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6.10 Further Reading - Biology

6.10 Further Reading - Biology


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  • Overview of Human Microbiome Project: commonfund.nih.gov/hmp/overview.aspx
  • Lawrence A. David and Eric J. Alm. (2011). Rapid evolutionary innovation during an Archaean

    genetic expansion. Nature, 469(7328):93-96.

  • A tutorial on 16S rRNA gene and its use in microbiome research: http://greengenes.lbl.gov/

    cgi-bin/JD_Tutorial/nph-Tutorial_2Main2.cgi

  • Dariush Mozaffarian, Tao Hao, Eric B. Rimm, Walter C. Willett, and Frank B. Hu. Changes in diet and lifestyle and long-term weight gain in women and men. The New England journal of medicine, 364(25):2392-2404.
  • JH Hehemann, G Correc, T Barbeyron, W Helbert, M Czjzek, and G Michel. (2010). Transfer of carbohydrate- active enzymes from marine bacteria to japanese gut microbiota. Nature, 464(5):908-12.
  • The Human Microbiome Jumpstart Reference Strains Consortium. A Catalog of Reference Genomes from the Human Microbiome. Science, 328(5981):994-999

Wolbachia: master manipulators of invertebrate biology

This Review focuses on intracellular Wolbachia, which are globally widespread Rickettsia-like bacteria that infect many arthropod species, as well as filarial nematodes.

The authors discuss recent advances in Wolbachia research, with an emphasis on genetics and genomics, ecology, evolution and applications to pest and disease control.

Wolbachia are primarily reproductive parasites that have several different effects on hosts, including feminization, induced parthenogenesis, male killing and a sperm–egg incompatibility that is known as cytoplasmic incompatibility. Wolbachia can effectively manipulate the biology of host cells, and have evolved mutualisms with their hosts. These and other effects of Wolbachia are discussed, as well as recent advances on the understanding of cytological interactions between bacteria and their host.

Maintenance of the global Wolbachia pandemic is discussed, including factors that affect the spread of Wolbachia, transfer between host species and persistence within a host lineage. The usefulness of multilocus strain typing to characterize the movement and diversity of these bacteria is also emphasized.

The evolutionary implications of Wolbachia infection are discussed, including the possible role of this endosymbiont in the promotion of reproductive isolation and speciation, as well as its potential to contribute to host genome evolution through horizontal transfer of genes from the bacteria into their host.

Finally, the authors outline possible practical applications of Wolbachia in pest and disease vector management strategies and highlight the main unanswered questions regarding Wolbachia biology.


Commentary on 2 Corinthians 5:6-10 [11-13] 14-17

What gives us the courage to do the right thing — to act on what our conscience calls us to do — when we know that we often will not be rewarded for it in this life?

Can we boldly defend the common good in the face of powerful detractors concerned solely with their own interests and agendas? And when we do speak the truth about what needs to be done in specific circumstances, can we do so with the love and forgiveness needed to bring about the justice we are calling for? These are some of the larger questions Paul grapples with in 2 Corinthians that provide a context for interpreting this passage.

The logic of double-negation

In 2 Corinthians 5:6-10, Paul asserts that we can be confident in all circumstances, whether we are &ldquoat home&rdquo or &ldquoaway&rdquo from either &ldquothe body&rdquo or &ldquothe Lord.&rdquo This theme resonates with his refrains in Philippians that &ldquoliving is Christ and dying is gain&rdquo (Philippians 1:21) and that in any and all circumstances — whether in plenty or in need — we can do all things through Christ who strengthens us (Philippians 4:12-13).

There is a logic of double-negation at work in these verses that runs throughout Paul&rsquos letters. This logic brings to the fore the point that God&rsquos &ldquoyes&rdquo — God&rsquos promise, which we receive in Jesus through the Spirit — is far greater than all our human distinctions and circumstances (2 Corinthians 1:18-22). In Galatians, for example, Paul states that through the Spirit we eagerly await the &ldquohope of righteousness&rdquo because &ldquoin Christ Jesus neither circumcision nor uncircumcision counts for anything&rdquo all that counts is &ldquofaith working through love&rdquo (Galatians 5:5-6). In 1 Corinthians, he makes clear that the foolishness and weakness of the cross of Christ embodies the fact that God&rsquos foolishness is wiser than human wisdom, and God&rsquos weakness is stronger than human strength (1 Corinthians 1:18-25) (my italics).

As depicted in the great hymn of Romans 8, Paul&rsquos point with these negations is to affirm that nothing — neither death nor life not angels, rulers, or powers not height or depth, nor anything else in all creation — can separate us from God&rsquos love in Christ Jesus our Lord (Romans 8:38-39). The love of God encompasses everything in reality. Grounded in God&rsquos love through Christ&rsquos grace and the Holy Spirit&rsquos communion, we can be what we have been called to be: an open statement of truth, commending ourselves with confidence to everyone&rsquos conscience before God, regardless of our circumstances (2 Corinthians 4:2 13:13).

Being at home or away from the body

Is Paul not introducing yet another dualism — another distinction — with his talk about being &ldquoat home&rdquo or &ldquoaway&rdquo from &ldquothe body&rdquo or &ldquothe Lord&rdquo? We can gain some insight on this question by taking a look at his &ldquofool&rsquos speech&rdquo regarding the &ldquosuper-apostles&rdquo who have defamed him and abused the Corinthians with their deceptive misuse of spiritual power.

