Towards pain-free diagnosis of skin diseases through multiplexed microneedles: biomarker extraction and detection using a highly sensitive blotting method

Immunodiagnostic microneedles provide a novel way to extract protein biomarkers from the skin in a minimally invasive manner for analysis in vitro. The technology could overcome challenges in biomarker analysis specifically in solid tissue, which currently often involves invasive biopsies. This study describes the development of a multiplex immunodiagnostic device incorporating mechanisms to detect multiple antigens simultaneously, as well as internal assay controls for result validation. A novel detection method is also proposed. It enables signal detection specifically at microneedle tips and therefore may aid the construction of depth profiles of skin biomarkers. The detection method can be coupled with computerised densitometry for signal quantitation. The antigen specificity, sensitivity and functional stability of the device were assessed against a number of model biomarkers. Detection and analysis of endogenous antigens (interleukins 1α and 6) from the skin using the device was demonstrated. The results were verified using conventional enzyme-linked immunosorbent assays. The detection limit of the microneedle device, at ≤10 pg/mL, was at least comparable to conventional plate-based solid-phase enzyme immunoassays.

The impact of location and type on the performance of Low-Voltage network connected Battery Energy Storage Systems

This paper assesses the impact of the location and configuration of Battery Energy Storage Systems (BESS) on Low-Voltage (LV) feeders. BESS are now being deployed on LV networks by Distribution Network Operators (DNOs) as an alternative to conventional reinforcement (e.g. upgrading cables and transformers) in response to increased electricity demand from new technologies such as electric vehicles. By storing energy during periods of low demand and then releasing that energy at times of high demand, the peak demand of a given LV substation on the grid can be reduced therefore mitigating or at least delaying the need for replacement and upgrade. However, existing research into this application of BESS tends to evaluate the aggregated impact of such systems at the substation level and does not systematically consider the impact of the location and configuration of BESS on the voltage profiles, losses and utilisation within a given feeder. In this paper, four configurations of BESS are considered: single-phase, unlinked three-phase, linked three-phase without storage for phase-balancing only, and linked three-phase with storage. These four configurations are then assessed based on models of two real LV networks. In each case, the impact of the BESS is systematically evaluated at every node in the LV network using Matlab linked with OpenDSS. The location and configuration of a BESS is shown to be critical when seeking the best overall network impact or when considering specific impacts on voltage, losses, or utilisation separately. Furthermore, the paper also demonstrates that phase-balancing without energy storage can provide much of the gains on unbalanced networks compared to systems with energy storage.

Detection of urease in the cell wall and membranes from leaf tissues of bromeliad species

Urea is an important nitrogen source for some bromeliad species, and in nature it is derived from the excretion of amphibians, which visit or live inside the tank water. Its assimilation is dependent on the hydrolysis by urease (EC: 3.5.1.5), and although this enzyme has been extensively studied to date, little information is available about its cellular location. In higher plants, this enzyme is considered to be present in the cytoplasm. However, there is evidence that urease is secreted by the bromeliad Vriesea gigantea, implying that this enzyme is at least temporarily located in the plasmatic membrane and cell wall. In this article, urease activity was measured in different cell fractions using leaf tissues of two bromeliad species: the tank bromeliad V. gigantea and the terrestrial bromeliad Ananas comosus (L.) Merr. In both species, urease was present in the cell wall and membrane fractions, besides the cytoplasm. Moreover, a considerable difference was observed between the species: while V. gigantea had 40% of the urease activity detected in the membranes and cell wall fractions, less than 20% were found in the same fractions in A. comosus. The high proportion of urease found in cell wall and membranes in V. gigantea was also investigated by cytochemical detection and immunoreaction assay. Both approaches confirmed the enzymatic assay. We suggest this physiological characteristic allows tank bromeliads to survive in a nitrogen-limited environment, utilizing urea rapidly and efficiently and competing successfully for this nitrogen source against microorganisms that live in the tank water.

