Development of classification and regression based QSAR models to predict rodent carcinogenic potency using oral slope factor

Carcinogenicity is among the toxicological endpoints posing the highest concern for human health. Oral slope factors (OSFs) are used to estimate quantitatively the carcinogenic potency or the risk associated with exposure to the chemical by oral route. Regulatory agencies in food and drug administration and environmental protection are employing quantitative structure-activity relationship (QSAR) models to fill the data gaps related with properties of chemicals affecting the environment and human health. In this background, we have developed quantitative structure-carcinogenicity regression models for rodents based on the carcinogenic potential of 70 chemicals with wide diversity of molecular structures, spanning a large number of chemical classes and biological mechanisms. All the developed models have been assessed according to the Organization for Economic Cooperation and Development (OECD) principles for the validation of QSAR models. We have also attempted to develop a carcinogenicity classification model based on Linear Discriminant Analysis (LDA). Developed regression and LDA models are rigorously validated internally as well as externally. Our in silico studies make it possible to obtain a quantitative interpretation of the structural information of carcinogenicity along with identification of the discriminant functions between lower and higher carcinogenic compounds by LDA. Pharmacological distribution diagrams (PDDs) are used as a visualizing technique for the identification and selection of chemicals with lower carcinogenicity. Constructive, informative and comparable interpretations have been observed in both cases of classification and regression based modeling.SK thanks the Department of Science and Technology, Government of India for awarding him a Senior Research fellowship under the INSPIRE scheme. KR thanks the Council of Scientific and Industrial Research (CSIR), New Delhi for awarding a major research project

Determination of charge spread, position resolution, energy resolution and gain uniformity of Gas Electron Multipliers (GEM)

Gas electron multipliers (GEM) detectors are gaseous detectors widely used for tracking and imaging applications due to their good position resolution, high efficiency at high irradiation rates, among other factors. In the present work, position resolution, charge spread, energy resolution and gain uniformity have been investigated experimentally for single and double GEM geometries using an Fe-55 source. The position resolution measurements have been performed by a novel method, using a high precision instrument for source movement and is found to be highly successful. The result shows that the double GEM can resolve positions with sigma values up to 36.8 micron and 54.6 micron in x and y directions, respectively. To validate the experimental results, a Garfield simulation work has been carried out on charge spread

Study of space charge phenomena in GEM-based detectors

Space charge accumulation within GEM holes is one of the vital phenomena which affects many of the key working parameters of the detector. This accumulation is found to be significantly affected by the initial primary charge configurations and applied GEM voltages since they determine charge sharing and the subsequent evolution of detector response. In this work, we have studied the effects of space charge phenomena on different parameters for single GEM detectors using a hybrid numerical model

Subduction Initiation Recorded in the Dadeville Complex of Alabama and Georgia, Southeastern United States

The Dadeville Complex of Alabama and Georgia (southeastern United States) represents the largest suite of exposed mafic-ultramafic rocks in the southern Appalachians. Due to poor preservation, chemical alteration, and tectonic reworking, a specific tectonic origin for the Dadeville Complex has been difficult to deduce. We obtained new whole-rock and mineral geochemistry coupled with zircon U-Pb geochronology to investigate the magmatic and metamorphic processes recorded by the Dadeville Complex, as well as the timing of these processes. Our data reveal an up-stratigraphic evolution in the geochemistry of the volcanic rocks, from forearc basalts to boninites. Our new U-Pb zircon crystallization data—obtained from three amphibolite samples—place the timing of forearc/protoarc volcanism no later than ca. 467 Ma. New thermobarometry suggests that the Dadeville Complex rocks subsequently experienced deep, high-grade metamorphism, at pressure-temperature conditions of \u3e7 kbar and \u3e760 °C. The data presented here support a model for formation of the Dadeville Complex in the forearc region of a subduction zone during subduction initiation and protoarc development, followed by deep burial/underthrusting of the complex during orogenesis

VI Jornades IET "Bretxa salarial i desigualtats de gènere en el mercat de treball"

Quantitative structure–property relationship (QSPR) models used for prediction of property of untested chemicals can be utilized for prioritization plan of synthesis and experimental testing of new compounds. Validation of QSPR models plays a crucial role for judgment of the reliability of predictions of such models. In the QSPR literature, serious attention is now given to external validation for checking reliability of QSPR models, and predictive quality is in the most cases judged based on the quality of predictions of property of a single test set as reflected in one or more external validation metrics. Here, we have shown that a single QSPR model may show a variable degree of prediction quality as reflected in some variants of external validation metrics like <i>Q</i>²_F1, <i>Q</i>²_F2, <i>Q</i>²_F3, CCC, and <i>r_m</i>² (all of which are differently modified forms of predicted variance, which theoretically may attain a maximum value of 1), depending on the test set composition and test set size. Thus, this report questions the appropriateness of the common practice of the “classic” approach of external validation based on a single test set and thereby derives a conclusion about predictive quality of a model on the basis of a particular validation metric. The present work further demonstrates that among the considered external validation metrics, <i>r_m</i>² shows statistically significantly different numerical values from others among which CCC is the most optimistic or less stringent. Furthermore, at a given level of threshold value of acceptance for external validation metrics, <i>r_m</i>² provides the most stringent criterion (especially with Δ<i>r</i>_<i>m</i>² at highest tolerated value of 0.2) of external validation, which may be adopted in the case of regulatory decision support processes

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis