Towards the detection of human papillomavirus infection by a reagentless electrochemical peptide biosensor

In the present work, we report first results about a technology using a conjugated copolymer poly(5-hydroxy-1,4-naphthoquinone-co-5-hydroxy-2-carboxyethyl-1,4-naphthoquinone) acting both as immobilizing and transducing element for reagentless immunosensor, and its application for the detection of HPV infection. It was shown that the reagentless immunosensor was able to detect the interaction between antigenic peptide L1 from HPV-16 major capsid protein, a dominant epitope involved in viral infection as well as in prophylactic vaccine, and the relevant antibody.     

A genetic programming method for protein motif discovery and protein classification

Proteins can be grouped into families according to some features such as hydrophobicity, composition or structure, aiming to establish common biological functions. This paper presents MAHATMA—memetic algorithm-based highly adapted tool for motif ascertainment—a system that was conceived to discover features (particular sequences of amino acids, or motifs) that occur very often in proteins of a given family but rarely occur in proteins of other families. These features can be used for the classification of unknown proteins, that is, to predict their function by analyzing their primary structure. Experiments were done with a set of enzymes extracted from the Protein Data Bank. The heuristic method used was based on genetic programming using operators specially tailored for the target problem. The final performance was measured using sensitivity, specificity and hit rate. The best results obtained for the enzyme dataset suggest that the proposed evolutionary computation method is effective in finding predictive features (motifs) for protein classification.     

Voltammetric investigations on the transition between dissolution, passivation and deposition characteristics of Ni, Cu and their alloys in fluorine based ionic liquid

Multisweep cyclic voltammetric (CV) responses of nickel, copper, Monel and nickel–copper alloy had been extensively studied and compared in a variety of non-aqueous solvents such as acetonitrile (AN), propylene carbonate (PC) and sulfolane containing triethylamine trishydrogen fluoride (TEA·3HF) ionic liquid. The quantity of dissolution as well as surface morphological transformation on the electrode surfaces as a result of anodic polarization were investigated using atomic absorption spectroscopy (AAS) and scanning electron microscopy (SEM) respectively. The nature of crystallites formed on the polarized electrode was characterized using X-ray diffraction (XRD). The voltammetric study clearly indicates that Ni, Monel and Ni–Cu alloy are passive and stable in neat TEA·3HF medium in the recorded potential region of CV. Surface morphology of Ni after polarization, reveals the generation of pits, whereas the evolution of small crystallites of CuF₂ are noted on the polarized alloy material, as evidenced by SEM pictures. Copper electrode shows reversible voltammetric characteristics with high charge recovery ratio (q_c/q_a) suggesting that in this medium, Cu can certainly serve as reference electrode. Addition of water in TEA·3HF medium increases the solubility and stability of these metal fluoride film. In solvents such as PC, AN and sulfolane containing TEA·3HF, Ni and their alloys exhibit remarkable passivity and the charge recovery ratio decreases to some extent for Cu. In TEA·3HF/AN medium, the dissolution of Cu is very high. The present investigation suggests that the relative stability of all the four electrodes in neat TEA·3HF and solvents containing 0.1 M TEA·3HF decreases in the order: Ni > Monel > Ni–Cu alloy > Cu and relative solubility of metal fluoride films in the three solvents increases in the order: PC < sulfolane < AN.     

Analysing green supply chain management practices in Brazil’s electrical/electronics industry using interpretive structural modelling

Industries need to adopt the environmental management concepts in the traditional supply chain management. The green supply chain management (GSCM) is an established concept to ensure environment-friendly activities in industry. This paper identifies the relationship of driving and dependence that exists between GSCM practices with regard to their adoption within Brazilian electrical/electronic industry with the help of interpretive structural modelling (ISM). From the results, we infer that cooperation with customers for eco-design practice is driving other practices, and this practice acts as a vital role among other practices. Commitment to GSCM from senior managers and cooperation with customers for cleaner production occupy the highest level.     

Color and COD retention by nanofiltration membranes

In the present study the application of the nanofiltration process was investigated mainly in the retention ofcolor and chemical oxygen demand (COD) present in textile industry wastewater. Nanofiltration experiments were carried out in a pilot unit, operating in crossflow. Three different types of spiral wound membranes, DK 1073, NF 45 and MPS 31 were used simultaneously in the same unit. The results of the tests showed that for color retention, the values were around 99% for the DK 1073 and NF 45 membranes and the 87% for COD retention for the DK 1073. The permeate flux for the different wastewaters varied from 30.5 to 70 L/h.m². Fouling was observed in all membranes due to the accumulation of molecular species close to the filtering surface. The process was efficient and promising for the reuse of wastewater from this type of industry.     

