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.     

Restoration of blood pressure by choline treatment in rats made hypotensive by haemorrhage

1. Intracerebroventricular (i.c.v.) injection of choline (25-150 micrograms) increased blood pressure in rats made acutely hypotensive by haemorrhage. Intraperitoneal administration of choline (60 mg kg-1) also increased blood pressure, but to a lesser extent. Following i.c.v. injection of 25 micrograms or 50 micrograms of choline, heart rate did not change, while 100 micrograms or 150 micrograms i.c.v. choline produced a slight and short lasting bradycardia. Choline (150 micrograms) failed to alter the circulating residual volume of blood in haemorrhaged rats. 2. The pressor response to i.c.v. choline (50 micrograms) in haemorrhaged rats was abolished by pretreatment with mecamylamine (50 micrograms, i.c.v.) but not atropine (10 micrograms, i.c.v.). The pressor response to choline was blocked by pretreatment with hemicholinium-3 (20 micrograms, i.c.v.). 3. The pressor response to i.c.v. choline (150 micrograms) was associated with a several fold increase in plasma levels of vasopressin and adrenaline but not of noradrenaline and plasma renin. 4. The pressor response to i.c.v. choline (150 micrograms) was not altered by bilateral adrenalectomy, but was attenuated by systemic administration of either phentolamine (10 mg kg-1) or the vasopressin antagonist [beta-mercapto-beta,beta-cyclopenta-methylenepropionyl1, O-Me-Tyr2,Arg8]-vasopressin (10 micrograms kg-1). 5. It is concluded that the precursor of acetylcholine, choline, can increase and restore blood pressure in acutely haemorrhaged rats by increasing central cholinergic neurotransmission. Nicotinic receptor activation and an increase in plasma vasopressin and adrenaline level appear to be involved in this effect of choline.     

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.     

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...     

Removal of metal ions using lignite in aqueous solution—Low cost biosorbents

Turkish lignite can be used as a new adsorption material for removing some toxic metals from aqueous solution. The adsorption of lignite (brown young coals) to remove copper (Cu²⁺), lead (Pb²⁺), and nickel (Ni²⁺) from aqueous solutions was studied as a function of pH, contact time, metal concentration and temperature. Adsorption equilibrium was achieved between 40 and 70 min for all studied cations except Pb²⁺, which is between 10 and 30 min. The adsorption capacities are 17.8 mg/g for Cu²⁺, 56.7 mg/g for Pb²⁺, 13.0 mg/g for Ni²⁺ for BC₁ (Ilg?n lignite) and 18.9 mg/g for Cu²⁺, 68.5 mg/g for Pb²⁺, 12.0 mg/g for Ni²⁺ for BC₂ (Beysehir lignite) and 7.2 mg/g for Cu²⁺, 62.3 mg/g for Pb²⁺, 5.4 mg/g for Ni²⁺ for AC (activated carbon). More than 67% of studied cations were removed by BC₁ and 60% BC₂, respectively from aqueous solution in single step. Whereas about 30% of studied cations except Pb²⁺, which is 90%, were removed by activated carbon. Effective removal of metal ions was demonstrated at pH values of 3.8–5.5. The adsorption isotherms were measured at 20 °C, using adsorptive solutions at the optimum pH value to determine the adsorption capacity. The Langmuir adsorption isotherm was used to describe observed sorption phenomena. The rise in temperature caused a slight decrease in the value of the equilibrium constant (K_c) for the sorption of metal ions. The mechanism for cations removal by the lignite includes ion exchange, complexation and sorption. The process is very efficient especially in the case of low concentrations of pollutants in aqueous solution, where common methods are either economically unfavorable or technically complicated.     

Adaptive three-dimensional cellular genetic algorithm for balancing exploration and exploitation processes

This paper presents a new adaptive algorithm that aims to control the exploration/exploitation trade-off dynamically. The algorithm is designed based on three-dimensional cellular genetic algorithms (3D-cGAs). In this study, our methodology is based on the change in the global selection pressure induced by dynamic tuning of the local selection rate. The parameter tuning of the local selection method is a way to define the global selection pressure. A diversity speed measure is used to guide the algorithm. Therefore, the integration of existing techniques helps in achieving our aims. A benchmark of well-known continuous test functions and real world problems was selected to investigate the effectiveness of the algorithm proposed. In addition, we provide a comparison between the proposed algorithm and other static and dynamic algorithms in order to study the different effects on the performance of the algorithms. Overall, the results show that the proposed algorithm provides the most desirable performance in terms of efficiency, efficacy, and speed for most problems considered. The results also confirm that problems of various characteristics require different selection pressures, which are difficult to be identified.     

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.