QSRlib: a software library for online acquisition of qualitative spatial relations from video

There is increasing interest in using Qualitative Spatial Relations as a formalism to abstract from noisy and large amounts of video data in order to form high level conceptualisations, e.g. of activities present in video. We present a library to support such work. It is compatible with the Robot Operating System (ROS) but can also be used stand alone. A number of QSRs are built in; others can be easily added

Protocol for a systematic review and thematic synthesis of patient experiences of central venous access devices in anti-cancer treatment

Background: Three types of central venous access devices (CVADs)—peripherally inserted central catheters (PICCs), skin-tunnelled central catheters (Hickman-type devices), and implantable chest wall Ports (Ports)—are routinely used in the intravenous administration of anti-cancer treatment. These devices avoid the need for peripheral cannulation and allow for home delivery of treatment. Assessments of these devices have tended to focus on medical and economic factors, but there is increased interest in the importance of patient experiences and perspectives in this area. The aim of this systematic review is to synthesise existing research regarding patient experiences of these CVADs to help clinicians guide, prepare, and support patients receiving CVADs for the administration of anti-cancer treatment. Method: A systematic search of MEDLINE, Embase, and CINAHL research databases will be carried out along with a supplementary reference list search. This review will include quantitative, qualitative, and mixed methods studies published in peer-review journals, reporting some aspect(s) of patient experiences or perspectives regarding the use of PICC, Hickman, or Port CVADs for the administration of anti-cancer drugs. The methodological quality and risk of bias of included papers will be assessed using the Mixed Methods Appraisal Tool (MMAT). Relevant outcome data will be extracted from included studies and analysed using a thematic synthesis approach. Discussion: The results section of the review will comprise thematic synthesis of quantitative studies, thematic synthesis of qualitative studies, and the aggregation of the two. Results will aim to offer an account of current understandings of patient experiences and perspective regarding PICC, Hickman-type, and Port devices in the context of anti-cancer treatment. Confidence in cumulative evidence will be assessed using the Confidence in the Evidence from Reviews of Qualitative research (CERQual) approach

Estimating the nuclear level density with the Monte Carlo shell model

A method for making realistic estimates of the density of levels in even-even nuclei is presented making use of the Monte Carlo shell model (MCSM). The procedure follows three basic steps: (1) computation of the thermal energy with the MCSM, (2) evaluation of the partition function by integrating the thermal energy, and (3) evaluating the level density by performing the inverse Laplace transform of the partition function using Maximum Entropy reconstruction techniques. It is found that results obtained with schematic interactions, which do not have a sign problem in the MCSM, compare well with realistic shell-model interactions provided an important isospin dependence is accounted for.Comment: 14 pages, 3 postscript figures. Latex with RevTex. Submitted as a rapid communication to Phys. Rev.

Dose and energy dependence of mechanical properties of focused electron beam induced pillar deposits from Cu(C5HF6O2)2

Bending and vibration tests performed inside the scanning electron microscope were used to mechanically characterize high-aspect pillars grown by focused electron-beam (FEB) induced deposition from the precursor Cu(C5HF6O2)2. Supported by finite element (FE) analysis the Young's modulus was determined from load-deflection measurements using cantilever-based force sensing and the material density from additional resonance vibration analysis. The pillar material consisted of a carbonaceous (C, O, F, H containing) matrix which embeds 5...10 at. % Cu deposited at 5 keV and 20 keV primary electron energy and 100 pA beam current, depending on primary electron energy. Young's moduli of the FEB deposits increased from 17+/-6 GPa to 25+/-8 GPa with increasing electron dose. The density of the carbonaceous matrix shows a dependence on the primary electron energy: 1.2+/-0.3 g cm-3 (5 keV) and 2.2+/-0.5 g cm-3 (20 keV). At a given primary energy a correlation with the irradiation dose is found. Quality factors determined from the phase relation at resonance of the fundamental pillar vibration mode were in the range of 150 to 600 and correlated to the deposited irradiation energy.Comment: 17 pages, 9 figures, 2 table

The STRANDS project: long-term autonomy in everyday environments

Thanks to the efforts of the robotics and autonomous systems community, the myriad applications and capacities of robots are ever increasing. There is increasing demand from end users for autonomous service robots that can operate in real environments for extended periods. In the Spatiotemporal Representations and Activities for Cognitive Control in Long-Term Scenarios (STRANDS) project (http://strandsproject.eu), we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots and deploying these systems for long-term installations in security and care environments. Our robots have been operational for a combined duration of 104 days over four deployments, autonomously performing end-user-defined tasks and traversing 116 km in the process. In this article, we describe the approach we used to enable long-term autonomous operation in everyday environments and how our robots are able to use their long run times to improve their own performance

