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 A new computed tomography showed no new lesions, and the second antibiotic regimen withdrawn.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.     

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.     

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.     

The use of lifetime functions in the optimization of interventions on existing bridges considering maintenance and failure costs

In the last decade, it became clear that life-cycle cost analysis of existing civil infrastructure must be used to optimally manage the growing number of aging and deteriorating structures. The uncertainties associated with deteriorating structures require the use of probabilistic methods to properly evaluate their lifetime performance. In this paper, the deterioration and the effect of maintenance actions are analyzed considering the performance of existing structures characterized by lifetime functions. These functions allow, in a simple manner, the consideration of the effect of aging on the decrease of the probability of survival of a structure, as well as the effect of maintenance actions. Models for the effects of proactive and reactive preventive maintenance, and essential maintenance actions are presented. Since the probability of failure is different from zero during the entire service life of a deteriorating structure and depends strongly on the maintenance strategy, the cost of failure is included in this analysis. The failure of one component in a structure does not usually lead to failure of the structure and, as a result, the safety of existing structures must be analyzed using a system reliability framework. The optimization consists of minimizing the sum of the cumulative maintenance and expected failure cost during the prescribed time horizon. Two examples of application of the proposed methodology are presented. In the first example, the sum of the maintenance and failure costs of a bridge in Colorado is minimized considering essential maintenance only and a fixed minimum acceptable probability of failure. In the second example, the expected lifetime cost, including maintenance and expected failure costs, of a multi-girder bridge is minimized considering reactive preventive maintenance actions.     

Condition, safety and cost profiles for deteriorating structures with emphasis on bridges

After an enormous investment in construction of highway networks undertaken in the second half of the 20th century, the highway networks of most European and North American countries are now completed or close to completion. As a result, the need in funding changed from building new highway structures to repair, rehabilitation, and replacement the existing ones. In this paper, a model for analyzing the evolution in time of probabilistic performance indicators of existing structures, in terms of condition, safety, and cost under no maintenance, preventive maintenance, and essential maintenance, is presented. This model integrates the current practice in bridge management systems based on visual inspections (condition index) with structural assessment (safety index) during the lifetime of existing structures. The proposed model allows the consideration of uncertainties in the performance deterioration process, times of application of maintenance actions, and in the effects of maintenance actions on the condition, safety, and life-cycle cost of structures by defining all parameters involved in the model as random variables. Interaction between condition and safety profiles is defined through probabilistic and deterministic relations. The probabilistic characteristics of the condition, safety, and cost profiles of deteriorating structures are computed by Monte-Carlo simulation. Several realistic examples, based on data on highway bridge components gathered in the United Kingdom, are presented.     

The fully informed particle swarm: simpler, maybe better

The canonical particle swarm algorithm is a new approach to optimization, drawing inspiration from group behavior and the establishment of social norms. It is gaining popularity, especially because of the speed of convergence and the fact that it is easy to use. However, we feel that each individual is not simply influenced by the best performer among his neighbors. We, thus, decided to make the individuals "fully informed." The results are very promising, as informed individuals seem to find better solutions in all the benchmark functions.     

An approach to accidents modeling based on compounds road environments

The most common approach to study the influence of certain road features on accidents has been the consideration of uniform road segments characterized by a unique feature. However, when an accident is related to the road infrastructure, its cause is usually not a single characteristic but rather a complex combination of several characteristics. The main objective of this paper is to describe a methodology developed in order to consider the road as a complete environment by using compound road environments, overcoming the limitations inherented in considering only uniform road segments. The methodology consists of: dividing a sample of roads into segments; grouping them into quite homogeneous road environments using cluster analysis; and identifying the influence of skid resistance and texture depth on road accidents in each environment by using generalized linear models. The application of this methodology is demonstrated for eight roads. Based on real data from accidents and road characteristics, three compound road environments were established where the pavement surface properties significantly influence the occurrence of accidents. Results have showed clearly that road environments where braking maneuvers are more common or those with small radii of curvature and high speeds require higher skid resistance and texture depth as an important contribution to the accident prevention.