A state estimator including conventional and synchronized phasor measurements

This paper presents an effective weighted least square formulation for the solution of the state estimation problem, considering conventional as well as synchronized phasor measurements. The proposed algorithm is based on a reference-free formulation, using both rectangular and polar coordinates for branch current phasor measurements, and alleviates any numerical problems encountered during initialization stage of the state estimation algorithm. Analytical equations are provided for the conventional and the phasor measurements and their corresponding partial derivatives with respect to state variables. The proposed algorithm is tested with a seven bus system and is compared with the traditional state estimator. Simulation results show that the proposed algorithm improves the precision greatly and gets better behavior as compared with the traditional state estimator.     

Spot addressing for microarray images structured in hexagonal grids

In this work, an efficient method for spot addressing in images, which are generated by the scanning of hexagonal structured microarrays, is proposed. Initially, the blocks of the image are separated using the projections of the image. Next, all the blocks of the image are processed separately for the detection of each spot. The spot addressing procedure begins with the detection of the high intensity objects, which are probably the spots of the image. Next, the Growing Concentric Hexagon algorithm, which uses the properties of the hexagonal grid, is introduced for the detection of the non-hybridized spots. Finally, the Voronoi diagram is applied to the centers of the detected spots for the gridding of the image. The method is evaluated using spots generated from the scanning of the Beadchip of Illumina, which is used for the detection of Single Nucleotide Polymorphisms in the human genome, and uses hexagonal structure for the location of the spots. For the evaluation, the detected centers for each of the spot in the image are compared to the centers of the annotation, obtaining up to 98% accuracy for the spot addressing procedure.     

Visual understanding industrial workflows under uncertainty on distributed service oriented architectures

Service Oriented Architecture (SOA) is an evolution of distributed computing based on the request/reply design paradigm for synchronous and asynchronous applications. In such architectures, application developers or system integrators can build scenarios by composing one or more services without knowing the services’ underlying implementations. In this paper, we adopt a SOA framework for on-line scheduling complex industrial workflows. The proposed SOA consists of three main layers; the input layer, the benchmarking layer, the prediction layer and the scheduler. The main innovation of our SOA architecture is the prediction layer that supports computer vision tools able to visually observe how an industrial workflow is actually executed. This way, we can automatically estimate the actual execution time for a process. In particular a new self-initialized visual tracker algorithm is proposed in this paper to robustly trace workers’ trajectory in a plant via visual observations. Then, part-to-whole curve matching is presented so as to find correspondences among the traced curve and the ideal one and thus improving scheduler efficiency. The input layer interoperably describes industrial operations using the XPDL (an XML-based) format. The benchmarker evaluates much faster than real-time and in an off-line mode how long it takes for an industrial workflow to be executed on a given resource guaranteeing an almost real-time implementation of the video processing algorithms on the plant. The last component of the proposed SOA is the scheduler with the goal to assign the workflows to the available resources. In this paper, a Maximum Benefit First (MBF) scheduler is presented which maximizes the total gain received by the industry when completing execution of all the beneficial operations with minimum violations of their delivery deadlines (that is with the minimum compensations). Experiments have been conducted on a real-world industrial plant of Nissan Iberica automobile construction indicating the efficiency of the proposed system.     

Finite Element Modelling of TRIP Steels

A constitutive model that describes the mechanical behaviour of steels exhibiting “Transformation Induced Plasticity” (TRIP) during martensitic transformation is presented. Multiphase TRIP steels are considered as composite materials with a ferritic matrix containing bainite and retained austenite, which gradually transforms into martensite. The effective properties and overall behaviour of TRIP steels are determined by using homogenization techniques for non‐linear composites. The developed constitutive model considers the different hardening behaviour of the individual phases and estimates the apportionment of plastic strain and stress between the individual phases of the composite. A methodology for the numerical integration of the resulting elastoplastic constitutive equations in the context of the finite element method is developed and the constitutive model is implemented in a general‐purpose finite element program. The prediction of the model in uniaxial tension agrees well with the experimental data. The problem of necking of a bar in uniaxial tension is studied in detail.     

