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Background A new series of indole-carbohydrazide-phenoxy-1,2,3-triazole-N-phenylacetamide hybrids 11a-o was designed based on molecular hybridization of the active pharmacophores of the potent α-glucosidase inhibitors. These compounds were synthesized and evaluated against α-glucosidase. Methods The fifteen various derivatives of indole-carbohydrazide-phenoxy-1,2,3-triazole-N-phenylacetamide scaffold were synthesized, purified, and fully characterized. These derivatives were evaluated against yeast α-glucosidase in vitro and in silico. ADMET properties of the most potent compounds were also predicted. Results All new derivatives 11a-o (IC50 values = 6.31 ± 0.03–49.89 ± 0.09 µM) are excellent α-glucosidase inhibitors in comparison to acarbose (IC50 value = 750.0 ± 10.0 µM) that was used as a positive control. Representatively, (E)-2-(4-((4-((2-(1H-indole-2-carbonyl)hydrazono)methyl) phenoxy)methyl)-1H-1,2,3-triazol-1-yl)-N-(4-methoxyphenyl)acetamide 11d with IC50 = 6.31 µM against MCF-7 cells, was 118.8-times more potent than acarbose. This compound is an uncompetitive inhibitor against α-glucosidase and showed the lowest binding energy at the active site of this enzyme in comparison to other potent compounds. Furthermore, computational calculations predicted that compound 11d can be an orally active compound. Conclusion According to obtained data, compound 11d can be a valuable lead compound for further structural development and assessments to obtain effective and potent new α-glucosidase inhibitors.

α-Glucosidase is responsible for glucose release of oligosaccharides and disaccharides in the intestine and increase postprandial hyperglycemia. Inhibition of this enzyme is a beneficial therapeutic method for glycemic control in diabetes. This study deals with the design and synthesis of 4,5-diphenylimidazole-N-phenylacetamide derivatives 7a–l and the screen of these compounds for their potential for α-glucosidase inhibition. All the synthesized compounds exhibited superior α-glucosidase inhibition (IC50 = 90.0–598.5 µM) as compared to standard inhibitor acarbose (IC50 = 750.0 µM). In contrast, these compounds were inactive against α-amylase. Among the synthesized compounds, compound 7h was the most potent inhibitor of this library and was a competitive inhibitor into α-glucosidase with Ki value = 86.3 μM. Docking study of the most potent compounds was performed to evaluate the binding interactions of these compounds with the active site of enzyme and to determine of binding energies of ligand–enzyme complexes. The results of this in silico study are in complete agreement with the results obtained from in vitro α-glucosidase inhibition assay. Docking study of the most potent compound demonstrated that it interacted with important residues in the active site of α-glucosidase. In vitro cytotoxic activity of the most potent compounds and in silico druglikeness/ADME/toxicity study of these compounds were evaluated.

A new series of phthalimide-benzamide-1,2,3-triazole hybrids 8a–k as α-glucosidase inhibitors was designed and synthesized. The biological evaluation of compounds 8a–k against yeast α-glucosidase demonstrated that all they have excellent inhibitory activity in comparison with standard inhibitor acarbose. Among them, the most potent compound was compound 8d with inhibitory activity 18.5-fold more than acarbose. Kinetic study revealed that α-glucosidase inhibition of compound 8d was the competitive type. Furthermore, docking study suggested that compound 8d is more stable than acarbose in the active site of α-glucosidase.

A thorough analysis of people’s sentiment about a business, an event or an individual is necessary for business development, event analysis and popularity assessment. Social networks are rich sources of obtaining user opinions about people, events and products. Sentiment analysis conducted using multiple user comments and messages on microblogs is an interesting field of data mining and natural language processing (NLP). Different techniques and algorithms have recently been developed for conducting sentiment analysis on Twitter. Different proposed classification and pure NLP-based methods have different behaviours in predicting sentiment orientation. In this study, we combined the results of the classic classifiers and NLP-based methods to propose a new approach for Twitter sentiment analysis. The proposed method uses a fuzzy measure for determining the importance of each classifier to make the final decision. Fuzzy measures are used with the Choquet fuzzy integral for fusing the classifier outputs in order to generate the final label. Our experiments with different Twitter sentiment datasets show that fuzzy integral-based classifier fusion improves the average accuracy of sentiment classification.