Automating condition monitoring of vegetation on railway trackbeds and embankments

Vegetation growing on railway trackbeds and embankments present potential problems. The presence of vegetation threatens the safety of personnel inspecting the railway infrastructure. In addition vegetation growth clogs the ballast and results in inadequate track drainage which in turn could lead to the collapse of the railway embankment. Assessing vegetation within the realm of railway maintenance is mainly carried out manually by making visual inspections along the track. This is done either on-site or by watching videos recorded by maintenance vehicles mainly operated by the national railway administrative body. A need for the automated detection and characterisation of vegetation on railways (a subset of vegetation control/management) has been identified in collaboration with local railway maintenance subcontractors and Trafikverket, the Swedish Transport Administration (STA). The latter is responsible for long-term planning of the transport system for all types of traffic, as well as for the building, operation and maintenance of public roads and railways. The purpose of this research project was to investigate how vegetation can be measured and quantified by human raters and how machine vision can automate the same process. Data were acquired at railway trackbeds and embankments during field measurement experiments. All field data (such as images) in this thesis work was acquired on operational, lightly trafficked railway tracks, mostly trafficked by goods trains. Data were also generated by letting (human) raters conduct visual estimates of plant cover and/or count the number of plants, either on-site or in-house by making visual estimates of the images acquired from the field experiments. Later, the degree of reliability of(human) raters’ visual estimates were investigated and compared against machine vision algorithms. The overall results of the investigations involving human raters showed inconsistency in their estimates, and are therefore unreliable. As a result of the exploration of machine vision, computational methods and algorithms enabling automatic detection and characterisation of vegetation along railways were developed. The results achieved in the current work have shown that the use of image data for detecting vegetation is indeed possible and that such results could form the base for decisions regarding vegetation control. The performance of the machine vision algorithm which quantifies the vegetation cover was able to process 98% of the im-age data. Investigations of classifying plants from images were conducted in in order to recognise the specie. The classification rate accuracy was 95%.Objective measurements such as the ones proposed in thesis offers easy access to the measurements to all the involved parties and makes the subcontracting process easier i.e., both the subcontractors and the national railway administration are given the same reference framework concerning vegetation before signing a contract, which can then be crosschecked post maintenance.A very important issue which comes with an increasing ability to recognise species is the maintenance of biological diversity. Biological diversity along the trackbeds and embankments can be mapped, and maintained, through better and robust monitoring procedures. Continuously monitoring the state of vegetation along railways is highly recommended in order to identify a need for maintenance actions, and in addition to keep track of biodiversity. The computational methods or algorithms developed form the foundation of an automatic inspection system capable of objectively supporting manual inspections, or replacing manual inspections.

Classification of Boal, Malvazia, Sercial and Verdelho wines based on terpenoid patterns

Thirty-six Madeira wine samples from Boal, Malvazia, Sercial and Verdelho white grape varieties were analyzed in order to estimate the free fraction of monoterpenols and C13 norisoprenoids (terpenoid compounds) using dynamic headspace solid phase micro-extraction (HS-SPME) technique coupled with gas chromatography–mass spectrometry (GC–MS). The average values from three vintages (1998–2000) show that these wines have characteristic profiles of terpenoid compounds. Malvazia wines exhibits the highest values of total free monoterpenols, contrary to Verdelho wines which had the lowest levels of terpenoids but produced the highest concentration of farnesol. The use of multivariate analysis techniques allows establishing relations between the compounds and the varieties under investigation. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to the obtained matrix data. A good separation and classification power between the four groups as a function of their varietal origin was observed.

Identification of lubricant contamination by biodiesel using vibration analysis and neural network

Purpose - The purpose of this paper is to provide information on lubricant contamination by biodiesel using vibration and neural network.Design/methodology/approach - The possible contamination of lubricants is verified by analyzing the vibration and neural network of a bench test under determinated conditions.Findings - Results have shown that classical signal analysis methods could not reveal any correlation between the signal and the presence of contamination, or contamination grade. on other hand, the use of probabilistic neural network (PNN) was very successful in the identification and classification of contamination and its grade.Research limitations/implications - This study was done for some specific kinds of biodiesel. Other types of biodiesel could be analyzed.Practical implications Contamination information is presented in the vibration signal, even if it is not evident by classical vibration analysis. In addition, the use of PNN gives a relatively simple and easy-to-use detection tool with good confidence. The training process is fast, and allows implementation of an adaptive training algorithm.Originality/value - This research could be extended to an internal combustion engine in order to verify a possible contamination by biodiesel.

