Diplocyclos palmatus (L.) C. Jeffrey is an important medicinal plant used in several reproductive medicines. It serves as a wide source of tetracyclic triterpens called cucurbitacins. Response surface methodology (RSM) with Box-Behnken design (BBD) was studied to optimize the production of cucurbitacins. RSM put forth the ideal conditions such as 1:30 SS ratio (g/mL), 80 rpm (mixing extraction speed), 150 µm mean particle size, 30 min extraction time and 50 °C using chloroform in continuous shaking extraction (CSE) and showed the highest cucurbitacin I (CUI) content (2.345 ± 0.1686 mg/g DW). Similarly, the highest yield of cucurbitacin B (CUB) (1.584 ± 0.15 mg/g DW) was recorded at ideal conditions (1:40 g/mL SS ratio and 60 min time and others similar to CUI). Among the tested extraction methods, the highest CUI, CUB, and CUI + B yield (1.437 ± 0.03, 0.782 ± 0.10, 2.17 ± 0.35 mg/g DW, respectively) as well as promising DPPH radical scavenging activity (25.06 ± 0.1 µgAAE/g DW) were recorded from the SBAE (steam bath assisted extraction). In addition, MAE and UAE revealed the highest inhibition of α-amylase (68.68%) and α-glucosidase (56.27%) enzymes, respectively. Fruit extracts showed potent anticancer activity against breast (MCF-7) and colon (HT-29) cancer cell lines (LC 50 -44.27 and 46.88 µg/mL, respectively). Our study proved that SS ratio, particle size and temperature were the most positively influencing variables and served to be the most efficient for the highest recovery of CUI and CUB. Based on the present study, the fruits of D. palmatus were revealed as a potent antioxidant, anti-diabetic and anticancer bio-resource that could be explored further to develop novel drug to manage diabetes, cancer and oxidative stress related disorders.The plants from the family Cucurbitaceae are commonly called as melons, squashes, and gourds which are traditionally used in the human diet. It comprises 122 genera and 940 species of which 31 genera and 94 species are found in India 1 . Diplocyclos palmatus (L.) C. Jeffrey is a slender-stemmed tendril climber commonly called as Shivlingi. Traditionally, this plant has been used in the folk medicine and possesses several activities such as gynaecological, anti-asthmatic, anti-convulsant, anti-venom, anti-inflammatory, androgenic and antioxidant 2-4 . The family Cucurbitaceae has been recognized as a rich source of bitter compounds, called cucurbitacins 5 . The cucurbitacins are highly unsaturated triterpenes containing many keto-, hydroxy-and acetoxy-groups. They are the major active components in traditional medicine, herbal remedies and Pharmacopoeia to treat various health issues such as hepatoprotective, anti-fertility, diabroticites cardiovascular and anti-inflammatory activities 6 . In a recent study, cucurbitacin I (CUI) has been identified as a strong inhibitor of the JAK2/STAT3 signaling pathway (a common oncogenic signaling pathway), which is constitutively activated in many types of cancer; hence, it is considered as a milestone in cancer therapy 7 . Similarly...

Background: Generalized linear models (GLM) are widely used to model social, medical and ecological data. Choosing predictors for building a good GLM is a widely studied problem. Likelihood based procedures like Akaike Information criterion and Bayes Information Criterion are usually used for model selection in GLM. The non-robustness property of likelihood based procedures in the presence of outliers or deviation from assumed distribution of response is widely studied in the literature. Results:The deviance based criterion (DBC) is modified to define a robust and consistent model selection criterion called robust deviance based criterion (RDBC). Further, bootstrap version of RDBC is also proposed. A simulation study is performed to compare proposed model selection criterion with the existing one. It indicates that the performance of proposed criteria is compatible with the existing one. A key advantage of the proposed criterion is that it is very simple to compute. Conclusions:The proposed model selection criterion is applied to arboreal marsupials data and model selection is carried out. The proposed criterion can be applied to data from any discipline mitigating the effect of outliers or deviation from the assumption of distribution of response. It can be implemented in any statistical software. In this article, R software is used for the computations.

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We propose penalized minimum φ-divergence estimator for parameter estimation and variable selection in logistic regression. Using an appropriate penalty function, we show that penalized φ-divergence estimator has oracle property. With probability tending to 1, penalized φ-divergence estimator identifies the true model and estimates nonzero coefficients as efficiently as if the sparsity of the true model was known in advance. The advantage of penalized φ-divergence estimator is that it produces estimates of nonzero parameters efficiently than penalized maximum likelihood estimator when sample size is small and is equivalent to it for large one. Numerical simulations confirm our findings.