Gene Expression Data Mining Reveals the Involvement of GPR55 and Its Endogenous Ligands in Immune Response,Cancer, and Differentiation

G protein-coupled receptor 55 (GPR55) is a recently deorphanized lipid- and peptide-sensing receptor. Its lipidic endogenous agonists belong to lysoglycerophospholipids, with lysophosphatidylinositol (LPI) being the most studied. Peptide agonists derive from fragmentation of pituitary adenylate cyclase-activating polypeptide (PACAP). Although GPR55 and its ligands were implicated in several physiological and pathological conditions, their biological function remains unclear. Thus, the aim of the study was to conduct a large-scale re-analysis of publicly available gene expression datasets to identify physiological and pathological conditions affecting the expression of GPR55 and the production of its ligands. The study revealed that regulation of GPR55 occurs predominantly in the context of immune activation pointing towards the role of the receptor in response to pathogens and in immune cell lineage determination. Additionally, it was revealed that there is almost no overlap between the experimental conditions affecting the expression of GPR55 and those modulating agonist production. The capacity to synthesize LPI was enhanced in various types of tumors, indicating that cancer cells can hijack the motility-related activity of GPR55 to increase aggressiveness. Conditions favoring accumulation of PACAP-derived peptides were different than those for LPI and were mainly related to differentiation. This indicates a different function of the two agonist classes and possibly the existence of a signaling bias.     

Stochastic finite element method for elasto‐plastic body

This paper proposes a Stochastic Finite Element Method (SFEM) for non‐linear elasto‐plastic bodies, as a generalization of the SFEM for linear elastic bodies developed by Ghanem and Spanos who applied the Karhunen–Loeve expansion and the polynomial chaos expansion for stochastic material properties and field variables, respectively. The key feature of the proposed SFEM is the introduction of two fictitious bodies whose behaviours provide upper and lower bounds for the mean of field variables. The two bounding bodies are rigorously obtained from a given distribution of material properties. The deformation of an ideal elasto‐plastic body of the Huber–von Mises type is computed as an illustrative example. The results are compared with Monte‐Carlo simulation. It is shown that the proposed SFEM can satisfactorily estimate means, variances and other probabilistic characteristics of field variables even when the body has a larger variance of the material properties. Copyright © 1999 John Wiley & Sons, Ltd.     

A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using 18F-FMISO-PET

Glioblastoma multiforme (GBM) is a highly invasive primary brain tumour that has poor prognosis despite aggressive treatment. A hallmark of these tumours is diffuse invasion into the surrounding brain, necessitating a multi-modal treatment approach, including surgery, radiation and chemotherapy. We have previously demonstrated the ability of our model to predict radiographic response immediately following radiation therapy in individual GBM patients using a simplified geometry of the brain and theoretical radiation dose. Using only two pre-treatment magnetic resonance imaging scans, we calculate net rates of proliferation and invasion as well as radiation sensitivity for a patient''s disease. Here, we present the application of our clinically targeted modelling approach to a single glioblastoma patient as a demonstration of our method. We apply our model in the full three-dimensional architecture of the brain to quantify the effects of regional resistance to radiation owing to hypoxia in vivo determined by [¹⁸F]-fluoromisonidazole positron emission tomography (FMISO-PET) and the patient-specific three-dimensional radiation treatment plan. Incorporation of hypoxia into our model with FMISO-PET increases the model–data agreement by an order of magnitude. This improvement was robust to our definition of hypoxia or the degree of radiation resistance quantified with the FMISO-PET image and our computational model, respectively. This work demonstrates a useful application of patient-specific modelling in personalized medicine and how mathematical modelling has the potential to unify multi-modality imaging and radiation treatment planning.     

