Providing the right stuff: feeding children with chronic renal failure

The progressive loss of renal function in children with chronic renal failure (CRF) has a negative influence on their nutritional status and statural growth. Supportive therapies with 1-25 dihydroxy-vitamin D3, recombinant erythropoietin and growth hormone have significantly improved the biochemical and clinical features but the success of these therapies is largely related to an appropriate diet, with adequate protein/caloric intakes. Children more than adults have minimal protein requirements to avoid malnutrition and growth impairment FAO/WHO and RDA recommendations save as guidelines for a correct diet in children with CRF. Following these allowances leads to a "normoproteic" diet, with a protein intake which is often half the unrestricted one in Western European countries, but which is still likely to be not enough to protect against renal deterioration. Indeed the European Study Group for Nutritional Treatment of CRF in children failed to show a significant effect of diet on the mean decline of glomerular filtration rate over two years.     

Determination of sulfur dioxide in wine using a quartz crystal microbalance

A new method for the analysis of both total and bound SO(2) in wine is proposed, based on a quartz crystal microbalance (QCM), and it is compared with the widely used Ripper method. The proposed method is faster than the Ripper's, and the instrumentation is either home-made or widely available. When both methods are applied to the same sample, the results obtained using the QCM method are bracketed in an interval less than one-tenth the size of that obtained using the Ripper method. Although the SO(2) concentrations found using the QCM method correlate well with the ones obtained with the Ripper method, the results are systematically higher, which can be explained as due to the absence of interferences known to affect the Ripper method.     

Cervical pain and proprioceptive sensitivity

With the intent of estimating the proprioceptive sensitivity of the cervical rachis, the Authors subjected 17 healthy volunteers and 20 patients suffering from various cervical disorders to a clinical head positioning test, which was previously experimented upon by several french Authors was modified under certain aspects by our equipe. The test, consists in flexion and extension of the head after maximal rotations to the left and right, then repositioning the head at the starting point "0" which corresponds to the crossing point of two orthogonal axis of a target. The positions are marked on the target by a laser beam situated on the helmet worn by the subject undergoing testing. The results obtained corresponding to the errors committed regarding point "0", permit us to confirm the reliability of the test and significant definitions between the control group and the patients suffering from cervical disorders.     

Localization of prothymosin alpha in the nucleus

Age differences in processing resources seem salient to age-related declines in secondary (or "recent") memory. Community-dwelling adults (N = 90, ages 30-80) completed 4 memory tests: Wechsler Memory Scale-Revised (WMS-R) Logical Memory (LM), Cowboy Story (CS), WMS-R Visual Reproductions (VR), and Extended Complex Figure Test (ECFT; Fastenau, in press). Two space-capacity measures (WMS-R Digit Span and Visual Memory Span) and 4 processing speed measures (cancellation and mental-tracking tasks) assessed processing resources. A statistical control procedure was used to isolate retrieval efficiency and measures contributions of age and processing resources to retrieval. A negative relationship between age and retrieval efficiency emerged on all measures (p < .05). The age effect was reduced 60% on LM and CS when processing resources were controlled, eliminated for VR, and unchanged on ECFT. It is possible that visual-spatial retrieval requires fewer processing resources than does verbal retrieval.     

BOO: Behavior-oriented ontology to describe participant dynamics in collocated design meetings

