Quantitative Structure-Activity Relationship Analysis of the Anticonvulsant Activity of Some Benzylacetamides Based on Genetic Algorithm-Based Multiple Linear Regression

Purpose: To develop the quantitative structure-activity relationship (QSAR) for predicting the anticonvulsant activity of α-substituted acetamido-N-benzylacetamide derivatives.Methods: AM1 semiempirical quantum chemical calculation method was used to find the optimum 3D geometry of the studied molecules. Two types of molecular descriptors, including the 2D autocorrelation and GETAWAY descriptors, were used to derive a quantitative relation between anticonvulsant activity and structural properties. The relevant molecular descriptors were selected by genetic algorithm-based multiple linear regression (GA-MLR) approach.Results: The high value of the correlation coefficient, R2 (0.900), indicate that the model was satisfactory.Conclusion: The proposed model has good stability, robustness and predictability when verified by internal and external validation.Keywords: Anticonvulsant, Benzylacetamides, 2D Autocorrelation, Quantitative structure-activity relationships, Multiple linear regression

Characterizing genomic alterations in cancer by complementary functional associations.

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

A combination of three surface modifiers for the optimal generation and application of natural hybrid nanopigments in a biodegradable resin

Our purpose was to improve the thermal, mechanical and optimal properties of an epoxy bioresin using optimum hybrid natural pigments previously synthesised in our lab. Next, we searched for the best combinations of factors in the synthesis of natural hybrid nanopigments and then incorporated them into the bioresin. We combined three structural modifiers in the nanopigment synthesis, surfactant, coupling agent (silane) and a mordant salt (alum), selected to replicate mordant textile dyeing with natural dyes. We used Taguchi s design L8 to seek final performance optimisation. We selected three natural dyes, chlorophyll, beta-carotene and beetroot extract, and used two laminar nanoclay types, montmorillonite and hydrotalcite. The thermal, mechanical and colorimetric characterisation of the composite obtained by mixing natural hybrid nanopigments (bionanocomposite) was made. The natural dye interactions with both nanoclays improved the thermal stabilities, colour performance and UV VIS light exposure stability of natural dyes and bioresins. The best bionanocomposite materials were found in an acidic pH [3, 4] environment and by modifying nanoclays with mordant and surfactant during the nanopigment synthesis processWe thank the Spanish Ministry of Economy and Competitiveness for funding Projects DPI2011-30090-C02-02 and DPI2015-68514-R.Micó Vicent, B.; Jordán Núñez, J.; Martinez Verdu, FM.; Balart Gimeno, RA. (2017). A combination of three surface modifiers for the optimal generation and application of natural hybrid nanopigments in a biodegradable resin. Journal of Materials Science. 52(2):889-898. https://doi.org/10.1007/s10853-016-0384-8S889898522Majdzadeh-Ardakani K, Nazari B (2010) Improving the mechanical properties of thermoplastic starch/poly(vinyl alcohol)/clay nanocomposites. 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Recent advances and perspectives on starch nanocomposites for packaging applications

Starch nanocomposites are popular and abundant materials in packaging sectors. The aim of this work is to review some of the most popular starch nanocomposite systems that have been used nowadays. Due to a wide range of applicable reinforcements, nanocomposite systems are investigated based on nanofiller type such as nanoclays, polysaccharides and carbonaceous nanofillers. Furthermore, the structures of starch and material preparation methods for their nanocomposites are also mentioned in this review. It is clearly presented that mechanical, thermal and barrier properties of plasticised starch can be improved with well-dispersed nanofillers in starch nanocomposites

Formulations of Plant Growth-Promoting Microbes for Field Applications

Development of a plant growth-promoting (PGP) microbe needs several steps starting with isolation of a pure culture, screening of its PGP or antagonistic traits by means of different efficacy bioassays performed in vitro, in vivo or in trials under greenhouse and/or field conditions. In order to maximize the potential of an efficient PGP microbe, it is essential to optimize mass multiplication protocols that promote product quality and quantity and a product formulation that enhances bioactivity, preserves shelf life and aids product delivery. Selection of formulation is very crucial as it can determine the success or failure of a PGP microbe. A good carrier material should be able to deliver the right number of viable cells in good physiological conditions, easy to use and economically affordable by the farmers. Several carrier materials have been used in formulation that include peat, talc, charcoal, cellulose powder, farm yard manure, vermicompost and compost, lignite, bagasse and press mud. Each formulation has its advantages and disadvantages but the peat based carrier material is widely used in different part of the world. This chapter gives a comprehensive analysis of different formulations and the quality of inoculants available in the market, with a case study conducted in five-states of India

Characterizing the Path Coverage of Random Wireless Sensor Networks

Wireless sensor networks are widely used in security monitoring applications to sense and report specific activities in a field. In path coverage, for example, the network is in charge of monitoring a path and discovering any intruder trying to cross it. In this paper, we investigate the path coverage properties of a randomly deployed wireless sensor network when the number of sensors and also the length of the path are finite. As a consequence, Boolean model, which has been widely used previously, is not applicable. Using results from geometric probability, we determine the probability of full path coverage, distribution of the number of uncovered gaps over the path, and the probability of having no uncovered gaps larger than a specific size. We also find the cumulative distribution function (cdf) of the covered part of the path. Based on our results on the probability of full path coverage, we derive a tight upper bound for the number of nodes guaranteeing the full path coverage with a desired reliability. Through computer simulations, it is verified that for networks with nonasymptotic size, our analysis is accurate where the Boolean model can be inaccurate