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RISS 인기검색어
- 검색키워드
- 저자 : Wohlleben, Wolfgang (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Shadow Economy, Tax Evasion, and Transfer Fraud - Definition, Measurement, and Data Problems
Hans-Georg Petersen,Ulrich Thiessen,Pierre Wohlleben 한국국제경제학회 2010 International Economic Journal Vol.24 No.4
The paper tries to shed some light on the definition of the shadow economy, in order to separate shadow activities from market activities and household production. A total income concept is applied, which is based on the labor force being engaged in market, shadow and household activities. Based on such a clear concept, tax evasion can be defined and identified in the market sector and is also usually taking place in the shadow economy, where it is often accompanied by evasion of social security contributions as well as transfer fraud. Money usage in the three sectors is then critically analyzed, and measurement as well as data problems are seriously taken into consideration. The results of our research project suggest that the size of the shadow economy as estimated with the currency approach often yields the highest possible values. Other approaches and plausibility considerations produce much lower values, which seem to be much more realistic. Consequently, policy considerations to strengthen the control mechanisms should be met with utmost skepticism.
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antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters
Weber, Tilmann,Blin, Kai,Duddela, Srikanth,Krug, Daniel,Kim, Hyun Uk,Bruccoleri, Robert,Lee, Sang Yup,Fischbach, Michael A,Mü,ller, Rolf,Wohlleben, Wolfgang,Breitling, Rainer,Takano, Eriko,Medema, Oxford University Press 2015 Nucleic acids research Vol.43 No.w1
<P><B>Abstract</B></P><P>Microbial secondary metabolism constitutes a rich source of antibiotics, chemotherapeutics, insecticides and other high-value chemicals. Genome mining of gene clusters that encode the biosynthetic pathways for these metabolites has become a key methodology for novel compound discovery. In 2011, we introduced antiSMASH, a web server and stand-alone tool for the automatic genomic identification and analysis of biosynthetic gene clusters, available at http://antismash.secondarymetabolites.org. Here, we present version 3.0 of antiSMASH, which has undergone major improvements. A full integration of the recently published ClusterFinder algorithm now allows using this probabilistic algorithm to detect putative gene clusters of unknown types. Also, a new dereplication variant of the ClusterBlast module now identifies similarities of identified clusters to any of 1172 clusters with known end products. At the enzyme level, active sites of key biosynthetic enzymes are now pinpointed through a curated pattern-matching procedure and Enzyme Commission numbers are assigned to functionally classify all enzyme-coding genes. Additionally, chemical structure prediction has been improved by incorporating polyketide reduction states. Finally, in order for users to be able to organize and analyze multiple antiSMASH outputs in a private setting, a new XML output module allows offline editing of antiSMASH annotations within the Geneious software.</P>
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