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Abstract Drug resistance poses a significant obstacle to the success of anti-cancer therapy in head and neck cancers (HNCs). We aim to develop a platform for visualizing and analyzing molecular expression alterations associated with HNC drug resistance. Through data mining, we convened differentially expressed molecules and context-specific signaling events involved in drug resistance. The driver genes, interaction networks and transcriptional regulations were explored using bioinformatics approaches. A total of 2364 differentially expressed molecules were identified in 78 distinct drug-resistant cells against 14 anti-cancer drugs, comprising 1131 mRNAs, 746 proteins, 62 lncRNAs, 257 miRNAs, 1 circRNA, and 166 post-translational modifications. Among these, 255 molecules were considerably, the signature driver genes of HNC drug resistance. Further, we also developed a landscape of signaling pathways and their cross-talk with diverse signaling modules involved in drug resistance. Additionally, a publicly-accessible database named “HNCDrugResDb” was designed with browse, query, and pathway explorer options to fetch and enrich molecular alterations and signaling pathways altered in drug resistance. HNCDrugResDb is also enabled with a Drug Resistance Analysis tool as an initial platform to infer the likelihood of resistance based on the expression pattern of driver genes. HNCDrugResDb is anticipated to have substantial implications for future advancements in drug discovery and optimization of personalized medicine approaches.

The renewable energy transition of oil- and gas-producing countries has specific peculiarities due to the ambivalent position of these countries in the global energy market, both as producers and consumers of energy resources. This task becomes even more challenging when the share of oil and gas in the country’s GDP is very high. These circumstances pose serious challenges for long-term energy policy development and require compromising decisions to better align the existing and newly created energy policies of the country. The scale, scope, and pace of changes in the transition process must be well balanced, considering the increasing pressure of economic and environmental factors. The objective of this paper is to develop models that allow the selection of the most appropriate scenario for renewable energy transition in an oil- and gas-producing country. The distinguishing feature of the proposed model is that alternatives in the decision matrix are presented as scenarios, composed of a set of energy resources and the level of their use. Linguistic descriptions of the alternative scenarios are formalized in the form of fuzzy statements. For the problem solution, four different Multiple-Criteria Decision-Making (MCDM) methods were used: the fuzzy simple additive weighting (F-SAW) method, the distance-based fuzzy TOPSIS method (Technique of Order Preference Similarity to the Ideal Solution), the ratio-analysis-based fuzzy MOORA method (Multi-Objective Optimization Model Based on the Ratio Analysis), and the fuzzy multi-criteria optimization and compromise solution method VIKOR (Serbian: VIekriterijumsko Kompromisno Rangiranje). This approach is illustrated using the example of the energy sector of Azerbaijan. The recommended solution for the country involves increasing natural gas (NG) moderately, maintaining hydro, and increasing solar notably and wind moderately.

In this Study, We applied Translog cost function with four Input (Labor, Capital, Energy and Material) and ISUR[1]method for analysis of the cost structure of 11 most Energy intensive sub sector industries (With 4 Digit ISIC[2]code) of Manufacture of other Non-Metallic Mineral Products (Code26) and Manufacture of Basic Metals (Code 27) during the period 1375-87. Input Share, Economies of Scale, Minimum Efficient Scale, Own-cross price elasticity and Morishima elasticity of substitution were calculated. The important results of this study is the detection of Scale effects, which reveals possibilities for increasing Scales (products) and reducing costs. The result of Cross-price and Morishima Elasticity of substitution shows that all Input substitution elasticities areElastic. This case indicates that the Firm's Manager of Energy intensive industries have a lot of options to substitute one input for the other inputs.

The present study was carried to determine the prevalence of Hepatitis A virus (HAV) IgM antibody among general population in Hyderabad since January 2002 to December 2011. A total of 4571 subjects were selected and divided into groups on the basis of age. The serum samples were screened for the presence of HAV IgM antibodies (anti-HAV IgM) using a commercially available enzyme linked immunosorbent assay (ELISA) kit. Of the 4571 serum samples, 4257 were negative and 314 (6.87%) were positive for the HAV IgM assay. The disease predominance was more in males (212) with 67.51% compared to females (102) with 32.48%. Anti-HAV seroprevalence was measured for the following age groups: Early (≤20 years), Middle (21-40 years) and Late (≥41 years). Average age-wise IgM-anti-HAV positivity from 2002-2011 was 234 of 314 in ≤20 years, 74.52%), 73 of 314 (21-40 years, 23.24%), and 7 of 314 (≥41 years, 2.22%). The present study will provide insights to the recent epidemiologic features of HAV infection in South India.