In that speech Paul refers to &ldquovisions and revelations&rdquo he experienced fourteen years prior, saying that he does not know whether they were &ldquoin the body or out of the body&rdquo (2 Corinthians 12:1-7). Paul himself has had such visions and revelations, which may indeed have been &ldquoout of the body&rdquo experiences. In these kinds of experiences we may have a powerful sense of union with God or sense of being &ldquoat home&rdquo with the Lord. Yet Paul is very clear: those experiences are no more sacred — no more weighted with authority — than others.

Why? Because the only power and authority we can ultimately rely on is the sufficiency of God&rsquos grace. Through that grace, power is &ldquomade perfect (teleitai, better translated as &ldquoreaches full maturity&rdquo) in weakness.&rdquo Indeed, our ultimate criterion is the weakness of Jesus&rsquo suffering body undergoing all of our vicissitudes, even to the point of death on a cross (2 Corinthians 12:8 cf. Philipians 2:8).

Walking by faith not sight

In fact, all that we do in our bodies will be manifest (phanerothenai) before &ldquothe judgment seat of Christ&rdquo — the eschatological place and time where and when Christ will judge all the living and the dead (2 Corinthians 5:10 Romans 2:16, 14:9-10). This reference to Christ&rsquos &ldquojudgment seat&rdquo is not a threat but a promise. Although we live in a world where technical savvy, wealth, and power seem continually to trump God&rsquos steadfast love, justice, and righteousness, we can be confident that the latter — described as God&rsquos mercies and consolation in 2 Corinthians — will prevail in the end (2 Corinthians 1:3 cf. Jeremiah 9:23-24).

Thus Paul&rsquos phrase — &ldquowe walk by faith, not sight&rdquo — fleshes out his earlier discussion of &ldquoseeing&rdquo the glory of Christ and &ldquobeing transformed&rdquo into the same image (2 Corinthians 3:18). Our &ldquoseeing&rdquo and &ldquobeing transformed&rdquo into Christ&rsquos image takes place not in some ethereal experience but in every aspect of our lives where we need to rely on, or put our trust in, God&rsquos grace. Wherever we are, we are accountable to God — and thus also to one another — for what we do in our bodies, whether good or evil. And God&rsquos grace is sufficient to give us the power to please God in all circumstances.

So being in &ldquoecstasy&rdquo (eksestemen, taken out of ourselves) before God does not immune us from being accountable for what we do with our bodies (2 Corinthians 5:13). Rather, knowing the fear of the Lord — that we are ultimately accountable to God and not to any other power — frees us to speak to speak the truth and to persuade others to do the same. Well known to God, we can confidently make ourselves known to others, even as we persuade them to reciprocate by living in the same confidence and sincerity (2 Corinthians 5:11-13).

Grounded in God&rsquos love, we can speak truth to one another — we can risk sincerity — even when we disagree or might be wrong. God is reconciling the entire world through Christ, in spite of anything we or others have done (2 Corinthians 5:19): God&rsquos promises are always a &ldquoyes.&rdquo Rooted in that &ldquoyes,&rdquo our lives can be an open statement of truth — regardless of where we find ourselves (2 Corinthians 1:20-22).


  • How to achieve breakthrough machine learning performance by combining deep neural networks with reinforcement learning
  • Reduces the learning curve by relying on the authors’ OpenAI Lab framework: requires less upfront theory, math, and programming expertise
  • Provides well-designed, modularized, and tested code examples with complete experimental data sets to illuminate the underlying algorithms
  • Includes case studies, practical tips, definitions, and other aids to learning and mastery
  • Prepares readers for exciting future advances in artificial general intelligence

Foreword xix
Preface xxi
Acknowledgments xxv
About the Authors xxvii

Chapter 1: Introduction to Reinforcement Learning 1
1.1 Reinforcement Learning 1
1.2 Reinforcement Learning as MDP 6
1.3 Learnable Functions in Reinforcement Learning 9
1.4 Deep Reinforcement Learning Algorithms 11
1.5 Deep Learning for Reinforcement Learning 17
1.6 Reinforcement Learning and Supervised Learning 19
1.7 Summary 21

Chapter 2: REINFORCE 25
2.1 Policy 26
2.2 The Objective Function 26
2.3 The Policy Gradient 27
2.4 Monte Carlo Sampling 30
2.5 REINFORCE Algorithm 31
2.6 Implementing REINFORCE 33
2.7 Training a REINFORCE Agent 44
2.8 Experimental Results 47
2.9 Summary 51
2.10 Further Reading 51
2.11 History 51

Chapter 3: SARSA 53
3.1 The Q- and V-Functions 54
3.2 Temporal Difference Learning 56
3.3 Action Selection in SARSA 65
3.4 SARSA Algorithm 67
3.5 Implementing SARSA 69
3.6 Training a SARSA Agent 74
3.7 Experimental Results 76
3.8 Summary 78
3.9 Further Reading 79
3.10 History 79

Chapter 4: Deep Q-Networks (DQN) 81
4.1 Learning the Q-Function in DQN 82
4.2 Action Selection in DQN 83
4.3 Experience Replay 88
4.4 DQN Algorithm 89
4.5 Implementing DQN 91
4.6 Training a DQN Agent 96
4.7 Experimental Results 99
4.8 Summary 101
4.9 Further Reading 102
4.10 History 102

