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Marma Science is one of the most distinctive concepts of Ayurveda. There are 107 marma sites in the body, and they are the conglomeration of muscles, veins, ligaments, bones, and joints. This peculiarity makes Marmamarma a somewhat vulnerable point, and any injury can lead to disability, dysfunction and demise. The cause of the damage can either be traumatic or iatrogenic; therefore, it becomes a necessity to rule out the exact location of the marma and anatomical structure responsible for the traumatic effects. Katiktarun being a Prishthagata marma, is prone to get injured during significant surgeries of the gluteal region and spine. Its injury can lead to delayed death. The aim of this study revolves around the anatomical entity responsible for delayed death caused by katiktarun injury. By identifying the location and structure involved in the marma, it might be possible to repair the structure and deferment the delayed end. Based on Ayurvedic literature and cadaveric observations, the superior margin of the sciatic notch (suprapiriform foraman) is considered as the position of Katiktarun Marma, whereas the neurovasculature associated with suprapiriform foramen is the causative structure of marma trauma symptoms.

The prediction of human externally visible characteristics (EVCs) based solely on DNA information has become an established approach in forensic and anthropological genetics in recent years. While for a large set of EVCs, predictive models have already been established using multinomial logistic regression (MLR), the prediction performances of other possible classification methods have not been thoroughly investigated thus far. Motivated by the question to identify a potential classifier that outperforms these specific trait models, we conducted a systematic comparison between the widely used MLR and three popular machine learning (ML) classifiers, namely support vector machines (SVM), random forest (RF) and artificial neural networks (ANN), that have shown good performance outside EVC prediction. As examples, we used eye, hair and skin color categories as phenotypes and genotypes based on the previously established IrisPlex, HIrisPlex, and HIrisPlex-S DNA markers. We compared and assessed the performances of each of the four methods, complemented by detailed hyperparameter tuning that was applied to some of the methods in order to maximize their performance. Overall, we observed that all four classification methods showed rather similar performance, with no method being substantially superior to the others for any of the traits, although performances varied slightly across the different traits and more so across the trait categories. Hence, based on our findings, none of the ML methods applied here provide any advantage on appearance prediction, at least when it comes to the categorical pigmentation traits and the selected DNA markers used here.

The present study was carried out with the aim to evaluate the bioactive constituent in Rosa damascena, which is commonly called Demask rose, medicinal plant with biological significance and belongs to Rosaceae. The analyses were performed in methanolic extract with two fraction, Butanol and ethyl acetate. The analysis reveals presence of 09 bioactive compounds. Out of nine compounds, five were derivatives of methyl ester. Chemical compounds found in selected plants possessMethanolic extract of plant in butanol and ethyl acetate fraction are found to possess significant potential to kill different bacterial species such as Pseudomonas aeruginosa, Klebsiella pneumoniae, Escherichia coli, and Proteus mirabillis. All these bacterial species were isolated from different sources such as pus, urine, vaginal sweat and sputum. Extract of Rosa damascena was able to kill all above mentioned species but was not capable to destroy Klebsiella oxytoca isolated from urine. Keywords: Bioactive compounds; GC-MS hyphenated technique; in-vitro Antibacterial assay; Medicinal plants; Rosa damascena http://dx.doi.org/10.19045/bspab.2018.700206