Ultrastructural changes in the ovary cells of engorged Rhipicephalus sanguineus female ticks treated with esters of ricinoleic acid from castor oil (Ricinus communis)

Rhipicephalus sanguineus is a widely distributed tick species that has adapted to the urban environment, and the dog is its main host. This species is also known as a vector and reservoir of diseases caused by bacteria, protozoa, and viruses. Currently, acaricides of synthetic chemical origin have been widely and indiscriminately used, leading to the development of resistance to these products by ticks and causing damage to the environment. Thus, these issues have made it necessary to seek other forms of controlling these ectoparasites. R. sanguineus was artificially infested in host New Zealand White rabbits, which were divided into four treatment groups: control (CG1 and CG2) and treatment (TG1 and TG2) groups. TG1 and TG2 hosts were provided with feed supplemented with esters of ricinoleic acid from castor oil at a concentration of 5 g/kg of feed for 7 and 15 days. Afterward, the ovaries of the female ticks were removed for analysis by transmission electron microscopy. The results showed ultrastructural changes in the somatic and germ cells of ovaries from TG1 and TG2 females, particularly with respect to chorion deposition, a protective membrane of the oocyte, as well as in the transport process of vitellogenic materials via the hemolymph and pedicel cells. Moreover, the mitochondria were less electron-dense and had cristae that were more disorganized than the mitochondria from CG1 and CG2 individuals. Thus, this study demonstrated the action of esters on the ovaries of R. sanguineus, signaling the prospect of a way to control this ectoparasite without affecting nontarget organisms or the environment.     

Fipronil (active ingredient of acaricide Frontline®) acting on the mice thyroid

Fipronil, active ingredient of Frontline® acaricide, is a phenyl‐pyrazolic derivative, and its efficacy in the elimination of several plagues, even in lower concentrations, has already been demonstrated; however, its effect on nontarget organisms has not been thoroughly explained. In this sense, the present study had the objective to evaluate the effects of different doses of fipronil in mice and determine its cytotoxic potential and possible compromises on nontarget organisms in artificial conditions. The results showed that animals exposed to fipronil presented significant alterations in the thyroidean tissue with evident follicular disorganization, decreased in the size of most follicles culminating with the rupture of smaller follicles and still with posterior fusion in a big and irregular “follicle” filled with colloid. The present study also revealed that the action of fipronil not only causes disorganization in the thyroidean tissue but alsoalters the chemical composition of the colloid itself (prehormone), modifying its proteic basis, which was confirmed by histochemical test for protein detection, making it clear that, in thyroids exposed to fipronil, the colloid was weakly positive or even negative for the presence of proteins in its constitution. Microsc. Res. Tech. 2011. © 2011 Wiley Periodicals, Inc.     

LS-VisionDraughts: improving the performance of an agent for checkers by integrating computational intelligence,reinforcement learning and a powerful search method

This paper presents LS-VisionDraughts: an efficient unsupervised evolutionary learning system for Checkers whose contribution is to automate the process of selecting an appropriate representation for the board states – by means of Evolutionary Computation – keeping a deep look-ahead (search depth) at the moment of choosing an adequate move. It corresponds to a player Multi Layer Perceptron Neural Network whose weights are updated through an evaluation function that is automatically adjusted by means of the Temporal Difference methods. A Genetic Algorithm automatically chooses a concise and efficient set of functions, which describe various scenarios associated with Checkers – called features – to represent the board states in the input layer of the Neural Network. It means that each individual of the Genetic Algorithm is a candidate set of features that is associated to a distinct Multi Layer Perceptron Neural Network. The output layer of the Neural Network is a real number (prediction) that indicates to which extent the input state is favorable to provide a better agent performance. In LS-VisionDraughts, a particular version of the search algorithm Alpha-Beta, called fail-soft Alpha-Beta, combined with Table Transposition, Iterative Deepening and ordered tree, uses this prediction value to choose the best move corresponding to the current board state. The best individual is chosen by means of numerous tournaments involving these selfsame Neural Networks. The architecture of LS-VisionDraught is inspired on the agent NeuroDraughts. However, the former system enhances the performance of the latter by automating the selection of the features through Evolutionary Computation and by replacing its Minimax search algorithm with the improved search strategy resumed above. This procedure allows for a 95 % reduction in the search runtime. Further, it remarkably increases the search tree depth. The results obtained from evaluative tournaments confirm the advances of LS-VisionDraughts compared to its opponents. It is however important to point out that LS-VisionDraughts learns practically without human supervision, contrary to the current automatic world champion Chinook, which has been built in a strongly supervised manner.     

