Medical genetics Books, bestsellers and new releases

Book SynopsisProgress and Future Directions in Exon Skipping and Inclusion Therapies: The Landscape of Oligonucleotide-Based Genetic Medicine.- Evolution and Breakthroughs in Exon Skipping and Splice Modulation: From Inception to Clinical Success.- An Overview of Recent Advances and Clinical Applications of Exon Skipping and Splice Modulation for Muscular Dystrophy and Various Genetic Diseases.- Milasen: The Emerging Era of Patient-Customized N-of-1 Antisense Oligonucleotides as Therapeutic Agents for Genetic Diseases.- Tips to Design Effective Splice-Switching Antisense Oligonucleotides for Exon Skipping and Exon Inclusion.- In Silico Prediction and Selection of Exon-Skipping Antisense Oligonucleotide Sequences Using eSkip-Finder.- Quantitative Evaluation of Exon Skipping in Immortalized Muscle Cells In Vitro.- Direct Reprogramming of Human DMD Fibroblasts into Myotubes for In Vitro Evaluation of Antisense-Mediated Exon Skipping and Exons 45-55 Skipping Accompanied by Rescue of Dystrophin Expression.- In Vitro Multiexon Skipping by Antisense PMOs in Dystrophic Dog and Exon 7-Deleted DMD Patient.- Creation of DMD Muscle Cell Model Using CRISPR-Cas9 Genome Editing to Test the Efficacy of Antisense-Mediated Exon Skipping.- In Vitro Evaluation of Exon Skipping in Disease Specific iPSC-Derived Myocytes.- Skipping of Duplicated Dystrophin Exons: In Vitro Induction and Assessment.- In Vivo Evaluation of Dystrophin Exon Skipping in mdx Mice.- Guide to Selection of Muscle-Homing Peptides after In Vivo Phage Display Biopanning.- Systemic Injection of Peptide-PMOs into Humanized DMD Mice and Detection by RT-PCR and ELISA.- In Vivo Evaluation of Single- and Multi-Exon Skipping in mdx52 Mice.- A Novel Zebrafish Model for Assessing In Vivo Delivery of Morpholino Oligomers.- Electrophysiological Evaluation in mdx52 Mouse Brain after Antisense-Mediated Exon 51 or 53 Skipping.- Use of Glucose/Fructose to Enhance the Exon Skipping Efficacy.- Systemic Intravenous Administration of Antisense Therapeutics for Combinatorial Dystrophin and Myostatin Exon Splice Modulation.- The Assembly of Fluorescently Labeled Peptide-Oligonucleotide Conjugates.- In Vivo Evaluation of Multiple Exon Skipping with Peptide-PMOs in Cardiac and Skeletal Muscles in Dystrophic Dogs.- Use of Tricyclo-DNA Antisense Oligonucleotides for Exon Skipping.- Optimization of 2',4'-BNA/LNA-Based Oligonucleotides for Splicing Modulation In Vitro.- Pre-mRNA Splicing Modulation by Antisense Oligonucleotides.- In Vitro Evaluation of Antisense-Mediated Exon Inclusion for Spinal Muscular Atrophy.- Systemic Injection of Antisense Oligos into Spinal Muscular Atrophy (SMA) Mice and Evaluation.- Morpholino-Mediated Exon Inclusion for Spinal Muscular Atrophy (SMA).- Exon Skipping by Ultrasound-Enhanced Delivery of Morpholino with Bubble Liposomes for Myotonic Dystrophy Model Mice.- Restoration of Dysferlin after Exon 32 Skipping in Patient Cells.- Morpholino-Mediated Exon Skipping Targeting Human ACVR1/ALK2 for Fibrodysplasia Ossificans Progressiva.- Exon Skipping of FcεRIβ for Allergic Diseases.- Exon-Skipping Using Antisense Oligonucleotides for Laminin-Alpha2-Deficient Muscular Dystrophy.

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£151.99

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Molecole Narrate Oltre il DNA

