03315nam 2200589 450 991046114360332120200520144314.00-8203-4817-1(CKB)3710000000462563(EBL)2169866(SSID)ssj0001543329(PQKBManifestationID)16133691(PQKBTitleCode)TC0001543329(PQKBWorkID)14629050(PQKB)10232032(MiAaPQ)EBC2169866(OCoLC)918892682(MdBmJHUP)muse46347(Au-PeEL)EBL2169866(CaPaEBR)ebr11091523(CaONFJC)MIL823966(EXLCZ)99371000000046256320141210h20152015 ub| 0engur|n|---|||||txtccrAfter Montaigne contemporary essayists cover the essays /edited by David Lazar and Patrick MaddenAthens :University of Georgia Press,[2015]©20151 online resource (269 p.)Description based upon print version of record.0-8203-4815-5 Includes bibliographical references and index.Cover; Contents; Acknowledgments; Introduction; 1. To the Reader, Sincerely; 2. Of Liars; 3. Of the Education of Children; 4. Of Prayers; 5. Of Thumbs; 6. Of Smells; 7. Of Cannibals; 8. How the Soul Discharges Its Emotions Against False Objects When Lacking Real Ones; 9. Of Constancy; 10. Of Giving the Lie; 11. Of Friendship; 12. Of Idleness; 13. Against Idleness; 14. Of the Affection of Fathers for Their Children; 15. Of Wearing My Red Dress [after "Of the Custom of Wearing Clothes"]; 16. Of the Power of the Imagination; 17. That Our Mind Hinders Itself; 18. Of Books and Huecos19. Of Diversion20. Of Sex, Embarrassment, and the Miseries of Old Age [after "On Some Verses of Virgil"]; 21. Of Sleep; 22. Of the Inconvenience of Greatness; 23. Of Solitude; 24. Of Age; 25. Of Practice; 26. The Ceremony of the Interview of Princes; 27. We Can Savour Nothing Pure; 28. Experience Necessary; Notes; A Note on the Translations; Contributors; Index; A; B; C; D; E; F; G; H; I; J; K; L; M; N; P; Q; R; S; T; V; W; YWriters of the modern essay can trace their chosen genre all the way back to Michel de Montaigne (1533-92). But save for the recent notable best seller How to Live: A Life of Montaigne by Sarah Bakewell, Montaigne is largely ignored. After Montaigne -a collection of twenty-four new personal essays intended as tribute- aims to correct this collective lapse of memory and introduce modern readers and writers to their stylistic forebear. Though it's been over four hundred years since he began writing his essays, Montaigne's writing is still fresh, and his use of the form as a means of self-exploraAmerican essays21st centuryElectronic books.American essays814/.6Montaigne Michel de1533-1592.172668Lazar David1957-Madden Patrick1971-MiAaPQMiAaPQMiAaPQBOOK9910461143603321After Montaigne2216749UNINA09815nam 2200601 450 99646640700331620230622193910.03-030-73351-3(CKB)5470000001298874(MiAaPQ)EBC6794567(Au-PeEL)EBL6794567(OCoLC)1281582862(PPN)258299126(EXLCZ)99547000000129887420220719d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierStatistical analysis of microbiome data /Somnath Datta and Subharup Guha, editorsCham, Switzerland :Springer,[2021]©20211 online resource (348 pages)Frontiers in probability and the statistical sciences3-030-73350-5 Intro -- Preface -- Acknowledgments -- Contents -- Part I Preprocessing and Bioinformatics Pipelines -- Denoising Methods for Inferring Microbiome Community Content and Abundance -- 1 Introduction -- 2 Common Algorithmic Denoising Strategies -- 3 Model-Based Denoising -- 3.1 Hierarchical Divisive Clustering -- 3.2 Finite Mixture Model -- 3.3 Denoising Long-Read