Heuristically accelerated reinforcement learning modularization for multi-agent multi-objective problems

This article presents two new algorithms for finding the optimal solution of a Multi-agent Multi-objective Reinforcement Learning problem. Both algorithms make use of the concepts of modularization and acceleration by a heuristic function applied in standard Reinforcement Learning algorithms to simplify and speed up the learning process of an agent that learns in a multi-agent multi-objective environment. In order to verify performance of the proposed algorithms, we considered a predator-prey environment in which the learning agent plays the role of prey that must escape the pursuing predator while reaching for food in a fixed location. The results show that combining modularization and acceleration using a heuristics function indeed produced simplification and speeding up of the learning process in a complex problem when comparing with algorithms that do not make use of acceleration or modularization techniques, such as Q-Learning and Minimax-Q.     

A colorimetric biosensor for the detection of foodborne bacteria

We have synthesized 10,12-pentacosadyinoic acid (PCDA) + N-[(2-tetradecanamide)-ethyl]-ribonamide (TDER) vesicles to determine the colorimetric response induced by pathogenic bacteria (Staphylococcus aureus and Escherichia coli). The addition of bacterial supernatants caused a colorimetric transition in TDER/PCDA vesicles, even in diluted concentrations, indicating that chemical interactions occur between the vesicles and bacterial compounds. Bacterial substrates released from S. aureus induced a greater color change compared to the color change induced by E. coli. S. aureus metabolites also induced a greater color change when TDER/PCDA vesicles were incorporated into the cellulose strips. We determined the energy involved in the interaction between the bacterial substrates and the TDER/PCDA vesicles to be exothermic (and very high) for both bacterial supernatants. In addition, we analyzed the colorimetric transition in the presence of other molecules, using apple juice as a food matrix. Both apple juice and apple juice inoculated with bacterial substrates induced a TDER/PCDA color change; however, the S. aureus supernatant induced a slightly greater color change in the vesicles both in the suspension and in the cellulose strips. TDER/PCDA vesicles show great potential to be used as a biosensor to detect food pathogens in intelligent food packaging.     

Tools and strategies for debugging distributed stream processing applications

Distributed data stream processing applications are often characterized by data flow graphs consisting of a large number of built‐in and user‐defined operators connected via streams. These flow graphs are typically deployed on a large set of nodes. The data processing is carried out on‐the‐fly, as tuples arrive at possibly very high rates, with minimum latency. It is well known that developing and debugging distributed, multi‐threaded, and asynchronous applications, such as stream processing applications, can be challenging. Thus, without domain‐specific debugging support, developers struggle when debugging distributed applications. In this paper, we describe tools and language support to support debugging distributed stream processing applications. Our key insight is to view debugging of stream processing applications from four different, but related, perspectives. First, debugging the semantics of the application involves verifying the operator‐level composition and inspecting the flows at the logical level. Second, debugging the user‐defined operators involves traditional source‐code debugging, but strongly tied to the stream‐level interactions. Third, debugging the deployment details of the application require understanding the runtime physical layout and configuration of the application. Fourth, debugging the performance of the application requires inspecting various performance metrics (such as communication rates, CPU utilization, etc.) associated with streams, operators, and nodes in the system. In light of this characterization, we developed several tools such as a debugger‐aware compiler and an associated stream debugger, composition and deployment visualizers, and performance visualizers, as well as language support, such as configuration knobs for logging and tracing, deployment configurations such as operator‐to‐process and process‐to‐node mappings, monitoring directives to inspect streams, and special sink adapters to intercept and dump streaming data to files and sockets, to name a few. We describe these tools in the context of Spade —a language for creating distributed stream processing applications, and System S —a distributed stream processing middleware under development at the IBM Watson Research Center. Published in 2009 by John Wiley & Sons, Ltd.     

