Transcriptional regulation of the yghJ-pppA-yghG-gspCDEFGHIJKLM cluster, encoding the type II secretion pathway in Enterotoxigenic Escherichia coli

The gene cluster gspCDEFGHIJKLM codes for various structural components of the type II secretion pathway which is responsible for the secretion of heat-labile enterotoxin by enterotoxigenic Escherichia coli (ETEC). In this work, we used a variety of molecular approaches to elucidate the transcriptional organization of the ETEC type II secretion system and to unravel the mechanisms by which the expression of these genes is controlled. We showed that the gspCDEFGHIJKLM cluster and three other upstream genes, yghJ, pppA, and yghG, are cotranscribed and that a promoter located in the upstream region of yghJ plays a major role in the expression of this 14-gene transcriptional unit. Transcription of the yghJ promoter was repressed 168-fold upon a temperature downshift from 37°C to 22°C. This temperature-induced repression was mediated by the global regulatory proteins H-NS and StpA. Deletion mutagenesis showed that the promoter region encompassing positions −321 to +301 relative to the start site of transcription of yghJ was required for full repression. The yghJ promoter region is predicted to be highly curved and bound H-NS or StpA directly. The binding of H-NS or StpA blocked transcription initiation by inhibiting promoter open complex formation. Unraveling the mechanisms of regulation of type II secretion by ETEC enhances our understanding of the pathogenesis of ETEC and other pathogenic varieties of E. coli.

An introduction to computational intelligence paradigms

This chapter presents an introduction to computational intelligence (CI) paradigms. A number of CI definitions are first presented to provide a general concept of this new, innovative computing field. The main constituents of CI, which include artificial neural networks, fuzzy systems, and evolutionary algorithms, are explained. In addition, different hybrid CI models arisen from synergy of neural, fuzzy, and evolutionary computational paradigms are discussed.

Development of an intelligent system for the solder paste printing process

A neurogenetic-based hybrid framework is developed where the main components within the framework are artificial neural networks (ANNs) and genetic algorithms (GAs). The investigation covers a mode of combination or hybridisation between the two components that is called task hybridisation. The combination between ANNs and GAs using task hybridisation leads to the development of a hybrid multilayer feedforward network, trained using supervised learning. This paper discusses the GA method used to optimize the process parameters, using the ANN developed as the process mode, in a solder paste printing process, which is part of the process in the surface mount technology (SMT) method. The results obtained showed that the GA-based optimization method works well under various optimization criteria

An efficient meta-heuristic algorithm for grid computing

A grid computing system consists of a group of programs and resources that are spread across machines in the grid. A grid system has a dynamic environment and decentralized distributed resources, so it is important to provide efficient scheduling for applications. Task scheduling is an NP-hard problem and deterministic algorithms are inadequate and heuristic algorithms such as particle swarm optimization (PSO) are needed to solve the problem. PSO is a simple parallel algorithm that can be applied in different ways to resolve optimization problems. PSO searches the problem space globally and needs to be combined with other methods to search locally as well. In this paper, we propose a hybrid-scheduling algorithm to solve the independent task- scheduling problem in grid computing. We have combined PSO with the gravitational emulation local search (GELS) algorithm to form a new method, PSO–GELS. Our experimental results demonstrate the effectiveness of PSO–GELS compared to other algorithms.

Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes

Cardiac complications of diabetes require continuous monitoring since they may lead to increased morbidity or sudden death of patients. In order to monitor clinical complications of diabetes using wearable sensors, a small set of features have to be identified and effective algorithms for their processing need to be investigated. This article focuses on detecting and monitoring cardiac autonomic neuropathy (CAN) in diabetes patients. The authors investigate and compare the effectiveness of classifiers based on the following decision trees: ADTree, J48, NBTree, RandomTree, REPTree, and SimpleCart. The authors perform a thorough study comparing these decision trees as well as several decision tree ensembles created by applying the following ensemble methods: AdaBoost, Bagging, Dagging, Decorate, Grading, MultiBoost, Stacking, and two multi-level combinations of AdaBoost and MultiBoost with Bagging for the processing of data from diabetes patients for pervasive health monitoring of CAN. This paper concentrates on the particular task of applying decision tree ensembles for the detection and monitoring of cardiac autonomic neuropathy using these features. Experimental outcomes presented here show that the authors' application of the decision tree ensembles for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the results obtained previously in the literature.

