Lipophilic profiling of Sorghum bicolor (L.) Moench seedlings vis-à-vis Chilo partellus (Swinhoe) larvae reveals involvement of biomarkers in sorghum-stem borer interactions

95-108Lipophilic metabolites play important role in the developmental process of insects, however, still there is no clarity on their involvement in plant resistance. Therefore, we carried out the lipophilic profile of host sorghum genotype seedlings and the Chilo partellus (Swinhoe) larvae, to understand the role and contribution of certain lipophilic metabolites in sorghum plant defense against the dreaded pest, spotted stem borer, C. partellus. There were variations in the form of presence or absence, along with significant differences in lipophilic metabolites across sorghum genotypes and the C. partellus larvae. The significantly higher contents of myristic acid, palmitic acid, linoleic acid, linolenic acid, eicosanoic acid and behenic acid in resistant sorghum genotypes; and linolenic acid, methyl 3-methoxytetradecanoate, myristic acid, oleic acid, palmitic acid, palmitoleic acid, lathosterol and squalene in C. partellus larvae were significantly lower than those fed on susceptible genotype, indicating their role in insect-plant biochemical disruptions. Myristic acid, methyl 3-methoxy-tetradecanoate, stearic acid, squalene, fucosterol, hexacontane, tetrapentacontane, palmitic acid, l-(+)-ascorbic acid 2,6-dihexa-decanoate, 2-pentadecanone, 6,10,14-trimethyl, lignoceric acid and stigmasterol in sorghum seedlings contributed to 60 to 100% variability in various biological and resistance parameters of C. partellus. However, myristic acid, linoleic acid, margaric acid, methyl 14-methylhexadecanoate, methyl 3-methoxytetradecanoate, stearic acid, palmitic acid, palmitoleic acid, eicosanoic acid, gamma-ergostenol, cholesterol, lathosterol, squalene, 1-triacontanol and n-pentadecanol in C. partellus larvae contributed to 64 to 100% variability in various biological and resistance parameters of C. partellus. The myristic acid, methyl 3-methoxytetradecanoate, palmitic acid, stearic acid and squalene present in both host plant and the test insect, contributed significantly to explain variability in resistance against C. partellus, thus could be used as biomarkers for sorghum-stem borer interactions

Predictive Analytics For Controlling Tax Evasion

Tax evasion is an illegal practice where a person or a business entity intentionally avoids paying his/her true tax liability. Any business entity is required by the law to file their tax return statements following a periodical schedule. Avoiding to file the tax return statement is one among the most rudimentary forms of tax evasion. The dealers committing tax evasion in such a way are called return defaulters. We constructed a logistic regression model that predicts with high accuracy whether a business entity is a potential return defaulter for the upcoming tax-filing period. For the same, we analyzed the effect of the amount of sales/purchases transactions among the business entities (dealers) and the mean absolute deviation (MAD) value of the �rst digit Benford's analysis on sales transactions by a business entity. We developed and deployed this model for the commercial taxes department, government of Telangana, India. Another technique, which is a much more sophisticated one, used for tax evasion, is known as Circular trading. Circular trading is a fraudulent trading scheme used by notorious tax evaders with the motivation to trick the tax enforcement authorities from identifying their suspicious transactions. Dealers make use of this technique to collude with each other and hence do heavy illegitimate trade among themselves to hide suspicious sales transactions. We developed an algorithm to detect the group of colluding dealers who do heavy illegitimate trading among themselves. For the same, we formulated the problem as finding clusters in a weighted directed graph. Novelty of our approach is that we used Benford's analysis to define weights and defined a measure similar to F1 score to find similarity between two clusters. The proposed algorithm is run on the commercial tax data set, and the results obtained contains a group of several colluding dealers