Depth Dependence of Nanoindentation Pile-Up Patterns in Copper Single Crystals

A study of the dependence of nanoindentation pile-up patterns on the indentation load and crystallographic orientation is presented. Three different orientations—(001), (011), and (111)—of single crystal copper were investigated. Experiments were conducted on a CSM ultra-nanoindentation tester using a Berkovich tip. The topographic images were obtained using an atomic force microscope. The evolution of pile-up patterns with different applied loads was observed. The results show that for applied loads equal to 0.45 mN and smaller the pile-up patterns do not depend on the crystallographic orientation of the indented surface; instead, they depend on the tip’s geometry. On the other hand, in the case of indentation loads bigger than 2 mN, pile-up patterns on the surfaces of (001)-, (011)-, and (111)-oriented single crystals have fourfold, twofold, and sixfold (or threefold) symmetry, respectively. An intermediate state was also reported. Furthermore, a detailed analysis of residual impressions with maximal applied loads equal to 2 mN and bigger reveals that both pile-up and sink-in patterns are present.     

Innovative method of Transthoracic Lung Cancer Biopsy of Parietal Lung Tumors under US Guidance after CT Pre-biopsy Planning as an Alternative to CT- Guided Biopsy

The authors compared their earlier results of the tumor biopsies performed under computer tomography （CT） guidance against the results of their recently modified-combined method of visualization and measurement of lung tumor parameters by CT imaging followed by US guided biopsy. In 238 （6.36%） of 3,745 patients in CT examination, the lung tumor was located peripherally, and obtaining histopathological confirmation was crucial to start the proper treatment. The patients were divided into two groups, the division was based on the biopsy method. Within the first group in I 18 patients, parietal lung tumor was confirmed and CT guided biopsy was performed. Within the second group, parietal lung tumor was confirmed in 120 patients and combined method of CT pre- biopsy planning and US guided biopsy were used. Pre-biopsy planning was performed using CT, measurements such as depth and distance of the tumor were made, obtained data were then saved on CT image and patient＇s skin was marked. Then obtained results were analyzed using test for two proportions. The analysis of the results confirmed the higher efficacy of the combined method taking into account the number of complications （p 〈 0.01） and diagnostic histopathological results （P 〈 0.001）. Suggested innovative method, involving both CT pre-biopsy planning and US guided biopsy, allows to analyze simultaneously static CT and dynamic US images and it is considered to be an easy and effective method comparing to relying on static in nature CT images only. Our method is related to a lower complication risk rate.     

Towards Increased Understanding of Open Data Use for Software Development

Information Systems Frontiers - The way open data resources of varied type and volume are used by software applications remains only partly known. In this study, following CRoss-Industry Standard...     

Fundamentals of modelling concurrency using discrete relational structures

We consider relational structures such that is a set and are two binary relations on . For a number of different classes of structures we show that any structure can be represented as the intersection of its maximal extensions. Such a property – called extension completeness – can be seen as a generalisation of Szpilrajn's theorem which states that each partial order is the intersection of its total order extensions. When can be interpreted as causality and as ‘weak’ causality we obtain a model of concurrent histories generalising that based on causal partial orders. Received 1 February 1994 / 6 May 1996     

Identification of Wiener models for dynamic and steady‐state performance with application to solid oxide fuel cell

This work is concerned with identification of Wiener models (a linear dynamic part connected in series with a nonlinear dynamic one). A neural network with one hidden layer is used as the nonlinear block of the model, two network configurations are considered. For model identification three algorithms are described. In the first case model accuracy only in transient conditions is considered, only the dynamic data is used for model training. In the next two algorithms model accuracy in both transient and steady‐state conditions is considered, dynamic and steady‐state data sets are used. The steady‐state model errors are taken into account by an additional term in the minimized cost‐function or by additional inequality constraints. For comparison of discussed algorithms and model structures, identification of a Wiener model of a solid oxide fuel cell (SOFC) process is considered. It is shown that the best results are obtained by the algorithm 2 which minimizes at the same time both dynamic and steady‐state model errors, additional constraints used in the algorithm 3 are computationally quite demanding.