Facilitating meetings is not an easy task. To assist the facilitator, we have been designing intelligent support systems, which can help contextual sensemaking, decision making and action. However, these systems are constructed based on behavioral models that provide guidelines to understand participant behaviors. This paper presents an ontology to describe participants’ behaviors in collaborative design meetings and rules that correlate them with the group’s acceptance of the final product. This ontology describes the group dynamics at collocated meetings, using verbal and non-verbal cues of attention shifts and attention maintenance as its basic constructs. The objective of creating this ontology was to better understand face-to-face meetings to eventually help meeting facilitators identify issues that may lead to dissatisfaction with the final product through behavioral cues. The ontology was derived through extensive analysis of a series of engineering design session videos. The design group was composed of experts with similar backgrounds, but working in different divisions of the same company. Different points of view were argued and decisions were made at the end of each meeting. After each meeting, participants were asked to asynchronously commit to the decisions made in the group. Our ontology can be used to identify the factors that lead to an undesired outcome, and now serves as a basis for a new project, which uses rules to support design meetings, improve final artifact acceptance and reduce rework. Our conclusions point out correlations between designers’ behaviors and future artifact acceptance and actions that interrupt or bring back group attention. The ontology was validated through application to other meeting situations. These findings may guide software developers in the creation of tools to support group design, and may be applied by an intelligent system.     

Applying machine learning techniques for scaling out data quality algorithms in cloud computing environments

Deduplication is the task of identifying the entities in a data set which refer to the same real world object. Over the last decades, this problem has been largely investigated and many techniques have been proposed to improve the efficiency and effectiveness of the deduplication algorithms. As data sets become larger, such algorithms may generate critical bottlenecks regarding memory usage and execution time. In this context, cloud computing environments have been used for scaling out data quality algorithms. In this paper, we investigate the efficacy of different machine learning techniques for scaling out virtual clusters for the execution of deduplication algorithms under predefined time restrictions. We also propose specific heuristics (Best Performing Allocation, Probabilistic Best Performing Allocation, Tunable Allocation, Adaptive Allocation and Sliced Training Data) which, together with the machine learning techniques, are able to tune the virtual cluster estimations as demands fluctuate over time. The experiments we have carried out using multiple scale data sets have provided many insights regarding the adequacy of the considered machine learning algorithms and proposed heuristics for tackling cloud computing provisioning.     

Supporting Unified Distributed Management and Autonomic Decisions: Design,Implementation and Deployment

Nowadays, the prevailing use of networks based on traditional centralized management systems reflects on a fast increase of the management costs. The growth in the number of network equipments and services reinforces the need to distribute the management responsibilities throughout the network devices. In this approach, each device executes common network management functionalities, being part of the overall network management platform. In this paper, we present a Unified Distributed Network Management (UDNM) framework that provides a unified (wired and wireless) management network solution, where further different network services can take part of this infrastructure, e.g., flow monitoring, accurate routing decisions, distributed policies dissemination, etc. This framework is divided in two main components: (A) Situation awareness, which sets up initial information through bootstrapping, discovery, fault-management process and exchange of management information; (B) Autonomic Decision System (ADS) that performs distributed decisions in the network with incomplete information. We deploy the UDNM framework in a testbed which involves two cities (\(\approx\)250 km between), different standards (IEEE 802.3, IEEE 802.11 and IEEE 802.16e) and network technologies, such as, wired virtual grid, wireless ad-hoc gateways, ad-hoc mobile access devices. The UDNM framework integrates management functionalities into the managed devices, proving to be a lightweight and easy-respond framework. The performance analysis shows that the UDNM framework is feasible to unify devices management functionalities and to take accurate decisions on top of a real network.     

Robotic control in knee joint replacement surgery

A brief history of robotic systems in knee arthroplasty is provided. The place of autonomous robots is then discussed and compared to more recent 'hands-on' robotic systems that can be more cost effective. The case is made for robotic systems to have a clear justification, with improved benefits compared to those from cheaper navigation systems. A number of more recent, smaller, robot systems for knee arthroplasty are also described. A specific example is given of an active constraint medical robot, the ACROBOT system, used in a prospective randomized controlled trial of unicondylar robotic knee arthroplasty in which the robot was compared to conventional surgery. The results of the trial are presented together with a discussion of the need for measures of accuracy to be introduced so that the efficacy of the robotic surgery can be immediately identified, rather than have to wait for a number of years before long-term clinical improvements can be demonstrated.