The timing of Neotropical speciation dynamics: A reconstruction of Myiopagis flycatcher diversification using phylogenetic and paleogeographic data

Neotropical forests have brought forth a large proportion of the world`s terrestrial biodiversity, but the underlying evolutionary mechanisms and their timing require further elucidation. Despite insights gained from phylogenetic studies, uncertainties about molecular clock rates have hindered efforts to determine the timing of diversification processes. Moreover, most molecular research has been detached from the extensive body of data on Neotropical geology and paleogeography. We here examine phylogenetic relationships and the timing of speciation events in a Neotropical flycatcher genus (Myiopagis) by using calibrations from modern geologic data in conjunction with a number of recently developed DNA sequence dating algorithms and by comparing these estimates with those based on a range of previously proposed molecular clock rates. We present a well-supported hypothesis of systematic relationships within the genus. Our age estimates of Myiopagis speciation events based on paleogeographic data are in close agreement with nodal ages derived from a ""traditional"" avian mitochondrial 2%/My clock, while contradicting other clock rates. Our comparative approach corroborates the consistency of the traditional avian mitochondrial clock rate of 2%/My for tyrant-flycatchers. Nevertheless, our results argue against the indiscriminate use of molecular clock rates in evolutionary research and advocate the verification of the appropriateness of the traditional clock rate by means of independent calibrations in individual studies. (C) 2009 Elsevier Inc. All rights reserved.

Verifiable Visualization for Isosurface Extraction

Visual representations of isosurfaces are ubiquitous in the scientific and engineering literature. In this paper, we present techniques to assess the behavior of isosurface extraction codes. Where applicable, these techniques allow us to distinguish whether anomalies in isosurface features can be attributed to the underlying physical process or to artifacts from the extraction process. Such scientific scrutiny is at the heart of verifiable visualization - subjecting visualization algorithms to the same verification process that is used in other components of the scientific pipeline. More concretely, we derive formulas for the expected order of accuracy (or convergence rate) of several isosurface features, and compare them to experimentally observed results in the selected codes. This technique is practical: in two cases, it exposed actual problems in implementations. We provide the reader with the range of responses they can expect to encounter with isosurface techniques, both under ""normal operating conditions"" and also under adverse conditions. Armed with this information - the results of the verification process - practitioners can judiciously select the isosurface extraction technique appropriate for their problem of interest, and have confidence in its behavior.

A pointcut-based coverage analysis approach for aspect-oriented programs

Aspect-oriented programming (AOP) is a promising technology that supports separation of crosscutting concerns (i.e., functionality that tends to be tangled with, and scattered through the rest of the system). In AOP, a method-like construct named advice is applied to join points in the system through a special construct named pointcut. This mechanism supports the modularization of crosscutting behavior; however, since the added interactions are not explicit in the source code, it is hard to ensure their correctness. To tackle this problem, this paper presents a rigorous coverage analysis approach to ensure exercising the logic of each advice - statements, branches, and def-use pairs - at each affected join point. To make this analysis possible, a structural model based on Java bytecode - called PointCut-based Del-Use Graph (PCDU) - is proposed, along with three integration testing criteria. Theoretical, empirical, and exploratory studies involving 12 aspect-oriented programs and several fault examples present evidence of the feasibility and effectiveness of the proposed approach. (C) 2010 Elsevier Inc. All rights reserved.

A transformational language for mutant description

Mutation testing has been used to assess the quality of test case suites by analyzing the ability in distinguishing the artifact under testing from a set of alternative artifacts, the so-called mutants. The mutants are generated from the artifact under testing by applying a set of mutant operators, which produce artifacts with simple syntactical differences. The mutant operators are usually based on typical errors that occur during the software development and can be related to a fault model. In this paper, we propose a language-named MuDeL (MUtant DEfinition Language)-for the definition of mutant operators, aiming not only at automating the mutant generation, but also at providing precision and formality to the operator definition. The proposed language is based on concepts from transformational and logical programming paradigms, as well as from context-free grammar theory. Denotational semantics formal framework is employed to define the semantics of the MuDeL language. We also describe a system-named mudelgen-developed to support the use of this language. An executable representation of the denotational semantics of the language is used to check the correctness of the implementation of mudelgen. At the very end, a mutant generator module is produced, which can be incorporated into a specific mutant tool/environment. (C) 2008 Elsevier Ltd. All rights reserved.

