Matrix metalloproteinase expression in rat pancreatic islets

OBJECTIVE: To determine whether the type of prosthetic material and technique of placement influenced long-term complications after repair of incisional hernias. DESIGN: Retrospective cohort analytic study. SETTING: University-affiliated hospital. PATIENTS: Two hundred patients undergoing open repair of abdominal incisional hernias with prosthetic material between 1985 and 1994. INTERVENTIONS: Four types of prosthetic material were used and placed either as an onlay, underlay, sandwich, or finger interdigitation technique. The materials were monofilamented polypropylene mesh (Marlex, Davol Inc, Cranston, RI), double-filamented mesh (Prolene, Ethicon Inc, Somerville, NJ), expanded polytetrafluroethylene patch (Gore-Tex, WL Gore & Associates, Phoenix, Ariz) or multifilamented polyester mesh (Mersilene, Ethicon Inc). MAIN OUTCOME MEASURES: The incidence of recurrence and complications such as enterocutaneous fistula, bowel obstruction, and infection with each type of material and technique of repair were compared with univariate and multivariate analysis. RESULTS: On univariate analysis, multifilamented polyester mesh had a significantly higher mean number of complications per patient (4.7 vs 1.4-2.3; P<.002), a higher incidence of fistula formation (16% vs 0%-2%; P<.001), a greater number of infections (16% vs 0%-6%; P<.05), and more recurrent hernias (34% vs 10%-14%; P<.05) than the other materials used. The additional mean length of stay to treat complications was also significantly longer (30 vs 3-7 days; P<.001) when polyester mesh was used. The deleterious effect of polyester mesh on long-term complications was confirmed on multiple logistic regression (P=.002). The technique of placement had no influence on outcome. CONCLUSION: Polyester mesh should no longer be used for incisional hernia repair.     

Epigallocatechin-3-Gallate-Loaded Liposomes Favor Anti-Inflammation of Microglia Cells and Promote Neuroprotection

Microglia-mediated neuroinflammation is recognized to mainly contribute to the progression of neurodegenerative diseases. Epigallocatechin-3-gallate (EGCG), known as a natural antioxidant in green tea, can inhibit microglia-mediated inflammation and protect neurons but has disadvantages such as high instability and low bioavailability. We developed an EGCG liposomal formulation to improve its bioavailability and evaluated the neuroprotective activity in in vitro and in vivo neuroinflammation models. EGCG-loaded liposomes have been prepared from phosphatidylcholine (PC) or phosphatidylserine (PS) coated with or without vitamin E (VE) by hydration and membrane extrusion method. The anti-inflammatory effect has been evaluated against lipopolysaccharide (LPS)-induced BV-2 microglial cells activation and the inflammation in the substantia nigra of Sprague Dawley rats. In the cellular inflammation model, murine BV-2 microglial cells changed their morphology from normal spheroid to activated spindle shape after 24 h of induction of LPS. In the in vitro free radical 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, EGCG scavenged 80% of DPPH within 3 min. EGCG-loaded liposomes could be phagocytized by BV-2 cells after 1 h of cell culture from cell uptake experiments. EGCG-loaded liposomes improved the production of BV-2 microglia-derived nitric oxide and TNF-α following LPS. In the in vivo Parkinsonian syndrome rat model, simultaneous intra-nigral injection of EGCG-loaded liposomes attenuated LPS-induced pro-inflammatory cytokines and restored motor impairment. We demonstrated that EGCG-loaded liposomes exert a neuroprotective effect by modulating microglia activation. EGCG extracted from green tea and loaded liposomes could be a valuable candidate for disease-modifying therapy for Parkinson’s disease (PD).     

