Evolution of JAK-STAT pathway components : mechanisms and role in immune system development

Background
Lying downstream of a myriad of cytokine receptors, the Janus kinase (JAK) – Signal transducer and activator of transcription (STAT) pathway is pivotal for the development and function of the immune system, with additional important roles in other biological systems. To gain further insight into immune system evolution, we have performed a comprehensive bioinformatic analysis of the JAK-STAT pathway components, including the key negative regulators of this pathway, the SH2-domain containing tyrosine phosphatase (SHP), Protein inhibitors against Stats (PIAS), and Suppressor of cytokine signaling (SOCS) proteins across a diverse range of organisms.

Results
Our analysis has demonstrated significant expansion of JAK-STAT pathway components co-incident with the emergence of adaptive immunity, with whole genome duplication being the principal mechanism for generating this additional diversity. In contrast, expansion of upstream cytokine receptors appears to be a pivotal driver for the differential diversification of specific pathway components.

Conclusion
Diversification of JAK-STAT pathway components during early vertebrate development occurred concurrently with a major expansion of upstream cytokine receptors and two rounds of whole genome duplications. This produced an intricate cell-cell communication system that has made a significant contribution to the evolution of the immune system, particularly the emergence of adaptive immunity.

A globally convergent MC algorithm with an adaptive learning rate

This brief deals with the problem of minor component analysis (MCA). Artificial neural networks can be exploited to achieve the task of MCA. Recent research works show that convergence of neural networks based MCA algorithms can be guaranteed if the learning rates are less than certain thresholds. However, the computation of these thresholds needs information about the eigenvalues of the autocorrelation matrix of data set, which is unavailable in online extraction of minor component from input data stream. In this correspondence, we introduce an adaptive learning rate into the OJAn MCA algorithm, such that its convergence condition does not depend on any unobtainable information, and can be easily satisfied in practical applications.

Adaptive mechanically controlled lubrication mechanism found in articular joints

Articular cartilage is a highly efficacious water-based tribological system that is optimized to provide low friction and wear protection at both low and high loads (pressures) and sliding velocities that must last over a lifetime. Although many different lubrication mechanisms have been proposed, it is becoming increasingly apparent that the tribological performance of cartilage cannot be attributed to a single mechanism acting alone but on the synergistic action of multiple "modes" of lubrication that are adapted to provide optimum lubrication as the normal loads, shear stresses, and rates change. Hyaluronic acid (HA) is abundant in cartilage and synovial fluid and widely thought to play a principal role in joint lubrication although this role remains unclear. HA is also known to complex readily with the glycoprotein lubricin (LUB) to form a cross-linked network that has also been shown to be critical to the wear prevention mechanism of joints. Friction experiments on porcine cartilage using the surface forces apparatus, and enzymatic digestion, reveal an "adaptive" role for an HA-LUB complex whereby, under compression, nominally free HA diffusing out of the cartilage becomes mechanically, i.e., physically, trapped at the interface by the increasingly constricted collagen pore network. The mechanically trapped HA-LUB complex now acts as an effective (chemically bound) "boundary lubricant"-reducing the friction force slightly but, more importantly, eliminating wear damage to the rubbing/shearing surfaces. This paper focuses on the contribution of HA in cartilage lubrication; however, the system as a whole requires both HA and LUB to function optimally under all conditions.

A hybrid Kansei engineering system using the self-organizing map neural network

Kansei Engineering (KE), a technology founded in Japan initially for product design, translates human feelings into design parameters. Although various intelligent approaches to objectively model human functions and therelationships with the product design decisions have been introduced in KE systems, many or the approaches are not able to incorporate human subjective feelings and preferenees into the decision-making process. This paper proposes a new hybrid KE system that attempts to make the machine-based decision-making process closely resembles the real-world practice. The proposed approach assimilates human perceptive and associative abililities into the decision-making process of the computer. A number of techniques based on the Self-Organizing Map (SOM) neural network are employed in the backward KE system to reveal the underlying data structures that are involved in the decision-making process. A case study on interior design is presented to evaluate the efficacy of the proposed approach. The results obtained demonstrate tbe effectiveness of the proposed approach in developing an intelligent KE system which is able to combine huiiUUI feelings and preferences into its decision making process.