Chapter 5: Improving DQN 103

5.1 Target Networks 104
5.2 Double DQN 106
5.3 Prioritized Experience Replay (PER) 109
5.4 Modified DQN Implementation 112
5.5 Training a DQN Agent to Play Atari Games 123
5.6 Experimental Results 128
5.7 Summary 132
5.8 Further Reading 132

Part II: Combined Methods 133

Chapter 6: Advantage Actor-Critic (A2C) 135
6.1 The Actor 136
6.2 The Critic 136
6.3 A2C Algorithm 141
6.4 Implementing A2C 143
6.5 Network Architecture 148
6.6 Training an A2C Agent 150
6.7 Experimental Results 157
6.8 Summary 161
6.9 Further Reading 162
6.10 History 162

Chapter 7: Proximal Policy Optimization (PPO) 165
7.1 Surrogate Objective 165
7.2 Proximal Policy Optimization (PPO) 174
7.3 PPO Algorithm 177
7.4 Implementing PPO 179
7.5 Training a PPO Agent 182
7.6 Experimental Results 188
7.7 Summary 192
7.8 Further Reading 192

Chapter 8: Parallelization Methods 195
8.1 Synchronous Parallelization 196
8.2 Asynchronous Parallelization 197
8.3 Training an A3C Agent 200
8.4 Summary 203
8.5 Further Reading 204

Chapter 9: Algorithm Summary 205

Part III: Practical Details 207

Chapter 10: Getting Deep RL to Work 209
10.1 Software Engineering Practices 209
10.2 Debugging Tips 218
10.3 Atari Tricks 228
10.4 Deep RL Almanac 231
10.5 Summary 238

Chapter 11: SLM Lab 239
11.1 Algorithms Implemented in SLM Lab 239
11.2 Spec File 241
11.3 Running SLM Lab 246
11.4 Analyzing Experiment Results 247
11.5 Summary 249

Chapter 12: Network Architectures 251
12.1 Types of Neural Networks 251
12.2 Guidelines for Choosing a Network Family 256
12.3 The Net API 262
12.4 Summary 271
12.5 Further Reading 271

Chapter 13: Hardware 273
13.1 Computer 273
13.2 Data Types 278
13.3 Optimizing Data Types in RL 280
13.4 Choosing Hardware 285
13.5 Summary 285

Part IV: Environment Design 287

Chapter 14: States 289
14.1 Examples of States 289
14.2 State Completeness 296
14.3 State Complexity 297
14.4 State Information Loss 301
14.5 Preprocessing 306
14.6 Summary 313

Chapter 15: Actions 315
15.1 Examples of Actions 315
15.2 Action Completeness 318
15.3 Action Complexity 319
15.4 Summary 323
15.5 Further Reading: Action Design in Everyday Things 324

Chapter 16: Rewards 327
16.1 The Role of Rewards 327
16.2 Reward Design Guidelines 328
16.3 Summary 332

Chapter 17: Transition Function 333
17.1 Feasibility Checks 333
17.2 Reality Check 335
17.3 Summary 337

Appendix A: Deep Reinforcement Learning Timeline 343

Appendix B: Example Environments 345
B.1 Discrete Environments 346
B.2 Continuous Environments 350


