LEADER 01966nam 2200493 450 001 9910798669403321 005 20230808195115.0 010 $a92-2-130388-8 035 $a(CKB)3710000000843168 035 $a(EBL)4661524 035 $a(MiAaPQ)EBC4661524 035 $a(Au-PeEL)EBL4661524 035 $a(CaPaEBR)ebr11253367 035 $a(OCoLC)958384910 035 $a(EXLCZ)993710000000843168 100 $a20160916h20162016 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 00$aWorld employment social outlook 2016 $etransforming jobs to end poverty /$fInternational Labour Office 210 1$aGeneva, [Switzerland] :$cInternational Labour Office,$d2016. 210 4$dİ2016 215 $a1 online resource (192 p.) 300 $aDescription based upon print version of record. 311 $a92-2-130387-X 320 $aIncludes bibliographical references. 330 $aThis report shows that decent work is vital to reducing poverty. Poverty has tended to decline in many emerging and developing countries, whereas it has tended to increase in the majority of advanced economies, including in terms of working poverty. The report examines the role that policy can play, particularly with economic policies, employment programmes, enterprise development, social protection and social dialogue. It also discusses the role of international labour standards. 606 $aUnemployment$y21st century 606 $aEmployment (Economic theory)$y21st century 606 $aLabor market 615 0$aUnemployment 615 0$aEmployment (Economic theory) 615 0$aLabor market. 676 $a331.1 712 02$aInternational Labour Office. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910798669403321 996 $aWorld employment social outlook 2016$93685024 997 $aUNINA LEADER 00771nam a2200205 i 4500 001 991004346038407536 005 20241007095631.0 008 241007s1969 it drf 001 0 ita d 040 $aBibl. Dip.le Aggr. Scienze Umane e Sociali$bita$cSocioculturale Scs 041 0 $aita 082 04$a233$223 100 1 $aSaitta, Armando$050346 245 10$aStato senza scettro e cittadini principi :$bmanuale di educazione civica per il triennio delle scuole medie superiori /$cArmando Saitta 250 $aRistampa 260 $aMessina ;$aFirenze :$bD'Anna,$c1969 300 $a218 p. ;$c24 cm 650 4$aEducazione civica$xScuole medie inferiori$vTesti scolastici 912 $a991004346038407536 996 $aStato senza scettro e cittadini principi$94215869 997 $aUNISALENTO LEADER 05719nam 2200721Ia 450 001 9910958642403321 005 20251117062842.0 010 $a9781848162525 010 $a1848162529 035 $a(CKB)1000000000767482 035 $a(EBL)1193219 035 $a(SSID)ssj0000519663 035 $a(PQKBManifestationID)12215641 035 $a(PQKBTitleCode)TC0000519663 035 $a(PQKBWorkID)10508618 035 $a(PQKB)10025286 035 $a(MiAaPQ)EBC1193219 035 $a(WSP)00002028 035 $a(Au-PeEL)EBL1193219 035 $a(CaPaEBR)ebr10688048 035 $a(CaONFJC)MIL491649 035 $a(OCoLC)780417054 035 $a(Perlego)845560 035 $a(EXLCZ)991000000000767482 100 $a20090404d2008 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRegulatory genomics $eproceedings of the 3rd annual RECOMB workshop : National University of Singapore, Singapore 17-18 July 2006 /$feditors, Leong Hon Wai, Sung Wing-Kin, Eleazar Eskin 205 $a1st ed. 210 $aLondon $cImperial College Press$dc2008 215 $a1 online resource (144 p.) 225 1 $aSeries on advances in bioinformatics and computational biology,$x1751-6404 ;$v8 300 $aDescription based upon print version of record. 311 08$a9781848162518 311 08$a1848162510 320 $aIncludes bibliographical references and index. 