Active learning of user’s preferences estimation towards a personalized 3D navigation of geo-referenced scenes

The current technological evolutions enter 3D geo-informatics into their digital age, enabling new potential applications in the field of virtual tourism, pleasure, entertainment and cultural heritage. It is argued that 3D information provides the natural way of navigation. However, personalization is a key aspect in a navigation system, since a route that incorporates user preferences is ultimately more suitable than the route with the shortest distance or travel time. Usually, user’s preferences are expressed as a set of weights that regulate the degree of importance of the scene metadata on the route selection process. These weights, however, are defined by the users, setting the complexity to the user’s side, which makes personalization an arduous task. In this paper, we propose an alternative approach in which metadata weights are estimated implicitly and transparently to the users, transferring the complexity to the system side. This is achieved by introducing a relevance feedback on-line learning strategy which automatically adjusts metadata weights by exploiting information fed back to the system about the relevance of user’s preferences judgments given in a form of pair-wise comparisons. Practically implementing a relevance feedback algorithm presents the limitation that several pair-wise comparisons (samples) are required to converge to a set of reliable metadata weights. For this reason, we propose in this paper a weight rectification strategy that improves weight estimation by exploiting metadata interrelations defined through an ontology. In the sequel, a genetic optimization algorithm is incorporated to select the most user preferred routes based on a multi-criteria minimization approach. To increase the degree of personalization in 3D navigation, we have also introduced an efficient algorithm for estimating 3D trajectories around objects of interest by merging best selected 2D projected views that contain faces which are mostly preferred by the users. We have conducted simulations and comparisons with other approaches either in the field of on-line learning or route selection using objective metrics in terms of precision and recall values. The results indicate that our system yields on average a 13.76 % improvement of precision as regards the learning strategy and an improvement of 8.75 % regarding route selection. In addition, we conclude that the ontology driven weight rectification strategy can reduce the number of samples (pair-wise comparisons) required of 76 % to achieve the same precision. Qualitative comparisons have been also performed using a use case route scenario in the city of Athens.     

The Interplay between Aquaporin-1 and the Hypoxia-Inducible Factor 1α in a Lipopolysaccharide-Induced Lung Injury Model in Human Pulmonary Microvascular Endothelial Cells

Aquaporin-1 (AQP1), a water channel, and the hypoxia-inducible factor 1α (HIF1A) are implicated in acute lung injury responses, modulating among others pulmonary vascular leakage. We hypothesized that the AQP1 and HIF1A systems interact, affecting mRNA, protein levels and function of AQP1 in human pulmonary microvascular endothelial cells (HPMECs) exposed to lipopolysaccharide (LPS). Moreover, the role of AQP1 in apoptosis and wound healing progression was examined. Both AQP1 mRNA and protein expression levels were higher in HPMECs exposed to LPS compared to untreated HPMECs. However, in the LPS-exposed HIF1A-silenced cells, the mRNA and protein expression levels of AQP1 remained unaltered. In the permeability experiments, a statistically significant volume increase was observed at the 360 s time-point in the LPS-exposed HPMECs, while LPS-exposed HIF1A-silenced HPMECs did not exhibit cell swelling, implying a dysfunctional AQP1. AQP1 did not seem to affect cell apoptosis yet could interfere with endothelial migration and/or proliferation. Based on our results, it seems that HIF1A silencing negatively affects AQP1 mRNA and protein expression, as well as AQP1 function, in the setting of lung injury.     

Molecular Biomarkers in Peri-Implant Health and Disease: A Cross-Sectional Pilot Study

Background: The aim of this feasibility study was to investigate the concentration level of CCL-20/MIP-3α, BAFF/BLyS, IL-23, RANKL, and Osteoprotegerin in the Peri-Implant Crevicular Fluid (PICF), from patients diagnosed with peri-implant mucositis and peri-implantitis, and to compare them with PICF from patients with healthy implants. Methods: Participants with at least one dental implant with healthy peri-implant tissues, peri-implant mucositis, or peri-implantitis were included. PICF was collected using paper strips from healthy and diseased peri-implant sites (n = 19). Biomarker levels were analyzed using a custom Multiplex ELISA Assay Kit. Results: In comparison to peri-implant health, the peri-implant mucositis group showed an increased concentration of CCL-20 MIP-3α, BAFF/BLyS, IL-23, RANKL, and Osteoprotegerin. The peri-implantitis group had the lowest median concentration of Osteoprotegerin (1963 ng/mL); this group had a similar concentration of RANKL (640.84 ng/mL) when compared to the peri-implant health group. BAFF/BLyS (17.06 ng/mL) showed the highest concentration in the peri-implantitis group. Conclusions: This feasibility study suggests that IL-23 and RANKL may help to elucidate the pathogenesis during the conversion from peri-implant health to peri-implantitis. Further research is required in BAFF/BLyS for the early diagnosis of peri-implantitis.     

