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An effort was made to analyze metabolome and transcriptome profiles of Ferula persica via GC-MS and RNA-seq data. The analysis of the essential oils extracted from both flower and root tissues demonstrated the prominence of monoterpene constituents, while sesquiterpene compounds were present in the lower magnitudes. Considering transcriptome analysis, 2127 differentially expressed genes were found between root and flower: 396 transcripts were up-regulated in root, while 1731 exhibited an up-regulation pattern in flower. Out of 2127 transcripts, 86 were annotated as Terpene Synthases (TPSs), of which 83 TPSs were classified subsequently into five individual sub-families of TPS-a (33), TPS-b (42), TPS-c (2), TPSe-f (3), and TPS-g (3). Several transcription factor families were recognized among the differentially expressed genes, suggesting their direct or indirect regulatory roles for the biosynthesis of terpenoids in F. persica. Finally, according to our phylogenetic results, both F. assa-foetida and F. gummosa were placed in the same clade, while F. persica was lonely settled in one monophyletic clade, with the estimated divergence time of 2.99 Million Years Ago (MYA) between F. gummosa and F. assa-foetida, and 3.87 MYA between F. persica and two other Ferula species. [ABSTRACT FROM AUTHOR]

We add a new phase, called reforming phase, to support vector data description (SVDD) between the training and testing phases. The reforming phase enables us to reconsider the SVDD's assumption of the uniformity of features in calculating the distance of an object to the center of hypersphere. In the reforming phase, the features are assumed as a group of experts who have different impacts in overall outlier detection. In doing so, the proportion of each feature in the distance of an object to the center of hypersphere is specified. Subsequently, the opinions of the experts about the label of the corresponding object are determined based on these measured proportions. By using different group decision-making methods for aggregating the opinions of the experts, a large variety of new models are obtained based on one SVDD's trained model. Specially, we utilize a kind of ordered weighted averaging operator as group decision-making method and introduce cDFS-SVDD based on this method. cDFS-SVDD performs runtime feature selection and calculates the distance of an object to the center of hypersphere dynamically at test time based on these selected features. We apply the method to the anomaly detection problem in mobile ad hoc networks as well as two UCI datasets by which the performance of SVDD improves significantly in separating the target and outlier objects. [ABSTRACT FROM AUTHOR]

In this investigation, the proficiency of a number of magnetic carbon-based nano-adsorbents is evaluated in pre-purification process of the crude paclitaxel extract obtained from fresh needles of yew tree ( Taxus baccata L.). The effectiveness and removal ability of color and impurities from crude extracts, for three novel candidate nano-adsorbents (i.e., FeO nanoparticles (FeONps), graphite oxide (GO), and their hybrids FeONps/GO) are compared with commercial graphite in three different solvents. In general, both HPLC and UV-Vis spectroscopy results demonstrate that in less polar solvent (i.e., dichloromethane), the adsorption is greatly affected by the electrostatic attractions, while in more polar solvents (i.e., acetone and ethanol) π-π electron interactions taking place between adsorbent and adsorbate are the most dominant factors in sorption. Considering decolorization efficiency, purity of taxol, recovery and reusability of adsorbents, FeONps/GO (50 g/L) in dichloromethane is selected as the best medium for pre-purification of paclitaxel. Additionally, in kinetic studies the sorption equilibrium can be reached within 120 min, and the experimental data are well fitted by the pseudo-second-order model. The Langmuir sorption isotherm model correlates well with the sorption equilibrium data for the crude extract concentration (500-2,000 mg/L). Our findings display promising applications of FeONps/GO, as a cost-effective nano-adsorbent, to provide a suitable vehicle toward improvement of paclitaxel pre-purification. [ABSTRACT FROM AUTHOR]

Many pregnant Muslim women fast during Ramadan. Leptin has an important role in the reproductive system and hormones. In this study, FSH, LH, oestrogen, progesterone and leptin were measured in the first, second and fourth week of Ramadan and the second week post-Ramadan, in 30 fasting pregnant women. Data were analysed using repeated measures ANOVA by SPSS. The weight and BMI did not change during the study. A significant change in FSH, oestrogen, progesterone and leptin was observed (p < 0.05). The lowest value of FSH was in the second week of Ramadan. Progesterone increased at the end of Ramadan and the second week after. Oestrogen increased significantly during Ramadan and decreased after Ramadan. A decreasing trend was seen in LH during the Ramadan and 2 weeks after (p < 0.1). Leptin decreased significantly 2 weeks after Ramadan. We found poor weight gain and hypoleptinaemia in pregnant fasted women during the study. Food restriction in pregnant fasted women during Ramadan may induce poor weight gain during pregnancy. These data confirm that Ramadan fasting by pregnant women may have potential risks during pregnancy. We recommend further study to evaluate long-term effects of Ramadan fasting during pregnancy in different countries with different food habits and traditions, to obtain reliable and documented data. [ABSTRACT FROM AUTHOR]