Informatics Publishing Limited

Devinder Singh ¹, Manish Sharma ¹, Ajay Kumar Sharma ²

Affiliations
1 Department of Zoology and Environmental Sciences, Punjabi University, Patiala, Punjab, IN
2 Entomology Division, Defense R&D Establishment (DRDE), DRDO, Gwalior, Madhya Pradesh,, IN

Abstract

The utility of cytochrome oxidase I (COI) gene has been tested for the identification of three species of forensically important flesh flies of the genus Parasarcophaga (Dipera: Sarcophagidae). A 450 bp fragment of the COI gene has been sequenced for 12 specimens, representing three species of Genus Parasarcophaga. Nucleotide divergences were calculated using Tamura 3 Parameter distance model. For graphical display of the patterns of divergence among the species Neighbor-joining and Parsimonious trees were also made using this software. Musca autumnalis was taken as outgroup in the phylogenetic analysis. Phylogenetic analysis of the sequenced segments showed that all sarcophagid specimens were properly assigned into three species, which indicated the possibility of separating congeneric species with the help of short fragments

Keywords

COI, Forensic Entomology, Sarcophagidae, MTDNA, Species Identification

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Bhaskar Basu ¹, Manish Sharma ¹, C. S. Gupta ², S. Thippeswamy ¹, A. V. Jeyagopal ¹, G. B. Joshi ¹, R. Mohanty ³

Affiliations
1 Department of Atomic Energy (DAE), Shillong 793 019, IN
2 DAE, New Delhi 110 066, IN
3 DAE, Hyderabad 500 016, IN

Abstract

No Abstract.

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Sanat Sharma ¹, Raj Sharma ¹, Manish Sharma ¹, Syed Sajid Ali ¹, Tabish Ahmed ¹, Devender Sachdeva ¹

Affiliations
1 Late Shree Baliram Kashyap Memorial Government Medical College, Jagdalpur, Chhattisgarh, IN

Abstract

A shampoo is a cleaning aid for the hair and is counted amongst the foremost beauty products. Shampoo is a hair care product that is used for the removal of oils, skin particles, dandruff, environmental pollutants and other contaminant particles that gradually build up in hair. The goal of using shampoo is to remove the unwanted build up without stripping out so much sebum as to make hair unmanageable. Shampoo is generally made by combining a surfactant, most often sodium lauryl sulfate or sodium laureth sulfate, with a co-surfactant, most often cocamidopropyl betaine in water to form a thick, viscous liquid. Other essential ingredients include salt (sodium chloride), which is used to adjust the viscosity, a preservative and fragrance.Today's shampoo formulations are beyond the stage of pure cleaning of the hair. Additional benefits are expected, e.g. conditioning, smoothing of the hair surface, good health of hair, i.e, hair free of dandruff, dirt, grease and lice and all, its safety benefits are expected. As the scalp is one of the most highly permeable part of the body, products applied to the scalp go directly to the blood, without being filtered in any way. So it is very important to know and understand the effects of ingredients used in shampoo formulations. Proteolysis is the directed degradation (digestion) of proteins by cellular enzymes called proteases or by intramolecular digestion. In the present research represent the proteolysis effectiveness of shampoos based on synthetic ingredients.

Keywords

Shampoo, SLS-Sodium Lauryl Sulphate, Skin, Baldnss, Proteolysis, Insecticide.

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Manish Sharma , Shweta Garg

Abstract

• Purpose – The paper provides an overview of the impact of urbanization on the health of Indians and the dynamic market of Functional Foods mapping the competitive landscape and the firm’s marketing effort.
• Design/methodology/approach – The study involves exhaustive desk research to explore, assimilate and analyse data to derive relevant information on market and marketing of Functional Foods in India. A variety of sources including the company sources, government agencies, consulting reports, scholarly papers and expert opinions have been explored.
• Findings – The functional food market in India is growing at a rapid pace as health as a value has percolated in the Indian society undergoing dramatic demographic shifts. The functional food market is innovation driven and dominated by Multinational companies. Ayurveda forms the basis of many Functional Food products sold in the Indian market and has been tapped predominantly by the domestic firms. Edible oil and dairy are two most important functional foods categories.
• Research limitations/implications – The research is based on the data from secondary sources and does not involve primary data collection. The research also suffers from a limitation of not having scholarly papers on Functional Foods market in the Indian context. To overcome this limitation the sources from industry, consulting reports and expert opinions have been referred.
• Originality/value – To the authors’ knowledge, this is the first paper that provides insights on the Functional Foods market in India and may form the basis for further empirical research to gain consumer insights.

