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1.

図書
図書
1. A student's guide to Python for physical modeling. 2nd ed (: pbk)
Jesse M. Kinder and Philip Nelson
出版情報: Princeton : Princeton University Press, c2021  xiii, 223 p. ; 26 cm
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目次情報: 続きを見る
Let's Go
Getting Started with Python / 1：
Algorithms and algorithmic thinking / 1.1：
Algorithmic thinking / 1.1.1：
States / 1.1.2：
What does a = a + 1 mean? / 1.1.3：
Symbolic versus numerical / 1.1.4：
Launch Python / 1.2：
IPython console / 1.2.1：
Error messages / 1.2.2：
Sources of help / 1.2.3：
Good practice: Keep a log / 1.2.4：
Python modules / 1.3：
Import / 1.3.1：
From … import / 1.3.2：
NumPy and PyPlot / 1.3.3：
Python expressions / 1.4：
Numbers / 1.4.1：
Arithmetic operations and predefined functions / 1.4.2：
Good practice: Variable names / 1.4.3：
More about functions / 1.4.4：
Organizing Data / 2：
Objects and their methods / 2.1：
Lists, tuples, and arrays / 2.2：
Creating a list or tuple / 2.2.1：
NumPy arrays / 2.2.2：
Filling an array with values / 2.2.3：
Concatenation of arrays / 2.2.4：
Accessing array elements / 2.2.5：
Arrays and assignments / 2.2.6：
Slicing / 2.2.7：
Flattening an array / 2.2.8：
Reshaping an array / 2.2.9：
T2 Lists and arrays as indices / 2.2.10：
Strings / 2.3：
Raw strings / 2.3.1：
Formatting strings with the format () method / 2.3.2：
T2 Formatting strings with % / 2.3.3：
Structure and Control / 3：
Loops / 3.1：
For loops / 3.1.1：
While loops / 3.1.2：
Very long loops / 3.1.3：
Infinite loops / 3.1.4：
Array operations / 3.2：
Vectorizing math / 3.2.1：
Matrix math / 3.2.2：
Reducing an array / 3.2.3：
Scripts / 3.3：
The Editor / 3.3.1：
T2 Other editors / 3.3.2：
First steps to debugging / 3.3.3：
Good practice: Commenting / 3.3.4：
Good practice: Using named parameters / 3.3.5：
Good practice: Units / 3.3.6：
Contingent behavior: Branching / 3.4：
The if statement / 3.4.1：
Testing equality of floats / 3.4.2：
Nesting / 3.5：
Data In, Results Out / 4：
Importing data / 4.1：
Obtaining data / 4.1.1：
Bringing data into Python / 4.1.2：
Exporting data / 4.2：
Data files / 4.2.1：
Visualizing data / 4.3：
The plot command and its relatives / 4.3.1：
Log axes / 4.3.2：
Manipulate and embellish / 4.3.3：
Replacing curves / 4.3.4：
T2 More about figures and their axes / 4.3.5：
T2 Error bars / 4.3.6：
3D graphs / 4.3.7：
Multiple plots / 4.3.8：
Subplots / 4.3.9：
Saving figures / 4.3.10：
T2 Using figures in other applications / 4.3.11：
First Computer Lab / 5：
HIV example / 5.1：
Explore the model / 5.1.1：
Fit experimental data / 5.1.2：
Bacterial example / 5.2：
Random Number Generation and Numerical Methods / 5.2.1：
Writing your own functions / 6.1：
Defining functions in Python / 6.1.1：
Updating functions / 6.1.2：
Arguments, keywords, and defaults / 6.1.3：
Return values / 6.1.4：
Functional programming / 6.1.5：
Random numbers and simulation / 6.2：
Simulating coin flips / 6.2.1：
Generating trajectories / 6.2.2：
Histograms and bar graphs / 6.3：
Creating histograms / 6.3.1：
Finer control / 6.3.2：
Contour plots, surface plots, and heat maps / 6.4：
Generating a grid of points / 6.4.1：
Contour plots / 6.4.2：
Surface plots / 6.4.3：
Heat maps / 6.4.4：
Numerical solution of nonlinear equations / 6.5：
General real functions / 6.5.1：
Complex roots of polynomials / 6.5.2：
Solving systems of linear equations / 6.6：
Numerical integration / 6.7：
Integrating a predefined function / 6.7.1：
Integrating your own function / 6.7.2：
Oscillatory integrands / 6.7.3：
