OWL‐based acquisition and editing of computer‐interpretable guidelines with the CompGuide editor

Computer‐Interpretable Guidelines (CIGs) are the dominant medium for the delivery of clinical decision support, given the evidence‐based nature of their source material. Therefore, these machine‐readable versions have the ability to improve practitioner performance and conformance to standards, with availability at the point and time of care. The formalisation of Clinical Practice Guideline knowledge in a machine‐readable format is a crucial task to make it suitable for the integration in Clinical Decision Support Systems. However, the current tools for this purpose reveal shortcomings with respect to their ease of use and the support offered during CIG acquisition and editing. In this work, we characterise the current landscape of CIG acquisition tools based on the properties of guideline visualisation, organisation, simplicity, automation, manipulation of knowledge elements, and guideline storage and dissemination. Additionally, we describe the CompGuide Editor, a tool for the acquisition of CIGs in the CompGuide model for Clinical Practice Guidelines that also allows the editing of previously encoded guidelines. The Editor guides the users throughout the process of guideline encoding and does not require proficiency in any programming language. The features of the CIG encoding process are revealed through a comparison with already established tools for CIG acquisition.     

Piperacillin/tazobactam‐induced neurotoxicity in a hemodialysis patient: A case report

Antibiotics are potentially a cause of neurotoxicity in dialysis patients, the most common are the beta‐lactams as ceftazidime and cefepime, and few cases have been reported after piperacillin/tazobactam use. This report presents a case of a hypertensive and diabetic 67‐year‐old woman in regular hemodialysis, which previously had a stroke. She was hospitalized presenting pneumonia, which was initially treated with cefepime. Two days after treatment, she presented dysarthria, left hemiparesis, ataxia, and IX and X cranial nerves paresis. Computed tomography showed no acute lesions and cefepime neurotoxicity was hypothesized, and the antibiotic was replaced by piperacillin/tazobactam. The neurologic signs disappeared; however, 4 days after with piperacillin/tazobactam treatment, the neurological manifestations returned. A new computed tomography showed no new lesions, and the second antibiotic regimen withdrawn. After two hemodialysis sessions, the patient completely recovered from neurological manifestations. The patient presented sequentially neurotoxicity caused by two beta‐lactams antibiotics. This report meant to alert clinicians that these antibiotics have dangerous neurological effects in chronic kidney disease patients.     

The influence of magnetron sputtered silver and titanium coatings on the compaction and properties of surface modified PM Ti-48Al powders

Thin layers of silver and titanium were deposited onto commercial Ti–₄₈Al particles by magnetron sputtering prior to their consolidation by hot isostatic pressing and subsequent heat treatment. Based on picnometry and electron probe microanalysis results, average values of 1.5 and 2.5 at.%, respectively, were obtained for the silver and titanium contents in the coated particles. The surface modified Ti–₄₈Al particles exhibited improved sintering ability than the unmodified ones. The consolidated samples have duplex microstructures formed by γ-TiAl and γ-TiAl + α₂-Ti₃Al grains. Ag-rich nanoprecipitates were detected in the microstructure of the compacted Ti-₄₈Al + Ag sample. The coatings are no longer visible at the grains boundaries after a subsequent homogenization heat treatment at 1200°C. The highest values of hardness and Young’s modulus were obtained for the Ti–₄₈Al + Ag sample, associated with a higher density and a lower percentage of pores.     

Evaluation of the thermal and mechanical properties of poly(ε‐caprolactone), low‐density polyethylene,and their blends

The thermal and mechanical properties of low‐density polyethylene (LDPE), poly(ε‐caprolactone) (PCL), and their blends were evaluated. Differential scanning calorimetry showed that increasing the PCL content of the blend did not change the LDPE melting temperature, but reduced the crystallinity by up to 16.8%. This behavior was related to interactions between the PCL chains and the crystalline phase of LDPE. Tensile strength and elongation at break values for the blends were lower than those for the pure polymers, which suggested an incompatibility between the polymers. The values for Young's modulus under tensile increased when PCL was added to LDPE. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 91:3909–3914, 2004     

Development and implementation of clinical guidelines: An artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.     

Spray drying conditions and encapsulating composition effects on formation and properties of sodium diclofenac microparticles

This paper reports the preparation and characterization of sustained release sodium diclofenac microparticles by spray drying. Aqueous dispersions of ethyl cellulose (Surelease®) and Eudragit RS 30 D® were evaluated as controlled release polymers. The product properties (product moisture, size distribution, particle morphology, flow properties, total drug load, in-vitro dissolution studies, and encapsulating efficiency) were determined as a function of inlet temperature of the spray drying, the feed flow rate and composition of the microencapsulating formulation. In general, lower values of the product moisture content were observed at higher drying temperatures. The spray-dried product was composed mainly by rounded-shape and multi-size particles. The mean particle diameters of the Eudragit based microparticles tended to be slight bigger than the Surelease based microparticles. The spray dried microparticles showed delayed drug dissolution rates, sustaining the drug release for several hours. These findings confirm the feasibility of the spray drying for preparation of microparticles with sustained release properties. The physical and chemical properties of the microparticles can be changed by varying the spray drying parameters as well as the microencapsulating formulation.     

