helices structure based on three dimensional of dimer functional structure. The differences of amino acid into the structural and functional domains of 14-3-3 plant protein were identified and can explain the functional diversity of different isoforms. The phylogenic protein trees were built by the maximum parsimony and neighborjoining procedures of Clustal X alignments and PAUP software for phylogenic analysis.

Knowledge extraction using visualization of hemoglobin parameters to identify thalassemia

The analysis of large amounts of data is better performed by humans when represented in a graphical format. Therefore, a new research area called the Visual Data Mining is being developed endeavoring to use the number crunching power of computers to prepare data for visualization, allied to the ability of humans to interpret data presented graphically.This work presents the results of applying a visual data mining tool, called FastMapDB to detect the behavioral pattern exhibited by a dataset of clinical information about hemoglobinopathies known as thalassemia. FastMapDB is a visual data mining tool that get tabular data stored in a relational database such as dates, numbers and texts, and by considering them as points in a multidimensional space, maps them to a three-dimensional space. The intuitive three-dimensional representation of objects enables a data analyst to see the behavior of the characteristics from abnormal forms of hemoglobin, highlighting the differences when compared to data from a group without alteration.

Morphological analysis of pits formed on Al 2024-T3 in chloride aqueous solution

It has been used a new image analysis method, based on segmentation by shape parameters, for pits morphology examination from Al 2024 aluminum-copper alloy in chloride aqueous solution. Corrosion behavior of this alloys in naturally aerated 3.5% NaCl solution has been investigated through open circuit potential measurements. Afterwards, pits have been characterized by image analysis taking density and size measurements right from corroded surfaces. Morphological investigation has been conducted for profiles, cut orthogonally from mean surface planes, and observed through light microscopy. Image analysis data could demonstrate that pits are wider than deep, evoluting for conical, quasi-conical or irregular shapes. Most pits have presented a quasi-conical morphology, but the wider ones have evoluted to an irregular shape influenced by sub-surface microstructure. Image analysis based on shape segmentation could enhance the differences on morphological behavior. (C) 2004 Elsevier B.V. All rights reserved.

Thermogravimetry on calcined mass basis - hydrated cement phases and pozzolanic activity quantitative analysis

When cement hydrated compositions are analyzed by usual initial mass basis TG curves to calculate mass losses, the higher is the amount of additive added or is the combined water content, the higher is the cement 'dilution' in the initial mass of the sample. In such cases, smaller mass changes in the different mass loss steps are obtained, due to the actual smaller content of cement in the initial mass compositions. To have a same mass basis of comparison, and to avoid erroneous results of initial components content there from, thermal analysis data and curves have to be transformed on cement calcined basis, i.e. on the basis of cement oxides mass present in the calcined samples or on the sample cement initial mass basis.The paper shows and discusses the fundamentals of these bases of calculation, with examples on free and combined water analysis, on calcium sulfate hydration during false cement set and on quantitative evaluation and comparison of pozzolanic materials activity.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

Predicting body and carcass characteristics of 2 broiler chicken strains using support vector regression and neural network models

As a new modeling method, support vector regression (SVR) has been regarded as the state-of-the-art technique for regression and approximation. In this study, the SVR models had been introduced and developed to predict body and carcass-related characteristics of 2 strains of broiler chicken. To evaluate the prediction ability of SVR models, we compared their performance with that of neural network (NN) models. Evaluation of the prediction accuracy of models was based on the R-2, MS error, and bias. The variables of interest as model output were BW, empty BW, carcass, breast, drumstick, thigh, and wing weight in 2 strains of Ross and Cobb chickens based on intake dietary nutrients, including ME (kcal/bird per week), CP, TSAA, and Lys, all as grams per bird per week. A data set composed of 64 measurements taken from each strain were used for this analysis, where 44 data lines were used for model training, whereas the remaining 20 lines were used to test the created models. The results of this study revealed that it is possible to satisfactorily estimate the BW and carcass parts of the broiler chickens via their dietary nutrient intake. Through statistical criteria used to evaluate the performance of the SVR and NN models, the overall results demonstrate that the discussed models can be effective for accurate prediction of the body and carcass-related characteristics investigated here. However, the SVR method achieved better accuracy and generalization than the NN method. This indicates that the new data mining technique (SVR model) can be used as an alternative modeling tool for NN models. However, further reevaluation of this algorithm in the future is suggested.