Classification is the most important issues that have gained much attention in various fields such as health and medicine. Especially in survival models, classification represents a main objective and it is also one of the main purposes in data mining. Among data mining methods used for classification, implementation of the decision tree due to its simplicity and understandable and accurate results, has gained much attention and popularity. In this paper, first we generate the observations by using Monte-Carlo simulation from hazard model with the three degrees of complexity in different levels of censorship 0 to 70%. Then the accuracy of classification in the Cox and the decision tree models is compared for the number of samples 1000, 5000 and 10,000 by area under the ROC curve(AUC) and the ROC-test.

The objective of this study was to characterize the heavily fertilized soils of northern Iran for their phosphorus (P) fractions and sorption properties for assessing their P availability status in order to minimize P applications and to reduce the danger of P loss to water sources. Different P fractions, namely, total, organic, inorganic, water extractable, and NH4-lactate extractable P varied widely among the studied soils, indicating a wide variability of P supplying capacity of these soils. High values of degree of phosphorus saturation were observed in soils with low amounts of organic carbon (C) and may indicate high P susceptibility to surface runoff and leaching. The maximum P buffering capacity (MPBC) and adsorption isotherm of P were highest in the sandy clay soil from Jamaleddinkola, which also contained high amounts of iron (Fe), aluminium (Al) and clay particles, and lowest in sandy soil from Deseleh, which contained very high initial NH4-lactate extractable P and the lowest content of Fe, Al...

Spatial patterns for several soil parameters such soil texture, Exchangeable Sodium Percentage (ESP), Electrical Conductivity (ECe), soil pH, Cation Exchange Capacity (CEC) were examined in saline and sodic soils in Arsanjan plain, Southern Iran, in order to identify their spatial distribution for implementation of a site-specific management. Soil samples were collected from 0-30, 30-60 and 60-90 cm soil depths at 85 sampling sites. Data were analyzed both statistically and geostatistically on the basis of the semivariogram. The spatial distribution model and spatial dependence level varied between soil parameters. Soil pH and ESP had the minimum and maximum variability at all depths, respectively. Soil properties indicated moderate to strong spatial dependence. ECe exhibited moderate spatial dependence at three depths; pH and ESP had a moderate spatial dependence at 0-30 cm and strong spatial dependence at 30-60 and 60-90 cm depths. Clay and CEC exhibited strong spatial dependence for the 0-30 cm and weak spatial dependence at 30-60 and 60-90 cm depths. Sand and silt had a non-spatial dependence at 0-30 cm and weak spatial dependency at 30-60 and 60-90 cm depths. The spatial variability in small distances of ECe, CEC, pH and ESP generally increased with depth. All geostatistical range values were greater than 1168 m. The results reported herein indicated that the strong spatial dependency of soil properties would lead to the extrinsic factors such as ground water level and drainage. It is important to know the spatial dependence of soil parameters, as management parameters with strong spatial dependence will be more readily managed and an accurate site-specific scheme for precision farming more easily developed.

