Adaptive immunity to rhinoviruses: Sex and age matter

Background: Rhinoviruses (RV) are key triggers in acute asthma exacerbations. Previous studies suggest that men suffer from infectious diseases more frequently and with greater severity than women. Additionally, the immune response to most infections and vaccinations decreases with age. Most immune function studies do not account for such differences, therefore the aim of this study was to determine if the immune response to rhinovirus varies with sex or age. Methods: Blood mononuclear cells were isolated from 63 healthy individuals and grouped by sex and age (≤50 years old and ≥52 years old). Cells were cultured with rhinovirus 16 at a multiplicity of infection of 1. The chemokine IP-10 was measured at 24 h as an index of innate immunity while IFNγ and IL-13 were measured at 5 days as an index of adaptive immunity. Results: Rhinovirus induced IFNγ and IL-13 was significantly higher in ≤50 year old women than in age matched men (p < 0.02 and p < 0.05) and ≥52 year old women (p < 0.02 and p > 0.005). There was no sex or age based difference in rhinovirus induced IP-10 expression. Both IFNγ and IL-13 were negatively correlated with age in women but not in men. Conclusions: This study suggests that pre-menopausal women have a stronger adaptive immune response to rhinovirus infection than men and older people, though the mechanisms responsible for these differences remain to be determined. Our findings highlight the importance of gender and age balance in clinical studies and in the development of new treatments and vaccines.

A new single-phase frequency adaptive Phase-Locked Loop (PLL) for harmonic measurement

The paper presents an improved Phase-Locked Loop (PLL) for measuring the fundamental frequency and selective harmonic content of a distorted signal. This information can be used by grid interfaced devices and harmonic compensators. The single-phase structure is based on the Synchronous Reference Frame (SRF) PLL. The proposed PLL needs only a limited number of harmonic stages by incorporating Moving Average Filters (MAF) for eliminating the undesired harmonic content at each stage. The frequency dependency of MAF in effective filtering of undesired harmonics is also dealt with by a proposed method for adaptation to frequency variations of input signal. The method is suitable for high sampling rates and a wide frequency measurement range. Furthermore, an extended model of this structure is proposed which includes the response to both the frequency and phase angle variations. The proposed algorithm is simulated and verified using Hardware-in-the-Loop (HIL) testing.

Threat of resource loss: The role of self-regulation in adaptive task performance

Changes at work are often accompanied with the threat of, or actual, resource loss. Through an experiment, we investigated the detrimental effect of the threat of resource loss on adaptive task performance. Self-regulation (i.e., task focus and emotion control) was hypothesized to buffer the negative relationship between the threat of resource loss and adaptive task performance. Adaptation was conceptualized as relearning after a change in task execution rules. Threat of resource loss was manipulated for 100 participants undertaking an air traffic control task. Using discontinuous growth curve modeling, 2 kinds of adaptation—transition adaptation and reacquisition adaptation—were differentiated. The results showed that individuals who experienced the threat of resource loss had a stronger drop in performance (less transition adaptation) and a subsequent slower recovery (less reacquisition adaptation) compared with the control group who experienced no threat. Emotion control (but not task focus) moderated the relationship between the threat of resource loss and transition adaptation. In this respect, individuals who felt threatened but regulated their emotions performed better immediately after the task change (but not later on) compared with those individuals who felt threatened and did not regulate their emotions as well. However, later on, relearning (reacquisition adaptation) under the threat of resource loss was facilitated when individuals concentrated on the task at hand.

A Quasi-Newton Adaptive Algorithm for Generalized Symmetric Eigenvalue Problem

In this paper, we first recast the generalized symmetric eigenvalue problem, where the underlying matrix pencil consists of symmetric positive definite matrices, into an unconstrained minimization problem by constructing an appropriate cost function, We then extend it to the case of multiple eigenvectors using an inflation technique, Based on this asymptotic formulation, we derive a quasi-Newton-based adaptive algorithm for estimating the required generalized eigenvectors in the data case. The resulting algorithm is modular and parallel, and it is globally convergent with probability one, We also analyze the effect of inexact inflation on the convergence of this algorithm and that of inexact knowledge of one of the matrices (in the pencil) on the resulting eigenstructure. Simulation results demonstrate that the performance of this algorithm is almost identical to that of the rank-one updating algorithm of Karasalo. Further, the performance of the proposed algorithm has been found to remain stable even over 1 million updates without suffering from any error accumulation problems.

