Protection of normal cells from irradiation bystander effects by silica-flufenamic acid nanoparticles

The development of a myriad of nanoparticles types has opened new possibilities for the diagnostics and treatment of many diseases, especially for cancer. However, most of the researches done so far do not focus on the protection of normal cells surrounding a tumor from irradiation bystander effects that might lead to cancer recurrence. Gap-junctions are known to be involved in this process, which leads to genomic instability of neighboring normal cells, and flufenamic acid (FFA) is included in a new group of gap-junction blockers recently discovered. The present work explores the use of mesoporous silica nanoparticles MCM-41 functionalized with 3-Aminopropyltriethoxysilane (APTES) for anchoring the flufenamic acid for its prolonged and controlled release and protection from radiation bystander effects. MCM-41 and functionalized samples were structurally and chemically characterized with multiple techniques. The biocompatibility of all samples was tested in a live/dead assay performed in cultured MRC-5 and HeLa cells. HeLa cells cultured were exposed to 50?Gy of gamma-rays and the media transferred to fibroblast cells cultured separately. Our results show that MCM-41 and functionalized samples have high biocompatibility with MCR-5 and HeLa cells, and most importantly, the FFA delivered by these NPs was able to halt apoptosis, one of main bystander effects.     

Preparation of Macroscopic Block‐Copolymer‐Based Gyroidal Mesoscale Single Crystals by Solvent Evaporation

Properties arising from ordered periodic mesostructures are often obscured by small, randomly oriented domains and grain boundaries. Bulk macroscopic single crystals with mesoscale periodicity are needed to establish fundamental structure–property correlations for materials ordered at this length scale (10–100 nm). A solvent‐evaporation‐induced crystallization method providing access to large (millimeter to centimeter) single‐crystal mesostructures, specifically bicontinuous gyroids, in thick films (>100 µm) derived from block copolymers is reported. After in‐depth crystallographic characterization of single‐crystal block copolymer–preceramic nanocomposite films, the structures are converted into mesoporous ceramic monoliths, with retention of mesoscale crystallinity. When fractured, these monoliths display single‐crystal‐like cleavage along mesoscale facets. The method can prepare macroscopic bulk single crystals with other block copolymer systems, suggesting that the method is broadly applicable to block copolymer materials assembled by solvent evaporation. It is expected that such bulk single crystals will enable fundamental understanding and control of emergent mesostructure‐based properties in block‐copolymer‐directed metal, semiconductor, and superconductor materials.     

A method for outlier detection based on cluster analysis and visual expert criteria

Outlier detection is an important problem occurring in a wide range of areas. Outliers are the outcome of fraudulent behaviour, mechanical faults, human error, or simply natural deviations. Many data mining applications perform outlier detection, often as a preliminary step in order to filter out outliers and build more representative models. In this paper, we propose an outlier detection method based on a clustering process. The aim behind the proposal outlined in this paper is to overcome the specificity of many existing outlier detection techniques that fail to take into account the inherent dispersion of domain objects. The outlier detection method is based on four criteria designed to represent how human beings (experts in each domain) visually identify outliers within a set of objects after analysing the clusters. This has an advantage over other clustering-based outlier detection techniques that are founded on a purely numerical analysis of clusters. Our proposal has been evaluated, with satisfactory results, on data (particularly time series) from two different domains: stabilometry, a branch of medicine studying balance-related functions in human beings and electroencephalography (EEG), a neurological exploration used to diagnose nervous system disorders. To validate the proposed method, we studied method outlier detection and efficiency in terms of runtime. The results of regression analyses confirm that our proposal is useful for detecting outlier data in different domains, with a false positive rate of less than 2% and a reliability greater than 99%.     

Modelling the vibration response of a gas turbine using machine learning

This work deals with modelling the vibration response of a gas turbine obtained during the start-up process until reaching the nominal speed for power generation. Analysing the vibrations of a complex systems like a gas turbine is useful for the diagnostic of faults or damages in the internal mechanical components of the different stages that integrate a turbine. This work focuses on the study of the shaft vibrations of the bearing radial type mounted between the shaft and the bearing compressor associated with the speed of the turbine. This relationship is studied using experimental data collected from a particular gas turbine model. In particular, we propose a methodology to synthesize a computational model following a supervised learning approach implemented through different machine learning techniques, including a multi-layers perceptron network, support vector machine (SVM), random forest (RF) and genetic programming (GP) with local search. Results show that SVM, RF and GP perform very well in this task, producing accurate predictive models. Moreover, there are some interesting trade-offs between the methods, regarding generalization error, overfitting and model interpretability that are relevant for future applications and research.     