Condition monitoring and fault prediction via an adaptive neural network

This paper describes the application of an adaptive neural network, called Fuzzy ARTMAP (FAM), to handle fault prediction and condition monitoring problems in a power generation station. The FAM network, which is supplemented with a pruning algorithm, is used as a classifier to predict different machine conditions, in an off-line learning mode. The process under scrutiny in the power plant is the Circulating Water (CW) system, with prime attention to monitoring the heat transfer efficiency of the condensers. Several phases of experiments were conducted to investigate the `optimum' setting of a set of parameters of the FAM classifier for monitoring heat transfer conditions in the power plant.

An adaptive fuzzy min-max conflict-resolving classifier

This paper describes a novel adaptive network, which agglomerates a procedure based on the fuzzy min-max clustering method, a supervised ART (Adaptive Resonance Theory) neural network, and a constructive conflict-resolving algorithm, for pattern classification. The proposed classifier is a fusion of the ordering algorithm, Fuzzy ARTMAP (FAM) and the Dynamic Decay Adjustment (DDA) algorithm. The network, called Ordered FAMDDA, inherits the benefits of the trio, viz . an ability to identify a fixed order of training pattern presentation for good generalisation; stable and incrementally learning architecture; and dynamic width adjustment of the weights of hidden nodes of conflicting classes. Classification performance of the Ordered FAMDDA is assessed using two benchmark datasets. The performances are analysed and compared with those from FAM and Ordered FAM. The results indicate that the Ordered FAMDDA classifier performs at least as good as the mentioned networks. The proposed Ordered FAMDDA network is then applied to a condition monitoring problem in a power generation station. The process under scrutiny is the Circulating Water (CW) system, with prime attention to condition monitoring of the heat transfer efficiency of the condensers. The results and their implications are analysed and discussed.

Adaptive fast finite-time multiple-surface sliding control for a class of uncertain non-linear systems

This paper concerns the adaptive fast finite-time multiple-surface sliding control (AFFTMSSC) problem for a class of high-order uncertain non-linear systems of which the upper bounds of the system uncertainties are unknown. By using the fast control Lyapunov function and the method of so-called adding a power integrator merging with adaptive technique, a recursive design procedure is provided, which guarantees the fast finite-time stability of the closed-loop system. Further, it is proved that the control input is bounded.

Development of a speaker recognition system using wavelets and artificial neural networks

This paper addresses the problem of speaker recognition from speech signals. The study focuses on the development of a speaker recognition system comprising two modules: a wavelet-based feature extractor, and a neural-network-based classifier. We have conducted a number of experiments to investigate the applicability of Discrete Wavelet Transform (D WT) in extracting discriminative features from the speech signals, and have examined various models from the Adaptive Resonance Theory (ART) family of neural networks in classijjing the extracted features. The results indicate that DWT could be a potential feature extraction tool for speaker recognition. In addition, the ART-based classijiers have yielded very promising recognition accuracy at more than 81%.

Terminal sliding mode control strategy design for second-order nonlinear system

This study mainly focuses on the terminal sliding mode control (TSMC) strategy design, including an adaptive terminal sliding mode control (ATSMC) and an exact-estimator-based terminal sliding mode control (ETSMC) for second-order nonlinear dynamical systems. In the ATSMC system, an adaptive bound estimation for the lump uncertainty is proposed to ensure the system stability. On the other hand, an exact estimator is designed for exact estimating system uncertainties to solve the trouble of chattering phenomena caused by a sign function in ATSMC law in despite of the utilization of a fixed value or an adaptive tuning algorithm for the lumped uncertainty bound. The effectiveness of the proposed control schemes can be verified in numerical simulations.

