N,N-dimethylglycyl-amido derivative of minocycline and 6-demethyl-6-desoxytetracycline, two new glycylcyclines highly effective against tetracycline-resistant gram-positive cocci

The in vitro activities of the N,N-dimethylglycyl-amino derivative of minocycline (DMG-MINO) and 6-dimethyl-6-dexoxytetracycline (DMG-DMDOT), members of a new generation of tetracyclines, were evaluated by an agar dilution method and were compared with those of tetracycline and minocycline against 224 tetracycline-resistant and 73 tetracycline-susceptible recent clinical isolates of gram-positive cocci, including multiple-antibiotic-resistant methicillin-resistant Staphylococcus aureus and penicillin-resistant Streptococcus pneumoniae. The MICs of DMG-MINO and DMG-DMDOT were up to 500- to 2,000-fold lower than those of tetracycline against methicillin-resistant S. aureus and Streptococcus pneumoniae (MIC for 50% of strains tested [MIC50], < 0.06 microgram/ml). Against Streptococcus groups A, B, C, and G and Enterococcus faecalis, the MIC50 was 0.5 microgram/ml. MIC50s were greater only for coagulase-negative staphylococci (2 micrograms/ml). These data indicate that DMG-MINO and DMG-DMDOT are very potent drugs, and further in vitro and in vivo studies are warranted.     

Zum Einsatz synthetischer Pyrethroide im vorbeugenden Holzschutz

The synthetic pyrethroids fenvalerate and cypermethrine, natural pyrethrum and lindane were investigated as wood preservatives under conditions of application to determine their contribution to indoor pollution. While pyrethrum degraded within 10 days, fenvalerate and cypermethrine showed high stability and long-term remaining in wood and no volatility into gasphase. In contrast to lindane they do not contribute to indoor pollution.     

Methacrylate-functionalized POSS influence on cross-linking and mechanical properties of styrene-butadiene rubber

Iranian Polymer Journal - The addition of methacrylate-functional polyhedral oligomeric silsesquioxane (MA-POSS) nanoparticles to styrene-butadiene rubber (SBR) composites was evaluated in terms of...     

Use of ultrasonic energy in reactive dyeing of cellulosic fabrics

The use of ultrasonic energy in dyeing cotton fabrics with monochlorotriazine and vinylsulphone reactive dyes has been studied spectrophotometrically. The dyeings carried out conventionally and by the use of ultrasonic techniques were compared in terms of percentage exhaustion, total amount of dye transferred to the washing bath after dyeing, fastness properties and colour values of the dyed materials. Overall results have shown that the use of ultrasound in reactive dyeing of cellulosic fabrics can result in energy savings, less water consumption, increased depth of colour and better process conditions.     

Unsupervised Multiway Data Analysis: A Literature Survey

Two-way arrays or matrices are often not enough to represent all the information in the data and standard two-way analysis techniques commonly applied on matrices may fail to find the underlying structures in multi-modal datasets. Multiway data analysis has recently become popular as an exploratory analysis tool in discovering the structures in higher-order datasets, where data have more than two modes. We provide a review of significant contributions in the literature on multiway models, algorithms as well as their applications in diverse disciplines including chemometrics, neuroscience, social network analysis, text mining and computer vision.     

Approximate probabilistic optimization using exact-capacity-approximate-response-distribution (ECARD)

Probabilistic structural design deals with uncertainties in response (e.g. stresses) and capacity (e.g. failure stresses). The calculation of the structural response is typically expensive (e.g., finite element simulations), while the capacity is usually available from tests. Furthermore, the random variables that influence response and capacity are often disjoint. In previous work we have shown that this disjoint property can be used to reduce the cost of obtaining the probability of failure via Monte Carlo simulations. In this paper we propose to use this property for an approximate probabilistic optimization based on exact capacity and approximate response distributions (ECARD). In Approximate Probabilistic Optimization Using ECARD, the change in response distribution is approximated as the structure is re-designed while the capacity distribution is kept exact, thus significantly reducing the number of expensive response simulations. ECARD may be viewed as an extension of SORA (Sequential Optimization and Reliability Assessment), which proceeds with deterministic optimization iterations. In contrast, ECARD has probabilistic optimization iterations, but in each iteration, the response distribution is approximated so as not to require additional response calculations. The use of inexpensive probabilistic optimization allows easy incorporation of system reliability constraints and optimal allocation of risk between failure modes. The method is demonstrated using a beam problem and a ten-bar truss problem. The former allocates risk between two different failure modes, while the latter allocates risk between members. It is shown that ECARD provides most of the improvement from risk re-allocation that can be obtained from full probabilistic optimization.     

Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange

Prediction of stock price index movement is regarded as a challenging task of financial time series prediction. An accurate prediction of stock price movement may yield profits for investors. Due to the complexity of stock market data, development of efficient models for predicting is very difficult. This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. The models are based on two classification techniques, artificial neural networks (ANN) and support vector machines (SVM). Ten technical indicators were selected as inputs of the proposed models. Two comprehensive parameter setting experiments for both models were performed to improve their prediction performances. Experimental results showed that average performance of ANN model (75.74%) was found significantly better than that of SVM model (71.52%).     

A comprehensive study on mid-level representation and ensemble learning for emotional analysis of video material

Multimedia Tools and Applications - In today’s society where audio-visual content such as professionally edited and user-generated videos is ubiquitous, automatic analysis of this content is...