Comprehensive and Highly Accurate Measurements of Crane Runways, Profiles and Fastenings.

The process of surveying crane runways has been continually refined due to the competitive situation, modern surveying instruments, additional sensors, accessories and evaluation procedures. Guidelines, such as the International Organization for Standardization (ISO) 12488-1, define target values that must be determined by survey. For a crane runway these are for example the span, the position and height of the rails. The process has to be objective and reproducible. However, common processes of surveying crane runways do not meet these requirements sufficiently. The evaluation of the protocols, ideally by an expert, requires many years of experience. Additionally, the recording of crucial parameters, e.g., the wear of the rail, or the condition of the rail fastening and rail joints, is not regulated and for that reason are often not considered during the measurement. To solve this deficit the Advanced Rail Track Inspection System (ARTIS) was developed. ARTIS is used to measure the 3D position of crane rails, the cross-section of the crane rails, joints and, for the first time, the (crane-rail) fastenings. The system consists of a monitoring vehicle and an external tracking sensor. It makes kinematic observations with the tracking sensor from outside the rail run, e.g., the floor of an overhead crane runway, possible. In this paper we present stages of the development process of ARTIS, new target values, calibration of sensors and results of a test measurement.

Pulmonary arterial hypertension therapy may be safe and effective in patients with systemic sclerosis and borderline pulmonary artery pressure.

OBJECTIVE: Borderline pulmonary arterial hypertension (PAH), characterized by a marked exercise-induced increase in pulmonary artery pressure (PAP) with normal resting values, may precede overt PAH in systemic sclerosis (SSc). We undertook the present study to investigate whether PAH treatment is safe in these patients and might attenuate hemodynamic progression. METHODS: SSc patients with borderline PAH underwent right heart catheterization at baseline, after a 12-month observation period, and subsequently after 6 months of bosentan therapy. Changes in mean PAP at 50W during the observation period versus during therapy were compared. RESULTS: Ten patients completed the study. Mean PAP at rest, at 50W, and during maximal exercise increased significantly during the observation period (mean ± SD increases of 2.5 ± 3.0 mm Hg [P = 0.03], 4.0 ± 2.9 mm Hg [P = 0.002], and 6.8 ± 4.1 mm Hg [P = 0.0005], respectively) and tended to decrease during the treatment period (decreases of 2.5 ± 3.9 mm Hg [P = 0.07], 1.5 ± 4.5 mm Hg [P = 0.32], and 1.8 ± 7.0 mm Hg [P = 0.43], respectively). The changes during the observation period versus the therapy period were significantly different (P = 0.03 at rest, P = 0.01 at 50W [primary end point], and P = 0.02 during maximal exercise). The changes in resting pulmonary vascular resistance were also significantly different during the observation period (increase of 8 ± 25 dynes · seconds · cm(-5) ) versus during the therapy period (decrease of 45 ± 22 dynes · seconds · cm(-5) ) (P < 0.0005). Changes in resting pulmonary arterial wedge pressure were not significantly different between the observation period and the treatment period, despite the significant increase during the observation period (2.6 ± 2.5 mm Hg [P = 0.01]). No relevant adverse effects were reported. CONCLUSION: In SSc patients with borderline abnormal pulmonary hemodynamics, resting and exercise PAP may increase significantly within 1 year of observation. Bosentan might be safe and effective to attenuate these changes. Randomized controlled trials are warranted to confirm the exploratory findings of this hypothesis-generating pilot study.

Borderline pulmonary arterial pressure is associated with decreased exercise capacity in scleroderma.

RATIONALE: Pulmonary arterial hypertension is associated with impaired exercise capacity and decreased survival in patients with scleroderma. Randomized controlled studies showed significant benefit of targeted therapies in patients with a resting mean pulmonary arterial pressure (MPAP) greater than 25 mm Hg. The clinical relevance of pulmonary arterial pressure values in the upper normal range is unknown. OBJECTIVES: To examine the clinical relevance of pulmonary arterial pressure in scleroderma patients. METHODS: After a noninvasive screening program, 29 patients with systemic sclerosis without significant lung fibrosis and without known pulmonary arterial hypertension underwent right heart catheterization and simultaneous cardiopulmonary exercise test. A six-minute walk distance (6MWD) was determined within 48 hours. MEASUREMENTS AND MAIN RESULTS: A resting MPAP above the median (17 mm Hg) was associated with decreased 6MWD (396 +/- 71 vs. 488 +/- 76 m; P < 0.005) and peak Vo(2) (76 +/- 11% vs. 90 +/- 24%; P = 0.05). Resting pulmonary vascular resistance was inversely correlated with 6MWD (r = 0.45; P < 0.05). At 25 and 50W, MPAP above the median (23 and 28 mm Hg) was associated with decreased 6MWD (P < 0.005; P < 0.0005). At peak exercise, MPAP showed no association with 6MWD or peak Vo(2); however, cardiac index was positively (r = 0.45; P < 0.05) and pulmonary vascular resistance was negatively correlated with 6MWD (r = -0.38; P < 0.05). CONCLUSIONS: MPAP and resistance in the upper normal range at rest and moderate exercise are associated with decreased exercise capacity and may indicate early pulmonary vasculopathy in patients with systemic sclerosis. Investigations on the prognostic and therapeutic implications of such borderline findings are warranted. Clinical trial registered with http://www.clinicaltrials.gov (NCT00609349).