Apert syndrome: Analysis of associated brain malformations and conformational changes determined by surgical treatment

Apert Syndrome, also called acrocephalosyndactylia type 1, is characterized by craniostenosis with early fusion of sutures of the vault and/ or cranial base, associated to mid-face hypoplasia, symmetric syndactylia of the hands and feet and other systemic malformations. CNS malformations and intracranial hypertension are frequently observed in these patients. Early surgical treatment aims to minimize the deleterious effects of intracranial hypertension. Fronto-orbital advancement, the usual surgical technique, increases the intracranial volume and improves the disposition of encephalic structures previously deformed by a short skull. This study analyzes CNS alterations revealed by magnetic resonance in 18 patients presenting Apert Syndrome, and the conformational alterations in the encephalic structures after surgical treatment. The patients' age in February 2001 ranged from 14 to 322 months (m=107). Image study included brain magnetic resonance showing ventricular enlargement in five cases (27.8%), corpus callosum hypoplasia in five cases (27.8%), septum pellucidum hypoplasia in five cases (27.8%), cavum vergae in two cases (11.1%) and, arachnoid cyst in the posterior fossa in two cases (11.1%). Absence of CNS alterations was noted in 44.4% of cases. A corpus callosum morphologic index was established by dividing its height by its length, which revealed values that ranged from 0.4409 to 1.0237. The values of this index were correlated to the occurrence or absence of surgical treatment (p=0.012; t=2.83). Data analysis allowed the conclusion that the corpus callosum morphologic measure quantified the conformational alterations of the cerebral structures determined by the surgical treatment.

An environment for knowledge discovery in biology

This paper describes a data mining environment for knowledge discovery in bioinformatics applications. The system has a generic kernel that implements the mining functions to be applied to input primary databases, with a warehouse architecture, of biomedical information. Both supervised and unsupervised classification can be implemented within the kernel and applied to data extracted from the primary database, with the results being suitably stored in a complex object database for knowledge discovery. The kernel also includes a specific high-performance library that allows designing and applying the mining functions in parallel machines. The experimental results obtained by the application of the kernel functions are reported. © 2003 Elsevier Ltd. All rights reserved.

Conceptual model for adaptable and extensible visual data exploration

Interactive visual representations complement traditional statistical and machine learning techniques for data analysis, allowing users to play a more active role in a knowledge discovery process and making the whole process more understandable. Though visual representations are applicable to several stages of the knowledge discovery process, a common use of visualization is in the initial stages to explore and organize a sometimes unknown and complex data set. In this context, the integrated and coordinated - that is, user actions should be capable of affecting multiple visualizations when desired - use of multiple graphical representations allows data to be observed from several perspectives and offers richer information than isolated representations. In this paper we propose an underlying model for an extensible and adaptable environment that allows independently developed visualization components to be gradually integrated into a user configured knowledge discovery application. Because a major requirement when using multiple visual techniques is the ability to link amongst them, so that user actions executed on a representation propagate to others if desired, the model also allows runtime configuration of coordinated user actions over different visual representations. We illustrate how this environment is being used to assist data exploration and organization in a climate classification problem.

Delayed tooth replantation after root surface treatment with sodium hypochlorite and sodium fluoride: Histomorphometric analysis in rats

In cases of delayed tooth replantation, non-vital periodontal ligament remnants have been removed with sodium hypochlorite in an attempt to control root resorption. Nevertheless, reports of its irritating potential in contact with the alveolar connective tissue have been described. Therefore, this study evaluated the healing process on delayed replantation of rat teeth, after periodontal ligament removal by different treatment modalities. Twenty-four rats, assigned to 3 groups (n=8), had their upper right incisor extracted and left on the workbench for desiccation during 60 min. Afterwards, the teeth in group I were immersed in saline for 2 min. In group II, root surfaces were scrubbed with gauze soaked in saline for 2 min; and in group III, scrubbing was done with gauze soaked in 1% sodium hypochlorite solution. Thereafter, root surfaces were etched with 37% phosphoric acid and immersed in 2% acidulate-phosphate sodium fluoride solution, at pH 5.5. Root canals were filled with a calcium hydroxide-based paste and the teeth were replanted. The animals were sacrificed 60 days postoperatively and the pieces containing the replanted teeth were processed and paraffin- embedded. Semi-serial transversally sections were obtained from the middle third of the root and stained with hematoxylin and eosin for histomorphometric analysis. Data were analyzed statistically using Kruskal-Wallis and Dunn's tests. The results showed that root structure and cementum extension were more affected by resorption in group III (p<0.05). All groups were affected by root resorption but the treatment performed in group III was the least effective for its control. The treatment accomplished in groups I and II yielded similar results to each other.

