Biofilm formation on bone grafts and bone graft substitutes: comparison of different materials by a standard in vitro test and microcalorimetry.

We analyzed the initial adhesion and biofilm formation of Staphylococcus aureus (ATCC 29213) and S. epidermidis RP62A (ATCC 35984) on various bone grafts and bone graft substitutes under standardized in vitro conditions. In parallel, microcalorimetry was evaluated as a real-time microbiological assay in the investigation of biofilm formation and material science research. The materials beta-tricalcium phosphate (beta-TCP), processed human spongiosa (Tutoplast) and poly(methyl methacrylate) (PMMA) were investigated and compared with polyethylene (PE). Bacterial counts (log(10) cfu per sample) were highest on beta-TCP (S. aureus 7.67 +/- 0.17; S. epidermidis 8.14 +/- 0.05) while bacterial density (log(10) cfu per surface) was highest on PMMA (S. aureus 6.12 +/- 0.2, S. epidermidis 7.65 +/- 0.13). Detection time for S. aureus biofilms was shorter for the porous materials (beta-TCP and processed human spongiosa, p < 0.001) compared to the smooth materials (PMMA and PE), with no differences between beta-TCP and processed human spongiosa (p > 0.05) or PMMA and PE (p > 0.05). In contrast, for S. epidermidis biofilms the detection time was different (p < 0.001) between all materials except between processed human spongiosa and PE (p > 0.05). The quantitative analysis by quantitative culture after washing and sonication of the material demonstrated the importance of monitoring factors like specific surface or porosity of the test materials. Isothermal microcalorimetry proved to be a suitable tool for an accurate, non-invasive and real-time microbiological assay, allowing the detection of bacterial biomass without removing the biofilm from the surface.

Automatic Mapping and Classification of Spatial Environmental Data

The paper deals with the development and application of the generic methodology for automatic processing (mapping and classification) of environmental data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve the problem of spatial data mapping (regression). The Probabilistic Neural Network (PNN) is considered as an automatic tool for spatial classifications. The automatic tuning of isotropic and anisotropic GRNN/PNN models using cross-validation procedure is presented. Results are compared with the k-Nearest-Neighbours (k-NN) interpolation algorithm using independent validation data set. Real case studies are based on decision-oriented mapping and classification of radioactively contaminated territories.

What drives invasibility? A multi-model inference test and spatial modelling of alien plant species richness patterns in northern Portugal

Understanding and anticipating biological invasions can focus either on traits that favour species invasiveness or on features of the receiving communities, habitats or landscapes that promote their invasibility. Here, we address invasibility at the regional scale, testing whether some habitats and landscapes are more invasible than others by fitting models that relate alien plant species richness to various environmental predictors. We use a multi-model information-theoretic approach to assess invasibility by modelling spatial and ecological patterns of alien invasion in landscape mosaics and testing competing hypotheses of environmental factors that may control invasibility. Because invasibility may be mediated by particular characteristics of invasiveness, we classified alien species according to their C-S-R plant strategies. We illustrate this approach with a set of 86 alien species in Northern Portugal. We first focus on predictors influencing species richness and expressing invasibility and then evaluate whether distinct plant strategies respond to the same or different groups of environmental predictors. We confirmed climate as a primary determinant of alien invasions and as a primary environmental gradient determining landscape invasibility. The effects of secondary gradients were detected only when the area was sub-sampled according to predictions based on the primary gradient. Then, multiple predictor types influenced patterns of alien species richness, with some types (landscape composition, topography and fire regime) prevailing over others. Alien species richness responded most strongly to extreme land management regimes, suggesting that intermediate disturbance induces biotic resistance by favouring native species richness. Land-use intensification facilitated alien invasion, whereas conservation areas hosted few invaders, highlighting the importance of ecosystem stability in preventing invasions. Plants with different strategies exhibited different responses to environmental gradients, particularly when the variations of the primary gradient were narrowed by sub-sampling. Such differential responses of plant strategies suggest using distinct control and eradication approaches for different areas and alien plant groups.

Automatic filtering and substantiation of drug safety signals

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions

Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities.