A direct adaptive neural command controller design for an unstable helicopter

This paper presents an off-line (finite time interval) and on-line learning direct adaptive neural controller for an unstable helicopter. The neural controller is designed to track pitch rate command signal generated using the reference model. A helicopter having a soft inplane four-bladed hingeless main rotor and a four-bladed tail rotor with conventional mechanical controls is used for the simulation studies. For the simulation study, a linearized helicopter model at different straight and level flight conditions is considered. A neural network with a linear filter architecture trained using backpropagation through time is used to approximate the control law. The controller network parameters are adapted using updated rules Lyapunov synthesis. The off-line trained (for finite time interval) network provides the necessary stability and tracking performance. The on-line learning is used to adapt the network under varying flight conditions. The on-line learning ability is demonstrated through parameter uncertainties. The performance of the proposed direct adaptive neural controller (DANC) is compared with feedback error learning neural controller (FENC).

From vision to actions: Towards adaptive and autonomous humanoid robots

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions.

Active Adaptive Gyrostabiliser Control System

A gyrostabiliser control system and method for stabilising marine vessel motion based on precession information only. The control system employs an Automatic Gain Control (AGC) precession controller (60). This system operates with a gain factor that is always being gradually minimized so as to let the gyro flywheel (12) develop as much precession as possible - the higher the precession, the higher the roll stabilising moment. This continuous gain change provides adaptation to changes in sea state and sailing conditions. The system effectively predicts the likelihood of maximum precession being reached. Should this event be detected, then the gain is rapidly increased so as to provide a breaking precession torque. Once the event has passed, the system again attempts to gradually decrease the gain.

A pattern adaptive thinning algorithm

A simple sequential thinning algorithm for peeling off pixels along contours is described. An adaptive algorithm obtained by incorporating shape adaptivity into this sequential process is also given. The distortions in the skeleton at the right-angle and acute-angle corners are minimized in the adaptive algorithm. The asymmetry of the skeleton, which is a characteristic of sequential algorithm, and is due to the presence of T-corners in some of the even-thickness pattern is eliminated. The performance (in terms of time requirements and shape preservation) is compared with that of a modern thinning algorithm.

Adaptive significance of the Indian caste system: an ecological perspective

Indian society is an agglomeration of several thousand endogamous groups or castes each with a restricted geographical range and a hereditarily determine mode of subsistence. These reproductively isolated castes may be compared to biological species, and the society thought of as a biological community with each caste having its specific ecological niche. In this paper we examine the ecological-niche relationships of castes which are directly dependent on natural resources. Evidence is presented to show that castes living together in the same region had so organized their pattern of resource use as to avoid excessive intercaste competition for limiting resources. Furthermore, territorial division of the total range of the caste regulated intra-caste competition. Hence, a particular plant or animal resource in a given locality was used almost exclusively by a given lineage within a caste generation after generation. This favoured the cultural evolution of traditions ensuring sustainable use of natural resources. This must have contributed significantly to the stability of Indian caste society over several thousand years. The collapse of the base of natural resources and increasing monetarization of the economy has, however, destroyed the earlier complementarity between the different castes and led to increasing conflicts between them in recent years.

Brain-specific epigenetic markers of schizophrenia

Epigenetics plays a crucial role in schizophrenia susceptibility. In a previous study, we identified over 4500 differentially methylated sites in prefrontal cortex (PFC) samples from schizophrenia patients. We believe this was the first genome-wide methylation study performed on human brain tissue using the Illumina Infinium HumanMethylation450 Bead Chip. To understand the biological significance of these results, we sought to identify a smaller number of differentially methylated regions (DMRs) of more functional relevance compared with individual differentially methylated sites. Since our schizophrenia whole genome methylation study was performed, another study analysing two separate data sets of post-mortem tissue in the PFC from schizophrenia patients has been published. We analysed all three data sets using the bumphunter function found in the Bioconductor package minfi to identify regions that are consistently differentially methylated across distinct cohorts. We identified seven regions that are consistently differentially methylated in schizophrenia, despite considerable heterogeneity in the methylation profiles of patients with schizophrenia. The regions were near CERS3, DPPA5, PRDM9, DDX43, REC8, LY6G5C and a region on chromosome 10. Of particular interest is PRDM9 which encodes a histone methyltransferase that is essential for meiotic recombination and is known to tag genes for epigenetic transcriptional activation. These seven DMRs are likely to be key epigenetic factors in the aetiology of schizophrenia and normal brain neurodevelopment.

Non-stationary signal analysis by least mean fourth (LMF) adaptive algorithm

A modified least mean fourth (LMF) adaptive algorithm applicable to non-stationary signals is presented. The performance of the proposed algorithm is studied by simulation for non-stationarities in bandwidth, centre frequency and gain of a stochastic signal. These non-stationarities are in the form of linear, sinusoidal and jump variations of the parameters. The proposed LMF adaptation is found to have better parameter tracking capability than the LMS adaptation for the same speed of convergence.

Structured model-following neuro-adaptive design for attitude maneuver of rigid bodies

A new structured model-following adaptive approach is presented in this paper to achieve large attitude maneuvers of rigid bodies. First, a nominal controller is designed using the dynamic inversion philosophy. Next, a neuro- adaptive design is proposed to augment the nominal design in order to assure robust performance in the presence of parameter inaccuracies as well as unknown constant external disturbances. The structured approach proposed in this paper (where kinematic and dynamic equations are handled separately), reduces the complexity of the controller structure. From simulation studies, this adaptive controller is found to be very effective in assuring robust performance.