Probing the functionalization of gold surfaces and protein adsorption by PM-IRRAS

The control of morphology and coating of metal surfaces is essential for a number of organic electronic devices including photovoltaic cells and sensors. In this study, we monitor the functionalization of gold surfaces with 11-mercaptoundecanoic acid (MUA, HS(CH(2))(10)CO(2)H) and cysteamine, aiming at passivating the surfaces for application in surface plasmon resonance (SPR) biosensors. Using polarization-modulated infrared reflection-absorption spectroscopy (PM-IRRAS), cyclic voltammetry, atomic force microscopy and quartz crystal microbalance, we observed a time-dependent organization process of the adsorbed MUA monolayer with alkyl chains perpendicular to the gold surface. Such optimized condition for surface passivation was obtained with a systematic search for experimental parameters leading to the lowest electrochemical signal of the functionalized gold electrode. The ability to build supramolecular architectures was also confirmed by detecting with PM-IRRAS the adsorption of streptavidin on the MUA-functionalized gold. As the approaches used for surface functionalization and its verification with PM-IRRAS are generic, one may now envisage monitoring the fabrication of tailored electrodes for a variety of applications.

ENHANCING MULTISCALE FRACTAL DESCRIPTORS USING FUNCTIONAL DATA ANALYSIS

This work presents a novel approach in order to increase the recognition power of Multiscale Fractal Dimension (MFD) techniques, when applied to image classification. The proposal uses Functional Data Analysis (FDA) with the aim of enhancing the MFD technique precision achieving a more representative descriptors vector, capable of recognizing and characterizing more precisely objects in an image. FDA is applied to signatures extracted by using the Bouligand-Minkowsky MFD technique in the generation of a descriptors vector from them. For the evaluation of the obtained improvement, an experiment using two datasets of objects was carried out. A dataset was used of characters shapes (26 characters of the Latin alphabet) carrying different levels of controlled noise and a dataset of fish images contours. A comparison with the use of the well-known methods of Fourier and wavelets descriptors was performed with the aim of verifying the performance of FDA method. The descriptor vectors were submitted to Linear Discriminant Analysis (LDA) classification method and we compared the correctness rate in the classification process among the descriptors methods. The results demonstrate that FDA overcomes the literature methods (Fourier and wavelets) in the processing of information extracted from the MFD signature. In this way, the proposed method can be considered as an interesting choice for pattern recognition and image classification using fractal analysis.

Formalization of an architectural model for exception handling coordination based on CA action concepts

Architectures based on Coordinated Atomic action (CA action) concepts have been used to build concurrent fault-tolerant systems. This conceptual model combines concurrent exception handling with action nesting to provide a general mechanism for both enclosing interactions among system components and coordinating forward error recovery measures. This article presents an architectural model to guide the formal specification of concurrent fault-tolerant systems. This architecture provides built-in Communicating Sequential Processes (CSPs) and predefined channels to coordinate exception handling of the user-defined components. Hence some safety properties concerning action scoping and concurrent exception handling can be proved by using the FDR (Failure Divergence Refinement) verification tool. As a result, a formal and general architecture supporting software fault tolerance is ready to be used and proved as users define components with normal and exceptional behaviors. (C) 2010 Elsevier B.V. All rights reserved.

