LEADER 01143cam0-22003011i-450 001 990005526430403321 005 20240423143329.0 035 $a000552643 035 $aFED01000552643 035 $a(Aleph)000552643FED01 035 $a000552643 100 $a19990604d1942----km-y0itay50------ba 101 0 $aita 102 $aDE 105 $af---f---001yy 200 1 $a<>educazione di Alessandro Magno nell'enciclopedia Aristotelica in un trittico megalografico di Pompei del 2. stile$fMatteo Della Corte 210 $aMünchen$cBruckmann$d1942 215 $aP. 32-77, 2 tav.$d28 cm 300 $aEstratto da "Mittelungen des Deutschen Archäologischen Instituts. Römische Abteilung". Band 57, 1942, 1-4 700 1$aDella Corte,$bMatteo$f<1875-1962>$035781 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990005526430403321 952 $aARCH. X MISC. 13 (02)$bARCH. 15335$fFLFBC 952 $aARCH. BM MISC. 093 (12)$bARCH. 14533$fFLFBC 959 $aFLFBC 996 $aEducazione di Alessandro Magno nell'enciclopedia Aristotelica in un trittico megalografico di Pompei del 2. stile$9610224 997 $aUNINA LEADER 04242nam 22007335 450 001 9911015632903321 005 20250712072138.0 010 $a3-031-88121-4 024 7 $a10.1007/978-3-031-88121-3 035 $a(MiAaPQ)EBC32201075 035 $a(Au-PeEL)EBL32201075 035 $a(CKB)39615339000041 035 $a(DE-He213)978-3-031-88121-3 035 $a(OCoLC)1528960736 035 $a(EXLCZ)9939615339000041 100 $a20250707d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAutophagy - From Molecular Mechanisms to Flux Control in Health and Disease /$fedited by Ben Loos, Daniel J. Klionsky 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (446 pages) 225 1 $aLearning Materials in Biosciences,$x2509-6133 311 08$a3-031-88120-6 327 $aChapter 1. How to Learn, and Teach, Autophagy -- Chapter 2. Discerning autophagy pathway intermediates ? transmission electron microscopy techniques -- Chapter 3. Assessment of Autophagy: Correlative and Super-Resolution Microscopy Techniques -- Chapter 4. Autophagy receptors couple cargo destined to be degraded with the core autophagy machinery -- Chapter 5. The source of membrane during autophagy and the early steps in autophagosome formation -- Chapter 6. Autophagy and neurodegenerative diseases -- Chapter 7. Autophagy and intracellular membrane dynamics in aging and age-related diseases -- Chapter 8. Shear stress-dependent regulation of autophagy and metabolism in kidney epithelial cells -- Chapter 9. Autophagy assessment in diagnostic pathology: Focus on skeletal myopathies -- Chapter 10. The role of autophagy and mitophagy in cardiomyocyte ischemic injury -- Chapter 11. Chaperone-mediated autophagy in health and disease -- Chapter 12. A phenotypic screening routine for the identification of autophagic flux inducers. 330 $aThis textbook describes the autophagy pathway with all its key molecular mechanisms and its physiological functions from yeast to eukaryotes in a didactic and reader-friendly manner. It provides the most critical aspects that need to be understood to foster research and clinical translation in this area. Autophagy activity, mechanism and control in the context of cellular fate are central to this book, underpinned by human pathologies of priority. Further, key chapters describing major techniques required to assess autophagy activity, and highlighting starting points for the research of potential drug candidates and clinical translation, offer detailed insight into practice and application. The work represents a comprehensive study guide that allows undergraduate and postgraduate students in biology and biomedicine to rapidly engage with the most critical and recent aspects of autophagy in health and its control of disease. Written in a style that may be favourable for its use in the classroom, this book can also serve as a valuable source for teaching in the biomedical and medical sciences. 