Interpreting single trial data using groupwise regularisation.

Univariate statistical approaches are often used for the analysis of neuroimaging data but are unable to detect subtle interactions between different components of brain activity. In contrast, multivariate approaches that use classification as a basis are well-suited to detect such interactions, allowing the analysis of neuroimaging data on the single trial level. However, multivariate approaches typically assign a non-zero contribution to every component, making interpretation of the results troublesome. This paper introduces groupwise regularisation as a novel method for finding sparse, and therefore easy to interpret, models that are able to predict the experimental condition to which single trials belong. Furthermore, the obtained models can be constrained in various ways by placing features extracted from the data that are thought to belong together into groups. In order to learn models from data, we introduce a new algorithm that makes use of stability conditions that have been derived in this paper. The algorithm is used to classify multisensor EEG signals recorded for a motor imagery task using (groupwise) regularised logistic regression as the underlying classifier. We show that regularisation dramatically reduces the number of features without reducing the classification rate. This improves model interpretability as it finds features in the data such as mu and beta desynchronisation in the motor cortex contralateral to the imagined movement. By choosing particular groupings we can constrain the regularised solutions such that a lower number of sensors is used or a model is obtained that generalises well over subjects. The identification of a small number of groups of features that best explain the data make groupwise regularisation a useful new tool for single trial analysis.

On semi-blind source separation using spatial constraints with applications in EEG analysis.

Blind source separation (BSS) techniques, such as independent component analysis (ICA), are increasingly being used in biomedical signal processing applications, including the analysis of multichannel electroencephalogram (EEG) and magnetoencephalogram (MEG) signals. These methods estimate a set of sources from the observed data, which reflect the underlying physiological signal generating and mixing processes, noise and artifacts. In practice, BSS methods are often applied in the context of additional information and expectations regarding the spatial or temporal characteristics of some sources of interest, whose identification requires complicated post-hoc analysis or, more commonly, manual selection by human experts. An alternative would be to incorporate any available prior knowledge about the source signals or locations into a semi-blind source separation (SBSS) approach, effectively by imposing temporal or spatial constraints on the underlying source mixture model. This work is concerned with biomedical applications of SBSS using spatial constraints, particularly for artifact removal and source tracking in EEG analysis, and provides definitions of different types of spatial constraint along with general guidelines on how these can be implemented in conjunction with conventional BSS methods.

Independent component analysis for biomedical signals.

Independent component analysis (ICA) is increasing in popularity in the field of biomedical signal processing. It is generally used when it is required to separate measured multi-channel biomedical signals into their constituent underlying components. The use of ICA has been facilitated in part by the free availability of toolboxes that implement popular flavours of the techniques. Fundamentally ICA in biomedicine involves the extraction and separation of statistically independent sources underlying multiple measurements of biomedical signals. Technical advances in algorithmic developments implementing ICA are reviewed along with new directions in the field. These advances are specifically summarized with applications to biomedical signals in mind. The basic assumptions that are made when applying ICA are discussed, along with their implications when applied particularly to biomedical signals. ICA as a specific embodiment of blind source separation (BSS) is also discussed, and as a consequence the criterion used for establishing independence between sources is reviewed and this leads to the introduction of ICA/BSS techniques based on time, frequency and joint time-frequency decomposition of the data. Finally, advanced implementations of ICA are illustrated as applied to neurophysiologic signals in the form of electro-magnetic brain signals data.

Expression of adhesion molecules in allergic lung diseases.