Comparing diagnostic tests with missing data

When missing data occur in studies designed to compare the accuracy of diagnostic tests, a common, though naive, practice is to base the comparison of sensitivity, specificity, as well as of positive and negative predictive values on some subset of the data that fits into methods implemented in standard statistical packages. Such methods are usually valid only under the strong missing completely at random (MCAR) assumption and may generate biased and less precise estimates. We review some models that use the dependence structure of the completely observed cases to incorporate the information of the partially categorized observations into the analysis and show how they may be fitted via a two-stage hybrid process involving maximum likelihood in the first stage and weighted least squares in the second. We indicate how computational subroutines written in R may be used to fit the proposed models and illustrate the different analysis strategies with observational data collected to compare the accuracy of three distinct non-invasive diagnostic methods for endometriosis. The results indicate that even when the MCAR assumption is plausible, the naive partial analyses should be avoided.

A neural networks study of quinone compounds with trypanocidal activity

This work investigates neural network models for predicting the trypanocidal activity of 28 quinone compounds. Artificial neural networks (ANN), such as multilayer perceptrons (MLP) and Kohonen models, were employed with the aim of modeling the nonlinear relationship between quantum and molecular descriptors and trypanocidal activity. The calculated descriptors and the principal components were used as input to train neural network models to verify the behavior of the nets. The best model for both network models (MLP and Kohonen) was obtained with four descriptors as input. The descriptors were T(5) (torsion angle), QTS1 (sum of absolute values of the atomic charges), VOLS2 (volume of the substituent at region B) and HOMO-1 (energy of the molecular orbital below HOMO). These descriptors provide information on the kind of interaction that occurs between the compounds and the biological receptor. Both neural network models used here can predict the trypanocidal activity of the quinone compounds with good agreement, with low errors in the testing set and a high correctness rate. Thanks to the nonlinear model obtained from the neural network models, we can conclude that electronic and structural properties are important factors in the interaction between quinone compounds that exhibit trypanocidal activity and their biological receptors. The final ANN models should be useful in the design of novel trypanocidal quinones having improved potency.

A chemometric study of the 5-HT1A receptor affinities presented by arylpiperazine compounds

Arylpiperazine compounds are promising 5-HT1A receptor ligands that can contribute for accelerating the onset of therapeutic effect of selective serotonin reuptake inhibitors. In the present work, the chemometric methods HCA, PCA, KNN, SIMCA and PLS were employed in order to obtain SAR and QSAR models relating the structures of arylpiperazine compounds to their 5-HT1A receptor affinities. A training set of 52 compounds was used to construct the models and the best ones were obtained with nine topological descriptors. The classification and regression models were externally validated by means of predictions for a test set of 14 compounds and have presented good quality, as verified by the correctness of classifications, in the case of pattern recognition studies, and b, the high correlation coefficients (q(2) = 0.76, r(2) = 0.83) and small prediction errors for the PLS regression. Since the results are in good agreement with previous SAR studies, we can suggest that these findings can help in the search for 5-HT1A receptor ligands that are able to improve antidepressant treatment. (c) 2007 Elsevier Masson SAS. All rights reserved.

Artificial Neural Networks and the Study of the Psychoactivity of Cannabinoid Compounds

Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher`s weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.

Studies of Design and Assembly Defects on Integrated and Modular Architectures

It is known that despite companies’ efforts to improve the quality of their products, design and assembly defects results in large repair costs both in terms of repair and providing feedback to the origin of the defect. The purpose of this paper is to study these types of defects and the defect rates in design and assembly. The paper presents a web based questionnaire answered by 29 companies. The result shows that the defect rate (defects per product) spanned from 0.01 to 10. Also, design and assembly defects covered 46%, 23% respectively, of all occurred defects. A case study is also presented, performed at a company who recently implemented a modular architecture. In this company, defects from 5 700 integrated product architectures are compared with defects from 431 modular architectures. The average defect rate increased by 21.5% – from 0.65 to 0.79 – when a more modular architecture has been implemented. Furthermore, the study showed that the assembly defects have decreased while the design defects increased. The results presented in this paper will also support the development of the MPV (Module Property Verification) method which is briefly described.