E-commerce trust management system reliability

Various solutions have been proposed in managing trust relationship between trading partners in eCommerce environment. Determine the reliability of trust management systems in eCommerce is most difficult issue due to highly dynamic nature of eCommerce environments. As trust management systems depend on the feedback ratings provided by the trading partners, they are fallible to strategic manipulation of the feedback ratings attacks. This paper addressed the challenges of trust management systems. The requirements of a reliable trust management are also discussed. In particular, we introduce an adaptive credibility model that distinguishes between credible feedback ratings and malicious feedback ratings by considering transaction size, frequency of ratings and majority vote to form a feedback ratings verification metric. The approach has been validated by simulation result.

Uplink power control via adaptive Hidden-Markov-Model-Based pathloss estimation

Dynamic variations in channel behavior is considered in transmission power control design for cellular radio systems. It is well known that power control increases system capacity, improves Quality of Service (QoS), and reduces multiuser interference. In this paper, an adaptive power control design based on the identification of the underlying pathloss dynamics of the fading channel is presented. Formulating power control decisions based on the measured received power levels allows modeling the fading channel pathloss dynamics in terms of a Hidden Markov Model (HMM). Applying the online HMM identification algorithm enables accurate estimation of the real pathloss ensuring efficient performance of the suggested power control scheme.

Living on the edge: how philopatry maintains adaptive potential.

Without genetic variation, species cannot cope with changing environments, and evolution does not proceed. In endangered species, adaptive potential may be eroded by decreased population sizes and processes that further reduce gene flow such as philopatry and local adaptations. Here, we focused on the philopatric and endangered loggerhead sea turtle (Caretta caretta) nesting in Cape Verde as a model system to investigate the link between adaptive potential and philopatry. We produced a dataset of three complementary genomic regions to investigate female philopatric behaviour (mitochondrial DNA), male-mediated gene flow (microsatellites) and adaptive potential (major histocompatibility complex, MHC). Results revealed genetically distinct nesting colonies, indicating remarkably small-scale philopatric behaviour of females. Furthermore, these colonies also harboured local pools of MHC alleles, especially at the margins of the population's distribution, which are therefore important reserves of additional diversity for the population. Meanwhile, directional male-mediated gene flow from the margins of distribution sustains the adaptive potential for the entire rookery. We therefore present the first evidence for a positive association between philopatry and locally adapted genomic regions. Contrary to expectation, we propose that philopatry conserves a high adaptive potential at the margins of a distribution, while asymmetric gene flow maintains genetic connectivity with the rest of the population.

Factories of the future : a system of systems engineering perspective

Factories of the Future will be distinguished by intelligent machines, automation, human factors integration and knowledge management. Modelling and simulation is recognised as a key enabling technology essential to economic, social and environmental sustainability of future manufacturing systems. This talk will explore the history, recent achievements and directions in modelling and simulation for 21st century factories and supply chains. A systems science approach is employed, from stakeholder engagement through participative modelling to self-tuning and self-assembling simulations. Our contributions lower the cost of the application of modelling and simulation to manufacturing processes, enabling real time planning, dynamic risk analysis, dashboards and 3D visualisation. This realisation of the virtual factory integrates human factors and decisions into the core technology platform. The implications to future manufacturing enterprises are explored through a series of case studies from aerospace, mining and small and medium manufacturing enterprises.

Hydrological modelling with a dynamic neural fuzzy inference system

Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) is a Takagi-Sugeno-type fuzzy inference system for online learning which can be applied for dynamic time series prediction. Data from Heshui catchment (2,275 km2) which is rural catchment in China, comprising daily time series of rainfall and discharge from January 1, 1990 to January 21, 2006 were analyzed. Rainfall and discharge antecedents were the inputs used for the DENFIS and ANFIS models and the output was discharge at the present time. DENFIS model results were compared with the results obtained from the physically-based University Regina Hydrologic Model (URHM) and an Adaptive Network-based Fuzzy Inference System (ANFIS) which employs offline learning. Our analysis shows that DENFIS results are better or at least comparable to URHM, but almost identical to ANFIS.