Endothelial adherence and migration of leukocytes into tissue is mediated by different sets of adhesion molecules. The expression of these sets might not only preselect the types of leukocytes that enter the inflammatory sites, but also activate these leukocytes, induce adherence to epithelial cells, and cause the release of cytokines. Atopic asthma, extrinsic allergic alveolitis, and sarcoidosis as examples of immunologic lung diseases were investigated for the expression of adhesion molecules. Bronchial biopsies in chronic obstructive lung disease (COPD) and resected lung tissue of juvenile emphysema were chosen for controls. Immunohistochemistry was done on sections from bronchial and transbronchial biopsies and on smears from bronchoalveolar lavage cells. In all three types of immune disorders, lymphocytes expressed the integrins alpha4/beta1 (VLA4) and ICAM3, whereas lymphocytes in COPD bronchitis and in emphysema controls were unreactive. Eosinophils in atopic asthma bronchitis in contrast to COPD bronchitis also expressed both VLA4 and ICAM3. The expression of VCAM1 on endothelial cells was only seen in atopic asthma and was related to disease activity. The expression of other adhesion molecules was nonspecific. Expression of VCAM1 on endothelial cells and its ligand VLA4 on lymphocytes and eosinophils seems to be a specific event in atopic asthma. Expression of VLA4 and ICAM3 on lymphocytes, however, might be a specific event in all three immune reactions.

Ex vivo effects of low-dose rivaroxaban on specific coagulation assays and coagulation factor activities in patients under real life conditions.

Global coagulation assays display variable effects at different concentrations of rivaroxaban. The aim of this study is to quantify the ex vivo effects of low-dose rivaroxaban on thrombophilia screening assays and coagulation factor activities based on the administration time, and to show how to mask possible interferences. Plasma samples from 40 patients receiving rivaroxaban 10 mg daily were investigated to measure activities of clotting factor II, V, VII, VIII, IX, XI, XII and XIII; protein C- and protein S-levels; lupus anticoagulants; anticardiolipin IgG and IgM; D-dimer, heparin-platelet factor 4 (HPF4) antibodies and screening tests for von Willebrand disease (VWD). Two hours after rivaroxaban administration, the activities of clotting factors were significantly decreased to different extents, except for factor XIII. Dilution of plasma samples resulted in neutralisation of these interferences. The chromogenic protein C activity assay was not affected by rivaroxaban. Depending on the timing of tablet intake in relation to blood sampling protein S activity was measured falsely high when a clotting assay was used. False-positive results for lupus anticoagulants were observed depending on the assay system used and the administration time of rivaroxaban. ELISA-based assays such as anticardiolipin IgG and IgM, D-dimer, HPF4-antibodies and the turbidimetric assays for VWD were not affected by rivaroxaban. Specific haemostasis clotting tests should be performed directly prior to rivaroxaban intake. Assay optimisation in the presence of rivaroxaban can be achieved by plasma dilution. Immunologic assays are not influenced by rivaroxaban, while chromogenic assays can be used, when they do not depend on factor Xa.

Directional variance adjustment: bias reduction in covariance matrices based on factor analysis with an application to portfolio optimization.

Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation.

Accurate determination of rivaroxaban levels requires different calibrator sets but not addition of antithrombin.

Rivaroxaban is a direct factor Xa inhibitor, which can be monitored by anti-factor Xa chromogenic assays. This ex vivo study evaluated different assays for accurate determination of rivaroxaban levels. Eighty plasma samples from patients receiving rivaroxaban (Xarelto) 10 mg once daily and 20 plasma samples from healthy volunteers were investigated using one anti-factor Xa assay with the addition of exogenous antithrombin and two assays without the addition of antithrombin. Two different lyophilised rivaroxaban calibration sets were used for each assay (low concentration set: 0, 14.5, 59.6 and 97.1 ng/ml; high concentration set: 0, 48.3, 101.3, 194.2 and 433.3 ng/ml). Using a blinded study design, the rivaroxaban concentrations determined by the assays were compared with concentrations measured by HPLC-MS/MS. All assays showed a linear relationship between the rivaroxaban concentrations measured by HPLC-MS/MS and the optical density of the anti-FXa assays. However, the assay with the addition of exogenous antithrombin detected falsely high concentrations of rivaroxaban even in plasma samples from controls who had not taken rivaroxaban (intercept values using the high calibrator set and the low calibrator set: +26.49 ng/ml and +13.71 ng/ml, respectively). Plasma samples, initially determined by the high calibrator setting and containing rivaroxaban concentrations <25 ng/ml, had to be re-run using the low calibrator setting for precise measurement. In conclusion, anti-factor Xa chromogenic assays that use rivaroxaban calibrators at different concentration levels can be used to measure accurately a wide range of rivaroxaban concentrations ex vivo. Assays including exogenous antithrombin are unsuitable for measurement of rivaroxaban.