Evolutionary optimization: a big data perspective

Stochastic search techniques such as evolutionary algorithms (EA) are known to be better explorer of search space as compared to conventional techniques including deterministic methods. However, in the era of big data like most other search methods and learning algorithms, suitability of evolutionary algorithms is naturally questioned. Big data pose new computational challenges including very high dimensionality and sparseness of data. Evolutionary algorithms' superior exploration skills should make them promising candidates for handling optimization problems involving big data. High dimensional problems introduce added complexity to the search space. However, EAs need to be enhanced to ensure that majority of the potential winner solutions gets the chance to survive and mature. In this paper we present an evolutionary algorithm with enhanced ability to deal with the problems of high dimensionality and sparseness of data. In addition to an informed exploration of the solution space, this technique balances exploration and exploitation using a hierarchical multi-population approach. The proposed model uses informed genetic operators to introduce diversity by expanding the scope of search process at the expense of redundant less promising members of the population. Next phase of the algorithm attempts to deal with the problem of high dimensionality by ensuring broader and more exhaustive search and preventing premature death of potential solutions. To achieve this, in addition to the above exploration controlling mechanism, a multi-tier hierarchical architecture is employed, where, in separate layers, the less fit isolated individuals evolve in dynamic sub-populations that coexist alongside the original or main population. Evaluation of the proposed technique on well known benchmark problems ascertains its superior performance. The algorithm has also been successfully applied to a real world problem of financial portfolio management. Although the proposed method cannot be considered big data-ready, it is certainly a move in the right direction.

Extending support to customised multi-point haptic devices in CHAI3D

 CHAI3D is a widely accepted haptic SDK in the society because it is open-source and provides support to devices from different vendors. In many cases, CHAI3D and its related demos are used for benchmarking various haptic collision and rendering algorithms. However, CHAI3D is designed for off-the-shelf single-point haptic devices only, and it does not provide native support to customised multi-point haptic devices. In this paper, we aim to extend the existing CHAI3D framework and provide a standardized routine to support customised, single/multi-point haptic devices. Our extension aims at two issues: Intra-device communication and Inter-device communication. Therefore, our extension includes an HIP wrapper layer to concurrently handle multiple HIPs of a single device, and a communication layer to concurrently handle multiple position, orientation and force calculations of multiple haptic devices. Our extension runs on top of a custom-built 8-channel device controller, although other offthe shelf controllers can also be integrated easily. Our extension complies with the CHAI3D design framework and advanced provide inter-device communication capabilities for multi-device operations. With straightforward conversion routines, existing CHAI3D demos can be adapted to multi-point demos, supporting real-time parallel collision detection and force rendering.

Selective visuo-haptic rendering of heterogeneous objects in 'parallel universes'

 Haptic rendering of complex models is usually prohibitive due to its much higher update rate requirement compared to visual rendering. Previous works have tried to solve this issue by introducing local simulation or multi-rate simulation for the two pipelines. Although these works have improved the capacity of haptic rendering pipeline, they did not take into consideration the situation of heterogeneous objects in one scenario, where rigid objects and deformable objects coexist in one scenario and close to each other. In this paper, we propose a novel idea to support interactive visuo-haptic rendering of complex heterogeneous models. The idea incorporates different collision detection and response algorithms and have them seamlessly switched on and off on the fly, as the HIP travels in the scenario. The selection of rendered models is based on the hypothesis of “parallel universes”, where the transition of rendering one group of models to another is totally transparent to users. To facilitate this idea, we proposed a procedure to convert the traditional single universe scenario into a “multiverse” scenario, where the original models are grouped and split into each parallel universe, depending on the scenario rendering requirement rather than just locality. We also proposed to add simplified visual objects as background avatars in each parallel universe to visually maintain the original scenario while not overly increase the scenario complexity. We tested the proposed idea in a haptically-enabled needle thoracostomy training environment and the result demonstrates that our idea is able to substantially accelerate visuo-haptic rendering with complex heterogeneous scenario objects.

Towards designing an email classification system using multi-view based semi-supervised learning

The goal of email classification is to classify user emails into spam and legitimate ones. Many supervised learning algorithms have been invented in this domain to accomplish the task, and these algorithms require a large number of labeled training data. However, data labeling is a labor intensive task and requires in-depth domain knowledge. Thus, only a very small proportion of the data can be labeled in practice. This bottleneck greatly degrades the effectiveness of supervised email classification systems. In order to address this problem, in this work, we first identify some critical issues regarding supervised machine learning-based email classification. Then we propose an effective classification model based on multi-view disagreement-based semi-supervised learning. The motivation behind the attempt of using multi-view and semi-supervised learning is that multi-view can provide richer information for classification, which is often ignored by literature, and semi-supervised learning supplies with the capability of coping with labeled and unlabeled data. In the evaluation, we demonstrate that the multi-view data can improve the email classification than using a single view data, and that the proposed model working with our algorithm can achieve better performance as compared to the existing similar algorithms.