Part I THE PRELIMINARIES

1.3 There is Nothing such as PRACTICALS 5

1.4 Datasets in R and Internet 6

1.4.1 List of Web-sites containing DATASETS 7

1.5.3 Is subscribing to R-Mailing List useful? 10

1.6 R and its Interface with other Software 11

2.2 Simple Arithmetics and a Little Beyond 16

2.2.1 Absolute Values, Remainders, etc. 16

2.2.4 Trigonometric Functions 18

2.2.6 Special Mathematical Functions 21

2.3 Some Basic R Functions 22

2.3.1 Summary Statistics 23

2.3.3 factors, levels, etc. 26

2.3.4 Control Programming 27

2.3.5 Other Useful Functions 29

2.4 Vectors and Matrices in R 33

2.5 Data Entering and Reading from Files 41

2.5.2 Reading Data from External Files 43

2.6 Working with Packages 44

2.7 R Session Management 45

2.9 Complements, Problems, and Programs 46

3 Data Preparation and Other Tricks 49

3.2 Manipulation with Complex Format Files 50

3.3 Reading Datasets of Foreign Formats 55

3.4 Displaying R Objects 56

3.5 Manipulation Using R Functions 57

3.6 Working with Time and Date 59

3.8 Scripts and Text Editors for R 64

3.8.1 Text Editors for Linuxians 64

3.10 Complements, Problems, and Programs 65

4 Exploratory Data Analysis 67

4.1 Introduction: The Tukey&rsquos School of Statistics 67

4.2 Essential Summaries of EDA 68

4.3 Graphical Techniques in EDA 71

4.3.3 Histogram Extensions and the Rootogram 79

4.4 Quantitative Techniques in EDA 91

4.5 Exploratory Regression Models 95

4.7 Complements, Problems, and Programs 100

Part II PROBABILITY AND INFERENCE

5 Probability Theory 105

5.2 Sample Space, Set Algebra, and Elementary Probability 106

5.3.1 Sampling: The Diverse Ways 114

5.3.2 The Binomial Coefficients and the Pascals Triangle 118

5.3.3 Some Problems Based on Combinatorics 119

5.4 Probability: A Definition 122

5.4.1 The Prerequisites 122

5.4.2 The Kolmogorov Definition 127

5.5 Conditional Probability and Independence 130

5.7 Random Variables, Expectations, and Moments 133

5.7.2 Expectation of Random Variables 136

5.8 Distribution Function, Characteristic Function, and Moment Generation Function 143

5.9.1 The Markov Inequality 145

5.9.2 The Jensen&rsquos Inequality 145

5.9.3 The Chebyshev Inequality 146

5.10 Convergence of Random Variables 146

5.10.1 Convergence in Distributions 147

5.10.2 Convergence in Probability 150

5.10.3 Convergence in rth Mean 150

5.10.4 Almost Sure Convergence 151

5.11 The Law of Large Numbers 152

5.11.1 The Weak Law of Large Numbers 152

5.12 The Central Limit Theorem 153

5.12.1 The de Moivre-Laplace Central Limit Theorem 153

5.12.2 CLT for iid Case 154

5.12.3 The Lindeberg-Feller CLT 157

5.12.4 The Liapounov CLT 162

5.13.1 Intuitive, Elementary, and First Course Source 165

5.13.2 The Classics and Second Course Source 166

5.13.3 The Problem Books 167

5.13.4 Other Useful Sources 167

5.13.5 R for Probability 167

5.14 Complements, Problems, and Programs 167

6 Probability and Sampling Distributions 171

6.2 Discrete Univariate Distributions 172

6.2.1 The Discrete Uniform Distribution 172

6.2.2 The Binomial Distribution 173

6.2.3 The Geometric Distribution 176

6.2.4 The Negative Binomial Distribution 178

6.2.5 Poisson Distribution 179

6.2.6 The Hypergeometric Distribution 182

6.3 Continuous Univariate Distributions 184

6.3.1 The Uniform Distribution 184

6.3.2 The Beta Distribution 186

6.3.3 The Exponential Distribution 187

6.3.4 The Gamma Distribution 188

6.3.5 The Normal Distribution 189

6.3.6 The Cauchy Distribution 191

6.3.7 The t-Distribution 193

6.3.8 The Chi-square Distribution 193

6.3.9 The F-Distribution 194

6.4 Multivariate Probability Distributions 194

6.4.1 The Multinomial Distribution 194

6.4.2 Dirichlet Distribution 195

6.4.3 The Multivariate Normal Distribution 195

6.4.4 The Multivariate t Distribution 196

6.5 Populations and Samples 196

6.6 Sampling from the Normal Distributions 197

6.7 Some Finer Aspects of Sampling Distributions 201

6.7.1 Sampling Distribution of Median 201

6.7.2 Sampling Distribution of Mean of Standard Distributions 201

6.8 Multivariate Sampling Distributions 203

6.8.1 Noncentral Univariate Chi-square, t, and F Distributions 203

6.8.2 Wishart Distribution 205

6.8.3 Hotellings T2 Distribution 206

6.9 Bayesian Sampling Distributions 206

6.11 Complements, Problems, and Programs 208

7 Parametric Inference 209

7.2 Families of Distribution 210

7.2.1 The Exponential Family 212

7.4.2 Minimal Sufficiency 219

7.5 Likelihood and Information 220

7.5.1 The Likelihood Principle 220

7.5.2 The Fisher Information 226

7.6.1 Maximum Likelihood Estimation 231

7.6.2 Method of Moments Estimator 239

7.7 Comparison of Estimators 241

7.7.1 Unbiased Estimators 241

7.7.2 Improving Unbiased Estimators 243

7.8 Confidence Intervals 245

7.9 Testing Statistical Hypotheses&ndashThe Preliminaries 246

7.10 The Neyman-Pearson Lemma 251