Correlation between the intensity of venous reflux in the saphenofemoral junction and morphological changes of the great saphenous vein by duplex scanning in patients with primary varicosis

Aim. One of the major causes of chronic venous disease is venous reflux, the identification and quantification of which are important for diagnosis. Duplex scanning allows for the detection and quantification of reflux in individual veins. Evaluation of the great saphenous vein in primary varicosis is necessary for its preservation. Objective of the study is to evaluate a possible correlation between the intensity of reflux at the saphenofemoral junction, diameter alterations of the incompetent great saphenous vein and the practical effect of such correlation. Also to compare the clinical severity of the CEAP classification with such parameters.Methods. Three hundred limbs were submitted to duplex evaluation of their insufficient saphenous veins. Vein diameter was measured on five different points. Velocity and flow at reflux peak and reflux time were determined. The saphenous vein's diameters were correlated with velocity, flow and time. The three latter parameters and diameters were compared with clinical severity according to CEAP.Results. Correlation was found between the saphenous vein's diameters, velocity and flow. No correlation was observed between time and diameter in the thigh's upper and middle thirds. When comparing diameter, velocity and flow with CEAP clinical severity classification, an association was observed. The correlation between reflux time with clinical severity was weak.Conclusion. Reflux time is a good parameter for identifying the presence of reflux, but not for quantifying it. Velocity and peak flow were better parameters for evaluating reflux intensity as they were correlated with great saphenous vein alterations, and were associated with the disease's clinical severity. [Int Angiol 2010;29:323-30]

Detection of SPM and IMP metallo-beta-lactamases in clinical specimens of Pseudomonas aeruginosa from a Brazilian public tertiary hospital

Phenotypic and genotypic SPM and IMP metallo-beta-lactamases (MBL) detection and also the determination of minimal inhibitory concentrations (MIC) to imipenem, meropenem and ceftazidime were evaluated in 47 multidrug-resistant Pseudomonas aeruginosa isolates from clinical specimens. Polymerase chain reaction detected 14 positive samples to either bla(SPM) or bla(IMP) genes, while the best phenotypic assay (ceftazidime substrate and mercaptopropionic acid inhibitor) detected 13 of these samples. Imipenem, meropenem and ceftazidime MICs were higher for MBL positive compared to MBL negative isolates. We describe here the SPM and IMP MBL findings in clinical specimens of P. aeruginosa from the University Hospital of Botucatu Medical School, São Paulo, Brazil, that reinforce local studies showing the high spreading of bla(SPM) and bla(IMP) genes among Brazilian clinical isolates.

The performance of conventional and fluorescence-based methods for occlusal caries detection An in vivo study with histologic validation

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Comparison of leaf morphology and anatomy among Malva sylvestris (gerânio-aromático), Pelargonium graveolens (falsa-malva) and Pelargonium odoratissimum (gerânio-de-cheiro)

Malva sylvestris é comumente confundida com Pelargonium graveolens e Pelargonium odoratissimum devido às semelhanças na morfologia foliar. As folhas de M. sylvestris possuem antocianinas com propriedades citotóxicas, antiinflamatória, antitumoral e antioxidante já comprovadas cientificamente. As folhas de P. odoratissimum apresentam óleo essencial com propriedades antibacteriana e espasmolítica, e o óleo essencial da folha de P. graveolens possui atividade antimicrobiana e antifúngica. O objetivo deste estudo foi analisar morfo-anatomicamente as folhas destas espécies, apontando diferenças que possam ser utilizadas para esclarecer controvérsias na sua utilização como planta medicinal. Com a finalidade de se comparar anatomicamente a estrutura de cada planta, as amostras foram observadas por Microscopia de Luz e Microscopia Eletrônica de Varredura (MEV). A anatomia foliar entre as espécies foi bem distinta. Malva sylvestris apresentou tricomas do tipo capitado, estrelado e tector, além de drusas e células mucilaginosas. A distinção entre P. graveolens e P. odoratissimum foi observada em relação aos tricomas. Ambas as espécies apresentaram tricomas glandulares e tectores, sendo que P. graveolens se diferencia pela maior altura dos tricomas tectores e menor quantidade destes em relação ao P. odoratissimum. Este trabalho permitiu constatar diferenças anatômicas, auxiliando na taxonomia e classificação entre estas espécies.