T2 Parameter dependence / 6.7.4：
Numerical solution of differential equations / 6.8：
Reformulating the problem / 6.8.1：
Solving an ODE / 6.8.2：
Other ODE solvers / 6.8.3：
Vector fields and streamlines / 6.9：
Vector fields / 6.9.1：
Streamlines / 6.9.2：
Second Computer Lab / 7：
Generating and plotting trajectories / 7.1：
Plotting the displacement distribution / 7.2：
Rare events / 7.3：
The Poisson distribution / 7.3.1：
Waiting times / 7.3.2：
Images and Animation / 8：
Image processing / 8.1：
Images as NumPy arrays / 8.1.1：
Saving and displaying images / 8.1.2：
Manipulating images / 8.1.3：
Displaying data as an image / 8.2：
Animation / 8.3：
Creating animations / 8.3.1：
Saving animations / 8.3.2：
HTML movies
T2 Using an encoder
Conclusion / 8.3.3：
Third Computer Lab / 9：
Convolution / 9.1：
Python tools for image processing / 9.1.1：
Averaging / 9.1.2：
Smoothing with a Gaussian / 9.1.3：
Denoising an image / 9.2：
Emphasizing features / 9.3：
T2 Image files and arrays / 9.4：
Advanced Techniques / 10：
Dictionaries and generators / 10.1：
Dictionaries / 10.1.1：
Special function arguments / 10.1.2：
List comprehensions and generators / 10.1.3：
Tools for data science / 10.2：
Series and data frames with pandas / 10.2.1：
Machine learning with scikit-learn / 10.2.2：
Next steps / 10.2.3：
Symbolic computing / 10.3：
Wolfram Alpha / 10.3.1：
The SymPy library / 10.3.2：
Other alternatives / 10.3.3：
First passage revisited / 10.3.4：
Writing your own classes / 10.4：
A random walk class / 10.4.1：
When to use classes / 10.4.2：
Get Going
Installing Python / A：
Install Python and Spyder / A.1：
Graphical installation / A.1.1：
Command line installation / A.1.2：
Setting up Spyder / A.2：
Working directory / A.2.1：
Interactive graphics / A.2.2：
Script template / A.2.3：
Restart / A.2.4：
Keeping up to date / A.3：
Installing FFmpeg / A.4：
Installing ImageMagick / A.5：
Command Line Tools / B：
The command line / B.1：
Navigating your file system / B.1.1：
Creating, renaming, moving, and removing files / B.1.2：
Creating and removing directories / B.1.3：
Python and Conda / B.1.4：
Text editors / B.2：
Version control / B.3：
How Git works / B.3.1：
Installing and using Git / B.3.2：
Tracking changes and synchronizing repositories / B.3.3：
Summary of useful workflows / B.3.4：
Troubleshooting / B.3.5：
Jupyter Notebooks / B.4：
Getting started / C.1：
Launch Jupyter Notebooks / C.1.1：
Open a notebook / C.1.2：
Multiple notebooks / C.1.3：
Quitting Jupyter / C.1.4：
T2 Setting the default directory / C.1.5：
Cells / C.2：
Code cells / C.2.1：
Graphics / C.2.2：
Markdown cells / C.2.3：
Edit mode and command mode / C.2.4：
Sharing / C.3：
More details / C.4：
Pros and cons / C.5：
Errors and Error Messages / D：
Python errors in general / D.1：
Some common errors / D.2：
Python 2 versus Python 3 / E：
Division / E.1：
Print command / E.2：
User input / E.3：
More assistance / E.4：
Under the Hood / F：
Assignment statements / F.1：
Memory management / F.2：
Functions / F.3：
Scope / F.4：
Name collisions / F.4.1：
Variables passed as arguments / F.4.2：
Summary / F.5：
Answers to "Your Turn" Questions / G：
Acknowledgments
Recommended Reading
Index
Let's Go
Getting Started with Python / 1：
Algorithms and algorithmic thinking / 1.1：
2.

電子ブック
ＥＢ
2. PROCEEDINGS OF INTERNATIONAL CONGRESS ON GRAPHENE, 2D MATERIALS AND APPLICATIONS (2D MATERIALS 2019)
出版情報: AIP Conference Proceedings (American Institute of Physics) , AIP Publishing, 2021
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3.