15 in stock

£26.99

John Wiley & Sons Inc Success Probability Estimation with Applications

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Book SynopsisWith a focus on estimating the success probability of an experiment, this book provides an introduction to the various statistical techniques involved in medical research and drug development addressing the theoretical and practical aspects of the topic.Table of ContentsPreface xv Acknowledgments xvii Acronyms xix Introduction xxi I.1 Overview of clinical trials xxii I.2 Success rates of clinical trials xxiv I.3 Success probability xxv I.4 Starting from practice xxvii PART I SUCCESS PROBABILITY ESTIMATION IN PLANNING AND ANALYZING CLINICAL TRIALS 1 Basic statistical tools 3 1.1 Pointwise estimation 4 1.2 Confidence interval estimation, conservative estimation 6 1.3 The statistical hypotheses, the statistical test and the type I error for one-tailed tests 10 1.4 The power function and the type II error 11 1.5 The p-value 14 1.6 The success probability and its estimation 17 1.7 Basic statistical tools for two tailed tests 19 1.8 Other statistical hypotheses and tests 23 2 Reproducibility Probability Estimation 25 2.1 Pointwise RP estimation 26 2.2 RP-testing 29 2.3 The RP estimate and the p-value 32 2.4 Statistical lower bounds for the RP 35 2.5 The stability criterion for statistical significance 37 2.6 Other stability criteria for statistical significance 40 2.7 Comparing stability criteria 43 2.8 Regulatory agencies and the single study 45 2.9 The RP for two-tailed tests 46 2.10 Discussing Situation I in Section I.4.1 49 3 Sample Size Estimation 51 3.1 The classical paradigm of sample size determination 52 3.2 SP estimation for adapting the sample size 55 3.3 Launching the trial in practice 57 3.4 Practical aspects of SSE 60 3.5 Frequentist conservative SSE 67 3.6 Optimal frequentist CSSE 70 3.7 Bayesian CSSE 75 3.8 A comparison of CSSE strategies 80 3.9 Discussing Situations I and II in Section I.4 83 3.10 Sample size estimation for the two-tailed setting 85 4 Robustness and Corrections in Sample Size Estimation 89 4.1 CSSE strategies with different effect sizes in phases II and III 90 4.2 Comparing CSSE strategies in different Scenarios 91 4.3 Corrections for CSSE strategies 94 4.4 A comparison among Corrected CSSE strategies 97 PART II SUCCESS PROBABILITY ESTIMATION FOR SOME WIDELY USED STATISTICAL TESTS 5 General parametric SP estimation 105 5.1 The parametric model 105 5.2 Power, SP and noncentrality parameter estimation 106 5.3 RP estimation and testing 107 5.4 Sample size estimation 108 5.5 Statistical tests included in the model 109 6 SP estimation for Student’s t statistical tests 113 6.1 Test for two means equal variances 114 6.1.1 Power and RP estimation 114 6.2 Test for two means unequal variances 117 6.3 On Student’s t RP estimates 120 7 SP estimation for Gaussian distributed test statistics 123 7.1 Test for two proportions 123 7.2 Test for survival: the log-rank test 127 8 SP estimation for Chi-square statistical tests 133 8.1 Test for two multinomial distributions: 2 x C comparative trial 133 8.2 Test for S couples of binomial distributions: the Mantel-Haenszel test 137 8.3 On χ2 RP estimates 141 9 General nonparametric SP estimation with - applications to the Wilcoxon test 143 9.1 The nonparametric model 144 9.2 General nonparametric SP estimation 145 9.3 The Wilcoxon rank-sum test 146 A Tables of quantiles 161 B Tables of RP estimates for the one-tailed Z test 169 References 179 Topic index 185 Author index 193

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Springer A Guide to Methods in the Biomedical Sciences

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Book SynopsisThousands of methods have been developed in the various biomedical disciplines, and those covered in this book represent the basic, essential and most widely used methods in several different disciplines. Table of Contents1: Detection And Analysis Of Proteins A. Introduction B. Basic methods for protein analysis C. Characterization of primary, secondary, tertiary and quaternary structures of proteins 2: Detection And Analysis Of Nucleic Acids A. Introduction B. Basic methods for nucleic acid analysis C. DNA sequencing D. Detection and quantitation of RNA 3: Recombinant DNA Techniques: Cloning And Manipulation Of DNA A. Introduction B. Plasmid and viral vectors C. Libraries D. Site Directed Mutagenesis E. Polymerase chain reaction (PCR) F. Screening for differentially expressed genes (DEGs) G. Promoter-protein interactions H. Silencing gene expression I. Forensics and DNA technology 4: Antibody-Based Techniques A. Introduction B. Monoclonal antibodies C. Purification and use of antibodies D. Flow cytometry and fluorescence activated cell sorting (FACS) E. Other assays using antibodies 5: Microscopy: Imaging Of Biological Specimens A. Introduction B. Light microscopy C. Fluorescence and laser confocal microarray microscopy D. Electron Microscopy (EM) E. Magnetic resonance imaging (MRI) 6: The Derivation And Manipulation Of Experimental Animals In Biomedical Sciences A. Introduction B. Inbred strains of mice C. Congenic strains of mice D. Transgenic mice E. Targeted transgenes: 'knockout' and 'knockin' mice F. Other uses of mice in biomedical research References Some excellent methodology manuals Index

