Survival model in oral squamous cell carcinoma based on clinicopathological parameters,molecular markers and support vector machines

The aim of the present study is to find an intelligent and efficient model, based on Support Vector Machines (SVM), able to predict prognosis in patients with oral squamous cell carcinoma (OSCC). A total of 34 clinical and molecular variables were studied in 69 patients suffering from an OSCC. Variables were selected by means of two methods applied in parallel (Non-concave penalty and Newton’s methods). The implementation of a predictive model was performed using the SVM as a classifier algorithm. Finally, its classification ability was evaluated by discriminant analysis. Recurrence, number of recurrences, and TNM stage have been identified as the most relevant prognosis factors with both used methods. Classification rates reached 97.56% and 100% for alive and dead patients, respectively (overall classification rate of 98.55%). SVM techniques build tools able to predict with high accuracy the survival of a patient with OSCC.     

Scheduling of complex manufacturing systems with Petri nets and genetic algorithms: a case on plastic injection moulds

This paper introduces significant improvements on a previous published work that addresses complex production scheduling problems using Petri nets (PNs) and genetic algorithms (GAs). The PN model allows a formal representation of the manufacturing system and of the special constraints of this kind of system, while the GA generates a near-optimal schedule through the structure provided by the PN. The corresponding manufacturing system is associated with a flexible job shop environment with features such as the fabrication of multiple parts and precedence relationships between such parts and assembly operations, in which the objective is the minimisation of the total weighted tardiness. As part of the modelling stage, a mixed integer linear programming formulation is proposed for this framework. The fabrication of a chess mould in a Colombian company is used in two ways: to introduce a proposed normalisation operator that improves the results by reducing the search space of the GA and to illustrate the use of PN modelling the special aforementioned constraints as well as the encoding of the chromosome used by the GA. The proposed approach was tested on randomly generated instances, and their performance was measure against optimal solutions or solutions provided by algorithms presented in previous work. The results confirm the relevance of this approach to schedule such complex manufacturing systems.     

Quantitative profiling of built environment bacterial and fungal communities reveals dynamic material dependent growth patterns and microbial interactions

Indoor microbial communities vary in composition and diversity depending on material type, moisture levels, and occupancy. In this study, we integrated bacterial cell counting, fungal biomass estimation, and fluorescence-assisted cell sorting (FACS) with amplicon sequencing of bacterial (16S rRNA) and fungal (ITS) communities to investigate the influence of wetting on medium density fiberboard (MDF) and gypsum wallboard. Surface samples were collected longitudinally from wetted materials maintained at high relative humidity (~95%). Bacterial and fungal growth patterns were strongly time-dependent and material-specific. Fungal growth phenotypes differed between materials: spores dominated MDF surfaces while fungi transitioned from spores to hyphae on gypsum. FACS confirmed that most of the bacterial cells were intact (viable) on both materials over the course of the study. Integrated cell count and biomass data (quantitative profiling) revealed that small changes in relative abundance often resulted from large changes in absolute abundance, while negative correlations in relative abundances were explained by rapid growth of only one group of bacteria or fungi. Comparisons of bacterial-bacterial and fungal-bacterial networks suggested a top-down control of fungi on bacterial growth, possibly via antibiotic production. In conclusion, quantitative profiling provides novel insights into microbial growth dynamics on building materials with potential implications for human health.     

Electrolysis of progesterone with conductive‐diamond electrodes

BACKGROUND: Progesterone is considered an endocrine disruptor chemical. It can be found in industrial discharges, municipal wastewaters, and, in some instances, even in treated effluents at the level of ng dm^?3. RESULTS: Conductive diamond electrolysis can be used to remove progesterone from aqueous solutions. Increases in current density lead to less efficient processes, indicating mass transfer control of the process rate. Occurrence of chlorides in the electrolytic media favors the depletion of progesterone compared with sulphates, although it does not affect the mineralization rate. Independently of the solubilizing agent used, the process behaves similarly during a first stage of the electrolysis (at the four ranges of pollutant concentration studied). However, in a second stage, the rate changes abruptly due to reduced action of hydroxyl radicals in methanol media. CONCLUSIONS: Progesterone can be removed efficiently by conductive diamond electrolysis from aqueous solutions within the range of initial concentrations 10^?2 to 10² mg dm^?3. The process efficiency increases with the current density. Removal rate does not depend on the nature of the electrolyte, but this parameter affects the intermediates formed during the experiment. When pure methanol is used as solubilizing agent, only direct electro‐oxidation takes place. Copyright © 2012 Society of Chemical Industry     

Electrochemical synthesis of poly(3′-alkyl-terthiophenes). Characterization and applications

A new series of polymers obtained from 3′-alkyl-terthiophene monomers have been electropolymerized aimed at using them as raw materials for the development of electronic devices, e.g., solar cell and organic light-emitting diodes, among others. The polymers were characterized by infrared and UV–Vis spectroscopy and cyclic voltammetry. Cyclic voltammetry results revealed that during polymerization both terthiophene system and substituent groups are oxidized, but these processes are reversible. The products were tested in solar cells and the maximum yield obtained was 0.01%.     

