A study of the protein composition of the zona pellucida of mammalian oocytes and zygotes by a method of microelectrophoresis in polyacrylamide gel

It is known that drugs are not unlimitedly stable. Dilutions of the antineoplastic agent daunoblastin used in the clinic and experimental work, were examined, whether they could be stored for several days. By means of impulscytophotometry, spectral-photometric measurements, counting of surviving cells and estimation of DNA-synthesis after culturing pieces of tissue in the presence of daunoblastin it could be established that this drug, diluted even four days ago, still possesses sufficient activity.     

水解淫羊藿苷糖基的土壤细菌筛选

从20种土壤细菌中筛选出了对淫羊藿苷水解能力高的菌株Rhodanobncter sp．GS3054．该菌株30℃下发酵3～4d的发酵液提酶后对淫羊藿苷水解能力最佳；水解淫养藿苷成低糖基苷的最适反应条件为：50℃、pH6．0、底物20mg／mL，反应时间24h．     

Donor Heart Preservation with Hydrogen Sulfide: A Systematic Review and Meta-Analysis

Preclinical studies have shown that postconditioning with hydrogen sulfide (H₂S) exerts cardioprotective effects against myocardial ischemia-reperfusion injury (IRI). The aim of this study was to appraise the current evidence of the cardioprotective effects of H₂S against IRI in order to explore the future implementation of H₂S in clinical cardiac transplantation. The current literature on H₂S postconditioning in the setting of global myocardial ischemia was systematically reviewed and analyzed, performing meta-analyses. A literature search of the electronic databases Medline, Embase and Cinahl identified 1835 studies that were subjected to our pre-defined inclusion criteria. Sixteen studies were considered eligible for inclusion. Postconditioning with H₂S showed significant robust effects with regard to limiting infarct size (standardized mean difference (SMD) = −4.12, 95% CI [−5.53–−2.71], p < 0.00001). Furthermore, H₂S postconditioning consistently resulted in a significantly lower release of cardiac injury markers, lower levels of oxidative stress and improved cardiac function. Postconditioning with slow-releasing H₂S donors offers a valuable opportunity for novel therapies within cardiac preservation for transplantation. Before clinical implication, studies evaluating the long-term effects of H₂S treatment and effects of H₂S treatment in large animal studies are warranted.     

An integrated interval-valued intuitionistic fuzzy AHP-TOPSIS methodology to determine the safest route for cash in transit operations: a real case in Istanbul

Neural Computing and Applications - The Cash in Transit (CIT) deals with the money distribution and picking up between depot(s), central bank, bank branches, Automated Teller Machines (ATMs),...     

Antineuraminidase serum antibodies in natural influenza A and immunization with influenza vaccines

Parallel HI and virus-elution-from-erythrocytes-inhibition (a simplified method for titration of neuraminidase antibody) tests were used for examinations of 1117 blood serum specimens from 440 adults and children under study, 5250 single serum specimens from healthy subjects from birth to 65 years of age, 38 paired serum specimens from children who experienced influenza A/Texas/1/77 disease in the epidemic of 1979-1980, and 590 paired serum specimens from subjects immunized with influenza vaccines. In 7%-23% of influenza patients and immunized subjects antibody rise was observed to only one of the influenza A virus surface antigens, hemagglutinin or neuraminidase. The protective activity of antibody to influenza A virus neuraminidase was as good as that of antihemagglutinins. Both kinds of antibody interacted in protection against the disease. Antineuraminidase antibody was found to affect the decrease in severity of the infectious process in natural infection with influenza A. The formation of immunological memory in the system of synthesis of antihemagglutinins and antineuraminidase antibodies was shown to have features in common. The pattern of heterologous immune responses in immunized subjects and patients with influenza showed all antigenic varieties of neuraminidase N2 as well as neuraminidases N1 and N2 to share common cross-reacting determinants.     

Gain adaptation in sliding mode control of robotic manipulators

In this paper, a novel scheme is proposed to adapt the gains of a sliding mode controller (SMC) so that the problems faced in its practical implementations as a motion controller are overcome. A Lyapunov function is selected for the design of the SMC and an MIT rule is used for gain adaptation. The criterion that is minimized for gain adaptation is selected as the sum of the squares of the control signal and the sliding surface function. This novel approach is tested on a scara-type robot manipulator. The experimental results presented prove its efficacy.     

A combined fuzzy linear regression and fuzzy multiple objective programming approach for setting target levels in quality function deployment

Quality function deployment (QFD) is a systematic process for translating customer needs into engineering characteristics, and then communicating them throughout the enterprise in a way to ensure that details are quantified and controlled. The inherent fuzziness of relationships in QFD modeling justifies the use of fuzzy regression for estimating the relationships between both customer needs and engineering characteristics, and among engineering characteristics. Albeit QFD aims to maximize customer satisfaction, requirements related to enterprise satisfaction such as cost budget, extendibility, and technical difficulty also need to be considered. This paper presents a fuzzy multiple objective decision framework that includes not only fulfillment of engineering characteristics to maximize customer satisfaction, but also maximization of extendibility and minimization of technical difficulty of engineering characteristics as objectives subject to a financial budget constraint to determine target levels of engineering characteristics in product design. A real-world quality improvement problem is presented to illustrate the application of the decision approach.     

Object manipulation with a variable-stiffness robotic mechanism using deep neural networks for visual semantics and load estimation

Neural Computing and Applications - In recent years, the computer vision applications in the robotics have been improved to approach human-like visual perception and scene/context understanding....     

An integrated decision making approach for ERP system selection

Enterprise resource planning (ERP) systems have gained major prominence by enabling companies to streamline their operations, leverage and integrate business data process. In order to implement an ERP project successfully, it is necessary to select an ERP system which can be aligned with the needs of the company. Thus, a robust decision making approach for ERP software selection requires both company needs and characteristics of the ERP system and their interactions to be taken into account. This paper develops a novel decision framework for ERP software selection based on quality function deployment (QFD), fuzzy linear regression and zero–one goal programming. The proposed framework enables both company demands and ERP system characteristics to be considered, and provides the means for incorporating not only the relationships between company demands and ERP system characteristics but also the interactions between ERP system characteristics through adopting the QFD principles. The presented methodology appears as a sound investment decision making tool for ERP systems as well as other information systems. The potential use of the proposed decision framework is illustrated through an application.     

A multilayer incremental neural network architecture for classification

A new multilayer incremental neural network (MINN) architecture and its performance in classification of biomedical images is discussed. The MINN consists of an input layer, two hidden layers and an output layer. The first stage between the input and first hidden layer consists of perceptrons. The number of perceptrons and their weights are determined by defining a fitness function which is maximized by the genetic algorithm (GA). The second stage involves feature vectors which are the codewords obtained automaticaly after learning the first stage. The last stage consists of OR gates which combine the nodes of the second hidden layer representing the same class. The comparative performance results of the MINN and the backpropagation (BP) network indicates that the MINN results in faster learning, much simpler network and equal or better classification performance.