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15 in stock

£85.49

Springer New York Statistical Human Genetics Methods and Protocols 1666 Methods in Molecular Biology

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Book SynopsisThe aim of this volume is to make computer programs for analyzing human genetic data more easily accessible to the beginner.Statistical Human Genetics: Methods and Protocols, Second Edition provides updated and new chapters detailing genetic terms, analysis software, and how to interpret the program outputs.Table of Contents1. Statistical Genetic TerminologyRobert Elston, Jaya Satagopan, and Shuying Sun 2. Identification of Genotype ErrorsJeffery O’Connell and Yin Yao 3. Detecting Pedigree Relationship ErrorsLei Sun 4. Identifying Cryptic RelationshipsLei Sun, Apostolos Dimitromanolakis, and Wei-Min Chen 5. Estimating Allele FrequenciesIndra Adrianto and Courtney Montgomery 6. Testing Departure from Hardy-Weinberg ProportionsJian Wang and Sanjay Shete 7. Estimating Disequilibrium CoefficientsMaren Vens and Andreas Ziegler 8. Detecting Familial AggregationAdam C. Naj and Terri H. Beaty 9. Estimating Heritability from Twin StudiesKatrina Grasby, Karin J.H. Verweij, Miriam A. Mosing, Brendan P. Zietsch, and Sarah E. Medland 10. Estimating Heritability from Nuclear Family and Pedigree DataMurielle Bochud 11. Correcting for AscertainmentWarren Ewens and Robert C. Elston 12. Segregation Analysis using the Unified ModelXiangqing Sun 13. Design Considerations for Genetic Linkage and Association StudiesJérémie Nsengimana and D Timothy Bishop 14. Model-based Linkage Analysis of a Quantitative Trait Yeunjoo E. Song, Sunah Song, and Audrey H. Schnell 15. Model-based Linkage Analysis of a Binary TraitRita M. Cantor 16. Model-free Linkage Analysis of a Quantitative TraitNathan J. Morris and Catherine M. Stein 17. Model-free Linkage Analysis of a Binary TraitWei Xu, Jin Ma, Celia M.T. Greenwood, Andrew D. Paterson, and Shelley B. Bull 18. Single Marker Association Analysis for Unrelated SamplesGang Zheng, Ao Yuan, Qizhai Li, and Joseph L. Gastwirth 19. Single Marker Family-based Association Analysis Conditional on Parental InformationRen-Hua Chung, Daniel D. Kinnamon, and Eden R. Martin 20. Single Marker Family-based Association Analysis not Conditional on Parental InformationJunghyun Namkung and Sungho Won 21. Calibrating Population Stratification in Association AnalysisHuaizhen Qin and Xiaofeng Zhu 22. Cross-phenotype Association Analysis using Summary Statistics from GWASXiaoyin Li and Xiaofeng Zhu 23. Haplotype Inference Sunah Song, Xin Li, and Jing Li 24. Multi-SNP Haplotype Analysis Methods for Association AnalysisDaniel O. Stram 25. The Analysis of Ethnic MixturesXiaofeng Zhu and Heming Wang 26. Detecting Multiethnic Rare VariantsWeiwei Ouyang, Xiaofeng Zhu, and Huaizhen Qin 27. Identifying Gene Interaction NetworksDanica Wiredja and Gurkan Bebek 28. Structural Equation ModelingCatherine M. Stein, Nathan J. Morris, Noémi B. Hall, and Nora L. Nock 29. Mendelian RandomizationSandeep Grover, Fabiola Del Greco M, Catherine M. Stein, and Andreas Ziegler 30. Preprocessing and Quality Control for Whole Genome Sequences from the Illumina HiSeq X PlatformMarvin N. Wright 31. Statistical Approaches for Processing and Analyzing Microbiome DataXuan Zhu, Jian Wang, Cielito Reyes-Gibby, and Sanjay Shete

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£111.20

IGI Global Optimized Genetic Programming Applications: Emerging Research and Opportunities

15 in stock

Book SynopsisData is more valuable than ever in the twenty-first century, and tremendous amounts of data are being generated every second. With a fast-growing information industry, engineers are required to develop new tools and techniques that increase human capabilities of mining useful knowledge from the vast amounts of data.Optimized Genetic Programming Applications: Emerging Research and Opportunities is an essential reference source that explores the concept of genetic programming and its role in managing engineering problems. It also examines genetic programming as a supervised machine learning technique, focusing on implementation and application. As a resource that details both the theoretical aspects and implementation of genetic programming, this book is a useful source for academicians, biological engineers, computer programmers, scientists, researchers, and upper-level students seeking the latest research on genetic programming.

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15 in stock

£200.70