This study attempts to evaluate the nutrient element and carbohydrate distribution within Water-Stable Aggregates (WSA) of two natural ecosystems, native forest and pasturelands, under different land uses. Soil samples were collected from depths of (0-20) cm in Typic Haploxeroll soils. The overall pattern indicated that Mean Weight Diameter (MWD) and WSA were greater in the pasture and forest soils compared with the adjacent cultivated soils and aggregates of1.0 mm size were dominant in the uncultivated soils, whereas the cultivated soils comprised aggregates of the sizeor = 0.5 mm. Distribution of organic carbon, nitrogen, phosphorus and carbohydrates within the WSA showed preferential enrichment of these parameters in the macroaggregate fraction (4.75-1.0 mm) for the uncultivated soils and microaggregate fraction (0.25 mm) for the cultivated soils. Average distribution of total exchangeable bases within WSA showed that cultivation of forest pastureland soils significantly led to reduce in these nutrient in the 4.75-2.0 mm fraction and increase in concentration of these cations in0.25 mm fraction. Since smaller aggregates are preferentially removed by erosion, this study emphasizes the need for sustainable soil management practices that they will minimize nutrient loss when forest or pastures lands are converted to cropland.

The volume of XML data exchange is explosively increasing, and the need for efficient mechanisms of XML data management is vital. Many XML storage models have been proposed for storing XML DTD-independent documents in relational database systems. Benchmarking is the best way to highlight pros and cons of different approaches. In this study, we use a common benchmarking scheme, known as XMark to compare the most cited and newly proposed DTD-independent methods in terms of logical reads, physical I/O, CPU time and duration. We show the effect of Label Path, extracting values and storing in another table and type of join needed for each method-s query answering., {"references":["M. Fernndez, Y. Kadiyska, D. Suciu, A. Morishima and W. C. Tan,\n\"Silkroute: A framework for publishing relational data in xml,\". ACM\nTrans. Database Syst. Vol .27, No. 4, pp. 438-493, 2002.","F. Yergeau, T. Bray, J. Paoli, C. M. Sperberg-McQueen and E. Maler,\n\"Extendible Markup Language (XML),\". W3C Recommendation, 2004.\nAvailable at http://www.w3.org/XML/","P. O-Neil, E. O-Neil, S. Pal, I. Cseri and G. Schaller, \"ORDPATHs:\nInsert-Friendly XML Node Labels,\". Proc. ACM SIGMOD. France pp.