Combining stochastic programming and optimal control to decompose multistage stochastic optimization problems

The paper suggests a possible cooperation between stochastic programming and optimal control for the solution of multistage stochastic optimization problems. We propose a decomposition approach for a class of multistage stochastic programming problems in arborescent form (i.e. formulated with implicit non-anticipativity constraints on a scenario tree). The objective function of the problem can be either linear or nonlinear, while we require that the constraints are linear and involve only variables from two adjacent periods (current and lag 1). The approach is built on the following steps. First, reformulate the stochastic programming problem into an optimal control one. Second, apply a discrete version of Pontryagin maximum principle to obtain optimality conditions. Third, discuss and rearrange these conditions to obtain a decomposition that acts both at a time stage level and at a nodal level. To obtain the solution of the original problem we aggregate the solutions of subproblems through an enhanced mean valued fixed point iterative scheme.     

Reasoning with truth values on compacted fuzzy chained rules

In this paper, we consider the problem of executing a fuzzy knowledge base (FKB) with rule chaining. The inference process used as starting point is the one based on forward reasoning functions which, obtained from the compositional rule of inference, permits performing the execution of rules in the truth space. This way the process is totally independent from the universes of discourse in which the different variables are defined, allowing a homogeneous treatment for all the variables in the FKB. The execution of the rules is interpreted as the "propagation" of linguistic truth values of the linguistic truth variable that reflect the linguistic degree of fulfillment of each of the propositions in the rules. This execution process is analyzed in two fields of application: control systems, where it is customary to assume t-norm operators as implication functions, and the aggregation process is implemented through the maximum operator and expert systems applications, where other implication functions may be needed and t-norm operators are generally used as aggregation operators. For both of these situations, we present a compaction mechanism which allows a noticeable part of the operations to be performed a priori, thus achieving an important computation time saving.     

A comparison of several neural networks to predict the execution times in injection molding production for automotive industry

In the industrial environment, specifically in the automotive industry, an accurate prediction of execution times for each production task is very useful in order to plan the work and to optimize the human, technical and material resources. In this paper, we applied several regression neural networks to predict the execution times of the tasks in the production of parts for plastic injection molds. These molds are used to make a variety of car components in automotive industry. The prediction is based on the geometric features of the mold parts to be made. The accuracy of the predicted times is high enough to be used as a tool for the design stage of the mold parts, e.g. guiding the design process in order to get the lowest production time.     

Landmark detection in mobile robotics using fuzzy temporal rules

Detection of landmarks is essential in mobile robotics for navigation tasks like building topological maps or robot localization. Doors are one of the most common landmarks since they show the topological structure of indoor environments. In this paper, the novel paradigm of fuzzy temporal rules is used for detecting doors from the information of ultrasound sensors. This paradigm can be used both to model the necessary knowledge for detection and to consider the temporal variation of several sensor signals. Experimental results using a Nomad 200 mobile robot in a real environment produce 91% of doors were correctly detected, which show the reliability and robustness of the system.     

Polytope ARTMAP: pattern classification without vigilance based on general geometry categories

This paper proposes polytope ARTMAP (PTAM), an adaptive resonance theory (ART) network for classification tasks which does not use the vigilance parameter. This feature is due to the geometry of categories in PTAM, which are irregular polytopes whose borders approximate the borders among the output predictions. During training, the categories expand only towards the input pattern without category overlap. The category expansion in PTAM is naturally limited by the other categories, and not by the category size, so the vigilance is not necessary. PTAM works in a fully automatic way for pattern classification tasks, without any parameter tuning, so it is easier to employ for nonexpert users than other classifiers. PTAM achieves lower error than the leading ART networks on a complete collection of benchmark data sets, except for noisy data, without any parameter optimization.     

Identification and estimation of carotenoids in lyophilised meals of four Vicia species at five different stages of growth

The carotenoids from Vicia sativa, V. villosa, V. ervilia and V. monanthos at five different stages of growth, were identified and quantitatively determined. All of them showed significant quantities of carotene, lutein, zeaxanthin, violaxanthin, neoxanthin and traces of a pigment that was probably cryptoxanthin. V. villosa contained the highest amount of carotenoids. The four Vicia spp. showed higher levels of lutein and zeaxanthin than lucerne (alfalfa) meals.     

Polynomial Kernel Discriminant Analysis for 2D visualization of classification problems

Neural Computing and Applications - In multivariate classification problems, 2D visualization methods can be very useful to understand the data properties whenever they transform the n-dimensional...