Hasil untuk "Biology"

E. Widdowson

D. Sibley, F. Monsma

M. Cheng

M. Karplus, G. Petsko

A. H. Weatherley, H. S. Gill, J. Casselman

A. J. Lotka

J. Jacquez

R. Littell, R. Mead, R. Curnow et al.

INTRODUCTION The Need for Statistics Types of Data The Use of Computers in Statistics PROBABILITY AND DISTRIBUTIONS Probability Populations and Samples Means and Variances The Normal Distribution Sampling Distributions ESTIMATION AND HYPOTHESIS TESTING Estimation of the Population Mean Testing Hypotheses about the Population Mean Population Variance Unknown Comparison of Samples A Pooled Estimate of Variance A SIMPLE EXPERIMENT Randomization and Replication Analysis of a Completely Randomized Design with Two Treatments A Completely Randomized Design with Several Treatments Testing Overall Variation Between the Treatments CONTROL OF RANDOM VARIATION BY BLOCKING Local Control of Variation Analysis of a Randomized Block Design Meaning of the Error Mean Square Latin Square Designs Multiple Latin Squares Design The Benefit of Blocking and the Use of Natural Blocks PARTICULAR QUESTIONS ABOUT TREATMENTS Treatment Structure Treatment Contrasts Factorial Treatment Structure Main Effects and Interactions Analysis of Variance for a Two-Factor Experiment Partial Factorial Structure Comparing Treatment Means - Are Multiple Comparison Methods Helpful? MORE ON FACTORIAL TREATMENT STRUCTURE More than Two Factors Factors with Two Levels The Double Benefit of Factorial Structure Many Factors and Small Blocks The Analysis of Confounded Experiments Split Plot Experiments Analysis of a Split Plot Experiment Experiments Repeated at Different Sites THE ASSUMPTIONS BEHIND THE ANALYSIS Our Assumptions Normality Variance Homogeneity Additivity Transformations of Data for Theoretical Reasons A More General Form of Analysis Empirical Detection of the Failure of Assumptions and Selection of Appropriate Transformations Practice and Presentation STUDYING LINEAR RELATIONSHIPS Linear Regression Assessing the Regression Line Inferences about the Slope of a Line Prediction Using a Regression Line Correlation Testing Whether the Regression is Linear Regression Analysis Using Computer Packages MORE COMPLEX RELATIONSHIPS Making the Crooked Straight Two Independent Variables Testing the Components of a Multiple Relationship Multiple Regression Possible Problems in Computer Multiple Regression LINEAR MODELS The Use of Models Models for Factors and Variables Comparison of Regressions Fitting Parallel Lines Covariance Analysis Regression in the Analysis of Treatment Variation NONLINEAR MODELS Advantages of Linear and Nonlinear Models Fitting Nonlinear Models to Data Inferences about Nonlinear Parameters Exponential Models Inverse Polynomial Models Logistic Models for Growth Curves THE ANALYSIS OF PROPORTIONS Data in the Form of Frequencies The 2 ' 2 Contingency Table More than Two Situations or More than Two Outcomes General Contingency Tables Estimation of Proportions Sample Sizes for Estimating Proportions MODELS AND DISTRIBUTIONS FOR FREQUENCY DATA Models for Frequency Data Testing the Agreement of Frequency Data with Simple Models Investigating More Complex Models The Binomial Distribution The Poisson Distribution Generalized Models for Analyzing Experimental Data Log-Linear Models Logit Analysis of Response Data MAKING AND ANALYZING SEVERAL EXPERIMENTAL MEASUREMENTS Different Measurements on the Same Units Interdependence of Different Variables Repeated Measurements Joint (Bivariate) Analysis Indices of Combined Yield Investigating Relationships with Experimental Data ANALYZING AND SUMMARIZING MANY MEASUREMENTS Introduction to Multivariate Data Principal Component Analysis Covariance or Correlation Matrix Cluster Analysis Similarity and Dissimilarity Measures Hierarchical Clustering Comparison of PCA and Cluster Analysis CHOOSING THE MOST APPROPRIATE EXPERIMENTAL DESIGN The Components of Design Units and Treatments Replication and Precision Different Levels of Variation and Within-Unit Replication Variance Components and Split Plot Designs Randomization Managing with Limited Resources Factors with Quantitative Levels Screening and Selection On-Farm Experiments SAMPLING FINITE POPULATIONS Experiments and Sample Surveys Simple Random Sampling Stratified Random Sampling Cluster Sampling, Multistage Sampling and Sampling Proportional to Size Ratio and Regression Estimates REFERENCES APPENDIX INDEX

M. Soulé

C onservation biology, a new stage in the application of science to conservation problems, addresses the biology of species, communities, and ecosystems that are perturbed, either directly or indirectly, by human activities or other agents. Its goal is to provide principles and tools for preserving biological diversity. In this article I describe conservation biology, define its fundamental propositions, and note a few of its contributions. I also point out that ethical norms are a genuine part of conservation biology, as they are in all missionor crisis-oriented disciplines.

D. Sonenshine

M. Winston

S. Morrison, Nobuko Uchida, Nobuko Uchida et al.

S. Prusiner, M. Scott, S. DeArmond et al.

Kolja Henckel, Kai J. Runte, T. Bekel et al.

B. Levine

A. Fernie, R. Trethewey, A. Krotzky et al.

W. Cawthorn, Jaswinder K. Sethi

Rui-Sheng Wang, A. Saadatpour, R. Albert

Fernando Parisio

In this work, we investigate how quantum coherence can scatter among the several off-diagonal elements of an arbitrary quantum state, defining coherence dispersion ($Δ_{\rm c}$). It turns out that this easily computable quantity is maximized for intermediate values of an appropriate entropy, a prevalent signature of complexity quantifiers across different fields, from linguistics and information science to evolutionary biology. By focusing on out-of-equilibrium systems, we use the developed framework to address a simplified model of cellular energetics, involving remanent coherence. Within the context of this model, the precise energy of 30.5 kJ/mol (the yield of ATP-ADP conversion) causes the temperature range where $Δ_{\rm c}$ is maximized to be compatible with temperatures for which unicellular life is reported to exist. Low levels of coherence suffice to support this conclusion.

Mohamed Amine Ouchdiri, Hamza Faquir, Saad Benjelloun et al.

Reaction-diffusion systems offer a powerful framework for understanding self-organized patterns in biological systems, yet controlling these patterns remains a significant challenge. As a consequence, we present a rigorous framework of optimal control for a class of coupled reaction-diffusion systems. The couplings are justified by the shared regulatory mechanisms encountered in synthetic biology. Furthermore, we introduce inputs and polynomial input-gain functions to guarantee well-posedness of the control system while maintaining biological relevance. As a result, we formulate an optimal control problem and derive necessary optimality conditions. We demonstrate our framework on an instance of such equations modeling the Nodal-Lefty interactions in mammalian cells. Numerical simulations showcase the effectiveness in directing pattern towards diverse targeted ones.