Quantifying the spatial configuration of hydraulic conductivity (K) in heterogeneous geological environments is essential for accurate predictions of contaminant transport, but is difficult because of the inherent limitations in resolution and coverage associated with traditional hydrological measurements. To address this issue, we consider crosshole and surface-based electrical resistivity geophysical measurements, collected in time during a saline tracer experiment. We use a Bayesian Markov-chain-Monte-Carlo (McMC) methodology to jointly invert the dynamic resistivity data, together with borehole tracer concentration data, to generate multiple posterior realizations of K that are consistent with all available information. We do this within a coupled inversion framework, whereby the geophysical and hydrological forward models are linked through an uncertain relationship between electrical resistivity and concentration. To minimize computational expense, a facies-based subsurface parameterization is developed. The Bayesian-McMC methodology allows us to explore the potential benefits of including the geophysical data into the inverse problem by examining their effect on our ability to identify fast flowpaths in the subsurface, and their impact on hydrological prediction uncertainty. Using a complex, geostatistically generated, two-dimensional numerical example representative of a fluvial environment, we demonstrate that flow model calibration is improved and prediction error is decreased when the electrical resistivity data are included. The worth of the geophysical data is found to be greatest for long spatial correlation lengths of subsurface heterogeneity with respect to wellbore separation, where flow and transport are largely controlled by highly connected flowpaths.

Computer Aided Detection and measurement of peripheral artery disease.

Computer-Aided Tomography Angiography (CTA) images are the standard for assessing Peripheral artery disease (PAD). This paper presents a Computer Aided Detection (CAD) and Computer Aided Measurement (CAM) system for PAD. The CAD stage detects the arterial network using a 3D region growing method and a fast 3D morphology operation. The CAM stage aims to accurately measure the artery diameters from the detected vessel centerline, compensating for the partial volume effect using Expectation Maximization (EM) and a Markov Random field (MRF). The system has been evaluated on phantom data and also applied to fifteen (15) CTA datasets, where the detection accuracy of stenosis was 88% and the measurement accuracy was with an 8% error.

Skeletal muscle mitochondrial energetics are associated with maximal aerobic capacity and walking speed in older adults.

BACKGROUND: Lower ambulatory performance with aging may be related to a reduced oxidative capacity within skeletal muscle. This study examined the associations between skeletal muscle mitochondrial capacity and efficiency with walking performance in a group of older adults. METHODS: Thirty-seven older adults (mean age 78 years; 21 men and 16 women) completed an aerobic capacity (VO peak) test and measurement of preferred walking speed over 400 m. Maximal coupled (State 3; St3) mitochondrial respiration was determined by high-resolution respirometry in saponin-permeabilized myofibers obtained from percutanous biopsies of vastus lateralis (n = 22). Maximal phosphorylation capacity (ATP) of vastus lateralis was determined in vivo by P magnetic resonance spectroscopy (n = 30). Quadriceps contractile volume was determined by magnetic resonance imaging. Mitochondrial efficiency (max ATP production/max O consumption) was characterized using ATP per St3 respiration (ATP/St3). RESULTS: In vitro St3 respiration was significantly correlated with in vivo ATP (r = .47, p = .004). Total oxidative capacity of the quadriceps (St3*quadriceps contractile volume) was a determinant of VO peak (r = .33, p = .006). ATP (r = .158, p = .03) and VO peak (r = .475, p < .0001) were correlated with preferred walking speed. Inclusion of both ATP/St3 and VO peak in a multiple linear regression model improved the prediction of preferred walking speed (r = .647, p < .0001), suggesting that mitochondrial efficiency is an important determinant for preferred walking speed. CONCLUSIONS: Lower mitochondrial capacity and efficiency were both associated with slower walking speed within a group of older participants with a wide range of function. In addition to aerobic capacity, lower mitochondrial capacity and efficiency likely play roles in slowing gait speed with age.

Effects of computing parameters and measurement locations on the estimation of 3D NPS in non-stationary MDCT images.