The Gestational Effects of Maternal Bone Marker Molecules on Fetal Growth,Metabolism and Long-Term Metabolic Health: A Systematic Review

Fetal exposure in adverse environmental factors during intrauterine life can lead to various biological adjustments, affecting not only in utero development of the conceptus, but also its later metabolic and endocrine wellbeing. During human gestation, maternal bone turnover increases, as reflected by molecules involved in bone metabolism, such as vitamin D, osteocalcin, sclerostin, sRANKL, and osteoprotegerin; however, recent studies support their emerging role in endocrine functions and glucose homeostasis regulation. Herein, we sought to systematically review current knowledge on the effects of aforementioned maternal bone biomarkers during pregnancy on fetal intrauterine growth and metabolism, neonatal anthropometric measures at birth, as well as on future endocrine and metabolic wellbeing of the offspring. A growing body of literature converges on the view that maternal bone turnover is likely implicated in fetal growth, and at least to some extent, in neonatal and childhood body composition and metabolic wellbeing. Maternal sclerostin and sRANKL are positively linked with fetal abdominal circumference and subcutaneous fat deposition, contributing to greater birthweights. Vitamin D deficiency correlates with lower birthweights, while research is still needed on intrauterine fetal metabolism, as well as on vitamin D dosing supplementation during pregnancy, to diminish the risks of low birthweight or SGA neonates in high-risk populations.     

Peripheral Inflammatory Markers TNF-α and CCL2 Revisited: Association with Parkinson’s Disease Severity

One of the major mediators of neuroinflammation in PD is tumour necrosis factor alpha (TNF-α), which, similar to other cytokines, is produced by activated microglia and astrocytes. Although TNF-α can be neuroprotective in the brain, long-term neuroinflammation and TNF release can be harmful, having a neurotoxic role that leads to death of oligodendrocytes, astrocytes, and neurons and, therefore, is associated with neurodegeneration. Apart from cytokines, a wide family of molecules with homologous structures, namely chemokines, play a key role in neuro-inflammation by drawing cytotoxic T-lymphocytes and activating microglia. The objective of the current study was to examine the levels of the serum TNF-α and CCL2 (Chemokine (C-C motif) ligand 2), also known as MCP-1 (Monocyte Chemoattractant Protein-1), in PD patients compared with healthy controls. We also investigated the associations between the serum levels of these two inflammatory mediators and a number of clinical symptoms, in particular, disease severity and cognition. Such an assessment may point to their prognostic value and provide some treatment hints. PD patients with advanced stage on the Hoehn–Yahr scale showed an increase in TNF-α levels compared with PD patients with stages 1 and 2 (p = 0.01). Additionally, the UPDRS score was significantly associated with TNF-α levels. CCL2 levels, however, showed no significant associations.     

Directed Evolution of Phi Class Glutathione Transferases Involved in Multiple-Herbicide Resistance of Grass Weeds and Crops

The extensive application of herbicides in crop cultivation has indisputably led to the emergence of weed populations characterized by multiple herbicide resistance (MHR). This phenomenon is associated with the enhanced metabolism and detoxifying ability of endogenous enzymes, such as phi class glutathione transferases (GSTFs). In the present work, a library of mutant GSTFs was created by in vitro directed evolution via DNA shuffling. Selected gstf genes from the weeds Alopecurus myosuroides and Lolium rigidum, and the cereal crops Triticum durum and Hordeum vulgare were recombined to forge a library of novel chimeric GSTFs. The library was activity screened and the best-performing enzyme variants were purified and characterized. The work allowed the identification of enzyme variants that exhibit an eight-fold improvement in their catalytic efficiency, higher thermal stability (8.3 °C) and three-times higher inhibition sensitivity towards the herbicide butachlor. The crystal structures of the best-performing enzyme variants were determined by X-ray crystallography. Structural analysis allowed the identification of specific structural elements that are responsible for k_cat regulation, thermal stability and inhibition potency. These improved novel enzymes hold the potential for utilization in biocatalysis and green biotechnology applications. The results of the present work contribute significantly to our knowledge of the structure and function of phi class plant GSTs and shed light on their involvement in the mechanisms of MHR.