電子ブック
ＥＢ
3. PROCEEDINGS OF THE 2ND PHYSICS AND MATERIALS SCIENCE INTERNATIONAL SYMPOSIUM (PhyMaS 2.0)
出版情報: AIP Conference Proceedings (American Institute of Physics) , AIP Publishing, 2021
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4.

図書
図書
4. E2S2-CREATE and AIChE Waste Management Conference 2019 : Singapore 11-13 March 2019 (: pbk)
E2S2-CREATE and AIChE Waste Management Conference ; American Institute of Chemical Engineers ; E2S2-CREATE
出版情報: New York : AIChE , Red Hook, NY : Printed from e-media with permission by Curran Associates, 2021, c2019  62 p. ; 28 cm
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5.

電子ブック
ＥＢ
5. Multi-Access Edge Computing: Software Development at the Network Edge
Dario Sabella
出版情報: SpringerLink Books - AutoHoldings , Springer International Publishing, 2021
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目次情報: 続きを見る
Edge Computing Overview / Part I：
Principles of Edge Computing, Fog and Cloud Computing / 1：
Background on Cloud Computing / 1.1：
From Remote Cloud to Edge Cloud / 1.2：
Fog Computing / 1.2.1：
Edge Computing and MEC / 1.3：
References
MEC: Standards and Industry Associations Around Edge Computing / 2：
MEC, Edge Computing, 5G and Verticals / 2.1：
MEC Service Scenarios / 2.2：
ETSI MEC Standard / 2.3：
Standardization Landscape on Edge Computing / 2.4：
Industry Groups on Edge Computing / 2.5：
5GAA (5G Automotive Association) / 2.5.1：
5G-ACIA (5G Alliance for Connected Industries and Automation) / 2.5.2：
Wireless Broadband Alliance / 2.5.3：
Other Fora and Projects / 2.6：
Research Projects / 2.7：
Edge Computing Standards / Part II：
MEC Standards on Edge Platforms / 3：
The Central Role of MEC Host / 3.1：
MEC Architecture / 3.2：
MEC Applications and UE Applications / 3.2.1：
MEC Platform and MEC Host / 3.2.2：
MEC Orchestrator / 3.2.3：
MEC Platform Application Enablement / 3.3：
MEC App Assistance: MEC App Start-Up Procedure / 3.3.1：
MEC App Assistance: MEC App Graceful Termination/Stop / 3.3.2：
Service Availability Update and New Service Registration / 3.3.3：
Service Availability Query / 3.3.4：
Service Availability Notification / 3.3.5：
Traffic Rule Activation/Deactivation/Update / 3.3.6：
DNS Rule Activation/Deactivation / 3.3.7：
Resource Structures / 3.3.8：
ETSI MEC Standard: MEC Management / 3.4：
MEC Standards on Edge Services / 4：
Classification of MEC Services / 4.1：
General Principles for MEC Service APIs / 4.2：
Methods to Update a Resource / 4.2.1：
Asynchronous Operations / 4.2.2：
Remote MEC Service Consumption / 4.3：
Alternative Transport Protocols in MEC / 4.3.1：
Zenoh: An Alternative MEC Transport Protocol / 4.3.2：
Overview of MEC APIs / 4.4：
Radio Network Information API / 4.4.1：
Location API / 4.4.2：
UE Identity API / 4.4.3：
Bandwidth Management API / 4.4.4：
Multi-access Traffic Steering API / 4.4.5：
WLAN Information API / 4.4.6：
Fixed Access API / 4.4.7：
V2X Api / 4.4.8：
MEC Service APIs in Action / 5：
Radio Network Information (RNI) API / 5.1：
Resource URI Structure of RNI API / 5.1.1：
Services Offered to RNI API Consumers / 5.1.2：
Resource URI Structure of Location API / 5.2：
Services Offered to Location API Consumers / 5.2.2：
Multi-access Traffic Steering (MTS) API / 5.3：
Resource URI Structure of MTS API / 5.3.1：
Services Offered to MTS API Consumers / 5.3.2：
Y2X Information Service API / 5.4：
Resource URI Structure of V2X API / 5.4.1：