327 $aForeword; RECOMB Regulatory Genomics 2006 Organization; CONTENTS; Keynote Papers; Computational Prediction of Regulatory Elements by Comparative Sequence Analysis M. Tompa; A Tale of Two Topics - Motif Significance and Sensitivity of Spaced Seeds M. Li; Computational Challenges for Top-Down Modeling and Simulation of Biological Pathways S. Miyano; An Improved Gibbs Sampling Method for Motif Discovery via Sequence Weighting T. Jiang; Discovering Motifs with Transcription Factor Domain Knowledge F. Chin; Applications of ILP in Computational Biology A . Dress 327 $aOn the Evolution of Transcription Regulation Networks R. Shamir Systems Pharmacology in Cancer Therapeutics: Iterative Informatics-Experimental Interface E. Liu; Computational Structural Proteomics and Inhibitor Discovery R. Abagyan; Characterization of Transcriptional Responses to Environmental Stress by Differential Location Analysis H. Tang; A Knowledge-based Hybrid Algorithm for Protein Secondary Structure Prediction W. L. Hsu; Monotony and Surprise (Conservative Approaches to Pattern Discovery) A . Apostolic0; Evolution of Bacterial Regulatory Systems M. S. Gelfand; Contributed Papers 327 $aTScan: A Two-step De NOVO Motif Discovery Method 0. Abul, G. K. Sandve, and F. Drabbs1. Introduction; 2. Method; 2.1. Step 1; 2.2. Step 2; 2.2.1, Over-representation Conservation Scoring; 2.2.2. Frith et al. Scoring; 3. Experiments; 4. Conclusion; References; Redundancy Elimination in Motif Discovery Algorithms H. Leung and F. Chin; 1. Introduction; 2. Maximizing Likelihood; 3. The Motif Redundancy Problem; 3.1. The motif redundancy problem; 3.2. Formal definition; 4. Algorithm; 5. Experimental Results; 6. Concluding Remarks; Appendix; References 327 $aGAMOT: An Efficient Genetic Algorithm for Finding Challenging Motifs in DNA Sequences N. Karaoglu, S. Maurer-Stroh, and B. Manderick1. Introduction; 2. GA for Motif Finding; 3. An Efficient Algorithm (GAMOT); 3.1. Fast motif discovery; 3.2. The genetic algorithm; 4. Experimental Results; 4.1. Comparison with exhaustive search; 4.2. Comparison with GAI and GA2; 4.3. Comparison with other algorithms; 4.3.1. Quality of the solutions; 4.4. GAMOTparameters; 5. Conclusions and Future Work; References; Identification of Spaced Regulatory Sites via Submotif Modeling E. Wijaya and R. Kanagasabai 327 $a1. Introduction 2. Related Work; 3. Our Approach; 4. Problem Definition; 5. Algorithm SPACE; 5.1. Generation of candidate motifs; 5.2. Constrained frequent pattern mining; 5.2.1. Generalized gap; 5.2.2. Mining of constrained frequent patterns; 5.3. Significance testing and scoring; 6. Experimental Results; 6.1. Results on Tompa's benchmark data set; 6.2. Results on synthetic data set; 7. Discussion and Conclusions; References; Refining Motif Finders with E-value Calculations N. Nagarajan, P. Ng, and U. Keich; 1. Introduction; 2. Efficiently Computing E-values 327 $a3. Optimizing for E-values - Conspv 330 $aResearch in the field of gene regulation is evolving rapidly in the ever-changing scientific environment. Advances in microarray techniques and comparative genomics have enabled more comprehensive studies of regulatory genomics. The study of genomic binding locations of transcription factors has enabled a more comprehensive modeling of regulatory networks. In addition, complete genomic sequences and comparison of numerous related species have demonstrated the conservation of non-coding DNA sequences, which often provide evidence for cis-regulatory binding sites. Systematic methods to decipher 410 0$aSeries on advances in bioinformatics and computational biology,$x1751-6404 ;$v8. 606 $aGenetic regulation$vCongresses 606 $aGenomics$vCongresses 615 0$aGenetic regulation 615 0$aGenomics 676 $a572.865 701 $aEskin$b Eleazar$01652094 701 $aLeong$b Hon Wai$f1955-$01865910 701 $aSung$b Wing-Kin$01865911 712 12$aRECOMB Satellite Workshop on Regulatory Genomics. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910958642403321 996 $aRegulatory genomics$94473140 997 $aUNINA