Intelligent facial emotion recognition using a layered encoding cascade optimization model

In this research, we propose a facial expression recognition system with a layered encoding cascade optimization model. Since generating an effective facial representation is a vital step to the success of facial emotion recognition, a modified Local Gabor Binary Pattern operator is first employed to derive a refined initial face representation and we then propose two evolutionary algorithms for feature optimization including (i) direct similarity and (ii) Pareto-based feature selection, under the layered cascade model. The direct similarity feature selection considers characteristics within the same emotion category that give the minimum within-class variation while the Pareto-based feature optimization focuses on features that best represent each expression category and at the same time provide the most distinctions to other expressions. Both a neural network and an ensemble classifier with weighted majority vote are implemented for the recognition of seven expressions based on the selected optimized features. The ensemble model also automatically updates itself with the most recent concepts in the data. Evaluated with the Cohn-Kanade database, our system achieves the best accuracies when the ensemble classifier is applied, and outperforms other research reported in the literature with 96.8% for direct similarity based optimization and 97.4% for the Pareto-based feature selection. Cross-database evaluation with frontal images from the MMI database has also been conducted to further prove system efficiency where it achieves 97.5% for Pareto-based approach and 90.7% for direct similarity-based feature selection and outperforms related research for MMI. When evaluated with 90° side-view images extracted from the videos of the MMI database, the system achieves superior performances with >80% accuracies for both optimization algorithms. Experiments with other weighting and meta-learning combination methods for the construction of ensembles are also explored with our proposed ensemble showing great adpativity to new test data stream for cross-database evaluation. In future work, we aim to incorporate other filtering techniques and evolutionary algorithms into the optimization models to further enhance the recognition performance.

Optimisation of nonlinear motion cueing algorithm based on genetic algorithm

Motion cueing algorithms (MCAs) are playing a significant role in driving simulators, aiming to deliver the most accurate human sensation to the simulator drivers compared with a real vehicle driver, without exceeding the physical limitations of the simulator. This paper provides the optimisation design of an MCA for a vehicle simulator, in order to find the most suitable washout algorithm parameters, while respecting all motion platform physical limitations, and minimising human perception error between real and simulator driver. One of the main limitations of the classical washout filters is that it is attuned by the worst-case scenario tuning method. This is based on trial and error, and is effected by driving and programmers experience, making this the most significant obstacle to full motion platform utilisation. This leads to inflexibility of the structure, production of false cues and makes the resulting simulator fail to suit all circumstances. In addition, the classical method does not take minimisation of human perception error and physical constraints into account. Production of motion cues and the impact of different parameters of classical washout filters on motion cues remain inaccessible for designers for this reason. The aim of this paper is to provide an optimisation method for tuning the MCA parameters, based on nonlinear filtering and genetic algorithms. This is done by taking vestibular sensation error into account between real and simulated cases, as well as main dynamic limitations, tilt coordination and correlation coefficient. Three additional compensatory linear blocks are integrated into the MCA, to be tuned in order to modify the performance of the filters successfully. The proposed optimised MCA is implemented in MATLAB/Simulink software packages. The results generated using the proposed method show increased performance in terms of human sensation, reference shape tracking and exploiting the platform more efficiently without reaching the motion limitations.

Classification of team sport activities using a single wearable tracking device

Wearable tracking devices incorporating accelerometers and gyroscopes are increasingly being used for activity analysis in sports. However, minimal research exists relating to their ability to classify common activities. The purpose of this study was to determine whether data obtained from a single wearable tracking device can be used to classify team sport-related activities. Seventy-six non-elite sporting participants were tested during a simulated team sport circuit (involving stationary, walking, jogging, running, changing direction, counter-movement jumping, jumping for distance and tackling activities) in a laboratory setting. A MinimaxX S4 wearable tracking device was worn below the neck, in-line and dorsal to the first to fifth thoracic vertebrae of the spine, with tri-axial accelerometer and gyroscope data collected at 100Hz. Multiple time domain, frequency domain and custom features were extracted from each sensor using 0.5, 1.0, and 1.5s movement capture durations. Features were further screened using a combination of ANOVA and Lasso methods. Relevant features were used to classify the eight activities performed using the Random Forest (RF), Support Vector Machine (SVM) and Logistic Model Tree (LMT) algorithms. The LMT (79-92% classification accuracy) outperformed RF (32-43%) and SVM algorithms (27-40%), obtaining strongest performance using the full model (accelerometer and gyroscope inputs). Processing time can be reduced through feature selection methods (range 1.5-30.2%), however a trade-off exists between classification accuracy and processing time. Movement capture duration also had little impact on classification accuracy or processing time. In sporting scenarios where wearable tracking devices are employed, it is both possible and feasible to accurately classify team sport-related activities.