Liquid-phase blocking sandwich enzyme-linked immunosorbent assay for detection of antibodies against foot-and-mouth disease virus in water buffalo sera

Objective-To develop and apply the liquid-phase blocking sandwich ELISA (BLOCKING-ELISA) for the quantification of antibodies against foot-and-mouth disease virus (FMDV) strains O-1 Campos, A(24) Cruzeiro, and C-3 Indaial.Design-Antibody quantification.Sample Population-158 water buffalo from various premises of São Paulo Stale-Brazil. The sera were collected either from systemically vaccinated or nonvaccinated animals.Procedure-The basic reagents of BLOCKING-ELISA (capture and detector antibodies, virus antigens, and conjugate) were prepared and the reaction was optimized and standardized to quantify water buffalo antibodies against FMDV. An alternative procedure based on mathematical interpolation was adopted to estimate more precisely the antibody 50% competition liters in the BLOCKING-ELISA. These titers were compared with the virus-neutralization test (VNT) titers to determine the correlation between these techniques. The percentages of agreement, cutoff points, and reproducibility also were determined.Results-The antibody liters obtained in the BLOCKING-ELISA had high positive correlation coefficients with VNT, reaching values of 0.90 for O-1 Campos and C-3 Indaial, and 0.82 for the A(24) Cruzeiro (P < 0.0005). The cutoff points obtained by use of the copositivity and conegativity curves allowed determination of high levels of agreement between BLOCKLNG-ELISA and VNT antibody titers against the 3 FMDV strains analyzed.Conclusions-The results characterized by high cor relation coefficients, levels of agreement, and reproducibility indicate that the BLOCKING-ELISA may replace the conventional VNT for detection and quantification of antibodies from water buffalo sera to FMDV.

Leak detection in petroleum pipelines using a fuzzy system

A methodology for pipeline leakage detection using a combination of clustering and classification tools for fault detection is presented here. A fuzzy system is used to classify the running mode and identify the operational and process transients. The relationship between these transients and the mass balance deviation are discussed. This strategy allows for better identification of the leakage because the thresholds are adjusted by the fuzzy system as a function of the running mode and the classified transient level. The fuzzy system is initially off-line trained with a modified data set including simulated leakages. The methodology is applied to a small-scale LPG pipeline monitoring case where portability, robustness and reliability are amongst the most important criteria for the detection system. The results are very encouraging with relatively low levels of false alarms, obtaining increased leakage detection with low computational costs. (c) 2005 Elsevier B.V. All rights reserved.

Production grinding burn detection using acoustic emission and electric power signals

This work uses a monitoring system based on a PC platform, where the acoustic emission and electric power signals generated during the grinding process are used to investigate superficial burning occurrence in a surface grinding operation using two types of steel, three grinding conditions and an Al203 vitrified grinding wheel. Acoustic emission signals on the workpiece and grinding power were measured during a surface plunge operation until the grinding burn happened. From the results the standard deviation of the acoustic emission signal and the maximum electric power were calculated for each grinding pass. The proposed DPO parameter is the product between the power level and acoustic emission standard deviation. The results show that both signals can be used for burning detection, and the parameter DPO is the best indicator for the burning studied in this work. This can be explained by the high dispersion of the acoustic emission RMS level associated to the high power consumption when the grinding wheel lose its sharpness.

Fault identification in distribution lines using intelligent systems and statistical methods

The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder. © 2006 IEEE.

Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.