The goal of this study was to investigate the impact of computing parameters and the location of volumes of interest (VOI) on the calculation of 3D noise power spectrum (NPS) in order to determine an optimal set of computing parameters and propose a robust method for evaluating the noise properties of imaging systems. Noise stationarity in noise volumes acquired with a water phantom on a 128-MDCT and a 320-MDCT scanner were analyzed in the spatial domain in order to define locally stationary VOIs. The influence of the computing parameters in the 3D NPS measurement: the sampling distances bx,y,z and the VOI lengths Lx,y,z, the number of VOIs NVOI and the structured noise were investigated to minimize measurement errors. The effect of the VOI locations on the NPS was also investigated. Results showed that the noise (standard deviation) varies more in the r-direction (phantom radius) than z-direction plane. A 25 × 25 × 40 mm(3) VOI associated with DFOV = 200 mm (Lx,y,z = 64, bx,y = 0.391 mm with 512 × 512 matrix) and a first-order detrending method to reduce structured noise led to an accurate NPS estimation. NPS estimated from off centered small VOIs had a directional dependency contrary to NPS obtained from large VOIs located in the center of the volume or from small VOIs located on a concentric circle. This showed that the VOI size and location play a major role in the determination of NPS when images are not stationary. This study emphasizes the need for consistent measurement methods to assess and compare image quality in CT.

Automatic Comparison and Evaluation of Impressions Left by a Firearm on Fired Cartridge Cases

Recent years have been characterized by a series of publications in the field of firearms investigation questioning the reliability and objectivity of such examination. This research investigates new solutions to decrease the subjective component affecting the evaluation that follows the comparison of impressions left by a firearm on the surface of spent cartridge cases. An automatic comparison system based on 3D measurements has been developed and coupled to a bivariate evaluative model allowing assigning likelihood ratios. Based on a dataset of 79 pistols (all SIG Sauer 9 mm Luger caliber), the system shows a very high discriminating power and the LRs that it provides are very indicative of the true state under both the prosecution and the defense propositions. For example, likelihood ratios exceeding a billion are predominantly obtained when impressions originating from the same source are compared. The system is also characterized by relatively low rates (≤1%) of misleading evidence depending on the firearm considered.

The Role and Measurement of Social Capital in New Technology-Based Start-up Context

Tämän Pro gradu-tutkielman tavoitteena olirakentaa esiymmärrys sosiaalisen pääoman roolista ja mittaamisesta uuden teknologian start-up yrityksissä. Pääasiallisena tarkoituksena tässä tutkimuksessa olilöytää sosiaalisen pääoman ja start-up yrityksen tuloksellisuuden välille yhdistävä tekijä. Tutkimuksen empiirinen aineisto kerättiin pääasiallisesti kuuden OKO Venture Capitalin sijoitusportfolioon sisältyvien case-yritysten kvalitatiivisten teemahaastatteluiden sekä kvantitatiivisten kyselylomakkeiden avulla. Kvalitatiivisten haastatteluiden tulosten perusteella sosiaalisen pääoman ja tuloksellisuuden välille löytyi yhdistävä tekijä, jota käytettiin myöhemmin hyväksi kvantitatiivisessa kyselylomakkeessa. Tämän tutkielman tulokset osoittivat, että startegisen päätöksenteon kautta sosiaalinen pääoma vaikuttaa osittain start-up yritysten tuloksellisuuteen. Manageriaalisesti tärkempi löydös tässä tutkimuksessa oli kuitenkin se, että sosiaalista pääomaa voidaan käyttää hyväksi ennustettaessa uuden teknologian start-up yritysten tulevaisuuden kassavirtoja.

Cerebral oxygenation during the Richalet hypoxia sensitivity test and cycling time-trial performance in severe hypoxia.

