Polymorphic Signature of the Anti-inflammatory Activity of 2,2′- {[1,2-Phenylenebis(methylene)]bis(sulfanediyl)}bis(4,6- dimethylnicotinonitrile)

Weak noncovalent interactions are the basic forces in crystal engineering. Polymorphism in flexible molecules is very common, leading to the development of the crystals of same organic compounds with different medicinal and material properties. Crystallization of 2,2′- {[1,2-phenylenebis(methylene)]bis(sulfanediyl)}bis(4,6-dimethylnicotinonitrile) by evaporation at room temperature from ethyl acetate and hexane and from methanol and ethyl acetate gave stable polymorphs 4a and 4b, respectively, while in acetic acid, it gave metastable polymorph 4c. The polymorphic behavior of the compound has been visualized through singlecrystal X-ray and Hirshfeld analysis. These polymorphs are tested for anti-inflammatory activity via the complete Freund’s adjuvant-induced rat paw model, and compounds have exhibited moderate activities. Studies of docking in the catalytic site of cyclooxygenase-2 were used to identify potential anti-inflammatory lead compounds. These results suggest that the supramolecular aggregate structure, which is formed in solution, influences the solid state structure and the biological activity obtained upon crystallization

NEW AGRICULTURAL POLITICS IN TURKEY: THE ECONOMETRIC ASSESSMENT OF COTTON PRODUCTION AND YIELD 1925-2015

WOS: 000406235300039This study was conducted to evaluate causality between long-term relationships among production amount, cultivated area and yield of cotton lint for Turkey for the period 1925 to 2015, and to determine the strength and direction of the relationships by means of vector error correction model (VECM) and cointegration analysis. After taking the first differences of the original (non-stationary) time series data on cotton lint, stationary time series data were obtained and exposed to cointegration analysis to determine whether any long-term relationships among the variables exist and whether the series were integrated. Both production amount and cultivated area had a positive effect on yield. The effect of production amount on yield is more than that of cultivated area. In order to find the direction of the long term relationship and the short term effects, vector error correction model (VECM) analysis was used and the analysis presented evidence of a causality relationship between cotton production and yield in Turkey. Holt exponential smoothing method was used to forecast cultivated area, amount of production, and yield from the period 2016-2026. The results of Holt method revealed that cultivated area, amount of production, and yield are expected to increase for the above-mentioned time period. New agricultural policies should be formulated in order to cut back on an estimated 20 billion dollars forecasted to be spent on cotton imports between 2016 and 2026

Genetic characterization of autochthonous grapevine cultivars from Eastern Turkey by simple sequence repeats (SSRs)

In this research, two well-recognized standard grape cultivars, Cabernet Sauvignon and Merlot, together with eight historical autochthonous grapevine cultivars from Eastern Anatolia in Turkey, were genetically characterized by using 12 pairs of simple sequence repeat (SSR) primers in order to evaluate their genetic diversity and relatedness. All of the used SSR primers produced successful amplifications and revealed DNA polymorphisms, which were subsequently utilized to evaluate the genetic relatedness of the grapevine cultivars. Allele richness was implied by the identification of 69 alleles in 8 autochthonous cultivars with a mean value of 5.75 alleles per locus. The average expected heterozygosity and observed heterozygosity were found to be 0.749 and 0.739, respectively. Taking into account the generated alleles, the highest number was recorded in VVC2C3 and VVS2 loci (nine and eight alleles per locus, respectively), whereas the lowest number was recorded in VrZAG83 (three alleles per locus). Two main clusters were produced by using the unweighted pair-group method with arithmetic mean dendrogram constructed on the basis of the SSR data. Only Cabernet Sauvignon and Merlot cultivars were included in the first cluster. The second cluster involved the rest of the autochthonous cultivars. The results obtained during the study illustrated clearly that SSR markers have verified to be an effective tool for fingerprinting grapevine cultivars and carrying out grapevine biodiversity studies. The obtained data are also meaningful references for grapevine domestication

Fitting nonlinear growth models on weight in Mengali sheep through Bayesian inference

This article deals with the Bayesian analysis as an alternative to the classical approach for estimating the body growth of Mengali sheep breed of Balochistan, Pakistan. The parameters mature weight, integration constant, maturity rate and their credible intervals of four widely-used nonlinear sigmoidal growth models were estimated through Bayesian inference. Gompertz, Logistic, Brody and Von Bertalanffy models were fitted to average monthly body weight data of (n = 412) Mengali sheep from birth to 24 months of age for both sexes (male and female) and type of births (single and twin). The overall goodness of fit was checked by calculating Deviance Information Criteria (DIC) and the square of correlation (R 2 ) between observed body weight and the predicted value’s marginal density means. The DIC and R 2 values of the models ranged from 31.2 to 59.3 and 0.9702 to 0.9977, respectively. Our results revealed the superior performance of the Brody model in terms of lower DIC and higher R 2 values for male, female, single and twin birth sheep data, thus providing the overall best fit than the competing nonlinear growth models. The findings of this study indicate the potential of fitting complex nonlinear functions to weight-age relationship of animal data via Bayesian approach. Copyright 2019 Zoological Society of Pakistan