Services Offered to V2X API Consumers / 5.4.2：
Edge Computing Deployments / Part III：
MEC in Virtualized Environments / 6：
Principles of Virtualization / 6.1：
Network Functions Virtualization (NFV) / 6.2：
Open Source Frameworks on Cloud and NFV Technologies / 6.3：
MEC in NFV Environments / 6.4：
MEC, Virtual RAN, C-RAN, Open RAN / 6.4.1：
Virtualization Aspects in MEC / 6.4.2：
Cloud Native Computing / 6.5：
Edge Computing in 5G Networks / 7：
Overview of 5G Networks / 7.1：
5G System Architecture / 7.1.1：
5G Non-Standalone Deployments / 7.1.2：
5G Standalone Deployments / 7.1.3：
5G Deployment Phases / 7.1.4：
Session and Service Continuity in 5G Systems / 7.2：
Network Exposure Function in 5G Systems / 7.3：
Initial Edge Computing Support in 5G Systems / 7.4：
3GPP SA2 Edge Computing Support in Rel.15/16 / 7.4.1：
Further Edge Computing Support in 5G Rel.17 Systems / 7.5：
SA6 / 7.5.1：
SA5: Management Aspects of Edge Computing / 7.5.2：
MEC Synergized Architecture / 7.6：
MEC 5G Integration / 7.7：
MEC Support for Network Slicing and Verticals / 7.8：
MEC Federation and Mobility Aspects / 8：
Background of MEC Federation / 8.1：
GSMA Requirements on MEC Federation / 8.2：
MEC Federation in ETSI / 8.3：
Edge Resources Exposure: A Case Study / 8.4：
Application Offloading Use Case in MEC / 8.4.1：
MEC Mobility Aspects / 8.5：
Example of MEC Application and E2E Mobility / 8.5.1：
Edge Computing Software Development / Part IV：
Software Development for Edge Computing / 9：
MEC: The Application Developer Perspective / 9.1：
Phase 1: MEC Application Packaging and On-Boarding / 9.1.1：
Phase 2: MEC Application Instantiation and Communications / 9.1.2：
Phase 3: Usage of the MEC Platform and Services / 9.1.3：
Open Network System Services Software (OpenNESS) Toolkit / 9.2：
OpenNESS System Architecture / 9.2.1：
OpenNESS APIs / 9.2.2：
Example OpenNESS Application / 9.2.3：
The OpenNESS4J Library / 9.3：
Edge Application Authenticator / 9.3.1：
Edge Application Connector / 9.3.2：
Edge Application Notification Manager / 9.3.3：
MEC and Open Source / 9.4：
Akraino API Portal / 9.4.1：
ETSI MEC DECODE WG and Akraino / 9.4.2：
ServerlessOnEdge / 9.5：
Open Edge Computing-Toward Globally Consistent Edge Services / 9.6：
Open Edge Computing Vision / 9.6.1：
Open Edge Computing Initiative (OEC) / 9.6.2：
Edge Software Development-Challenges, Projects and Outlook / 9.6.3：
MEC in Action: Performance, Testing and Ecosystem Activities / 10：
Performance Assessment, Metrics, Best Practices and Guidelines / 10.1：
MEC Metrics / 10.1.1：
Performance Assessment of MEC / 10.1.2：
Measurement Methodology and Examples / 10.2：
Evaluation of Latency / 10.2.1：
Evaluation of Energy Efficiency (EE) / 10.2.2：
MEC Testing / 10.3：
OpenAPI Representations of MEC Services APIs / 10.4：
MEC Sandbox / 10.5：
Sandbox Access and Configuration / 10.5.1：
Sandbox MEC Service API Interaction / 10.5.2：
MEC Ecosystem: Proof-of-Concepts, Trials, Hackathons / 10.6：
MEC PoCs (Proof-of-Concepts) / 10.6.1：
Trials and Plugtests™ / 10.6.2：
MEC Hackathons / 10.6.3：
MEC Terminology (Phase 1 and Phase 2) / Annex A：
Functional Blocks and Reference Points in the MEC System / Annex B：
MEC Software Resources / Annex C：
All Quiz Results / Annex D：
Acknowledgements / Annex E：
Edge Computing Overview / Part I：
Principles of Edge Computing, Fog and Cloud Computing / 1：
Background on Cloud Computing / 1.1：
6.

電子ブック
ＥＢ
6. Explainable AI with Python
Gianfagna, Antonio Di Cecco
出版情報: SpringerLink Books - AutoHoldings , Springer International Publishing, 2021
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目次情報: 続きを見る
The Landscape / 1：
Examples of What Explainable AI Is / 1.1：
Learning Phase / 1.1.1：
Knowledge Discovery / 1.1.2：
Reliability and Robustness / 1.1.3：
What Have We Learnt from the Three Examples / 1.1.4：
Machine Learning and XAI / 1.2：
Machine Learning Tassonomy / 1.2.1：
Common Myths / 1.2.2：
The Need for Explainable AI / 1.3：
Explainability and Interpretability: Different Words to Say the Same Tiling or Not? / 1.4：
From World to Humans / 1.4.1：
Correlation Is Not Causation / 1.4.2：
So What Is the Difference Between Interpretability and Explainability? / 1.4.3：
Making Machine Learning Systems Explainable / 1.5：
The XAI Flow / 1.5.1：
The Big Picture / 1.5.2：
Do We Really Need to Make Machine Learning Models Explainable? / 1.6：
Summary / 1.7：
References
Explainable AI: Needs, Opportunities, and Challenges / 2：
Human in the Loop / 2.1：
Centaur XAI Systems / 2.1.1：
XAI Evaluation from "Human in the Loop Perspective" / 2.1.2：
How to Make Machine Learning Models Explainable / 2.2：
Intrinsic Explanations / 2.2.1：
Post Hoc Explanations / 2.2.2：
Global or Local Explainability / 2.2.3：
Properties of Explanations / 2.3：
Intrinsic Explainable Models / 2.4：
Loss Function / 3.1：
Linear Regression / 3.2：
Logistic Regression / 3.3：
Decision Trees / 3.4：
K-Nearest Neighbors (KNN) / 3.5：
Model-Agnostic Methods for XAI / 3.6：
Global Explanations: Permutation Importance and Partial Dependence Plot / 4.1：
Ranking Features by Permutation Importance / 4.1.1：
Permutation Importance on the Train Set / 4.1.2：
Partial Dependence Plot / 4.1.3：
Local Explanations: XAI with Shapley Additive explanations / 4.1.4：
Shapley Values: A Game Theoretical Approach / 4.2.1：
The First Use of SHAP / 4.2.2：
The Road to KernelSHAP / 4.2.3：
The Shapley Formula / 4.3.1：
How to Calculate Shapley Values / 4.3.2：
Local Linear Sunogate Models (LIME) / 4.3.3：
KernelSHAP Is a Unique Form of LIME / 4.3.4：
KernelSHAP and Interactions / 4.4：
The New York Cab Scenario / 4.4.1：
Train the Model with Preliminary Analysis / 4.4.2：
Making the Model Explainable with KernelShap / 4.4.3：
Interactions of Features / 4.4.4：
A Faster SHAP for Boosted Trees / 4.5：
Using TreeShap / 4.5.1：
Providing Explanations / 4.5.2：
A Naive Criticism to SHAP / 4.6：
Explaining Deep Learning Models / 4.7：
Agnostic Approach / 5.1：
Adversarial Features / 5.1.1：
Augmentations / 5.1.2：
Occlusions as Augmentations / 5.1.3：
Occlusions as an Agnostic XAI Method / 5.1.4：
Neural Networks / 5.2：
The Neural Network Structure / 5.2.1：
Why the Neural Network Is Deep? (Versus Shallow) / 5.2.2：
Rectified Activations (and Batch Normalization) / 5.2.3：
Saliency Maps / 5.2.4：
Opening Deep Networks / 5.3：
Different Layer Explanation / 5.3.1：
CAM (Class Activation Maps) and Grad-CAM / 5.3.2：
DeepShap/DeepLift / 5.3.3：
A Critic of Saliency Methods / 5.4：
What the Network Sees / 5.4.1：
Explainability Batch Normalizing Layer by Layer / 5.4.2：
Unsupervised Methods / 5.5：
Unsupervised Dimensional Reduction / 5.5.1：
Dimensional Reduction of Convolutional Filters / 5.5.2：
Activation Atlases: How to Tell a Wok from a Pan / 5.5.3：
Making Science with Machine Learning and XAI / 5.6：
Scientific Method in the Age of Data / 6.1：
Ladder of Causation / 6.2：
Discovering Physics Concepts with ML and XAI / 6.3：
The Magic of Autoencoders / 6.3.1：
Discover the Physics of Damped Pendulum with ML and XAI / 6.3.2：
Climbing the Ladder of Causation / 6.3.3：
Science in the Age of ML and XAI / 6.4：
Adversarial Machine Learning and Explainability / 6.5：
Adversarial Examples (AEs): Crash Course / 7.1：
Hands-On Adversarial Examples / 7.1.1：
Doing XAI with Adversarial Examples / 7.2：
Defending Against Adversarial Attacks with XAI / 7.3：
A Proposal for a Sustainable Model of Explainable AI / 7.4：
The XAI "Fil Rouge" / 8.1：
XAI and GDPR / 8.2：
F.A.S.T. XAI / 8.2.1：
Conclusions / 8.3：
Index / 8.4：
The Landscape / 1：
Examples of What Explainable AI Is / 1.1：
Learning Phase / 1.1.1：
7.