7.11 Uniformly Most Powerful Tests 256

7.12 Uniformly Most Powerful Unbiased Tests 260

7.12.1 Tests for the Means: One- and Two-Sample t-Test 263

7.13 Likelihood Ratio Tests 265

7.13.1 Normal Distribution: One-Sample Problems 266

7.13.2 Normal Distribution: Two-Sample Problem for the Mean 269

7.14 Behrens-Fisher Problem 270

7.15 Multiple Comparison Tests 271

7.15.1 Bonferroni&rsquos Method 272

7.16.3 Introductory Applications 275

7.17.2 Texts from the Last 30 Years 281

7.18 Complements, Problems, and Programs 281

8 Nonparametric Inference 283

8.2 Empirical Distribution Function and Its Applications 283

8.2.1 Statistical Functionals 285

8.3 The Jackknife and Bootstrap Methods 288

8.3.3 Bootstrapping Simple Linear Model* 292

8.4 Non-parametric Smoothing 294

8.4.1 Histogram Smoothing 294

8.4.3 Nonparametric Regression Models* 300

8.5 Non-parametric Tests 304

8.5.1 The Wilcoxon Signed-Ranks Test 305

8.5.2 The Mann-Whitney test 308

8.5.3 The Siegel-Tukey Test 309

8.5.4 The Wald-Wolfowitz Run Test 311

8.5.5 The Kolmogorov-Smirnov Test 312

8.5.6 Kruskal-Wallis Test* 314

8.7 Complements, Problems, and Programs 316

9 Bayesian Inference 317

9.2 Bayesian Probabilities 317

9.3 The Bayesian Paradigm for Statistical Inference 321

9.3.1 Bayesian Sufficiency and the Principle 321

9.3.2 Bayesian Analysis and Likelihood Principle 322

9.3.3 Informative and Conjugate Prior 322

9.3.4 Non-informative Prior 323

9.4 Bayesian Estimation 323

9.4.1 Inference for Binomial Distribution 323

9.4.2 Inference for the Poisson Distribution 326

9.4.3 Inference for Uniform Distribution 327

9.4.4 Inference for Exponential Distribution 328

9.4.5 Inference for Normal Distributions 329

9.5 The Credible Intervals 332

9.6 Bayes Factors for Testing Problems 333

9.8 Complements, Problems, and Programs 335

Part III STOCHASTIC PROCESSES AND MONTE CARLO

10 Stochastic Processes 339

10.2 Kolmogorov&rsquos Consistency Theorem 340

10.3.2 Classification of States 345

10.3.3 Canonical Decomposition of an Absorbing Markov Chain 347

10.3.4 Stationary Distribution and Mean First Passage Time of an Ergodic Markov Chain 350

10.3.5 Time Reversible Markov Chain 352

10.4 Application of Markov Chains in Computational Statistics 352

10.4.1 The Metropolis-Hastings Algorithm 353

10.4.3 Illustrative Examples 355

10.6 Complements, Problems, and Programs 361

11 Monte Carlo Computations 363

11.2 Generating the (Pseudo-) Random Numbers 364

11.2.1 Useful Random Generators 364

11.2.2 Probability Through Simulation 366

11.3 Simulation from Probability Distributions and Some Limit Theorems 373

11.3.1 Simulation from Discrete Distributions 373

11.3.2 Simulation from Continuous Distributions 380

11.3.3 Understanding Limit Theorems through Simulation 383

11.3.4 Understanding The Central Limit Theorem 386

11.4 Monte Carlo Integration 388

11.5 The Accept-Reject Technique 390

11.6 Application to Bayesian Inference 394

11.8 Complements, Problems, and Programs 397

Part IV LINEAR MODELS

12 Linear Regression Models 401

12.2 Simple Linear Regression Model 402

12.2.1 Fitting a Linear Model 403

12.2.2 Confidence Intervals 405

12.2.3 The Analysis of Variance (ANOVA) 407

12.2.4 The Coefficient of Determination 409

12.2.5 The &ldquolm&rdquo Function from R 410

12.2.6 Residuals for Validation of the Model Assumptions 412

12.2.7 Prediction for the Simple Regression Model 416

12.2.8 Regression through the Origin 417

12.3 The Anscombe Warnings and Regression Abuse 418

12.4 Multiple Linear Regression Model 421

12.4.1 Scatter Plots: A First Look 422

12.4.2 Other Useful Graphical Methods 423

12.4.3 Fitting a Multiple Linear Regression Model 427

12.4.4 Testing Hypotheses and Confidence Intervals 429

12.5 Model Diagnostics for the Multiple Regression Model 433

12.5.2 Influence and Leverage Diagnostics 436

12.6 Multicollinearity 441

12.6.1 Variance Inflation Factor 442

12.6.2 Eigen System Analysis 443

12.7 Data Transformations 445

12.7.2 Variance Stabilization 447

12.7.3 Power Transformation 449

12.8.1 Backward Elimination 453

12.8.2 Forward and Stepwise Selection 456

12.9.2 Industrial Applications 458

12.9.3 Regression Details 458

12.9.4 Modern Regression Texts 458

12.9.5 R for Regression 458

12.10 Complements, Problems, and Programs 458

13 Experimental Designs 461

13.2 Principles of Experimental Design 461

13.3 Completely Randomized Designs 462

13.3.2 Randomization in CRD 463

13.3.3 Inference for the CRD Models 465

13.3.4 Validation of Model Assumptions 470

13.3.5 Contrasts and Multiple Testing for the CRD Model 472

13.4.1 Randomization and Analysis of Balanced Block Designs 477

13.4.2 Incomplete Block Designs 481

13.4.3 Latin Square Design 484

13.4.4 Graeco Latin Square Design 487

13.5 Factorial Designs 490

13.5.1 Two Factorial Experiment 491

13.5.2 Three-Factorial Experiment 496