A Phenomic Scan of the Norfolk Island Genetic Isolate Identifies a Major Pleiotropic Effect Locus Associated with Metabolic and Renal Disorder Markers

Multiphenotype genome-wide association studies (GWAS) may reveal pleiotropic genes, which would remain undetected using single phenotype analyses. Analysis of large pedigrees offers the added advantage of more accurately assessing trait heritability, which can help prioritise genetically influenced phenotypes for GWAS analysis. In this study we performed a principal component analysis (PCA), heritability (h2) estimation and pedigree-based GWAS of 37 cardiovascular disease -related phenotypes in 330 related individuals forming a large pedigree from the Norfolk Island genetic isolate. PCA revealed 13 components explaining >75% of the total variance. Nine components yielded statistically significant h2 values ranging from 0.22 to 0.54 (P<0.05). The most heritable component was loaded with 7 phenotypic measures reflecting metabolic and renal dysfunction. A GWAS of this composite phenotype revealed statistically significant associations for 3 adjacent SNPs on chromosome 1p22.2 (P<1x10-8). These SNPs form a 42kb haplotype block and explain 11% of the genetic variance for this renal function phenotype. Replication analysis of the tagging SNP (rs1396315) in an independent US cohort supports the association (P = 0.000011). Blood transcript analysis showed 35 genes were associated with rs1396315 (P<0.05). Gene set enrichment analysis of these genes revealed the most enriched pathway was purine metabolism (P = 0.0015). Overall, our findings provide convincing evidence for a major pleiotropic effect locus on chromosome 1p22.2 influencing risk of renal dysfunction via purine metabolism pathways in the Norfolk Island population. Further studies are now warranted to interrogate the functional relevance of this locus in terms of renal pathology and cardiovascular disease risk.

A global convergence criterion for discrete-time multivariable adaptive systems

The paper deals with the basic problem of adjusting a matrix gain in a discrete-time linear multivariable system. The object is to obtain a global convergence criterion, i.e. conditions under which a specified error signal asymptotically approaches zero and other signals in the system remain bounded for arbitrary initial conditions and for any bounded input to the system. It is shown that for a class of up-dating algorithms for the adjustable gain matrix, global convergence is crucially dependent on a transfer matrix G(z) which has a simple block diagram interpretation. When w(z)G(z) is strictly discrete positive real for a scalar w(z) such that w-1(z) is strictly proper with poles and zeros within the unit circle, an augmented error scheme is suggested and is proved to result in global convergence. The solution avoids feeding back a quadratic term as recommended in other schemes for single-input single-output systems.

Isolation and characterization of novel microsatellite markers and their application for diversity assessment in cultivated groundnut (Arachis hypogaea)

Background: Cultivated peanut or groundnut (Arachis hypogaea L.) is the fourth most important oilseed crop in the world, grown mainly in tropical, subtropical and warm temperate climates. Due to its origin through a single and recent polyploidization event, followed by successive selection during breeding efforts, cultivated groundnut has a limited genetic background. In such species, microsatellite or simple sequence repeat (SSR) markers are very informative and useful for breeding applications. The low level of polymorphism in cultivated germplasm, however, warrants a need of larger number of polymorphic microsatellite markers for cultivated groundnut. Results: A microsatellite- enriched library was constructed from the genotype TMV2. Sequencing of 720 putative SSR-positive clones from a total of 3,072 provided 490 SSRs. 71.2% of these SSRs were perfect type, 13.1% were imperfect and 15.7% were compound. Among these SSRs, the GT/CA repeat motifs were the most common (37.6%) followed by GA/CT repeat motifs (25.9%). The primer pairs could be designed for a total of 170 SSRs and were optimized initially on two genotypes. 104 (61.2%) primer pairs yielded scorable amplicon and 46 (44.2%) primers showed polymorphism among 32 cultivated groundnut genotypes. The polymorphic SSR markers detected 2 to 5 alleles with an average of 2.44 per locus. The polymorphic information content (PIC) value for these markers varied from 0.12 to 0.75 with an average of 0.46. Based on 112 alleles obtained by 46 markers, a phenogram was constructed to understand the relationships among the 32 genotypes. Majority of the genotypes representing subspecies hypogaea were grouped together in one cluster, while the genotypes belonging to subspecies fastigiata were grouped mainly under two clusters. Conclusion. Newly developed set of 104 markers extends the repertoire of SSR markers for cultivated groundnut. These markers showed a good level of PIC value in cultivated germplasm and therefore would be very useful for germplasm analysis, linkage mapping, diversity studies and phylogenetic relationships in cultivated groundnut as well as related Arachis species.