図書
図書
7. AAAI technical tracks : computer vision I (pt. 2)
National Conference on Artificial Intelligence ; Association for the Advancement of Artificial Intelligence
出版情報: Palo Alto, Calif. : Association for the Advancement of Artificial Intelligence , Red Hook, NY : Printed with permission by Curran Associates, 2021  p. 1343-1797 ; 27 cm
シリーズ名: 35th AAAI Conference on Artificial Intelligence (AAAI-21) : online 2-9 February 2021 ; v. 2
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8.

図書
図書
8. Single-particle cryo-EM of biological macromolecules
edited by Robert M Glaeser, Eva Nogales, Wah Chiu
出版情報: Bristol : IOP Publishing, c2021  1 v. ; 27 cm
シリーズ名: Biophysical Society-IOP series
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目次情報: 続きを見る
Editor biographies
Section authors
Introduction and overview / 1：
Visualizing biological molecules to understand life's principles / 1.1：
A brief historical perspective on scattering-based structural biology methods / 1.1.1：
Unique capabilities of cryo-EM: polymers and viruses / 1.1.2：
Unique capabilities of cryo-EM: integral membrane proteins / 1.1.3：
Unique capabilities of cryo-EM: large assemblies / 1.1.4：
Unique capabilities of cryo-EM: scarce samples / 1.1.5：
Unique capabilities of cryo-EM: compositionally heterogeneous samples / 1.1.6：
Unique capabilities of cryo-EM: conformationally complex samples / 1.1.7：
Current limits of cryo-EM and things yet to come / 1.1.8：
Recovery of 3D structures from images of weak-phase objects / 1.2：
The signal that we care about is attributed to elastic scattering of electrons / 1.2.1：
The electron accumulates information as it passes through a specimen / 1.2.2：
The image wave function, and thus the image intensity, suffers from imperfections in the microscope optics / 1.2.3：
Intermediate summary: the image intensity is linear in the projected Coulomb potential of the object / 1.2.4：
Structure-factor phases, as well as amplitudes, are retained in the computed Fourier transforms of image intensities / 1.2.5：
The projection theorem: the Fourier transform of an image corresponds to a 2D 'central' section within the 3D Fourier transform of the object / 1.2.6：
The 3D object can be reconstructed from multiple projections / 1.2.7：
Similarities and differences between sub-tomogram averaging and single-particle cryo-EM / 1.2.8：
References
Sample preparation / 2：
Overview / 2.1：
Initial screening of samples in negative stain / 2.2：
Introduction / 2.2.1：
Negative staining for TEM / 2.2.2：
Purpose of negative staining when starting a project / 2.2.3：
Techniques for the preparation of negatively stained samples / 2.2.4：
Use of data processing to provide feedback to optimize samples for cryo-EM / 2.2.5：
Standard method of making grids for cryo-EM / 2.3：
Grids and support films / 2.3.1：
Plasma cleaning or 'glow discharging' grids / 2.3.2：
Types of apparatus used for plunge freezing / 2.3.3：
Blotting and plunging the grid using plunge freezers / 2.3.4：
Common issues faced in making grids for cryo-EM imaging / 2.3.5：
Requirement to make very thin specimens for cryo-EM / 2.4：
Inelastic electron scattering causes the image quality to deteriorate with increasing sample thickness values / 2.4.1：
The projection approximation may fail if the sample is too thick / 2.4.2：
Areas of a grid where the sample is obviously too thick can, and should be, avoided during data collection / 2.4.3：
Areas where the sample is much too thin, perhaps even air-dried, can sometimes be avoided just on the basis of their subjective appearance / 2.4.4：
Current strategies for optimizing preparation of cryo-grids / 2.5：
Behavior of particles in the thin film environment / 2.5.1：
Approaches to alter particle behavior in the thin film / 2.5.2：
New technologies for sample preparation / 2.5.3：