Modular Product Verifications Based on Design for Assembly

The desire to conquer markets through advanced product design and trendy business strategies are still predominant approaches in industry today. In fact, product development has acquired an ever more central role in the strategic planning of companies, and it has extended its influence to R&D funding levels as well. It is not surprising that many national R&D project frameworks within the EU today are dominated by product development topics, leaving production engineering, robotics, and systems on the sidelines. The reasons may be many but, unfortunately, the link between product development and the production processes they cater for are seldom treated in depth. The issue dealt with in this article relates to how product development is applied in order to attain the required production quality levels a company may desire, as well as how one may counter assembly defects and deviations through quantifiable design approaches. It is recognized that product verifications (tests, inspections, etc.) are necessary, but the application of these tactics often result in lead-time extensions and increased costs. Modular architectures improve this by simplifying the verification of the assembled product at module level. Furthermore, since Design for Assembly (DFA) has shown the possibility to identify defective assemblies, it may be possible to detect potential assembly defects already in the product and module design phase. The intention of this paper is to discuss and describe the link between verifications of modular architectures, defects and design for assembly. The paper is based on literature and case studies; tables and diagrams are included with the intention of increasing understanding of the relation between poor designs, defects and product verifications.

An approach to evaluate product verifications

Companies implement a module product assortment as a part of their strategy to, among others, shorten lead-times, increase the product quality and to create more product variants with fever parts. However, the increased number of variants becomes a challenging task for the personnel responsible for the product verifications. By implementing verifications at module level, so called MPV (Module Property Verification) several advantages ensue. The advantages is not only a decrease in cost of verifications, but also a decrease in repair times, occupied space, storages with spare parts, and repair tools. Further, MPV also give an increased product quality due to an increased understanding of which defects that may occur. As an approach to implement MPV, this paper discusses defects and verification processes based on a study at a Swedish company. It also describes a matrix which is used to map relations between company specific cost drivers and so called verification factors. The matrix may indicate cost drivers which have a large impact on the total cost of product verifications.

Thermal Performance of a Solarus CPC-Thermal Collector

The  aim  of  this  master  thesis  is  an  investigation  of  the  thermal  performance  of  a  thermal compound parabolic concentrating (CPC) collector from Solarus. The collector consists of two troughs with absorbers which are coated with different types of paint with  unknown  properties.  The  lower  and  upper  trough  of  the  collector  have  been  tested individually. In  order  to  accomplish  the  performance  of  the  two  collectors,  a  thorough  literature  study  in  the  fields  of  CPC  technology,  various  test  methods,  test  standards  for  solar thermal  collectors  as  well  as  the  latest  articles  relating  on  the  subject  were  carried  out. In addition, the set‐up of the thermal test rig was part of the thesis as well. The thermal  performance  was  tested  according  to  the  steady  state  test  method  as  described in the European standard 12975‐2. Furthermore, the thermal performance of  a  conventional  flat  plate  collector  was  carried  out  for  verification  of  the  test  method. The  CPC‐Thermal  collector  from  Solarus  was  tested  in  2013  and  the  results  showed  four  times  higher  values  of  the  heat  loss  coefficient  UL (8.4  W/m²K)  than  what  has been reported for a commercial collector from Solarus. This value was assumed to be too large and it was assumed that the large value was a result of the test method used that time. Therefore, another aim was the comparison of the results achieved in this work with the results from the tests performed in 2013. The results of the thermal performance showed that the optical efficiency of the lower trough of the CPC‐T collector is 77±5% and the corresponding heat loss coefficient UL 4.84±0.20  W/m²K.  The  upper  trough  achieved  an  optical  efficiency  of  75±6  %  and  a  heat loss coefficient UL of 6.45±0.27 W/m²K. The results of the heat loss coefficients  are  valid  for  temperature  intervals  between  20°C  and  80°C.  The  different  absorber paintings have a significant impact on the results, the lower trough performs overall better.  The  results  achieved  in  this  thesis  show  lower  heat  loss  coefficients UL and higher optical efficiencies compared to the results from 2013.