\n903-908, 2004.","T. Böhme and E. Rahm, \"Supporting Efficient Streaming and Insertion\nof XML Data in RDBMS,\". Proc. DIWeb. Latvia, pp. 70-81, 2004.","H. Lu, J. Xu Yu, G. Wang, S. Zheng, H. Jiang, G. Yu and A. Zhou,\n\"What makes the differences: benchmarking XML database\nimplementations,\". ACM Trans. Internet Techn. Vol. 5, No. 1, pp 154-\n194, 2005.","D. Florescu and D. Kossmann, \"Storing and Querying XML Data using\nan RDMBS,\". IEEE Data Eng. Bull. Vol. 22, No. 3, pp. 27-34, 1999.","M. Yoshikawa, T. Amagasa, T. Shimura, and S. Uemura, \"XRel: a pathbased\napproach to storage and retrieval of XML documents using\nrelational databases,\". ACM Trans. Internet Techn. Vol. 1, No. 1, pp.\n110-141, 2001.","H. Jiang, H. Lu, W. Wang and J. Xu Yu, \"Path Materialization\nRevisited: An Efficient Storage Model for XML Data,\". Proc. of ACM\nAustralasian Database Tech. Conf. Australia, pp. 85-94, 2002.","H. Jiang, H. Lu, W. Wang and J. Xu Yu, \"XParent: An Efficient\nRDBMS-Based XML Database System,\". Proc. of IEEE ICDE. USA,\npp. 335-336, 2002.\n[10] E. Cohen, H. Kaplan and T. Milo, \"Labeling Dynamic XML Trees,\".\nProc. of ACM PODS. USA, pp. 271-281, 2002.\n[11] S. Pal, I. Cseri, O. Seeliger, G. Schaller, L. Giakoumakis and V. Zolotov,\n\"Indexing XML Data Stored in a Relational Database,\". Proc. of VLDB.\nCanada, pp. 1134-1145, 2004.\n[12] S. Abiteboul, D. Quass, J. McHugh, J. Widom, J. Wiener, \"The Lorel\nQuery Language for Semistructured Data,\". Int. J. on Digital Libraries.\nVol. 1, No. 1, pp. 68-88, 1997.\n[13] A. Deutsch, M. Fernandez, D. Florescu, A. Levy and D. Suciu \"A Query\nLanguage for XML,\". Proc. of WWW. Canada, pp. 1155-1169, 1999.\n[14] D. Chamberlin, J. Robie and D. Florescu, \"Quilt: An XML Query\nLanguage for Heterogeneous Data Sources,\". Proc of WebDB (LNCS).\nUSA, pp. 53-62, 2000.\n[15] J. Clark and S. DeRose, \"XML Path Language (XPath) Version 1.0,\".\nW3C Recommendation, 1999. Available at\nhttp://www.w3.org/TR/xpath.\n[16] A. Bonifati and S. Ceri, \"Comparative Analysis of Five XML Query\nLanguages,\". ACM SIGMOD Record. Vol. 29, No. 1, pp. 68-79, 2000.\n[17] D. Lee and W. W. Chu, \"Comparative Analysis of Six XML Schema\nLanguages,\". ACM SIGMOD Record. Vol 29, No. 3, pp. 76-87, 2000.\n[18] A. Schmidt, M. Kersten, M. Windhouwer and F. Waas, \"Efficient\nRelational Storage and Retrieval of XML Documents,\". Proc. of WebDB\n(LNCS). USA, pp. 137-150, 2000.\n[19] T. Böhme and E. Rahm, \"Multi-User Evaluation of XML Data\nManagement Systems with XMach-1,\". Proc. of EEXTT (LNCS).\nGermany, pp. 148-159, 2003.\n[20] A. Schmidt, F. Waas, M. Kersten, M. Carey, I. Manolescu, and R. Busse,\n\"Xmark: A Benchmark for XML Data Management,\". Proc. of VLDB\nChina, pp. 974-985, 2002.\n[21] S. Boag, D. Chamberlin, M. F. Fern├índez , D. Florescu, J. Robie, J.\nSiméon, \"W3C XML Query (XQuery),\". W3C Candidate\nRecommendation. 2005. Available at http://www.w3.org/XML/Query/\n[22] M. Emadi, M. Rahgozar, A. Ardalan, A. Kazerani and M.M. Arian, \"A\nComparative Study of DTD-Independent XML Data Storage\nApproaches,\". 11th International CSI Computer Conference (CSICC'06).\nIran, pp. 624-628, 2006."]}