Quick fixing ATL transformations with speculative analysis

Model transformations are central components of most model-based software projects. While ensuring their correctness is vital to guarantee the quality of the solution, current transformation tools provide limited support to statically detect and fix errors. In this way, the identification of errors and their correction are nowadays mostly manual activities which incur in high costs. The aim of this work is to improve this situation. Recently, we developed a static analyser that combines program analysis and constraint solving to identify errors in ATL model transformations. In this paper, we present a novel method and system that uses our analyser to propose suitable quick fixes for ATL transformation errors, notably some non-trivial, transformation-specific ones. Our approach supports speculative analysis to help developers select the most appropriate fix by creating a dynamic ranking of fixes, reporting on the consequences of applying a quick fix, and providing a pre-visualization of each quick fix application. The approach integrates seamlessly with the ATL editor. Moreover, we provide an evaluation based on existing faulty transformations built by a third party, and on automatically generated transformation mutants, which are then corrected with the quick fixes of our catalogue.     

Recommender systems in model-driven engineering

Software and Systems Modeling - Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also...     

Carnivore fecal chemicals suppress feeding by Alpine goats (Capra hircus)

The efficacy of carnivore and ungulate fecal chemicals in suppressing the feeding behavior of Alpine goats (Capra hircus) was examined. In the first four experiments, goats were offered food covered with paper strips treated with fecal extracts of the Bengal tiger, Siberian tiger, African lion, and brown bear, respectively; food covered with solvent-treated and untreated (plain) papers served as controls in each experiment. Goats made fewer head entries into, and ate less food from, buckets containing fecal extracts. In the fifth experiment, goats were offered food covered with paper strips treated with fecal extracts of the puma, Dorcas gazelle, white-bearded gnu, and conspecifics; food covered with solvent-treated and plain papers again served as controls. The amounts of food consumed from buckets containing puma, gazelle, gnu, and solvent treatments were statistically indistinguishable, but less food was consumed from them than from buckets containing the goat-scented or plain papers. No significant differences among treatments were detected with respect to head entries. Field experiments are needed on the use of predator-derived chemicals to reduce damage by goats to vegetation.     

Precursor system of liquid crystalline phase containing propolis microparticles for the treatment of periodontal disease: development and characterization

Precursor systems of liquid crystalline phase were prepared using the surfactant PPG-5-Ceteth-20, isopropyl myristate, and water; gelatin microparticles containing propolis were then added into these systems. Homogeneity of dispersion, the in-system microparticle morphology, and sedimentation behavior of each formulation were evaluated. The rheological and mechanical properties (hardness, compressibility, and adhesiveness), the work of syringing, and the propolis release profile were also evaluated. All the formulations exhibited pseudoplastic flow and thixotropy, and they displayed storage modulus, loss modulus, dynamic viscosity, and loss tangent that depended on temperature, frequency, and composition. Mechanical properties varied significantly among the formulations being affected by changes in the composition and temperature. Raising the concentration of surfactant and adding propolis microparticles significantly decreased the work of syringing. The drug release was non-Fickian (anomalous) and there was no significant difference between the tested systems in the times required for 10%, 30%, and 50% release of the initial drug loading.     

Sulphur poisoning of palladium catalysts used for methane combustion: effect of the support

Four different supported palladium catalysts (using alumina, silica, zirconia and titania as supports), prepared by incipient wetness impregnation, were tested as catalysts for methane oxidation in presence of sulphur dioxide. The catalyst supported on zirconia showed the best performance, whereas the silica-supported one showed the fastest deactivation. Temperature-programmed desorption experiments of the poisoned catalysts suggest that SO(2) adsorption capacity of the support plays a key role in the catalyst poisoning. In order to study the effect of promoters, expected to improve the thermal stability and thioresistance of the catalyst, commercial zirconia modified by yttrium and lantane was tested as supports. It was found that the presence of these promoters does not improve the performance of the zirconia-supported catalyst. A deactivation model -- considering two different active sites (fresh and poisoning), pseudo-first order dependence on methane concentration and poisoning rate depending on sulphur concentration and fraction of non-poisoned palladium -- was used for modelling the deactivation behaviour.