Cyclooxygenase Inhibition Alters Proliferative,Migratory, and Invasive Properties of Human Glioblastoma Cells In Vitro

Prostaglandin E₂ (PGE₂) is known to increase glioblastoma (GBM) cell proliferation and migration while cyclooxygenase (COX) inhibition decreases proliferation and migration. The present study investigated the effects of COX inhibitors and PGE₂ receptor antagonists on GBM cell biology. Cells were grown with inhibitors and dose response, viable cell counting, flow cytometry, cell migration, gene expression, Western blotting, and gelatin zymography studies were performed. The stimulatory effects of PGE₂ and the inhibitory effects of ibuprofen (IBP) were confirmed in GBM cells. The EP2 and EP4 receptors were identified as important mediators of the actions of PGE₂ in GBM cells. The concomitant inhibition of EP2 and EP4 caused a significant decrease in cell migration which was not reverted by exogenous PGE₂. In T98G cells exogenous PGE₂ increased latent MMP2 gelatinolytic activity. The inhibition of COX1 or COX2 caused significant alterations in MMP2 expression and gelatinolytic activity in GBM cells. These findings provide further evidence for the importance of PGE₂ signalling through the EP2 and the EP4 receptor in the control of GBM cell biology. They also support the hypothesis that a relationship exists between COX1 and MMP2 in GBM cells which merits further investigation as a novel therapeutic target for drug development.     

SOKNL: A novel way of integrating K-nearest neighbours with adaptive random forest regression for data streams

Data Mining and Knowledge Discovery - Most research in machine learning for data streams has focused on classification algorithms, whereas regression methods have received a lot less attention....     

Design of a Control System Using an Artificial Neural Network to Optimize the Energy Efficiency of Water Distribution Systems

Sustainable management of water supply systems is a major challenge within the framework of the water-energy nexus. The main strategies to improve the operation of these systems are related to increasing the hydraulic and energy efficiency of pumping systems. In this context, this work presents a new artificial neural network (ANN) controller to improve the operation of water distribution systems (WDSs) that includes in its algorithm the specific energy consumption (SEC) as a decision parameter. Therefore, pressure control at the measuring points is also based on the energy efficiency of the pumps. The technique was applied to control the pressures in an experimental setup that emulates a WDS with two consumption zones with different topographies. For this purpose, the controller acted on a conventional pump, a booster pump and a control valve. To analyze the performance under the controller action, tests were performed emulating water-demand scenarios, introducing perturbations and changing the pressure setpoints. The real-time control performance was proven based on the dynamic performance, steady-state performance and SEC. The experimental results showed that the proposed controller kept the pressures close to the setpoints and provided a reduction in the SEC between 15.1% and 17.8%, compared with the uncontrolled system, and an economy that varied from 2.5% to 8.1% compared with the performance of the ANN based only on pressure control.

     

Effect of RF magnetron sputtering parameters on the optimization of the discharge capacity of ternary lithium oxide thin films

Journal of Materials Science: Materials in Electronics - The increasing demand for lithium-ion batteries has stimulated the investigation of new compounds in order to reduce the costs and the...     

Polyetherimide-LaNi5 composite films for hydrogen storage applications

This study investigates the preparation of polyetherimide (PEI) – LaNi₅ composites films for hydrogen storage. Prior to the polymer addition, LaNi₅ was ball-milled at different conditions (250, 350, and 450 RPM) and annealed at 500 °C for 1 h under vacuum. The composites were produced with BM-LaNi₅-350 (PEI/LaNi₅-350) and annealed BM-LaNi₅-350 (PEI/LaNi₅-350-TT). Membranes were successfully produced through solvent casting assisted by an ultrasonic bath. The particles dispersion and the film morphology did not change after hydrogenation cycles. In the H₂ sorption experiments at 43 °C and 20 bar, the films stored H₂ without incubation time; both samples reached a capacity of ~0.6 wt%. The H₂ sorption kinetics of PEI/LaNi₅-350 was comparable to that of BM-LaNi₅-350, whereas PEI/LaNi₅-350-TT presented significantly slower kinetics. LaNi₅ oxidation was hindered by PEI, showing that it can be explored to improve metal hydrides air resistance. The results demonstrated that PEI films filled with LaNi₅ are promising materials for hydrogen storage.     

Touch sensor and photovoltaic characteristics of CuSbS2 thin films

Spin-coated chalcostibite CuSbS₂ thin films (≈500 nm thick) were fabricated and the influence of the drying temperature on the structural, morphological, optical and thermoelectric properties of the films was investigated. Crystalline phase-pure chalcostibite has been obtained for the films dried at 180 °C and 210 °C, while below 180 °C these films are partially amorphous. Surprisingly, at drying temperature of 240 °C, a Cu_xS secondary phase appeared. The increase of the drying temperature leads to the increase of the particle size and the decrease of the optical band gap, which is interesting for optoelectronic applications. The highest power factor value was achieved for the film dried at 210 °C, due to the inexistence of secondary phases, which allowed realizing a stable thermoelectric touch sensor with a V_signal/_noise of 5. In addition, this film was tested as a photovoltaic (PV) device and a power conversion efficiency (PCE) of 0.030% with an open-circuit voltage (V_OC) of 0.36 V, a short-circuit current density (J_SC) of 0.278 mAcm^?2 and a fill factor (FF) of 0.27 were obtained. Therefore, this work evidences a pathway toward developing bi-functional devices with simultaneously thermoelectric touch sensor and photovoltaic functions.     

A computational literature review of football performance analysis through probabilistic topic modeling

This research aims to illustrate the potential use of concepts, techniques, and mining process tools to improve the systematic review process. Thus, a review was performed on two online databases (Scopus and ISI Web of Science) from 2012 to 2019. A total of 9649 studies were identified, which were analyzed using probabilistic topic modeling procedures within a machine learning approach. The Latent Dirichlet Allocation method, chosen for modeling, required the following stages: 1) data cleansing, and 2) data modeling into topics for coherence and perplexity analysis. All research was conducted according to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses in a fully computerized way. The computational literature review is an integral part of a broader literature review process. The results presented met three criteria: (1) literature review for a research area, (2) analysis and classification of journals, and (3) analysis and classification of academic and individual research teams. The contribution of the article is to demonstrate how the publication network is formed in this particular field of research, and how the content of abstracts can be automatically analyzed to provide a set of research topics for quick understanding and application in future projects.

     

The Impact of International Crises on Maritime Transportation Based Global Value Chains

Networks and Spatial Economics - International trade has evolved into global value chains, a worldwide network highly impacted by global crises. Since maritime transportation is the most important...