Effects of motor intervention in elderly patients with dementia: An analysis of randomized controlled trials

The objective of this study was to analyze randomized controlled trials published in the last decades involving motor intervention as a treatment for dementia, based on Physiotherapy Evidence Database (PEDro) criteria. A database search was performed using the following keywords: randomized controlled trial, dementia, physiotherapy, physical therapy, occupational therapy, physical education, motor approach, exercise, and physical activity. Ten trials were found: 4 related to physiotherapy, 3 to occupational therapy, 1 to physical education, and 2 to interdisciplinary motor intervention. The efficacy of motor intervention was confirmed in the following variables: psychosocial function, physical health and function, affective status, and caregiver's distress (P < .05). Results related to mobility were not significant (P > .05). Behavior, cognitive performance, activities of daily living, and risk of falls were not similar among the articles. From a total score of 10 points, with excellence characterized as the highest punctuation, the articles were classified between 3 and 7 by PEDro. Motor intervention was shown to be an alternative for minimizing physical and mental decline. PEDro has been confirmed as a very reliable tool to analyze studies and as an evaluation criteria, both qualitative and quantitative, allowing the establishment of motor intervention strategies for the treatment of patients with dementia. © 2007 Lippincott Williams & Wilkins, Inc.

Intelligent alarm processing

This paper presents the analysis that have been carried out in the alarm system of the DCRanger EMS. The intention of this study is to present the problem of alarm processing in electric energy control centers, its various aspects and operational difficulties due to operator needs. Some tests are produced in order to identify the desirable features an alarm system should possess in order to be of effective help in the operative duty. © 2006 IEEE.

An intelligent system to detect drilling problems through drilled cuttings return analysis

Cuttings return analysis is an important tool to detect and prevent problems during the petroleum well drilling process. Several measurements and tools have been developed for drilling problems detection, including mud logging, PWD and downhole torque information. Cuttings flow meters were developed in the past to provide information regarding cuttings return at the shale shakers. Their use, however, significantly impact the operation including rig space issues, interferences in geological analysis besides, additional personel required. This article proposes a non intrusive system to analyze the cuttings concentration at the shale shakers, which can indicate problems during drilling process, such as landslide, the collapse of the well borehole walls. Cuttings images are acquired by a high definition camera installed above the shakers and sent to a computer coupled with a data analysis system which aims the quantification and closure of a cuttings material balance in the well surface system domain. No additional people at the rigsite are required to operate the system. Modern Artificial intelligence techniques are used for pattern recognition and data analysis. Techniques include the Optimum-Path Forest (OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC). Field test results conducted on offshore floating vessels are presented. Results show the robustness of the proposed system, which can be also integrated with other data to improve the efficiency of drilling problems detection. Copyright 2010, IADC/SPE Drilling Conference and Exhibition.

Development banking in the Eastern Caribbean: the case of Saint Lucia and Grenada

Includes bibliography

Multi-relational algorithm for mining association rules in large databases

Multi-relational data mining enables pattern mining from multiple tables. The existing multi-relational mining association rules algorithms are not able to process large volumes of data, because the amount of memory required exceeds the amount available. The proposed algorithm MRRadix presents a framework that promotes the optimization of memory usage. It also uses the concept of partitioning to handle large volumes of data. The original contribution of this proposal is enable a superior performance when compared to other related algorithms and moreover successfully concludes the task of mining association rules in large databases, bypass the problem of available memory. One of the tests showed that the MR-Radix presents fourteen times less memory usage than the GFP-growth. © 2011 IEEE.