Enhancing thermal and dynamic-mechanical properties of epoxy reinforced by amino-functionalized microcrystalline cellulose

In this study, microcrystalline cellulose (MCC) was chemically modified with 3-(aminopropyl)triethoxysilane and added to epoxy to improve chemical, thermal and dynamic-mechanical characteristics of the composites. The composites were manufactured aided by sonication with 1.0%, 2.5%, or 5.0% wt/wt of untreated MCC or amino-functionalized MCC (MCC-Si). The epoxy/MCC-Si composites showed a decrease in the ─OH band by Fourier-transform infrared spectroscopy, and X-ray diffraction analysis indicated better dispersion. The incorporation of MCC-Si in epoxy resin decreased the heat of reaction, increased activation energy values (E_a) and pre-exponential factor (A), and did not affect thermal degradation. All conversion degree (α) versus temperature curves for the composites showed a sigmoidal shape. MCC-Si composites showed better dynamic-mechanical properties than the MCC counterparts, and the functionalization effect was evidenced in storage modulus (E') and loss modulus (E"). At 2.5% wt/wt of MCC-Si content an increase of 119% in E' at the glassy region, 127% in E' at the rubbery region and 173% in E" was observed compared to the neat resin, whereas the T_g barely changed among samples. Good adhesion between the amino-functionalized MCC and the epoxy matrix was observed at the fracture surface, evidencing that surface modification of MCC improves their chemical interaction.     

Hand gesture recognition from depth and infrared Kinect data for CAVE applications interaction

This paper presents a real-time framework that combines depth data and infrared laser speckle pattern (ILSP) images, captured from a Kinect device, for static hand gesture recognition to interact with CAVE applications. At the startup of the system, background removal and hand position detection are performed using only the depth map. After that, tracking is started using the hand positions of the previous frames in order to seek for the hand centroid of the current one. The obtained point is used as a seed for a region growing algorithm to perform hand segmentation in the depth map. The result is a mask that will be used for hand segmentation in the ILSP frame sequence. Next, we apply motion restrictions for gesture spotting in order to mark each image as a ‘Gesture’ or ‘Non-Gesture’. The ILSP counterparts of the frames labeled as “Gesture” are enhanced by using mask subtraction, contrast stretching, median filter, and histogram equalization. The result is used as the input for the feature extraction using a scale invariant feature transform algorithm (SIFT), bag-of-visual-words construction and classification through a multi-class support vector machine (SVM) classifier. Finally, we build a grammar based on the hand gesture classes to convert the classification results in control commands for the CAVE application. The performed tests and comparisons show that the implemented plugin is an efficient solution. We achieve state-of-the-art recognition accuracy as well as efficient object manipulation in a virtual scene visualized in the CAVE.     

Randomization can be a healer: consensus with dynamic omission failures

Wireless ad-hoc networks are being increasingly used in diverse contexts, ranging from casual meetings to disaster recovery operations. A promising approach is to model these networks as distributed systems prone to dynamic communication failures. This captures transitory disconnections in communication due to phenomena like interference and collisions, and permits an efficient use of the wireless broadcasting medium. This model, however, is bound by the impossibility result of Santoro and Widmayer, which states that, even with strong synchrony assumptions, there is no deterministic solution to any non-trivial form of agreement if n ? 1 or more messages can be lost per communication round in a system with n processes. In this paper we propose a novel way to circumvent this impossibility result by employing randomization. We present a consensus protocol that ensures safety in the presence of an unrestricted number of omission faults, and guarantees progress in rounds where such faults are bounded by ${f \,{\leq}\,\lceil \frac{n}{2} \rceil (n\,{-}\,k)\,{+}\,k\,{-}\,2}$ , where k is the number of processes required to decide, eventually assuring termination with probability 1.     

Enhanced default risk models with SVM+

Default risk models have lately raised a great interest due to the recent world economic crisis. In spite of many advanced techniques that have extensively been proposed, no comprehensive method incorporating a holistic perspective has hitherto been considered. Thus, the existing models for bankruptcy prediction lack the whole coverage of contextual knowledge which may prevent the decision makers such as investors and financial analysts to take the right decisions. Recently, SVM+ provides a formal way to incorporate additional information (not only training data) onto the learning models improving generalization. In financial settings examples of such non-financial (though relevant) information are marketing reports, competitors landscape, economic environment, customers screening, industry trends, etc. By exploiting additional information able to improve classical inductive learning we propose a prediction model where data is naturally separated into several structured groups clustered by the size and annual turnover of the firms. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed default risk model showed better predictability performance than the baseline SVM and multi-task learning with SVM.