Technology -- 4 Model Assessment -- 4.1 With Known Truth -- 4.1.1 Accuracy in ASV Identification -- 4.1.2 Accuracy in Read Assignments -- 4.2 With Unknown Truth -- 4.2.1 Assessment with UMIs -- 4.2.2 Clustering Stability -- 5 Conclusions -- References -- Statistical and Computational Methods for Analysis of Shotgun Metagenomics Sequencing Data -- 1 Introduction -- 2 Methods for Species Identification and Quantification of Microorganisms -- 3 Metagenome Assembly and Applications -- 3.1 de Bruijn Assembly of a Single Genome -- 3.2 Modification for Metagenome and Metagenome-Assembled Genomes -- 3.3 Compacted de Bruijn Graph -- 4 Estimation of Growth Rates for Metagenome-Assembled Genomes (MAGs) -- 5 Methods for Identifying Biosynthetic Gene Clusters -- 5.1 A Hidden Markov Model-Based Approach -- 5.2 A Deep Learning Approach -- 5.3 BGC Identification Based on Metagenomic Data -- 6 Future Directions -- References -- Bioinformatics Pre-Processing of Microbiome Data with An Application to Metagenomic Forensics -- 1 Introduction -- 2 Bioinformatics Pipeline -- 2.1 Microbiome Data -- 2.2 Quality Control -- 2.3 Taxonomic Profiling -- 2.3.1 MetaPhlAn2 -- 2.3.2 Kraken2 -- 2.3.3 Kaiju -- 2.4 Computing facilities -- 3 Methodology -- 3.1 Pre-Processing and Feature Selection -- 3.2 Exploration of Candidate Classifiers -- 3.3 The Ensemble Classifier -- 3.4 Class Imbalance -- 3.5 Performance Measures -- 3.6 Data Analysis -- 4 Results -- 5 Discussion -- 6 Data Acknowledgement -- 7 Code Availability.References -- Part II Exploratory Analyses of Microbial Communities -- Statistical Methods for Pairwise Comparison of Metagenomic Samples -- 1 Introduction -- 2 Microbial Community Comparison Methods Based on OTU Abundance Data -- 3 Microbial Community Comparison Measures Based on a Phylogenetic Tree -- 3.1 The Fst Statistic and Phylogenetic Test for Comparing Communities -- 3.2 UniFrac, W-UniFrac, VAW-UniFrac, and Generalized UniFrac for Comparing Microbial Communities -- 3.3 VAW-UniFrac for Comparing Communities -- 4 Alignment-Free Methods for the Comparison of Microbial Communities -- 5 A Tutorial on the Use of UniFrac Type and Alignment-Free Dissimilarity Measures for the Comparison of Metagenomic Samples -- 5.1 Analysis Steps for UniFrac, W-UniFrac, Generalized UniFrac, and VAW-UniFrac -- 5.2 Analysis Steps for the Comparison of Microbial Communities Based on Alignment-Free Methods -- 6 Discussion -- References -- Beta Diversity and Distance-Based Analysis of Microbiome Data -- 1 Introduction -- 2 Quantifying Dissimilarity: Common Beta Diversity Metrics -- 3 Ordination and Dimension Reduction -- 3.1 Principal Coordinates Analysis -- 3.2 Double Principal Coordinate Analysis -- 3.3 Biplots -- 3.4 Accounting for Compositionality -- 3.5 Model-Based Ordination Using Latent Variables -- 4 Distance-Based Hypothesis Testing -- 4.1 Permutation Tests -- 4.2 Kernel Machine Regression Tests -- 4.3 Sum of Powered Score Tests -- 4.4 Adaptive Tests -- 4.5 Comparison of Distance-Based Tests -- 5 Strengths, Weaknesses, and Future Directions -- References -- Part III Statistical Models and Inference -- Joint Models for Repeatedly Measured Compositional and Normally Distributed Outcomes -- 1 Introduction -- 2 Motivating Data -- 3 Statistical Models -- 3.1 The Multinomial Logistic Mixed Model (MLMM) -- 3.2 Dirichlet-Multinomial Mixed Model (DMMM).3.3 Goodness of Fit -- 4 Simulation Studies -- 4.1 Simulation Setting -- 4.2 Simulation Results -- 5 Data Analysis -- 6 Discussion -- 7 Software -- Appendix -- References -- Statistical Methods for Feature Identification in Microbiome Studies -- 1 Introduction -- 2 Differential Abundance Analysis -- 2.1 Compositional Methods -- 2.2 Count-Based Methods -- 2.3 Additional Notes -- 3 Mediation Analysis -- 4 Feature Identification Adjusting for Confounding -- 4.1 Covariate Adjustment -- 4.2 Model-Based Standardization -- 5 Summary -- References -- Statistical Methods for Analyzing Tree-Structured Microbiome Data -- 1 Introduction -- 2 Modeling Multivariate Count Data -- 2.1 Dirichlet-Multinomial Model -- 2.2 Dirichlet-Tree Multinomial Model -- 2.3 Implementation and Illustration -- 3 Estimating Microbial Compositions -- 3.1 Empirical Bayes Normalization -- 3.2 Phylogeny-Aware Normalization -- 3.3 Statistical Analysis of Compositional Data -- 3.4 Implementation and Illustration -- 4 Regression with Compositional Predictors -- 4.1 Constrained Lasso and Log-Ratio Lasso -- 4.2 Subcomposition Selection -- 4.3 Phylogeny-Aware Subcomposition Selection -- 4.4 Linear Regression and Variable Fusion -- 5 Additional References -- 6 Discussion -- References -- A Log-Linear Model for Inference on Bias in Microbiome Studies -- 1 Introduction -- 2 Methods -- 2.1 The Brooks' Data -- 2.2 Setup and Estimation -- 2.3 Inference -- 2.4 Testability of the Hypothesis -- 2.4.1 Example: Testable Hypotheses for Main Effects -- 2.4.2 Example: Testable Hypotheses for Interaction Effects -- 3 Simulations -- 3.1 Main Effect Simulation -- 3.2 Interaction Effect Simulation Based on the Brooks Data -- 4 Results -- 4.1 Simulation Results -- 4.2 Do Interactions Between Taxa Affect Bias in the Brooks' Data? -- 4.3 Plate and Sample Type Effects in the Brooks' Data -- 5 Discussion -- Appendix.References -- Part IV Bayesian Methods -- Dirichlet-Multinomial Regression Models with Bayesian Variable Selection for Microbiome Data -- 1 Introduction -- 2 Methods -- 2.1 Dirichlet-Multinomial Regression Models for Compositional Data -- 2.2 Variable Selection Priors -- 2.3 Network Priors -- 2.3.1 Unknown G -- 2.4 Dirichlet-Tree Multinomial Models -- 2.5 Posterior Inference -- 3 Simulated Data -- 3.1 Simulation Study for DM Regression Models -- 3.2 DM Sensitivity Analysis -- 3.3 Simulation Study for DTM Regression Models -- 3.4 DTM Sensitivity Analysis -- 4 Applications -- 4.1 Multi-omics Microbiome Study-Pregnancy Initiative (MOMS-PI) -- 4.2 Gut Microbiome Study -- 5 Conclusion -- References -- A Bayesian Approach to Restoring the Duality Between Principal Components of a Distance Matrix and Operational Taxonomic Units in Microbiome Analyses -- 1 Introduction -- 1.1 Motivating Datasets -- 1.2 Nonlinear or Stochastic Distances -- 1.3 Limitations of SVD-Based Approaches -- 2 A Bayesian Formulation -- 2.1 Posterior Density -- 3 Model Sum of Squares and Biplots -- 4 Posterior Inference -- 4.1 Gibbs Sampler -- 4.2 Dimension Reduction: Skinny Bayesian Technique -- 4.2.1 Subsetted Data Matrix -- 4.2.2 Lower Dimensional Parameters and Induced Posterior -- 4.2.3 Faster Inference Procedure -- 4.3 Model Parameter Estimates -- 5 Simulation Study -- 5.1 