13.5.3 Blocking in Factorial Experiments 502

13.7 Complements, Problems, and Programs 504

14 Multivariate Statistical Analysis - I 507

14.2 Graphical Plots for Multivariate Data 507

14.3 Definitions, Notations, and Summary Statistics for Multivariate Data 511

14.3.1 Definitions and Data Visualization 511

14.3.2 Early Outlier Detection 517

14.4 Testing for Mean Vectors : One Sample 520

14.4.1 Testing for Mean Vector with Known Variance-Covariance Matrix 520

14.4.2 Testing for Mean Vectors with Unknown Variance-Covariance Matrix 521

14.5 Testing for Mean Vectors : Two-Samples 523

14.6 Multivariate Analysis of Variance 526

14.6.1 Wilks Test Statistic 526

14.6.3 Pillai&rsquos Test Statistic 529

14.6.4 The Lawley-Hotelling Test Statistic 529

14.7 Testing for Variance-Covariance Matrix: One Sample 531

14.7.1 Testing for Sphericity 532

14.8 Testing for Variance-Covariance Matrix: k-Samples 533

14.9 Testing for Independence of Sub-vectors 536

14.11 Complements, Problems, and Programs 538

15 Multivariate Statistical Analysis - II 541

15.2 Classification and Discriminant Analysis 541

15.2.1 Discrimination Analysis 542

15.3 Canonical Correlations 544

15.4 Principal Component Analysis &ndash Theory and Illustration 547

15.4.2 Illustration Through a Dataset 549

15.5 Applications of Principal Component Analysis 553

15.5.1 PCA for Linear Regression 553

15.6.1 The Orthogonal Factor Analysis Model 561

15.6.2 Estimation of Loadings and Communalities 562

15.7.1 The Classics and Applied Perspectives 568

15.7.2 Multivariate Analysis and Software 568

15.8 Complements, Problems, and Programs 569

16 Categorical Data Analysis 571

16.2 Graphical Methods for CDA 572

16.2.1 Bar and Stacked Bar Plots 572

16.2.4 Pie Charts and Dot Charts 580

16.4 The Simpson&rsquos Paradox 588

16.5 The Binomial, Multinomial, and Poisson Models 589

16.5.1 The Binomial Model 589

16.5.2 The Multinomial Model 590

16.5.3 The Poisson Model 591

16.6 The Problem of Overdispersion 593

16.7 The �- Tests of Independence 593

16.9 Complements, Problems, and Programs 595

17 Generalized Linear Models 597

17.2 Regression Problems in Count/Discrete Data 597

17.3 Exponential Family and the GLM 600

17.4 The Logistic Regression Model 601

17.5 Inference for the Logistic Regression Model 602

17.5.1 Estimation of the Regression Coefficients and Related Parameters 602

17.5.2 Estimation of the Variance-Covariance Matrix of 𝛽̂ 606

17.5.3 Confidence Intervals and Hypotheses Testing for the Regression Coefficients 607


Contents

Examples of degeneracy are found in the genetic code, when many different nucleotide sequences encode the same polypeptide in protein folding, when different polypeptides fold to be structurally and functionally equivalent in protein functions, when overlapping binding functions and similar catalytic specificities are observed in metabolism, when multiple, parallel biosynthetic and catabolic pathways may coexist. More generally, degeneracy is observed in proteins of every functional class (e.g. enzymatic, structural, or regulatory), [4] [5] protein complex assemblies, [6] ontogenesis, [7] the nervous system, [8] cell signalling (crosstalk) and numerous other biological contexts reviewed in. [1]

Degeneracy contributes to the robustness of biological traits through several mechanisms. Degenerate components compensate for one another under conditions where they are functionally redundant, thus providing robustness against component or pathway failure. Because degenerate components are somewhat different, they tend to harbor unique sensitivities so that a targeted attack such as a specific inhibitor is less likely to present a risk to all components at once. [3] There are numerous biological examples where degeneracy contributes to robustness in this way. For instance, gene families can encode for diverse proteins with many distinctive roles yet sometimes these proteins can compensate for each other during lost or suppressed gene expression, as seen in the developmental roles of the adhesins gene family in Saccharomyces. [9] Nutrients can be metabolized by distinct metabolic pathways that are effectively interchangeable for certain metabolites even though the total effects of each pathway are not identical. [10] [11] In cancer, therapies targeting the EGF receptor are thwarted by the co-activation of alternate receptor tyrosine kinases (RTK) that have partial functional overlap with the EGF receptor (and are therefore degenerate), but are not targeted by the same specific EGF receptor inhibitor. [12] [13] Other examples from various levels of biological organization can be found in. [1]

Several theoretical developments have outlined links between degeneracy and important biological measurements related to robustness, complexity, and evolvability. These include:

