Differences between the CME fronts tracked by an expert, an automated algorithm, and the Solar Stormwatch project

Observations from the Heliospheric Imager (HI) instruments aboard the twin STEREO spacecraft have enabled the compilation of several catalogues of coronal mass ejections (CMEs), each characterizing the propagation of CMEs through the inner heliosphere. Three such catalogues are the Rutherford Appleton Laboratory (RAL)-HI event list, the Solar Stormwatch CME catalogue, and, presented here, the J-tracker catalogue. Each catalogue uses a different method to characterize the location of CME fronts in the HI images: manual identification by an expert, the statistical reduction of the manual identifications of many citizen scientists, and an automated algorithm. We provide a quantitative comparison of the differences between these catalogues and techniques, using 51 CMEs common to each catalogue. The time-elongation profiles of these CME fronts are compared, as are the estimates of the CME kinematics derived from application of three widely used single-spacecraft-fitting techniques. The J-tracker and RAL-HI profiles are most similar, while the Solar Stormwatch profiles display a small systematic offset. Evidence is presented that these differences arise because the RAL-HI and J-tracker profiles follow the sunward edge of CME density enhancements, while Solar Stormwatch profiles track closer to the antisunward (leading) edge. We demonstrate that the method used to produce the time-elongation profile typically introduces more variability into the kinematic estimates than differences between the various single-spacecraft-fitting techniques. This has implications for the repeatability and robustness of these types of analyses, arguably especially so in the context of space weather forecasting, where it could make the results strongly dependent on the methods used by the forecaster.

A robust sclera segmentation algorithm

Sclera segmentation is shown to be of significant importance for eye and iris biometrics. However, sclera segmentation has not been extensively researched as a separate topic, but mainly summarized as a component of a broader task. This paper proposes a novel sclera segmentation algorithm for colour images which operates at pixel-level. Exploring various colour spaces, the proposed approach is robust to image noise and different gaze directions. The algorithm’s robustness is enhanced by a two-stage classifier. At the first stage, a set of simple classifiers is employed, while at the second stage, a neural network classifier operates on the probabilities’ space generated by the classifiers at stage 1. The proposed method was ranked the 1st in Sclera Segmentation Benchmarking Competition 2015, part of BTAS 2015, with a precision of 95.05% corresponding to a recall of 94.56%.

A Genetic Algorithm for the selection of structural MRI features for classification of Mild Cognitive Impairment and Alzheimer's Disease

This work investigates the problem of feature selection in neuroimaging features from structural MRI brain images for the classification of subjects as healthy controls, suffering from Mild Cognitive Impairment or Alzheimer’s Disease. A Genetic Algorithm wrapper method for feature selection is adopted in conjunction with a Support Vector Machine classifier. In very large feature sets, feature selection is found to be redundant as the accuracy is often worsened when compared to an Support Vector Machine with no feature selection. However, when just the hippocampal subfields are used, feature selection shows a significant improvement of the classification accuracy. Three-class Support Vector Machines and two-class Support Vector Machines combined with weighted voting are also compared with the former and found more useful. The highest accuracy achieved at classifying the test data was 65.5% using a genetic algorithm for feature selection with a three-class Support Vector Machine classifier.

Modelling personal thermal sensations using C-Support Vector Classification (C-SVC) algorithm

The personalised conditioning system (PCS) is widely studied. Potentially, it is able to reduce energy consumption while securing occupants’ thermal comfort requirements. It has been suggested that automatic optimised operation schemes for PCS should be introduced to avoid energy wastage and discomfort caused by inappropriate operation. In certain automatic operation schemes, personalised thermal sensation models are applied as key components to help in setting targets for PCS operation. In this research, a novel personal thermal sensation modelling method based on the C-Support Vector Classification (C-SVC) algorithm has been developed for PCS control. The personal thermal sensation modelling has been regarded as a classification problem. During the modelling process, the method ‘learns’ an occupant’s thermal preferences from his/her feedback, environmental parameters and personal physiological and behavioural factors. The modelling method has been verified by comparing the actual thermal sensation vote (TSV) with the modelled one based on 20 individual cases. Furthermore, the accuracy of each individual thermal sensation model has been compared with the outcomes of the PMV model. The results indicate that the modelling method presented in this paper is an effective tool to model personal thermal sensations and could be integrated within the PCS for optimised system operation and control.

Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks

With the fast development of wireless communications, ZigBee and semiconductor devices, home automation networks have recently become very popular. Since typical consumer products deployed in home automation networks are often powered by tiny and limited batteries, one of the most challenging research issues is concerning energy reduction and the balancing of energy consumption across the network in order to prolong the home network lifetime for consumer devices. The introduction of clustering and sink mobility techniques into home automation networks have been shown to be an efficient way to improve the network performance and have received significant research attention. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. Then, a mobile sink communicates with each cluster head to collect data directly through short range communications. The ACO algorithm has been utilized in this work in order to find the optimal mobility trajectory for the mobile sink. Extensive simulation results from this research show that the proposed algorithm significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed for home automation networks.

A constrained recursive least squares algorithm for adaptive combination of multiple models

In this paper, we develop a novel constrained recursive least squares algorithm for adaptively combining a set of given multiple models. With data available in an online fashion, the linear combination coefficients of submodels are adapted via the proposed algorithm.We propose to minimize the mean square error with a forgetting factor, and apply the sum to one constraint to the combination parameters. Moreover an l1-norm constraint to the combination parameters is also applied with the aim to achieve sparsity of multiple models so that only a subset of models may be selected into the final model. Then a weighted l2-norm is applied as an approximation to the l1-norm term. As such at each time step, a closed solution of the model combination parameters is available. The contribution of this paper is to derive the proposed constrained recursive least squares algorithm that is computational efficient by exploiting matrix theory. The effectiveness of the approach has been demonstrated using both simulated and real time series examples.

A generalised background correction algorithm for a Halo Doppler lidar and its application to data from Finland

Current commercially available Doppler lidars provide an economical and robust solution for measuring vertical and horizontal wind velocities, together with the ability to provide co- and cross-polarised backscatter profiles. The high temporal resolution of these instruments allows turbulent properties to be obtained from studying the variation in radial velocities. However, the instrument specifications mean that certain characteristics, especially the background noise behaviour, become a limiting factor for the instrument sensitivity in regions where the aerosol load is low. Turbulent calculations require an accurate estimate of the contribution from velocity uncertainty estimates, which are directly related to the signal-to-noise ratio. Any bias in the signal-to-noise ratio will propagate through as a bias in turbulent properties. In this paper we present a method to correct for artefacts in the background noise behaviour of commercially available Doppler lidars and reduce the signal-to-noise ratio threshold used to discriminate between noise, and cloud or aerosol signals. We show that, for Doppler lidars operating continuously at a number of locations in Finland, the data availability can be increased by as much as 50 % after performing this background correction and subsequent reduction in the threshold. The reduction in bias also greatly improves subsequent calculations of turbulent properties in weak signal regimes.

Gaze stabilization testing: The efficiency and test-retest reliability of user operated control versus the parameter estimation (PEST) algorithm

The primary objective of this research study is to determine which form of testing, the PEST algorithm or an operator-controlled condition is most accurate and time efficient for administration of the gaze stabilization test

The SARS algorithm: detrending CoRoT light curves with Sysrem using simultaneous external parameters

Surveys for exoplanetary transits are usually limited not by photon noise but rather by the amount of red noise in their data. In particular, although the CoRoT space-based survey data are being carefully scrutinized, significant new sources of systematic noises are still being discovered. Recently, a magnitude-dependant systematic effect was discovered in the CoRoT data by Mazeh et al. and a phenomenological correction was proposed. Here we tie the observed effect to a particular type of effect, and in the process generalize the popular Sysrem algorithm to include external parameters in a simultaneous solution with the unknown effects. We show that a post-processing scheme based on this algorithm performs well and indeed allows for the detection of new transit-like signals that were not previously detected.