Rivaroxaban differentially influences ex vivo global coagulation assays based on the administration time.

It was the objective of this study to quantify the effects of rivaroxaban administration on global coagulation parameters associated with routine clinical procedures, we collected plasma samples from patients undergoing major orthopaedic surgery receiving rivaroxaban at various time points after drug administration. Forty-seven patients received rivaroxaban (10 mg daily) for venous thromboembolism prophylaxis. Blood samples were collected at four different time points: A) before surgery; B) before drug administration at day 4-5 after surgery (steady state of rivaroxaban); C) 2 hours (h) after drug administration and D) 12 h after drug administration. The prothrombin time (PT), activated partial thromboplastin time (aPTT), thrombin time (TT), antithrombin (AT) level, fibrinogen level by Clauss method (FibC), and derived fibrinogen (dFIB) level were assessed with various reagents. At 2 h after rivaroxaban administration, the PT and aPTT clotting times were significantly prolonged to different extents up to 1.4 fold, whereas 12 h after drug administration, no significant effect was observed. Rivaroxaban administration had no influence on the TT or the FibC concentration. The dFIB assay was differentially affected by rivaroxaban when different reagents were tested. The AT assay dependent on thrombin activity was not influenced by rivaroxaban, whereas the AT levels dependent on factor Xa activity were significantly increased by rivaroxaban. Clinicians should be aware of the time-dependent influence of rivaroxaban on factor Xa-dependent routine coagulation assays. Therefore, routine coagulation parameters should be assessed directly before drug administration to keep the interaction of rivaroxaban low.

Assessment of pulmonary arterial pressure during exercise in collagen vascular disease: echocardiography vs right-sided heart catheterization.

BACKGROUND: This study compared the results of exercise Doppler echocardiography (EDE) with right-sided heart catheterization (RHC) and evaluated the combination of EDE and cardiopulmonary exercise testing (CPET) as a screening method for early pulmonary vasculopathy in patients with connective tissue disease. METHODS: Patients (N = 52) with connective tissue disease (predominantly systemic sclerosis) and without known pulmonary arterial hypertension underwent both EDE and CPET. If systolic pulmonary arterial pressure (SPAP) was > 40 mm Hg during exercise or peak oxygen uptake (Vo(2)) was < 75% predicted, RHC was suggested. RESULTS: EDE showed an SPAP > 40 mm Hg during exercise in 26/52 patients. Additionally, CPET showed a peak Vo(2) < 75% predicted in 10/26 patients with SPAP 40 mm Hg in 25 patients (n = 1 at rest, n = 24 during exercise). SPAP values assessed by EDE showed no significant difference vs RHC at rest, 25 W, 50 W, and maximal exercise (difference [95% CI]: 0.3 [-2.7; 3.2], -1.3 [-7.1; 4.4], 0.9 [-7.7; 5.9], and -5.6 [-13.5; 2.2] mm Hg). Eight patients with exercise SPAP > 40 mm Hg had an exercise pulmonary arterial wedge pressure > 20 mm Hg, suggesting exercise-induced left ventricular diastolic dysfunction not detectable by EDE. CONCLUSIONS: EDE appears to be a reasonable noninvasive method to detect SPAP increase during exercise in connective tissue disease. In combination with CPET, it may be a useful screening tool for early pulmonary vasculopathy, although RHC remains the gold standard for hemodynamic assessment. TRIAL REGISTRATION: clinicaltrials.gov; Identifier: NCT00609349 (Early Recognition of Pulmonary Arterial Hypertension).

Model order estimation for blind source separation of multichannel magnetoencephalogram and electroencephalogram signals.

Most blind source separation (BSS) approaches - especially independent component analysis (ICA) - assume a noiseless mixture of the same number of sources as sensors. It is doubtful, however, whether this assumption actually holds for multichannel magnetoencephalogram (MEG) and electroencephalogram (EEG) measurements comprising a large number of channels. Corroborating and extending previous results, this work further examines the utility of second-order statistical methods based on probabilistic principal component analysis (PPCA) and factor analysis (FA) models for estimating the number of underlying sources in multichannel MEG and EEG. Compared with conventional PCA-based eigenvalue thresholding, both PPCA and FA approaches yield stable model order estimates which are almost independent of total signal power. The FA model provides a more optimal description of both MEG and EEG data than PPCA, in terms of balancing goodness-of-fit and parsimony. These findings add to the growing evidence that anisotropic 'sensor noise' may be a statistically robust characteristic of both the EEG and MEG, which most BSS algorithms and applications do not address.

Semi-blind identification of movement-related magnetoencephalogram components using a classification approach.