  • Theoretical arguments supported by simulations have proposed that degeneracy can lead to distributed forms of robustness in protein interaction networks. [14] Those authors suggest that similar phenomena is likely to arise in other biological networks and potentially may contribute to the resilience of ecosystems as well.
  • Tononi et al. have found evidence that degeneracy is inseparable from the existence of hierarchical complexity in neural populations. [8] They argue that the link between degeneracy and complexity is likely to be much more general.
  • Fairly abstract simulations have supported the hypothesis that degeneracy fundamentally alters the propensity for a genetic system to access novel heritable phenotypes[15] and that degeneracy could therefore be a precondition for open-ended evolution.
  • The three hypotheses above have been integrated in [3] where they propose that degeneracy plays a central role in the open-ended evolution of biological complexity. In the same article, it was argued that the absence of degeneracy within many designed (abiotic) complex systems may help to explain why robustness appears to be in conflict with flexibility and adaptability, as seen in software, systems engineering, and artificial life. [3]
  1. ^ abcd Edelman and Gally Gally, J. A. (2001). "Degeneracy and complexity in biological systems". Proceedings of the National Academy of Sciences, USA. 98 (24): 13763–13768. Bibcode:2001PNAS. 9813763E. doi:10.1073/pnas.231499798. PMC61115 . PMID11698650.
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  3. Mason, Paul H. (2 January 2015). "Degeneracy: Demystifying and destigmatizing a core concept in systems biology". Complexity. 20 (3): 12–21. Bibcode:2015Cmplx..20c..12M. doi:10.1002/cplx.21534.
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  5. Whitacre (2010). "Degeneracy: a link between evolvability, robustness and complexity in biological systems". Theoretical Biology and Medical Modelling. 7 (6): 6. arXiv: 0910.2586 . Bibcode:2009arXiv0910.2586W. doi:10.1186/1742-4682-7-6. PMC2830971 . PMID20167097.
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  7. Atamas (2005). "Les affinités électives". Pour la Science. 46: 39–43.
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  9. Wagner (2000). "The role of population size, pleiotropy and fitness effects of mutations in the evolution of overlapping gene functions". Genetics. 154 (3): 1389–1401. doi:10.1093/genetics/154.3.1389. PMC1461000 . PMID10757778.
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  11. Kurakin (2009). "Scale-free flow of life: on the biology, economics, and physics of the cell". Theoretical Biology and Medical Modelling. 6 (1): 6. doi:10.1186/1742-4682-6-6. PMC2683819 . PMID19416527.
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  13. Newman (1994). "Generic physical mechanisms of tissue morphogenesis: A common basis for development and evolution". Journal of Evolutionary Biology. 7 (4): 480. doi: 10.1046/j.1420-9101.1994.7040467.x .
  14. ^ ab
  15. Tononi Sporns, O. Edelman, G. M. et al. (1999). "Measures of degeneracy and redundancy in biological networks". Proceedings of the National Academy of Sciences, USA. 96 (6): 3257–3262. Bibcode:1999PNAS. 96.3257T. doi:10.1073/pnas.96.6.3257. PMC15929 . PMID10077671.
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  17. Guo Styles, C. A. Feng, Q. Fink, G. R. et al. (2000). "A Saccharomyces gene family involved in invasive growth, cell-cell adhesion, and mating". Proceedings of the National Academy of Sciences, USA. 97 (22): 12158–12163. Bibcode:2000PNAS. 9712158G. doi:10.1073/pnas.220420397. PMC17311 . PMID11027318.
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  19. Kitano (2004). "Biological robustness". Nature Reviews Genetics. 5 (11): 826–837. doi:10.1038/nrg1471. PMID15520792. S2CID7644586.
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  21. Ma and Zeng Zeng, AP (2003). "The connectivity structure, giant strong component and centrality of metabolic networks". Bioinformatics. 19 (11): 1423–1430. doi: 10.1093/bioinformatics/btg177 . PMID12874056.
  22. ^
  23. Huang Mukasa, A. Bonavia, R. Flynn, R. A. Brewer, Z. E. Cavenee, W. K. Furnari, F. B. White, F. M. et al. (2007). "Quantitative analysis of EGFRvIII cellular signaling networks reveals a combinatorial therapeutic strategy for glioblastoma". Proceedings of the National Academy of Sciences. 104 (31): 12867–72. Bibcode:2007PNAS..10412867H. doi:10.1073/pnas.0705158104. PMC1937558 . PMID17646646.
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  25. Stommel Kimmelman, AC Ying, H Nabioullin, R Ponugoti, AH Wiedemeyer, R Stegh, AH Bradner, JE et al. (2007). "Coactivation of receptor tyrosine kinases affects the response of tumor cells to targeted therapies". Science. 318 (5848): 287–90. Bibcode:2007Sci. 318..287S. doi:10.1126/science.1142946. PMID17872411. S2CID36607054.
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  27. Whitacre and Bender Bender, Axel (2010). "Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems". Theoretical Biology and Medical Modelling. 7 (20): 20. doi:10.1186/1742-4682-7-20. PMC2901314 . PMID20550663.
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  29. Whitacre and Bender Bender, A (2010). "Degeneracy: a design principle for achieving robustness and evolvability". Journal of Theoretical Biology. 263 (1): 143–153. arXiv: 0907.0510 . doi:10.1016/j.jtbi.2009.11.008. PMID19925810. S2CID11511132.

Because there are many distinct types of systems that undergo heritable variation and selection (see Universal Darwinism), degeneracy has become a highly interdisciplinary topic. The following provides a brief roadmap to the application and study of degeneracy within different disciplines.


6.10 Further Reading - Biology

Visitors. Materials here are from my course, Principles of Molecular Biology, Fall 2001. I have only slightly updated these materials since that semester I have tried to keep Internet links and references to basic books in the field current. Much of the information on core molecular biology remains sound, and many of these materials should still be useful to students taking basic molecular biology courses.

This is part of a larger site, with materials over a range of science. Home page. Among featured pages that may be of general interest.

Musings is an informal newsletter mainly highlighting recent science. It is intended as both fun and instructive. Items are posted a few times each week. See the Introduction on the Musings page for more information.

Unusual microbes. A brief discussion of some of the oddities of the microbial world, organisms that capture our imagination by being different.

Books: Suggestions for general science reading is an annotated book list. Many are for the general audience. The list includes some books of historic interest.

Some pages of specific course-related content that may be of wider interest are:
* Metric prefixes, from yotta to yocto
* Significant figures - a beginner's guide
* ChemSketch - An Introductory Guide.
* Chemistry practice problems. Links to practice quizzes, self-help worksheets, and more -- for a range of topics in general and organic chemistry.


More about the molecular biology materials.

Chapter handouts. All chapter handouts, as Word DOCs. The first handout is also available as a web page -- a quick way to see what one handout is like. Ch 1 handout web page.