Interception of the enzymatic conversion of farnesyl diphosphate to 5-epi-aristolochene by using a fluoro substrate analogue: 1-fluorogermacrene A from (2E,6Z)-6-fluorofarnesyl diphosphate

Tobacco 5-epi-aristolochene synthase (TEAS) catalyzes the Mg(II)-dependent cyclizations and rearrangements of (E,E)-farnesyl diphosphate (PP) to the bicyclic sesquiterpene hydrocarbon via a tightly bound (+)-germacrene A as a deprotonated intermediate. With the native enzyme, only a few percent of the putative germacrene A intermediate is released from the active site during the catalytic cycle. 6-Fluorofarnesyl PP was designed and synthesized with the aim of arresting the cyclization-rearrangement mechanism en route to 5-epi-aristolochene. Indeed, incubation of (2E,6Z)-6-fluorofarnesyl PP with recombinant TEAS afforded (-)-1-fluorogermacrene A as the sole product in 58% yield. Steady-state kinetic experiments with farnesyl PP and the 6-fluoro analogue showed that the overall catalytic efficiencies (k(cat)/K(m)) are essentially the same for both substrates. 1-Fluorogermacrene A was characterized by chromatographic properties (TLC, GC), MS, optical rotation, UV, IR and (1)H NMR data, and by heat-induced Cope rearrangement to (+)-1-fluoro-beta-elemene. (1)H NMR spectra at room temperature revealed that this (E,E)-configured fluorocyclodecadiene exists in solution as a 7:3 mixture of UU and UD conformers. 1-Fluorogermacrene A underwent trifluoroacetic acid-catalyzed cyclization to give three 1alpha-fluoroselinene isomers at a rate estimated to be about 1000 times slower than that of the similar cyclization of (+)-germacrene A to the parent selinenes.     

Activated carbon for hydrogen purification by pressure swing adsorption: Multicomponent breakthrough curves and PSA performance

In this work, multicomponent breakthrough experiments (binary H₂-CO₂, ternary H₂-CO₂-CO and five-component H₂-CO₂-CO-CH₄-N₂) were performed under different operating conditions in activated carbon extrudates to validate the mathematical model. A 10 steps one-column VPSA experiment was also performed. These experiments allow experimental validation of adsorption equilibrium predicted by the multicomponent extension of the Virial isotherm and a fixed-bed mathematical model. In the VPSA experiment, a 99.981% hydrogen purity stream (with 63 ppm of CO contamination) was obtained with a hydrogen recovery of 81.6% and an adsorbent productivity of .The mathematical model was also employed to assess the effect of operating conditions and the influence of step times and pressure equalizations in the PSA unit. It was verified that high-purity hydrogen (>99.99%) can be obtained using this adsorbent with recoveries higher than 75% and unit productivities of .     

Effect of catalysts in the quality of syngas and by-products obtained by co-gasification of coal and wastes. 1. Tars and nitrogen compounds abatement

The aim of this work is to analyse the possibility of using co-gasification technology to process coal mixed with wastes to take profit of its energy content and at the same time to minimize the environmental impact associated with the use of wastes and to diminish the costs of flue gas treatment. The addition to coal of different types of materials, like: pine based waste, petcoke and polyethylene (PE), was not found to give rise to any operational problems, regarding both the feeding system and gasification process and led to higher energy conversions, however, the gas presented higher tars and hydrocarbons content. Several catalysts were tested, such as, dolomite, olivine, nickel and magnesium oxides, zinc oxides and cobalt and molybdenum oxides. Catalyst action was analysed in tars release and also in ammonia compounds reduction. The presence of catalysts allowed increasing hydrogen release, whilst there was a decrease in hydrocarbons and tars contents. A nickel-magnesium oxide was the catalyst that led to the highest reduction in hydrocarbons and tars. This catalyst also led to the lowest NH₃ content in the fuel gas produced, due to the catalyst efficiency in NH₃ destruction.     

Towards adaptive learning with improved convergence of deep belief networks on graphics processing units

In this paper we focus on two complementary approaches to significantly decrease pre-training time of a deep belief network (DBN). First, we propose an adaptive step size technique to enhance the convergence of the contrastive divergence (CD) algorithm, thereby reducing the number of epochs to train the restricted Boltzmann machine (RBM) that supports the DBN infrastructure. Second, we present a highly scalable graphics processing unit (GPU) parallel implementation of the CD-k algorithm, which boosts notably the training speed. Additionally, extensive experiments are conducted on the MNIST and the HHreco databases. The results suggest that the maximum useful depth of a DBN is related to the number and quality of the training samples. Moreover, it was found that the lower-level layer plays a fundamental role for building successful DBN models. Furthermore, the results contradict the pre-conceived idea that all the layers should be pre-trained. Finally, it is shown that by incorporating multiple back-propagation (MBP) layers, the DBNs generalization capability is remarkably improved.