Multi‐criteria IoT resource discovery: a comparative analysis

The growth of real‐world objects with embedded and globally networked sensors allows to consolidate the Internet of things paradigm and increase the number of applications in the domains of ubiquitous and context‐aware computing. The merging between cloud computing and Internet of things named cloud of things will be the key to handle thousands of sensors and their data. One of the main challenges in the cloud of things is context‐aware sensor search and selection. Typically, sensors require to be searched using two or more conflicting context properties. Most of the existing work uses some kind of multi‐criteria decision analysis to perform the sensor search and selection, but does not show any concern for the quality of the selection presented by these methods. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi‐objective decision methods and their quality of selection comparing them with the Pareto‐optimality solutions. The gathered results allow to analyse and compare these algorithms regarding their behaviour, the number of optimal solutions and redundancy. Copyright © 2016 John Wiley & Sons, Ltd.     

A finite element model for the analysis of 3D axisymmetric laminated shells with piezoelectric sensors and actuators

This paper presents the development of a semi-analytical axisymmetric shell finite element model with piezoelectric layers using the 3D linear elasticity theory. The piezoelectric effect of the material could be used as sensors and/or actuators in way to control shell deformation. In the present 3D axisymmetric model, the equations of motion are expressed by expanding the displacement field using Fourier series in the circumferential direction. Thus, the 3D elasticity equations of motion are reduced to 2D equations involving circumferential harmonics. In the finite element formulation the dependent variables, electric potential and loading are expanded in truncated Fourier series. Special emphasis is given to the coupling between symmetric and anti-symmetric terms for laminated materials with piezoelectric rings. Numerical results obtained with the present model are found to be in good agreement with other finite element solutions.     

Joint evaluation of recovery and performance of a COTS DBMS in the presence of operator faults

A major cause of failures in large database management systems (DBMS) is operator/administrator faults. Although most of the complex DBMS available today have comprehensive recovery mechanisms, the effectiveness of these mechanisms is difficult to characterize. On the other hand, the tuning of a large database is very complex and database administrators tend to concentrate on performance tuning and disregard the recovery mechanisms. Above all, database administrators seldom have feedback on how good a given configuration is concerning recovery. This paper proposes an experimental approach to characterize both the performance and the recoverability of DBMS. Our approach is presented through a concrete example of benchmarking the performance and recovery of an Oracle DBMS running the standard TPC-C benchmark, extended to include two new elements: a faultload based on operator faults and measures related to recoverability. A classification of operator/administrator faults in DBMS is proposed. A set of tools have been designed and built to reproduce operator faults in an Oracle 8i DBMS, using exactly the same means used in the field by the real database administrator. This experimental approach is generic (i.e., can be applied to any DBMS) and is fully automatic. The paper ends with the discussion of the results and the proposal of guidelines to help database administrators in finding the balance between performance and recovery tuning.     

Non-destructive Investigation of Paintings on Canvas by Continuous Wave Terahertz Imaging and Flash Thermography

Terahertz (THz) imaging is increasingly used in the cultural heritage field. In particular, continuous wave (CW) and low frequency THz is attracting more attention. The first application of the THz technique inherent to the cultural heritage field dates back 10 years ago. Since 2006, tangible improvements have been conducted in the refinement of the technique, with the aim to produce clear maps useful for any art restorer. In this paper, a CW THz (0.1 THz) imaging system was used to inspect paintings on canvas both in reflection and in transmission modes. In particular, two paintings were analyzed: in the first one, similar materials and painting execution of the original artwork were used, while in the second one, the canvas layer is slightly different. Flash thermography was used herein together with the THz method in order to observe the differences in results for the textile support materials. A possible application of this method for the detection of artwork forgery requires some parameterization and analysis of various materials or thickness influence which will be addressed in a future study. In this work, advanced image processing techniques including principal component thermography (PCT) and partial least squares thermography (PLST) were used to process the infrared data. Finally, a comparison of CW THz and thermographic results was conducted.