 

Keywords

Functional Foods, Marketing, Health, India, Probiotics, Ayurveda

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Tarun ¹, Ramesh Verma ¹, Varun Arora ², Manish Sharma , Shalini Singh , Amit Arya ³

Affiliations
1 Department of General Medicine, B.P.S. G.M.C. (W), Khanpur Kalan, IN
2 Department of Community Medicine, PGIMS Rohtak, Haryana, IN
3 HCMS, IN

Abstract

In several ethnic populations including the relatively non-obese Indian population, the android pattern of body fat, typified by more upper body adiposity measured as waist hip ratio (WHR) was found to be a greater risk factor for type 2 diabetes than general obesity which is calculated by BMI. Various studies have shown that central obesity is common in Indians despite low rates of obesity. This is probably one of the reasons for a higher prevalence of diabetes in urban area To study the Anthropometric Indices & their relationship with diabetes in urban population of Rohtak, Haryana. Population based descriptive type of epidemiological study, design adopted was cross-sectional. Urban field practice area with population of 57000, attached to Dept. of Community Medicine PGIMS, Rohtak. 1003 subjects were selected using simple random sampling through random number table. Various anthropometric indices estimated were Weight, Height, Body Mass Index (BMI), Waist Hip Ratio, Waist Height Ratio. Fasting Blood Glucose estimation was done for detection of Diabetes using ADA criteria. ANOVA, chi square test, percentages & proportions. Out of 1003 study subjects, 81 were detected as diabetics & 103 were diagnosed as pre-diabetics. Mean weight of diabetics and pre diabetics was 65.77±12.94 kg and66±13.64kg, respectively which was significantly higher than that of non-diabetics 59.59±13.64kg (p < 0.001) implying a strong association of obesity with diabetes. Body mass index (BMI) was high in diabetics (26.11±4.31) as well as pre diabetics (25.68±4.98), classifying them as overweight as compared to normal BMI (22.99±4.20) in non-diabetics. (p< 0.001). Waist hip ratio was highest in diabetics (0.95±0.057) followed by in pre diabetics (0.92±0.069) and was least in non-diabetics (0.89±0.079). (p< 0.001)

Keywords

Anthropometric Indices, Diabetes.

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Sunali Mahajan ¹, Manish Sharma ¹, Banti Kumar ¹

Affiliations
1 Division of Statistics and Computer Science, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu (J&K), IN

Abstract

The traditional ordinary least squares procedure (OLS) is the most frequently used method for analyzing food grain production data (1983-2014), but ignore the presence of outliers or influential data points which may distort the regression estimates obtained from OLS. These data points may remain unnoticed and can have a strong adverse affect on the regression estimates. In this paper, two approaches i.e., robust M-regression and quantile regression to linear robust regression analysis are presented, as these methods provide formal procedure to overcome from the situation of outliers and influential observations and to reduce their influence on the final estimates of the regression co-efficients by using Cobb-Douglas production function. Moreover, 0.90^th quantile regression model comes out to be best on the basis of AIC (-47.17), SBIC (-36.91), elasticity of production, marginal value productivity, sign, size and the variables significant effect on foodgrain production than OLS and robust M-regression. Also, the variables NSA and AC were best in order to increase the food grain production on the basis of quantile 0.90^th regression, elasticity of production and MVP at 0.90^th quantile.

Keywords

Ordinary Least Square, Outliers, Robust Regression, Quantile Regression, M-Estimator, Food Grain Production.

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