Growth curve in Mengali sheep breed of Balochistan

Growth, one of the most essential traits for farm animals, is defined as an increase in tissues and organs of the animals per unit time and affected by genetic and environmental factors. The growth that has sigmoid form is explained reliably by nonlinear growth models (such as Monomolecular, Brody, Gompertz, Richards and Logistic). Information about parameters of these nonlinear models enables researcher to obtain beneficial clues for selection studies. Data on 2377 Mengali sheep kept at four different research stations (Experimental Station CASVAB, Quetta, (ESC), Mastung, Noshki and Quetta) at three different locations in Balochistan were analyzed using Gompertz growth model, W(t) = A*exp(-B*exp(-k*t) with non-linear regression methodology. Body weight values for all the sheep were recorded monthly from birth to 360th days of age. Body weight averages of these sheep in each period were used to define the weight-age relationship in Mengali sheep. Determination coefficient (R2) and Root of Mean Square Error (RMSE) were used to decide whether Gompertz growth model was appropriate for the body weight - age data from Mengali Sheep. Convergence was achieved after 5 iterations. The parameters A, B, and k of Gompertz growth model were 36.924, 2.043 and 0.010083, respectively. These parameter estimates were statistically significant (P<0.01). Root of Mean Square Error (RMSE) and Determination Coefficient (R2) were 1.022, 99.17% respectively. Besides, it was determined the observed and predicted weight values at each time period in Gompertz growth model were almost similar. These results reflected that Gompertz growth model reliably explained relationship between weight and age in Mengali sheep. As a result, Gompertz growth model fitted to the body weight - age data from Mengali sheep might help us to determine an accurate feed regime, maturity age, and problems in growth and development over time

Prediction of Selected Reproductive Traits of Indigenous Harnai Sheep under the Farm Management System via various Data Mining Algorithms

In this study, an attempt was made at predicting the values of selected reproductive parameters in Harnai sheep using different data mining algorithms (artificial neural networks - ANN, classification and regression trees - CART, chi-square automatic interaction detector - CHAID and multivariate adaptive regression splines - MARS) and indicating the most influential predictors of these traits. A total of 382 reproduction records including three predictors (month of lambing - MOL, age at first lambing - AFL and lambing weight - LW) and seven dependent (output) variables (services per conception - SPC, service period - SP, lambing interval - LI, twinning rate - TR, gestation length - GL, breeding efficiency - BE and fertility rate - FR) were used. A 10-fold cross-validation was applied to train and evaluate the models. The highest correlation coefficients (r) were found for LI (0.18 - 0.29; P?0.001), GL (0.05 - 0.21; P?0.001 to P>0.05) and FR (0.11 - 0.26; P?0.001 to P?0.05). For the remaining output variables, it was usually lower than 0.10. The smallest values of SD ratio (0.96 - 1.06) were found for LI, GL and FR. For the rest of the output variables, it was usually above 1.00. The measures of predictor importance to ANN, CART, CHAID and MARS were generally low. In conclusion, the applied method of reproductive parameters prediction was rather ineffective, indicating that more powerful input variables are required to obtain better prediction results. Copyright 2019 Zoological Society of Pakistan

A Bayesian approach for describing the growth of Chukar partridges

WOS: 000489299700001In this article, a Bayesian approach was employed for estimating the body growth of Chukar partridge (Alectoris chukar). Nonlinear growth models commonly used to estimate the growth curve of birds were fitted to weight-age data and estimates of model parameters and their credible intervals were obtained via a formal Bayesian framework. More specifically, the Gompertz, Brody, Logistic and von Bertalanffy growth models were fitted to weekly body weight data of 108 female and 72 male partridge chicks from hatch to 20 weeks of age. Deviance Information Criteria (DIC) and the coefficient of determination (R-2) were employed as the goodness of fit for comparing the fitted models. The DIC and R-2 values of the models ranged from 172.9 to 204.2 and 0.9765 to 0.9913, respectively. The von Bertalanffy model was found to provide the best fit in terms of lower DIC and higher R-2 values, followed by the Gompertz model for both male and female partridge data. The Bayesian approach was found to be adequate for fitting complex nonlinear functions to weight-age data of Chukar partridges