Extracellular Vesicles and Asthma—More Than Just a Co-Existence

Extracellular vesicles (EVs) are membranous structures, which are secreted by almost every cell type analyzed so far. In addition to their importance for cell-cell communication under physiological conditions, EVs are also released during pathogenesis and mechanistically contribute to this process. Here we summarize their functional relevance in asthma, one of the most common chronic non-communicable diseases. Asthma is a complex persistent inflammatory disorder of the airways characterized by reversible airflow obstruction and, from a long-term perspective, airway remodeling. Overall, mechanistic studies summarized here indicate the importance of different subtypes of EVs and their variable cargoes in the functioning of the pathways underlying asthma, and show some interesting potential for the development of future therapeutic interventions. Association studies in turn demonstrate a good diagnostic potential of EVs in asthma.     

Boosting the activity of FeOOH via integration of ZIF-12 and graphene to efficiently catalyze the oxygen evolution reaction

Transition metal oxyhydroxides have been used as promising electrocatalysts for water splitting however, their catalytic activity is restricted due to low surface area and poor conductivity. Herein, we report novel composite FeOOH@ZIF-12/graphene composite as electrocatalyst for water oxidation, whereby ZIF-12 provide extra surface for the FeOOH dispersion whilst graphene act as excellent electron mediator. The composite shows a low overpotential value of 291 mV to attain a current density of 10 mA cm^?2 and a low Tafel slope value of 78 mV dec^?1. The catalyst offers a maximum current density of 101 mA cm^?2, while it gives a turnover frequency (TOF) value of 0.031 s^?1 at an overpotential of 291 mV only. The excellent activity and remarkable stability of composite is attributed to highly conductive and porous support.     

Enhanced Image Detection on an ARM based Embedded System

This paper presents a new technique for thedetection of Integrated Circuits within images of Printed Circuit Boards autonomouslyand without the need to be assisted by CAD data. The technique is a key partof a suite of algorithms targeted for an embedded System On Chip architecturebased on the ARM7 platform for real time detection of PCB images for diagnosticpurposes. The technique has a significant reduction in complexity when comparedto conventional approaches such as the Hough Transform. The reduction in complexitymakes the approach ideal for an embedded vision application suchas the one described in this paper. This paper presents the technique, thetarget embedded architecture and results showing the reduction in complexitywhen compared to a Hough Transform.     

On using peak amplitude and rise time for AE source characterization

Acoustic Emission (AE) signals, which are electrical version of acoustic emissions, are usually analysed using a set of signal parameters. The major objective of signal analysis is to study the characteristics of the sources of emissions. Peak amplitude(P _a ) and rise time(R _t ) are two such parameters used for source characterization. In this paper, we theoretically investigate the efficiency ofP _a andR _t to classify and characterize AE sources by modelling the input stress pulse and transducer. Analytical expressions obtained forP _a andR _t clearly indicate their use and efficiency for source characterization. It is believed that these results may be of use to investigators in areas like control systems and signal processing also     

Investigations of surface characterization of silicone rubber due to tracking phenomena under a.c. and d.c. voltages

In the present work, tracking phenomena has been studied with silicone rubber material under the a.c. and d.c. voltages following IEC-587 standards. The surface condition of the tracked zone was analysed using wide angle X-ray diffraction (WAXD) and thermogravimetric differential thermal analysis (TG-DTA) studies. The tracking time was different for a.c. and d.c. voltages.     

Estimation of Residual Pepsin and Chymosin Activity in Curd,

Pasteurized milk (225 g) adjusted to pH 6.2 was set with 3.5 milk clotting units of chymosin (EC 3.4.23.4). The same amount of milk at pH 5.8 was set with 3.5 milk clotting units of porcine pepsin (EC 3.4.23.1). Fifteen minutes after clotting, the curd was broken, and curd and whey were separated by centrifugation at 3500 × g for 20 min. The curd (30 g) was extracted at pH 6.8 in 450 ml water or at pH 6.2 (chymosin) or 5.8 (pepsin) in 450 ml 1 M sodium chloride.Chymosin was completely released from the curd and accounted for by both methods of extraction. Pepsin was completely released and accounted for after extraction in 1 M sodium chloride at pH 5.8 but was partly inactivated during extraction at pH 6.8.Assay of curd extracts and whey by a linear agar diffusion test accounted for 102 ± 6% of the pepsin activity added to milk when the curd was extracted in 1 M sodium chloride. Extraction at pH 6.8 allowed recovery of only 63% of the activity. Chymosin recovery was 100 ± 5% by both methods of curd extraction.     

Development of an Expert System for Selection of Dam Type on Alluvium Foundations

. A knowledge-based expert system was developed to aid in the selection of the type of dam. The dam type selector expert system (DTSA ES) was designed to determine the type of dam on the alluvium foundations. Detailed expert knowledge is required to estimate the type of dam and to develop an expert system. The DTSA ES utilizes rules of thumb used by an expert for determining the selection of the type of dam. The DTSA ES was developed using a shell program. The expert system was tested on several dam sites in order to validate the decision obtained. The use of this expert system, containing knowledge about the selection of dam type, can be helpful to students, potential owners or contractors in selecting dam types. The current prototype always needs additional parameters for more detailed analyses of new developments. However, the current DTSA ES is designed to include existing information about dam types.     

Lung nodules detection using semantic segmentation and classification with optimal features

Neural Computing and Applications - Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of...     

A new approach based on a discrete hidden Markov model using the Rocchio algorithm for the diagnosis of heart valve diseases

Abstract: Application of the Doppler ultrasound technique in the diagnosis of heart diseases has been increasing in the last decade since it is non‐invasive, practicable and reliable. In this study, a new approach based on the discrete hidden Markov model (DHMM) is proposed for the diagnosis of heart valve disorders. For the calculation of hidden Markov model (HMM) parameters according to the maximum likelihood approach, HMM parameters belonging to each class are calculated by using training samples that only belong to their own classes. In order to calculate the parameters of DHMMs, not only training samples of the related class but also training samples of other classes are included in the calculation. Therefore HMM parameters that reflect a class's characteristics are more represented than other class parameters. For this aim, the approach was to use a hybrid method by adapting the Rocchio algorithm. The proposed system was used in the classification of the Doppler signals obtained from aortic and mitral heart valves of 215 subjects. The performance of this classification approach was compared with the classification performances in previous studies which used the same data set and the efficiency of the new approach was tested. The total classification accuracy of the proposed approach (95.12%) is higher than the total accuracy rate of standard DHMM (94.31%), continuous HMM (93.5%) and support vector machine (92.67%) classifiers employed in our previous studies and comparable with the performance levels of classifications using artificial neural networks (95.12%) and fuzzy‐C‐means/CHMM (95.12%).     

Prosthetic Hand Finger Control Using Fuzzy Sliding Modes

In order to improve the life quality of amputees, providing approximate manipulation ability of a human hand to that of a prosthetic hand is considered by many researchers. In this study, a biomechanical model of the index finger of the human hand is developed based on the human anatomy. Since the activation of finger bones are carried out by tendons, a tendon configuration of the index finger is introduced and used in the model to imitate the human hand characteristics and functionality. Then, fuzzy sliding mode control where the slope of the sliding surface is tuned by a fuzzy logic unit is proposed and applied to have the finger model to follow a certain trajectory. The trajectory of the finger model, which mimics the motion characteristics of the human hand, is pre-determined from the camera images of a real hand during closing and opening motion. Also, in order to check the robust behaviour of the controller, an unexpected joint friction is induced on the prosthetic finger on its way. Finally, the resultant prosthetic finger motion and the tendon forces produced are given and results are discussed.