The volume of XML data exchange is explosively increasing, and the need for efficient mechanisms of XML data management is vital. Many XML storage models have been proposed for storing XML DTD-independent documents in relational database systems. Benchmarking is the best way to highlight pros and cons of different approaches. In this study, we use a common benchmarking scheme, known as XMark to compare the most cited and newly proposed DTD-independent methods in terms of logical reads, physical I/O, CPU time and duration. We show the effect of Label Path, extracting values and storing in another table and type of join needed for each method's query answering., {"references":["M. Fernndez, Y. Kadiyska, D. Suciu, A. Morishima and W. C. Tan, \"Silkroute: A framework for publishing relational data in xml,''. ACM Trans. Database Syst. Vol .27, No. 4, pp. 438-493, 2002.","F. Yergeau, T. Bray, J. Paoli, C. M. Sperberg-McQueen and E. Maler, ''Extendible Markup Language (XML),''. W3C Recommendation, 2004. Available at http://www.w3.org/XML/","P. O'Neil, E. O'Neil, S. Pal, I. Cseri and G. Schaller, ''ORDPATHs: Insert-Friendly XML Node Labels,''. Proc. ACM SIGMOD. France pp. 903-908, 2004.","T. Böhme and E. Rahm, ''Supporting Efficient Streaming and Insertion of XML Data in RDBMS,''. Proc. DIWeb. Latvia, pp. 70-81, 2004.","H. Lu, J. Xu Yu, G. Wang, S. Zheng, H. Jiang, G. Yu and A. Zhou, ''What makes the differences: benchmarking XML database implementations,''. ACM Trans. Internet Techn. Vol. 5, No. 1, pp 154-194, 2005.","D. Florescu and D. Kossmann, ''Storing and Querying XML Data using an RDMBS,''. IEEE Data Eng. Bull. Vol. 22, No. 3, pp. 27-34, 1999.","M. Yoshikawa, T. Amagasa, T. Shimura, and S. Uemura, ''XRel: a path-based approach to storage and retrieval of XML documents using relational databases,''. ACM Trans. Internet Techn. Vol. 1, No. 1, pp. 110-141, 2001.","H. Jiang, H. Lu, W. Wang and J. Xu Yu, ''Path Materialization Revisited: An Efficient Storage Model for XML Data,''. Proc. of ACM Australasian Database Tech. Conf. Australia, pp. 85-94, 2002.","H. Jiang, H. Lu, W. Wang and J. Xu Yu, ''XParent: An Efficient RDBMS-Based XML Database System,''. Proc. of IEEE ICDE. USA, pp. 335-336, 2002. [10] E. Cohen, H. Kaplan and T. Milo, ''Labeling Dynamic XML Trees,''. Proc. of ACM PODS. USA, pp. 271-281, 2002. [11] S. Pal, I. Cseri, O. Seeliger, G. Schaller, L. Giakoumakis and V. Zolotov, ''Indexing XML Data Stored in a Relational Database,''. Proc. of VLDB. Canada, pp. 1134-1145, 2004. [12] S. Abiteboul, D. Quass, J. McHugh, J. Widom, J. Wiener, ''The Lorel Query Language for Semistructured Data,''. Int. J. on Digital Libraries. Vol. 1, No. 1, pp. 68-88, 1997. [13] A. Deutsch, M. Fernandez, D. Florescu, A. Levy and D. Suciu ''A Query Language for XML,''. Proc. of WWW. Canada, pp. 1155-1169, 1999. [14] D. Chamberlin, J. Robie and D. Florescu, \"Quilt: An XML Query Language for Heterogeneous Data Sources,''. Proc of Web DB (LNCS). USA, pp. 53-62, 2000. [15] J. Clark and S. DeRose, ''XML Path Language (XPath) Version 1.0,''. W3C Recommendation, 1999. Available at http://www.w3.org/TR/xpath. [16] A. Bonifati and S. Ceri, ''Comparative Analysis of Five XML Query Languages,''. ACM SIGMOD Record. Vol. 29, No. 1, pp. 68-79, 2000. [17] D. Lee and W. W. Chu, ''Comparative Analysis of Six XML Schema Languages,''. ACM SIGMOD Record. Vol 29, No. 3, pp. 76-87, 2000. [18] A. Schmidt, M. Kersten, M. Windhouwer and F. Waas, ''Efficient Relational Storage and Retrieval of XML Documents,''. Proc. of Web DB (LNCS). USA, pp. 137-150, 2000. [19] T. Böhme and E. Rahm, ''Multi-User Evaluation of XML Data Management Systems with XMach-1,''. Proc. of EEXTT (LNCS). Germany, pp. 148-159, 2003. [20] A. Schmidt, F. Waas, M. Kersten, M. Carey, I. Manolescu, and R. Busse, ''Xmark: A Benchmark for XML Data Management,''. Proc. of VLDB China, pp. 974-985, 2002. [21] S. Boag, D. Chamberlin, M. F. Fern├índez , D. Florescu, J. Robie, J. Siméon, ''W3C XML Query (XQuery),''. W3C Candidate Recommendation. 2005. Available at http://www.w3.org/XML/Query/ [22] M. Emadi, M. Rahgozar, A. Ardalan, A. Kazerani and M.M. Arian, ''A Comparative Study of DTD-Independent XML Data Storage Approaches,''. 11th International CSI Computer Conference (CSICC'06). Iran, pp. 624-628, 2006."]}