Genetic algorithm inversion of the average 1D crustal structure using local and regional earthquakes

Knowing the best 1D model of the crustal and upper mantle structure is useful not only for routine hypocenter determination, but also for linearized joint inversions of hypocenters and 3D crustal structure, where a good choice of the initial model can be very important. Here, we tested the combination of a simple GA inversion with the widely used HYPO71 program to find the best three-layer model (upper crust, lower crust, and upper mantle) by minimizing the overall P- and S-arrival residuals, using local and regional earthquakes in two areas of the Brazilian shield. Results from the Tocantins Province (Central Brazil) and the southern border of the Sao Francisco craton (SE Brazil) indicated an average crustal thickness of 38 and 43 km, respectively, consistent with previous estimates from receiver functions and seismic refraction lines. The GA + HYPO71 inversion produced correct Vp/Vs ratios (1.73 and 1.71, respectively), as expected from Wadati diagrams. Tests with synthetic data showed that the method is robust for the crustal thickness, Pn velocity, and Vp/Vs ratio when using events with distance up to about 400 km, despite the small number of events available (7 and 22, respectively). The velocities of the upper and lower crusts, however, are less well constrained. Interestingly, in the Tocantins Province, the GA + HYPO71 inversion showed a secondary solution (local minimum) for the average crustal thickness, besides the global minimum solution, which was caused by the existence of two distinct domains in the Central Brazil with very different crustal thicknesses. (C) 2010 Elsevier Ltd. All rights reserved.

Blockage of Angiotensin II type 2 receptor prevents thyroxine-mediated cardiac hypertrophy by blocking Akt activation

Although most of effects of Angiotensin II (Ang II) related to cardiac remodelling can be attributed to type 1 Ang II receptor (AT(1)R), the type 2 receptor (AT(2)R) has been shown to be involved in the development of some cardiac hypertrophy models. In the present study, we investigated whether the thyroid hormone (TH) action leading to cardiac hypertrophy is also mediated by increased Ang II levels or by change on AT(1)R and AT(2)R expression, which could contribute to this effect. In addition, we also evaluated the possible contribution of AT(2)R in the activation of Akt and in the development of TH-induced cardiac hypertrophy. To address these questions, Wistar rats were treated with thyroxine (T(4), 0.1 mg/kg BW/day, i.p.), with or without AT(2)R blocker (PD123319), for 14 days. Cardiac hypertrophy was identified based on heart/body weight ratio and confirmed by analysis of atrial natriuretic factor mRNA expression. Cardiomyocyte cultures were used to exclude the influence of TH-related hemodynamic effects. Our results demonstrate that the cardiac Ang II levels were significantly increased (80%, P < 0.001) as well as the AT(2)R expression (50%, P < 0.05) in TH-induced cardiac hypertrophy. The critical involvement of AT(2)R to the development of this cardiac hypertrophy in vivo was evidenced after administration of AT(2) blocker, which was able to prevent in 40% (P < 0.01) the cardiac mass gain and the Akt activation induced by TH. The role of AT(2)R to the TH-induced cardiomyocyte hypertrophy was also confirmed after using PD123319 in the in vitro studies. These findings improve understanding of the cardiac hypertrophy observed in hyperthyroidism and provide new insights into the generation of future therapeutic strategies.

Melatonin Protects CD4(+) T Cells from Activation-Induced Cell Death by Blocking NFAT-Mediated CD95 Ligand Upregulation