BACKGROUND: The Richalet hypoxia sensitivity test (RT), which quantifies the cardiorespiratory response to acute hypoxia during exercise at an intensity corresponding to a heart rate of ~130 bpm in normoxia, can predict susceptibility of altitude sickness. Its ability to predict exercise performance in hypoxia is unknown. OBJECTIVES: Investigate: (1) whether cerebral blood flow (CBF) and cerebral tissue oxygenation (O2Hb; oxygenated hemoglobin, HHb; deoxygenated hemoglobin) responses during RT predict time-trial cycling (TT) performance in severe hypoxia; (2) if subjects with blunted cardiorespiratory responses during RT show greater impairment of TT performance in severe hypoxia. STUDY DESIGN: Thirteen men [27 ± 7 years (mean ± SD), Wmax: 385 ± 30 W] were evaluated with RT and the results related to two 15 km TT, in normoxia and severe hypoxia (FIO2 = 0.11). RESULTS: During RT, mean middle cerebral artery blood velocity (MCAv: index of CBF) was unaltered with hypoxia at rest (p > 0.05), while it was increased during normoxic (+22 ± 12 %, p < 0.05) and hypoxic exercise (+33 ± 17 %, p < 0.05). Resting hypoxia lowered cerebral O2Hb by 2.2 ± 1.2 μmol (p < 0.05 vs. resting normoxia); hypoxic exercise further lowered it to -7.6 ± 3.1 μmol below baseline (p < 0.05). Cerebral HHb, increased by 3.5 ± 1.8 μmol in resting hypoxia (p < 0.05), and further to 8.5 ± 2.9 μmol in hypoxic exercise (p < 0.05). Changes in CBF and cerebral tissue oxygenation during RT did not correlate with TT performance loss (R = 0.4, p > 0.05 and R = 0.5, p > 0.05, respectively), while tissue oxygenation and SaO2 changes during TT did (R = -0.76, p < 0.05). Significant correlations were observed between SaO2, MCAv and HHb during RT (R = -0.77, -0.76 and 0.84 respectively, p < 0.05 in all cases). CONCLUSIONS: CBF and cerebral tissue oxygenation changes during RT do not predict performance impairment in hypoxia. Since the changes in SaO2 and brain HHb during the TT correlated with performance impairment, the hypothesis that brain oxygenation plays a limiting role for global exercise in conditions of severe hypoxia remains to be tested further.

R&D Performance Analysis: Case Studies on the Challenges and Promotion of the Evaluation and Measurement of R&D

Due to the intense international competition, demanding, and sophisticated customers, and diverse transforming technological change, organizations need to renew their products and services by allocating resources on research and development (R&D). Managing R&D is complex, but vital for many organizations to survive in the dynamic, turbulent environment. Thus, the increased interest among decision-makers towards finding the right performance measures for R&D is understandable. The measures or evaluation methods of R&D performance can be utilized for multiple purposes; for strategic control, for justifying the existence of R&D, for providing information and improving activities, as well as for the purposes of motivating and benchmarking. The earlier research in the field of R&D performance analysis has generally focused on either the activities and considerable factors and dimensions - e.g. strategic perspectives, purposes of measurement, levels of analysis, types of R&D or phases of R&D process - prior to the selection of R&Dperformance measures, or on proposed principles or actual implementation of theselection or design processes of R&D performance measures or measurement systems. This study aims at integrating the consideration of essential factors anddimensions of R&D performance analysis to developed selection processes of R&D measures, which have been applied in real-world organizations. The earlier models for corporate performance measurement that can be found in the literature, are to some extent adaptable also to the development of measurement systemsand selecting the measures in R&D activities. However, it is necessary to emphasize the special aspects related to the measurement of R&D performance in a way that make the development of new approaches for especially R&D performance measure selection necessary: First, the special characteristics of R&D - such as the long time lag between the inputs and outcomes, as well as the overall complexity and difficult coordination of activities - influence the R&D performance analysis problems, such as the need for more systematic, objective, balanced and multi-dimensional approaches for R&D measure selection, as well as the incompatibility of R&D measurement systems to other corporate measurement systems and vice versa. Secondly, the above-mentioned characteristics and challenges bring forth the significance of the influencing factors and dimensions that need to be recognized in order to derive the selection criteria for measures and choose the right R&D metrics, which is the most crucial step in the measurement system development process. The main purpose of this study is to support the management and control of the research and development activities of organizations by increasing the understanding of R&D performance analysis, clarifying the main factors related to the selection of R&D measures and by providing novel types of approaches and methods for systematizing the whole strategy- and business-based selection and development process of R&D indicators.The final aim of the research is to support the management in their decision making of R&D with suitable, systematically chosen measures or evaluation methods of R&D performance. Thus, the emphasis in most sub-areas of the present research has been on the promotion of the selection and development process of R&D indicators with the help of the different tools and decision support systems, i.e. the research has normative features through providing guidelines by novel types of approaches. The gathering of data and conducting case studies in metal and electronic industry companies, in the information and communications technology (ICT) sector, and in non-profit organizations helped us to formulate a comprehensive picture of the main challenges of R&D performance analysis in different organizations, which is essential, as recognition of the most importantproblem areas is a very crucial element in the constructive research approach utilized in this study. Multiple practical benefits regarding the defined problemareas could be found in the various constructed approaches presented in this dissertation: 1) the selection of R&D measures became more systematic when compared to the empirical analysis, as it was common that there were no systematic approaches utilized in the studied organizations earlier; 2) the evaluation methods or measures of R&D chosen with the help of the developed approaches can be more directly utilized in the decision-making, because of the thorough consideration of the purpose of measurement, as well as other dimensions of measurement; 3) more balance to the set of R&D measures was desired and gained throughthe holistic approaches to the selection processes; and 4) more objectivity wasgained through organizing the selection processes, as the earlier systems were considered subjective in many organizations. Scientifically, this dissertation aims to make a contribution to the present body of knowledge of R&D performance analysis by facilitating dealing with the versatility and challenges of R&D performance analysis, as well as the factors and dimensions influencing the selection of R&D performance measures, and by integrating these aspects to the developed novel types of approaches, methods and tools in the selection processes of R&D measures, applied in real-world organizations. In the whole research, facilitation of dealing with the versatility and challenges in R&D performance analysis, as well as the factors and dimensions influencing the R&D performance measure selection are strongly integrated with the constructed approaches. Thus, the research meets the above-mentioned purposes and objectives of the dissertation from the scientific as well as from the practical point of view.