Many biomedical signal processing applications involving the analysis of multi-channel electrophysiological recordings, such as the magnetoencephalogram (MEG) and electroencephalogram (EEG), increasingly employ blind source separation (BSS) techniques to estimate signal components reflecting artifacts and neurophysiological activity. While much research focuses on developing methods for automatic removal of artefact sources, comparatively little effort has been spent on trying to identify neurophysiological sources of interest, which is especially challenging in the absence of prior knowledge about their spatial or time-freqency characteristics. This work presents a method for identifying source signals exhibiting systematic and reliable time-frequency differences over clearly defined epochs associated with different 'system-states'. The proposed method uses annotated data and a classification approach to identify those sources which individually reflect significant differences between epochs (classes). Applied to segments of 275-channel MEG data from a visuo-motor task in which left, right or no finger movements occurred, the method selects only a small number of sources whose scalp topographies are consistent with primary sensorimotor cortical areas.

On estimating the signal subspace dimension of high-density multichannel magnetoencephalogram measurements.

Accurate estimates of the dimension and an (orthogonal) basis of the signal subspace of noise corrupted multi-channel measurements are essential for accurate identification and extraction of any signals of interest within that subspace. For most biomedical signals comprising very large numbers of channels, including the magnetoencephalogram (MEG), the "true" number of underlying signals ¿ although ultimately unknown ¿ is unlikely to be of the same order as the number of measurements, and has to be estimated from the available data. This work examines several second-order statistical approaches to signal subspace (dimension) estimation with respect to their underlying assumptions and their performance in high-dimensional measurement spaces using 151-channel MEG data. The purpose is to identify which of these methods might be most appropriate for modeling the signal subspace structure of high-density MEG data recorded under controlled conditions, and what are the practical consequences with regard to the subsequent application of biophysical modeling and statistical source separation techniques.

Tracking Epileptiform Activity in the Multichannel Ictal EEG using Spatially Constrained Independent Component Analysis.

Blind source separation (BSS) methods such as independent component analysis (ICA) are increasingly being used in biomedical signal processing for decomposition of multivariate time-series, such as the multichannel electroencephalogram (EEG), into a set of underlying sources, some of which may reflect clinically relevant neurophysiological activity such as epileptic seizures or spikes. Tracking and detecting signals of interest fundamentally requires at least some a priori knowledge or assumptions regarding the spatial and/or temporal characteristics of the target sources. While such prior information is conventionally used during post-processing, it seems equally sensible to incorporate any available information into the data decomposition process from the outset. This work presents an alternative approach to source tracking in multichannel EEG, which exploits prior knowledge of the spatial topographies of the scalp voltage distributions associated with the target sources. The predetermined target topographies are used in conjunction with spatially constrained ICA to extract target source waveforms which are uncontaminated by contributions from coactive and spatially correlated brain and artifact sources. These signals can then be further analyzed in terms of their morphological, spectral or statistical properties. As illustrated in the context of epileptiform EEG, this method is useful for tracking seizures.

Isotope fractionations in the biosynthesis of cell components by different fungi: a basis for environmental carbon flux studies.

Abstract The isotope fractionation of carbon from substrates possessing different isotope ratios into fatty acids of polar lipids and amino acids was determined for four different fungi (Rhizopus arrhizus, Mortierella isabellina, Fusarium solani, Aspergillus niger). Carbon isotope ratios of fungi closely followed that of the substrates. Palmitic acid (C16:0), derived from phospholipids, did not display a large carbon isotope fractionation against the substrate. Stearic acid (C18:0), however, was depleted in (13)C against C16:0 in all strains. The desaturation of C18:0 to oleic acid (C18:1omega9) had little effect on the carbon isotope ratio. The subsequent desaturation of C18:1omega9 to linolic acid (C18:2omega6,9) enriched the resulting C18:2omega6,9 by +3.9 per thousand and varied little among strains. This result is important because C18:2omega6,9 is often used as a biomarker in environmental studies. Most amino acids were enriched in (13)C compared to the substrates, but isoleucine and lysine were close to the isotope ratio of the substrate and phenylalanine and leucine were depleted. Interestingly, the carbon isotope ratios of many amino acids differed significantly among different species. A discriminant analysis based on the isotope ratio of four amino acids (Thr, Ile, Phe, Val) resolved the two phyla in the first discriminant function and all four strains in the first two discriminant functions and confirmed a taxon-specific manner of isotope fractionation. The derived rules provide the basis for the use of stable isotopes in environmental studies to elucidate the role of fungi in the carbon flow in the environment.