410 0$aLearning Materials in Biosciences,$x2509-6133 606 $aCytology 606 $aAging 606 $aBiochemistry 606 $aMetabolism 606 $aCytology 606 $aNeurosciences 606 $aCancer 606 $aCell Biology 606 $aCellular Senescence 606 $aMetabolic Pathways 606 $aNeuroscience 606 $aCancer Biology 615 0$aCytology. 615 0$aAging. 615 0$aBiochemistry. 615 0$aMetabolism. 615 0$aCytology. 615 0$aNeurosciences. 615 0$aCancer. 615 14$aCell Biology. 615 24$aCellular Senescence. 615 24$aMetabolic Pathways. 615 24$aNeuroscience. 615 24$aCancer Biology. 676 $a571.6 700 $aLoos$b Ben$01833105 701 $aKlionsky$b Daniel J$01833106 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911015632903321 996 $aAutophagy - from Molecular Mechanisms to Flux Control in Health and Disease$94408031 997 $aUNINA LEADER 05176nam 22006615 450 001 9910409668803321 005 20251114111232.0 010 $a1-4471-7493-3 024 7 $a10.1007/978-1-4471-7493-6 035 $a(CKB)4100000011254380 035 $a(MiAaPQ)EBC6207654 035 $a(DE-He213)978-1-4471-7493-6 035 $a(MiAaPQ)EBC6420168 035 $a(Au-PeEL)EBL6420168 035 $a(OCoLC)1155482662 035 $a(PPN)248395270 035 $a(EXLCZ)994100000011254380 100 $a20200520d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPrinciples of Data Mining /$fby Max Bramer 205 $a4th ed. 2020. 210 1$aLondon :$cSpringer London :$cImprint: Springer,$d2020. 215 $a1 online resource (576 pages) 225 1 $aUndergraduate Topics in Computer Science,$x1863-7310 311 08$a1-4471-7492-5 320 $aIncludes bibliographical references and index. 327 $aIntroduction to Data Mining -- Data for Data Mining -- Introduction to Classification: Naïve Bayes and Nearest Neighbour -- Using Decision Trees for Classification -- Decision Tree Induction: Using Entropy for Attribute Selection -- Decision Tree Induction: Using Frequency Tables for Attribute Selection -- Estimating the Predictive Accuracy of a Classifier -- Continuous Attributes -- Avoiding Overfitting of Decision Trees -- More About Entropy -- Inducing Modular Rules for Classification -- Measuring the Performance of a Classifier -- Dealing with Large Volumes of Data -- Ensemble Classification -- Comparing Classifiers -- Associate Rule Mining I -- Associate Rule Mining II -- Associate Rule Mining III -- Clustering -- Mining -- Classifying Streaming Data -- Classifying Streaming Data II: Time-dependent Data -- An Introduction to Neural Networks -- Appendix A ? Essential Mathematics -- Appendix B ? Datasets -- Appendix C ? Sources of Further Information -- Appendix D ? Glossary and Notation -- Appendix E ? Solutions to Self-assessment Exercises -- Index. 330 $aThis book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self-study, it aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. Principles of Data Mining includes descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift. The expanded fourth edition gives a detailed description of a feed-forward neural network with backpropagation and shows how it can be used for classification. 410 0$aUndergraduate Topics in Computer Science,$x1863-7310 606 $aInformation storage and retrieval 606 $aDatabase management 606 $aArtificial intelligence 606 $aComputer programming 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010 615 0$aInformation storage and retrieval. 615 0$aDatabase management. 615 0$aArtificial intelligence. 615 0$aComputer programming. 615 14$aInformation Storage and Retrieval. 615 24$aDatabase Management. 615 24$aArtificial Intelligence. 615 24$aProgramming Techniques. 676 $a006.312 700 $aBramer$b Max$4aut$4http://id.loc.gov/vocabulary/relators/aut$0849832 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910409668803321 996 $aPrinciples of Data Mining$91897503 997 $aUNINA