Sample tests (with answer keys). Information about using a note page on tests.

RasMol - An Introductory Guide. Help getting started with the RasMol computer program for viewing molecular structures on your computer. (Introduced in Weaver Ch 3 handout, in context of protein structures.)

Amides. A brief discussion of some properties of amides that you might not have expected. Peptide bonds are amide bonds, and this page is introduced in the Weaver Ch 3 handout, with proteins.

Unusual microbes. A brief discussion of some of the oddities of the microbial world, organisms that capture our imagination by being different.

Library matters. Information on using the library system, including electronic resources, and information on searching for articles, using databases such as PubMed (Medline). Includes sources of online journals, some of which are free. Parts of the page focus on UC Berkeley, but much of the information here will be generally useful to people not at UC. For example, PubMed is freely available to the public. Further, some may adapt the given information to their library system, and to other databases. Major topic areas here include: UC Berkeley library electronic journals journal articles PubMed searches citation searches. This page is also listed on the Site Home Page (under General resources), on course pages, and on the list of pages of Internet resources.

Molecular Biology Internet resources. Includes all links mentioned in class handouts. Also includes sections of molecular biology links beyond those in handouts.

Files available for download. Current files include a periodic table handout ChemFormula, a macro that helps you when using Microsoft Word for writing chemical expressions a kit for making your own buckyballs and some supplemental self-help materials for introductory/general chemistry. (Files for Molecular Biology class handouts are not on this page they are on the Handouts page.)

Metric prefixes, from yotta to yocto. Examples are shown, to help you relate to the size of the units that include these prefixes.

Further reading: Old articles. A list of some older articles that used to be referred to in various class handouts. Although these are no longer current, they may still be of interest.

Classic papers. This page lists sources of "classic papers", in both chemistry and molecular biology. Some are sources on the Internet, some are notes about printed collections. Reading some of the classic papers in a field can be a fun way to explore history -- and to discover the different style of scientific papers long ago.

Books: Suggestions for general science reading. Includes molecular biology, but also a wide range of science. (Also see the list of supplementary books in the Syllabus - Supplementary books section.)

Molecular Biology Tests Sample tests, with answer keys

The sample tests are actual tests from previous classes. A complete set is posted below, with answer keys. (For an actual class, I also hand out the sample for Test #1, to make sure everyone gets to see at least the first one before going into the test. The purpose of doing this is so you can see not only the contents of the test, but also what a real test looks like.)

Caution: Exactly what is covered, or even the order of topics, does not always agree from one semester to another. Therefore, you should not rely on sample tests as precise guides to test content. Approximate coverage of each test is shown below.

Those who are using my web site materials for self-study. You are welcome -- and encouraged -- to ask me questions when difficulties arise. (My contact information is at the bottom of each of my web pages.) It always helps if you include how you would answer the question and why. That lets me respond to what you are thinking, lets me focus my reply on where you are having trouble. Further, it gives me a feel for the level at which you are addressing the question -- which may vary depending on your background and course level. The level of discourse -- and your learning of the subject matter -- is enhanced by trying to focus on reasons, not simply answers.


6.10 Further Reading - Biology

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited.

Feature Papers represent the most advanced research with significant potential for high impact in the field. Feature Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review prior to publication.

The Feature Paper can be either an original research article, a substantial novel research study that often involves several techniques or approaches, or a comprehensive review paper with concise and precise updates on the latest progress in the field that systematically reviews the most exciting advances in scientific literature. This type of paper provides an outlook on future directions of research or possible applications.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to authors, or important in this field. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.


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Malnic B, Hirono J, Sato T, Buck LB: Combinatorial receptor codes for odors. Cell. 1999, 96: 713-723. 10.1016/S0092-8674(00)80581-4.

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Quignon P, Kirkness E, Cadieu E, Touleimat N, Guyon R, Renier C, Hitte C, Andre C, Fraser C, Galibert F: Comparison of the canine and human olfactory receptor gene repertoires. Genome Biol. 2003, 4: R80-10.1186/gb-2003-4-12-r80.

Olender T, Fuchs T, Linhart C, Shamir R, Adams M, Kalush F, Khen M, Lancet D: The canine olfactory subgenome. Genomics. 2004, 83: 361-372. 10.1016/j.ygeno.2003.08.009.

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Kirkness EF, Bafna V, Halpern AL, Levy S, Remington K, Rusch DB, Delcher AL, Pop M, Wang W, Fraser CM, Venter JC: The dog genome: survey sequencing and comparative analysis. Science. 2003, 301: 1898-1903. 10.1126/science.1086432.

Tacher S, Quignon P, Rimbault M, Dréano S, André C, Galibert F: Olfactory receptor sequence polymorphism within and between breeds of dogs. J Hered.

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9. Conclusion

Since Aristotle’s biological works comprise almost a third of his writings that have come down to us, and since these writings may have occurred early in his career, it is very possible that the influence of the biological works upon Aristotle’s other writings is considerable. Aristotle’s biological works (so often neglected) should be brought to the fore, not only in the history of biology, but also as a way of understanding some of Aristotle’s non-biological writings.


Watch the video: Audiobook Βιολογία Κατεύθυνσης Γ ΓΕΛ. Κεφ. 2: Αντιγραφή, έκφραση και ρύθμιση της γεν. πληρ. (November 2022).