The Conceptualisation and Measurement of Issue Ownership

Issue ownership means that some parties are considered by the public at large as being more able to deal with, or more attentive to, certain issues. The theory has been used to explain both party behaviour - parties are expected to focus on owned issues - and voter behaviour - when a voter considers a party to own an issue, this affects the odds of voting for that party. The purpose of this article is, first, to provide a look backward at the existing research through a literature review of the studies that were conducted in the past decade-and-a-half. Secondly, it takes stock of the current conceptualisation and argues that issue ownership is a multidimensional concept. Thereafter the article discusses how this multidimensionality affects both the role of issue ownership in voter and in party behaviour. Finally, the article outlines a number of shortcomings of the extant literature and discusses potential avenues for future research.

High Performance Electrical Drive System for a Biomechanical Test and Rehabilitation Equipment

Suunniteltiin ja rakennettiin suoraa vääntömomenttisäätöä soveltava taajuudenmuuttajakäyttö oikosulkumoottorin ohjaukseen korvaamaan passiivinen jarrukäyttö. Laite on kuntoutuslaite, jolla tehdään lihasvoiman mittauksia ja voimaharjoituksia. Selvitettiin kaupallisten moottoreiden ja taajuudenmuuttajien suoritusominaisuuksia ja tämän perusteella valittiin käyttöön sopivat laitteet. Työssä esitetään kaksi oikosulkumoottorin ohjaustapaa: vektorisäätö ja suora vääntömomenttisäätö. Merkittävin osa tästä työstä käsittelee - tarkan turvallisuussuunnitelman lisäksi - kuntoutuslaitteen prototyypin komponentteja, kokoamista ja suoritustestien tuloksia.

Brand Authenticity: An Integrative Framework and Measurement Scale

Although brand authenticity is gaining increasing interest in consumer behavior research and managerial practice, literature on its measurement and contribution to branding theory is still limited. This article develops an integrative framework of the concept of brand authenticity and reports the development and validation of a scale measuring consumers' perceived brand authenticity (PBA). A multi-phase scale development process resulted in a 15-item PBA scale measuring four dimensions: credibility, integrity, symbolism, and continuity. This scale is reliable across different brands and cultural contexts. We find that brand authenticity perceptions are influenced by indexical, existential, and iconic cues, whereby some of the latters' influence is moderated by consumers' level of marketing skepticism. Results also suggest that PBA increases emotional brand attachment and word-of-mouth, and that it drives brand choice likelihood through self-congruence for consumers high in self-authenticity.