Kinetics and Mechanisms of Homogeneous Catalytic Reactions. Part 11. Regioselective Hydrogenation of Quinoline Catalyzed by Rhodium Systems Containing 1,2-Bis(diphenylphosphino)ethane

Abstract  

The system prepared in situ by addition of two equivalents of 1,2-bis(diphenylphosphino)ethane (dppe) to Rh₂Cl₂(COE)₄ (COE = cyclooctene) showed to be an efficient and regioselective precatalyst for the hydrogenation of quinoline (Q). This reaction showed to be independent of the Q concentration and of fractional order on H₂ and catalyst concentrations (1.5 and 0.6, respectively). The fractional order on catalyst concentration indicates that several catalytic species with different activities are present in the reaction medium; however, the cationic species [Rh (dppe)₂]⁺ was the only phosphorous-containing compound detected by ³¹P{¹H} NMR. For the acac salt of this cationic bis(dppe) complex, a kinetic study led to the rate law r = {K₁k₂/(1 + K₁[H₂])}[M][H₂]²; [M(Q)(κ²-dppe)(κ¹-dppe)]⁺ was proposed as the catalytically active species (CAS) of the cycle. The general mechanism involves a reversible oxidative addition of H₂ to generate a dihydrido complex, which transfers the hydride ligands to the coordinated Q to yield species containing a 1,2-dihydroquinoline (DHQ) ligand, followed by a second oxidative addition of H₂, considered as the rate-determining step of the cycle; hydrogen transfer toward the DHQ ligand yields THQ, regenerates the CAS and restarts the catalytic cycle.     

Design methodology for the frequency shift method of islanding prevention and analysis of its detection capability

Islanding protection is one of the most important sources of discrepancy in grid‐connected photovoltaic systems. Even when islanding is not very likely to happen, regulations demand the photovoltaic inverters to implement effective protection methods. Due to its several advantages, the frequency shift method of islanding prevention, commonly known as Sandia Frequency Shift, is one of the most important active methods. This method implements a positive feedback of the frequency that tends to move it outside the trip limits in case of islanding. The method shows a very high detection capability, which depends on both the values of the method parameters and the characteristics of the load that remains in the same power section after islanding. This paper develops a mathematical analysis of the Sandia Frequency Shift method and proposes a new methodology to design its parameters as a trade‐off between the detection capability, which is evaluated as a function of the load characteristics, and the distortion that the method could introduce in the grid as a consequence of transitory frequency disturbances. The ability of this methodology to design the method parameters and achieve the highest detection capability is satisfactorily proved by means of both simulation and experimental results on a commercial photovoltaic inverter that implements the method once its parameters have been designed with the proposed methodology. Copyright © 2005 John Wiley & Sons, Ltd.     

A Business-Oriented Management Framework for Mobile Communication Systems

The incremental efforts needed to manage low-level radio access network decisions from a business-perspective have received little attention so far. This paper considers the influence of business-level indicators on network management decisions related to low-level network control mechanisms. It provides a formal understanding of all involved aspects, the representation of the adjustable parameters, and the network control mechanisms that enable the reconfiguration of access network entities from a business perspective (i.e., users?? information, operator??s goals). The effectiveness of our approach is validated through a simulation environment that we developed on OPNET.     

ASC,IL-18 and Galectin-3 as Biomarkers of Non-Alcoholic Steatohepatitis: A Proof of Concept Study

Non-alcoholic steatohepatitis (NASH) is a severe form of non-alcoholic fatty liver disease that is growing in prevalence. Symptoms of NASH become apparent when the disease has progressed significantly. Thus, there is a need to identify biomarkers of NASH in order to detect the disease earlier and to monitor disease severity. The inflammasome has been shown to play a role in liver diseases. Here, we performed a proof of concept study of biomarker analyses (cut-off points, positive and negative predictive values, receiver operating characteristic (ROC) curves, and likelihood ratios) on the serum of patients with NASH and healthy controls on apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC), interleukin (IL)-18, Galectin-3 (Gal-3), and C-reactive protein (CRP). ASC, IL-18, and Gal-3 were elevated in the serum of NASH patients when compared to controls. The area under the curve (AUC) for ASC was the highest (0.7317) with an accuracy of 68%, followed by IL-18 (0.7036) with an accuracy of 66% and Gal-3 (0.6891) with an accuracy of 61%. Moreover, we then fit a stepwise multivariate logistic regression model using ASC, IL-18, and Gal-3 to determine the probability of patients having a NASH diagnosis, which resulted in an AUC of 0.71 and an accuracy of 79%, indicating that combining these biomarkers increases their diagnostic potential for NASH. These results indicate that ASC, IL-18, and Gal-3 are reliable biomarkers of NASH and that combining these analytes increases the biomarker potential of these proteins.