Bilateral asynchronous acute epidural hematoma : a case report

BACKGROUND: Bilateral extradural hematomas have only rarely been reported in the literature. Even rarer are cases where the hematomas develop sequentially, one after removal of the other. Among 187 cases of operated epidural hematomas during past 4 years in our hospital, we found one case of sequentially developed bilateral epidural hematoma. CASE PRESENTATION: An 18-year-old conscious male worker was admitted to our hospital after a fall. After deterioration of his consciousness, an emergency brain CT scan showed a right temporoparietal epidural hematoma. The hematoma was evacuated, but the patient did not improve afterwards. Another CT scan showed contralateral epidural hematoma and the patient was reoperated. Postoperatively, the patient recovered completely. CONCLUSIONS: This case underlines the need for monitoring after an operation for an epidural hematoma and the need for repeat brain CT scans if the patient does not recover quickly after removal of the hematoma, especially if the first CT scan has been done less than 6 hours after the trauma. Intraoperative brain swelling can be considered as a clue for the development of contralateral hematoma

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

<p>Abstract</p> <p>Background</p> <p>Bioactivity profiling using high-throughput <it>in vitro </it>assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex <it>in vitro/in vivo </it>datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.</p> <p>Results</p> <p>The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated <it>in vitro </it>assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.</p> <p>Conclusion</p> <p>We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.</p

Financial and monetary policy responses to oil price shocks: evidence from oil-importing and oil-exporting countries

In this study, we investigate the financial and monetary policy responses to oil price shocks using a Structural VAR framework. We distinguish between net oil-importing and net oil-exporting countries. Since the 80s, a significant number of empirical studies have been published investigating the effect of oil prices on macroeconomic and financial variables. Most of these studies though, do not make a distinction between oil-importing and oil-exporting economies. Overall, our results indicate that the level of inflation in both net oil-exporting and net oil-importing countries is significantly affected by oil price innovations. Furthermore, we find that the response of interest rates to an oil price shock depends heavily on the monetary policy regime of each country. Finally, stock markets operating in net oil-importing countries exhibit a negative response to increased oil prices. The reverse is true for the stock market of the net oil-exporting countries. We find evidence that the magnitude of stock market responses to oil price shocks is higher for the newly established and/or less liquid stock market

Normal radial migration and lamination are maintained in dyslexia-susceptibility candidate gene homolog Kiaa0319 knockout mice

AbstractDevelopmental dyslexia is a common disorder with a strong genetic component, but the underlying molecular mechanisms are still unknown. Several candidate dyslexia-susceptibility genes, including KIAA0319, DYX1C1, and DCDC2, have been identified in humans. RNA interference experiments targeting these genes in rat embryos have shown impairments in neuronal migration, suggesting that defects in radial cortical migration could be involved in the disease mechanism of dyslexia. Here we present the first characterisation of a Kiaa0319 knockout mouse line. Animals lacking KIAA0319 protein do not show anatomical abnormalities in any of the layered structures of the brain. Neurogenesis and radial migration of cortical projection neurons are not altered, and the intrinsic electrophysiological properties of Kiaa0319-deficient neurons do not differ from those of wild-type neurons. Kiaa0319 overexpression in cortex delays radial migration, but does not affect final neuronal position. However, knockout animals show subtle differences suggesting possible alterations in anxiety-related behaviour and in sensorimotor gating. Our results do not reveal a migration disorder in the mouse model, adding to the body of evidence available for Dcdc2 and Dyx1c1 that, unlike in the rat in utero knockdown models, the dyslexia-susceptibility candidate mouse homolog genes do not play an evident role in neuronal migration. However, KIAA0319 protein expression seems to be restricted to the brain, not only in early developmental stages but also in adult mice, indicative of a role of this protein in brain function. The constitutive and conditional knockout lines reported here will be useful tools for further functional analyses of Kiaa0319

Deletion of iRhom2 protects against diet-induced obesity by increasing thermogenesis

Objective: Obesity is the result of positive energy balance. It can be caused by excessive energy consumption but also by decreased energy dissipation, which occurs under several conditions including when the development or activation of brown adipose tissue (BAT) is impaired. Here we evaluated whether iRhom2, the essential cofactor for the Tumour Necrosis Factor (TNF) sheddase ADAM17/TACE, plays a role in the pathophysiology of metabolic syndrome.Methods: We challenged WT versus iRhom2 KO mice to positive energy balance by chronic exposure to a high fat diet and then compared their metabolic phenotypes. We also carried out ex vivo assays with primary and immortalized mouse brown adipocytes to establish the autonomy of the effect of loss of iRhom2 on thermogenesis and respiration.Results: Deletion of iRhom2 protected mice from weight gain, dyslipidemia, adipose tissue inflammation, and hepatic steatosis and improved insulin sensitivity when challenged by a high fat diet. Crucially, the loss of iRhom2 promotes thermogenesis via BAT activation and beige adipocyte recruitment, enabling iRhom2 KO mice to dissipate excess energy more efficiently than WT animals. This effect on enhanced thermogenesis is cell-autonomous in brown adipocytes as iRhom2 KOs exhibit elevated UCP1 levels and increased mitochondrial proton leak.Conclusion: Our data suggest that iRhom2 is a negative regulator of thermogenesis and plays a role in the control of adipose tissue homeostasis during metabolic disease. (C) 2019 The Authors. Published by Elsevier GmbH