Over the past 20 y, the hormone melatonin was found to be produced in extrapineal sites, including cells of the immune system. Despite the increasing data regarding the biological effects of melatonin on the regulation of the immune system, the effect of this molecule on T cell survival remains largely unknown. Activation-induced cell death plays a critical role in the maintenance of the homeostasis of the immune system by eliminating self-reactive or chronically stimulated T cells. Because activated T cells not only synthesize melatonin but also respond to it, we investigated whether melatonin could modulate activation-induced cell death. We found that melatonin protects human and murine CD4(+) T cells from apoptosis by inhibiting CD95 ligand mRNA and protein upregulation in response to TCR/CD3 stimulation. This inhibition is a result of the interference with calmodulin/calcineurin activation of NFAT that prevents the translocation of NFAT to the nucleus. Accordingly, melatonin has no effect on T cells transfected with a constitutively active form of NFAT capable of migrating to the nucleus and transactivating target genes in the absence of calcineurin activity. Our results revealed a novel biochemical pathway that regulates the expression of CD95 ligand and potentially other downstream targets of NFAT activation. The Journal of Immunology, 2010, 184: 3487-3494.

Human monocytes but not dendritic cells are killed by blocking of autocrine cyclooxygenase activity

Dendritic cells (DCs), in peripheral tissues, derive mostly from blood precursors that differentiate into DCs under the influence of the local microenvironment. Monocytes constitute the main known DC precursors in blood and their infiltration into tissues is up-regulated during inflammation. During this process, the local production of mediators, like prostaglandins (PGs), influence significantly DC differentiation and function. In the present paper we show that treatment of blood adherent mononuclear cells with 10 mu M indomethacin, a dose achieved in human therapeutic settings, causes monocytes` progressive death but does not affect DCs viability or cell surface phenotype. This resistance of DCs was observed both for cells differentiated in vitro from blood monocytes and for a population with DCs characteristics already present in blood. This phenomenon could affect the local balance of antigen-presenting cells, influence the induction and pattern of immune responses developed under the treatment with non-steroidal anti-inflammatory drugs and, therefore, deserves further investigation. (C) 2009 Elsevier Inc. All rights reserved.

Worldwide sequence conservation of transmission-blocking vaccine candidate Pvs230 in Plasmodium vivax

Pfs230, surface protein of gametocyte/gamete of the human malaria parasite, Plasmodium falciparum, is a prime candidate of malaria transmission-blocking vaccine. Plasmodium vivax has an ortholog of Pfs230 (Pvs230), however, there has been no study in any aspects on Pvs230 to date. To investigate whether Pvs230 can be a vivax malaria transmission-blocking vaccine, we performed evolutionary and population genetic analysis of the Pvs230 gene (pvs230: PVX_003905). Our analysis of Pvs230 and its orthologs in eight Plasmodium species revealed two distinctive parts: an interspecies variable part (IVP) containing species-specific oligopeptide repeats at the N-terminus and a 7.5 kb interspecies conserved part (ICP) containing 14 cysteine-rich domains. Pvs230 was closely related to its orthologs, Pks230 and Pcys230, in monkey malaria parasites. Analysis of 113 pvs230 sequences obtained from worldwide, showed that nucleotide diversity is remarkably low in the non-repeat 8-kb region of pvs230 (theta pi = 0.00118) with 77 polymorphic nucleotide sites, 40 of which results in amino acid replacements. A signature of purifying selection but not of balancing selection was seen on pvs230. Functional and/or structural constraints may limit the level of polymorphism in pvs230. The observed limited polymorphism in pvs230 should ground for utilization of Pvs230 as an effective transmission-blocking vaccine. (C) 2011 Elsevier Ltd. All rights reserved.

Investigation of a new GRASP-based clustering algorithm applied to biological data

A large amount of biological data has been produced in the last years. Important knowledge can be extracted from these data by the use of data analysis techniques. Clustering plays an important role in data analysis, by organizing similar objects from a dataset into meaningful groups. Several clustering algorithms have been proposed in the literature. However, each algorithm has its bias, being more adequate for particular datasets. This paper presents a mathematical formulation to support the creation of consistent clusters for biological data. Moreover. it shows a clustering algorithm to solve this formulation that uses GRASP (Greedy Randomized Adaptive Search Procedure). We compared the proposed algorithm with three known other algorithms. The proposed algorithm presented the best clustering results confirmed statistically. (C) 2009 Elsevier Ltd. All rights reserved.