Preparation and characterization of proton‐exchange hybrid membranes

Two types of composite were prepared, based on a thermoplastic polymer, polyvinylidene fluoride (PVDF), and an elastomer, ethylene‐propylene‐diene terpolymer (EPDM), respectively. We obtained both series by addition of an inorganic proton‐conducting antimonic acid derivative (HSb) and polystyrene crosslinked with a small percentage of divinylbenzene (PS‐co‐DVB). From these composites, membranes were obtained and subjected to a heterogeneous‐phase sulfonation reaction with chlorosulfonic acid. All experimental materials were characterized from a morphological and electrical point of view, by means of techniques such as differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), non‐isothermal crystallization and complex impedance analysis. Copyright © 2005 Society of Chemical Industry     

Fullerene Reactivity in an Oxygen Plasma

Fullerene reactivity in an oxygen plasma was measured and compared with those determined under the same conditions for a comprehensive set of 50 carbon materials. The possible reasons for the high plasma reactivity of the fullerene sample in an oxygen plasma are discussed.     

Precision Oncology via NMR-Based Metabolomics: A Review on Breast Cancer

Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient’s unique disease and individual health status. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine).     

Standard goal programming with fuzzy hierarchies: a sequential approach

This paper proposes a pragmatic model for multi-objective decision-making processes involving clusters of objectives which have a decisional meaning for the decision maker (DM). We provide the DMs with a comfortable tool that allows them to express their preferences both by comparing criteria of the same cluster and via the comparison between the different clusters. In standard goal programming the importance of the goals is modeled by the introduction of preferential weights or/and the incorporation of pre-emptive priorities. However, in many cases the DM is not able to establish a precise preference structure. Even in the case of precise weights the solution does not match necessarily the relative weights or, in the case of precise pre-emptive priority, the result could be very restrictive. In order to overcome these drawbacks, in this paper the normalized unwanted deviations are interpreted in terms of achievement degrees of the goals and fuzzy relations are used to model the relative importance of the goals. Thus, we show how several methodologies from the fuzzy goal programming literature can be tailored for solving standard GP problems. We apply this new modeling to problems where there is a “natural” clustering between goals of the same class. We address this situation by solving two phases; in the first one each class is handled separately taking into account the hierarchy of their goals and, in the second phase, we integrate the results of the first phase and the imprecise hierarchy of the different classes. We formulate a new goal programming model called as sequential goal programming with fuzzy hierarchy model. Because many real situations involve decision making in this environment, our proposal can be a useful tool of broad application. A numerical example illustrates the methodology.     

Cell-protection mechanism through autophagy in HGFs/<Emphasis Type="Italic">S. mitis</Emphasis> co-culture treated with Chitlac-nAg

Silver-based products have been proven to be effective in retarding and preventing bacterial growth since ancient times. In the field of restorative dentistry, the use of silver ions/nanoparticles has been explored to counteract bacterial infections, as silver can destroy bacterial cell walls by reacting with membrane proteins. However, it is also cytotoxic towards eukaryotic cells, which are capable of internalizing nanoparticles. In this work, we investigated the biological effects of Chitlac-nAg, a colloidal system based on a modified chitosan (Chitlac), administered for 24–48?h to a co-culture of primary human gingival fibroblasts and Streptococcus mitis in the presence of saliva, developed to mimic the microenvironment of the oral cavity. We sought to determine its efficiency to combat oral hygiene-related diseases without affecting eukaryotic cells. Cytotoxicity, reactive oxygen species production, apoptosis induction, nanoparticles uptake, and lysosome and autophagosome metabolism were evaluated. In vitro results show that Chitlac-nAg does not exert cytotoxic effects on human gingival fibroblasts, which seem to survive through a homoeostasis mechanism involving autophagy. That suggests that the novel biomaterial Chitlac-nAg could be a promising tool in the field of dentistry.     

Multiple instance learning for classifying students in learning management systems

In this paper, a new approach based on multiple instance learning is proposed to predict student’s performance and to improve the obtained results using a classical single instance learning. Multiple instance learning provides a more suitable and optimized representation that is adapted to available information of each student and course eliminating the missing values that make difficult to find efficient solutions when traditional supervised learning is used. To check the efficiency of the new proposed representation, the most popular techniques of traditional supervised learning based on single instances are compared to those based on multiple instance learning. Computational experiments show that when the problem is regarded as a multiple instance one, performance is significantly better and the weaknesses of single-instance representation are overcome.     

On the convergence of a new time window discretization method for the traveling salesman problem with time window constraints

In this paper, we discuss convergence of a time window discretization method for the traveling salesman problem with time window constraints. This recently proposed discretization method leads to a lower bound to the minimization problem. Analysis shows that this method always guarantees convergence to the optimal solution. We also illustrate that the traditional method that gives feasible solutions does not always provide such a guarantee.     

Modelling of palm oil production using fuzzy expert system

The production of crude palm oil and palm kernel is a complex problem due to the influence of processing variables and environmental factors. These processing variables influence the amount of crude palm oil and palm kernel losses during processing. Instead of mathematical model, fuzzy logic approach provides a simpler and easier mechanism to describe the relationships between the processing variables and the amount of crude palm oil and palm kernel losses. In this study, four fuzzy expert system models were developed for each palm oil processing station. An approximation of centre of gravity method for defuzzification is also proposed to enable the development of the model using Microsoft^®Excel. For comparison purpose, the models were also developed using MATLAB^®. Results obtained from the Excel model are found to be very close to the results from MATLAB.     

The Inhibition of the Inducible Nitric Oxide Synthase Enhances the DPSC Mineralization under LPS-Induced Inflammation

Nitric oxide (NO) is a key messenger in physiological and pathological processes in mammals. An excessive NO production is associated with pathological conditions underlying the inflammation response as a trigger. Among others, dental pulp inflammation results from the invasion of dentin by pathogenic bacteria. Vital functions of pulp mesenchymal stem cells (DPSCs, dental pulp stem cells), such as mineralization, might be affected by the inducible NOS (iNOS) upregulation. In this context, the iNOS selective inhibition can be considered an innovative therapeutic strategy to counteract inflammation and to promote the regeneration of the dentin-pulp complex. The present work aims at evaluating two acetamidines structurally related to the selective iNOS inhibitor 1400W, namely CM544 and FAB1020, in a model of LPS-stimulated primary DPSCs. Our data reveal that CM544 and even more FAB1020 are promising anti-inflammatory compounds, decreasing IL-6 secretion by enhancing CD73 expression-levels, a protein involved in innate immunity processes and thus confirming an immunomodulatory role of DPSCs. In parallel, cell mineralization potential is retained in the presence of compounds as well as VEGF secretion, and thus their angiogenetic potential. Data presented lay the ground for further investigation on the anti-inflammatory potential of acetamidines selectively targeting iNOS in a clinical context.