IntroductionCritically ill patients can develop hyperglycaemia even if they do not have diabetes. Intensive insulin therapy decreases morbidity and mortality rates in patients in a surgical intensive care unit (ICU) and decreases morbidity in patients in a medical ICU. The effect of this therapy on patients in a mixed medical/surgical ICU is unknown. Our goal was to assess whether the effect of intensive insulin therapy, compared with standard therapy, decreases morbidity and mortality in patients hospitalised in a mixed ICU.MethodsThis is a prospective, randomised, non-blinded, single-centre clinical trial in a medical/surgical ICU. Patients were randomly assigned to receive either intensive insulin therapy to maintain glucose levels between 80 and 110 mg/dl (4.4 to 6.1 mmol/l) or standard insulin therapy to maintain glucose levels between 180 and 200 mg/dl (10 and 11.1 mmol/l). The primary end point was mortality at 28 days.ResultsOver a period of 30 months, 504 patients were enrolled. The 28-day mortality rate was 32.4% (81 of 250) in the standard insulin therapy group and 36.6% (93 of 254) in the intensive insulin therapy group (Relative Risk [RR]: 1.1; 95% confidence interval [CI]: 0.85 to 1.42). The ICU mortality in the standard insulin therapy group was 31.2% (78 of 250) and 33.1% (84 of 254) in the intensive insulin therapy group (RR: 1.06; 95%CI: 0.82 to 1.36). There was no statistically significant reduction in the rate of ICU-acquired infections: 33.2% in the standard insulin therapy group compared with 27.17% in the intensive insulin therapy group (RR: 0.82; 95%CI: 0.63 to 1.07). The rate of hypoglycaemia (≤ 40 mg/dl) was 1.7% in the standard insulin therapy group and 8.5% in the intensive insulin therapy group (RR: 5.04; 95% CI: 1.20 to 21.12).ConclusionsIIT used to maintain glucose levels within normal limits did not reduce morbidity or mortality of patients admitted to a mixed medical/surgical ICU. Furthermore, this therapy increased the risk of hypoglycaemia.Trial Registrationclinicaltrials.gov Identifiers: 4374-04-13031; 094-2 in 000966421

In design and manufacturing, mesh segmentation is required for FACE construction in boundary representation (BRep), which in turn is central for featurebased design, machining, parametric CAD and reverse engineering, among others. Although mesh segmentation is dictated by geometry and topology, this article focuses on the topological aspect (graph spectrum), as we consider that this tool has not been fully exploited. We preprocess the mesh to obtain a edgelength homogeneous triangle set and its Graph Laplacian is calculated. We then produce a monotonically increasing permutation of the Fiedler vector (2nd eigenvector of Graph Laplacian) for encoding the connectivity among part feature submeshes. Within the mutated vector, discontinuities larger than a threshold (interactively set by a human) determine the partition of the original mesh. We present tests of our method on large complex meshes, which show results which mostly adjust to B Rep FACE partition. The achieved segmentations properly locate most manufacturing features, although it requires human interaction to avoid over segmentation. Future work includes an iterative application of this algorithm to progressively sever features of the mesh left from previous submesh removals.

Necessary and sufficient conditions for the existence of limits of the form lim (x,y)→ (a,b) f (x, y) g(x, y) are given, under the hipothesis that f and g are real analytic functions near the point (a, b), and g has an isolated zero at (a, b). An algorithm (implemented in MAPLE 12) is also provided. This algorithm determines the existence of the limit, and computes it in case it exists. It is shown to be more powerful than the one found in the latest versions of MAPLE. The main tools used throughout are Hensel's Lemma and the theory of Puiseux series.

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Given a 2-manifold triangular mesh M ⊂ R 3 , with border, a parameterization of M is a FACE or trimmed surface F = {S, L 0 , . . . , L m }. F is a connected subset or region of a parametric surface S, bounded by a set of LOOPs L 0 , . . . , L m such that each L i ⊂ S is a closed 1-manifold having no intersection with the other L j LOOPs. The parametric surface S is a statistical fit of the mesh M. L 0 is the outermost LOOP bounding F and L i is the LOOP of the i-th hole in F (if any). The problem of parameterizing triangular meshes is relevant for reverse engineering, tool path planning, feature detection, re-design, etc. State-of-art mesh procedures parameterize a rectangular mesh M. To improve such procedures, we report here the implementation of an algorithm which parameterizes meshes M presenting holes and concavities. We synthesize a parametric surface S ⊂ R 3 which approximates a superset of the mesh M. Then, we compute a set of LOOPs trimming S, and therefore completing the FACE F = {S, L 0 , . . . , L m }. Our algorithm gives satisfactory results for M having low Gaussian curvature (i.e., M being quasi-developable or developable). This assumption is a reasonable one, since M is the product of manifold segmentation pre-processing. Our algorithm computes: (1) a manifold learning mapping φ : M → U ⊂ R 2 , (2) an inverse mapping S : W ⊂ R 2 → R 3 , with W being a rectangular grid containing and surpassing U . To compute φ we test IsoMap, Laplacian Eigenmaps and Hessian local linear embedding (best results with HLLE). For the back mapping (NURBS) S the crucial step is to find a control polyhedron P, which is an extrapolation of M. We calcu-B Oscar E. Ruiz