Generation Strategy -- 6 Data Analysis -- 6.1 Tobacco Data -- 6.2 Subway Data -- 7 Data Acknowledgement -- 8 Discussion -- Supplementary Materials -- Appendix -- Proof of Lemma 1 -- References -- Part V Special Topics -- Tree Variable Selection for Paired Case-Control Studies with Application to Microbiome Data -- 1 Introduction -- 2 Gini Index -- 2.1 Simulation Analysis -- 3 Multivariate Gini Index -- 3.1 Conditional Gini Index -- 4 Variable Importance -- 5 Analysis of Obesity Using Microbiome Data.6 Discussion -- Appendix -- References -- Networks for Compositional Data -- 1 Introduction -- 2 Methods -- 2.1 Learning Networks from Marginal Associations -- 2.1.1 ReBoot -- 2.1.2 SparCC -- 2.1.3 CCLasso -- 2.1.4 COAT -- 2.2 Learning Networks from Conditional Associations -- 2.2.1 SPIEC-EASI -- 2.2.2 gCoda -- 2.2.3 SPRING -- 3 Data-Generating Models -- 3.1 Null Models -- 3.2 Copula Models -- 3.3 Logistic-Normal Model -- 4 Results -- 4.1 Spurious (Partial) Correlations -- 4.2 Performance in Network Discovery -- 4.3 Case Studies in R -- 5 Future Directions -- References -- Index.Frontiers in probability and the statistical sciences.MicrobiologyStatistical methodsMicrobiologiathubEstadística matemàticathubMicroorganismesthubGenètica microbianathubLlibres electrònicsthubMicrobiologyStatistical methods.MicrobiologiaEstadística matemàticaMicroorganismesGenètica microbiana579Datta SomnathGuha SubharupMiAaPQMiAaPQMiAaPQBOOK996466407003316Statistical analysis of microbiome data2901910UNISA01426nas 2200433- 450 991062615570332120230403213018.02473-2397(DE-599)ZDB2703053-2(OCoLC)798816075(CKB)2560000000085097(CONSER)--2012203185(EXLCZ)99256000000008509720120711a20139999 --- aengur|||||||||||txtrdacontentcrdamediacrrdacarrierIEEE geoscience and remote sensing magazineNew York, NY :Institute of Electrical and Electronics Engineers1 online resourceRefereed/Peer-reviewed2168-6831 Geoscience and remote sensing magazineIEEE geosci. remote sens. mag.Earth sciencesRemote sensingPeriodicalsEarth sciencesRemote sensingfast(OCoLC)fst00900758Periodicals.fastGeography-GeneralEarth sciencesRemote sensingEarth sciencesRemote sensing.621Institute of Electrical and Electronics Engineers.IEEE Geoscience and Remote Sensing Society.JOURNAL9910626155703321IEEE geoscience and remote sensing magazine2574447UNINA01589ngm 2200409 a 450 991069919370332120091015142759.0(CKB)5470000002399955(OCoLC)451013588(EXLCZ)99547000000239995520091007d200u ma vengurmna|||m||||tdirdacontentcrdamediacrrdacarrierCaring for someone with Alzheimer'sBathing tips[electronic resource][Bethesda, Md.] :National Institutes of Health,[between 2000 and 2009?]Available as both streaming video (47 sec., WMV file, sd., col.) and downloadable video (47 sec., WMV file, sd., col.) files.Title from NIH SeniorHealth Videos table of contents page (viewed Oct. 6, 2009).Accompanied by transcript in HTML format.Cynthia D. Steele, RN, MPH, Johns Hopkins University Alzheimer's Disease Center, describes techniques for bathing Alzheimer's patients.Bathing tipsAlzheimer's diseasePatientsCarePopular worksBathsPopular worksDocumentary films.lcgftStreaming videos.lcgftAlzheimer's diseasePatientsCareBathsSteele Cynthia1947-1196275National Institutes of Health (U.S.)GPOGPOBOOK9910699193703321Caring for someone with Alzheimer's3452623UNINA