Data collection / 3：
Radiation damage in cryo-EM / 3.1：
Interaction cross sections, elastic, and inelastic interactions / 3.2.1：
Cryoprotection and primary, secondary, and tertiary radiation damage / 3.2.3：
Radiation damage dependence on electron energy / 3.2.4：
Practical implications of radiation damage: image averaging in cryo-EM / 3.2.5：
Resolution dependence and exposure weighting / 3.2.6：
Radiation damage versus beam-induced motion and charging / 3.2.7：
Low-dose protocols for recording images / 3.3：
Automated low-dose imaging / 3.3.1：
Improving throughput / 3.3.2：
Electron exposure levels used during high-resolution data collection / 3.3.3：
Practical considerations: defocus. stigmation, coma-free illumination, and phase plates / 3.4：
Why do we need to defocus the microscope? / 3.4.1：
Effects of defocus on the image and its information content / 3.4.2：
Defocus variation is necessary to obtain uniform information coverage in reciprocal space / 3.4.3：
Optical correction of astigmatism and coma aberrations / 3.4.4：
Use of phase plates to improve image contrast and the expected benefits / 3.4.5：
Practical considerations: movie-mode data acquisition / 3.5：
Magnification and resolution / 3.5.1：
Dose rate / 3.5.2：
Strategies for motion correction / 3.5.3：
Total dose or exposure time / 3.5.4：
File size of movie datasets / 3.5.5：
Summary / 3.5.6：
Data processing / 4：
Automated extraction of particles / 4.1：
From micrographs to particles / 4.2.1：
Manual selection / 4.2.2：
Unbiased automated approaches / 4.2.3：
Particle extraction / 4.2.4：
Cleaning up the results through classification / 4.2.5：
CTF estimation and image correction (restoration) / 4.3：
CTF estimation / 4.3.1：
Image correction / 4.3.2：
Magnification distortion / 4.3.3：
Concluding remarks / 4.3.4：
Merging data from structurally homogeneous subsets / 4.4：
How many particle images are needed for a 3D reconstruction? / 4.4.1：
Obtaining a 3D reconstruction / 4.4.2：
Acknowledgments
3D classification of structurally heterogeneous particles / 4.5：
Global 3D classification / 4.5.1：
Masked 3D classification / 4.5.3：
3D classification of particles with pseudo-symmetry / 4.5.4：
Dealing with continuous motions / 4.5.5：
Conclusion / 4.5.6：
Preferred orientation: how to recognize and deal with adverse effects / 4.6：
Protein interaction with the air-water interface / 4.6.1：
Preferred orientation and its effects in cryo-EM / 4.6.2：
Quantifying preferred orientation and its effects on cryo-EM reconstructions / 4.6.3：
Overcoming the effects of preferred orientation / 4.6.4：
Areas of research / 4.6.5：
B factors and map sharpening / 4.7：
An ideal 3D reconstruction has a predictable radial amplitude spectrum / 4.7.1：
Actual 3D reconstructions feature dampened amplitudes at high frequencies / 4.7.2：
Several factors contribute to signal decay at high frequencies / 4.7.3：
Gaussian falloff, parametrized by a B factor, is a useful model of signal loss / 4.7.4：
Estimating B factors / 4.7.5：
Sharpening a map / 4.7.6：
A single inverse Gaussian filter using a global B factor does not always lead to the optimal map / 4.7.7：
Optical aberrations and Ewald sphere curvature / 4.8：
Further considerations on the aberration function ¿(s) / 4.8.1：
Common types of aberrations / 4.8.2：
Practical considerations for aberration correction / 4.8.3：
Thick objects and the Ewald sphere / 4.8.4：
Ewald sphere correction / 4.8.5：
Map validation / 5：
Measures of resolution: FSC and local resolution / 5.1：
The 'gold-standard' FSC / 5.2.1：
Resolution thresholds / 5.2.2：
FSC artifacts due to masking, filtration, and CTF / 5.2.3：
Local resolution / 5.2.4：
Resolution anisotropy / 5.2.5：
Recognizing the effect of bias and over-fitting / 5.3：
Introduction and nature of the problem arising from iterative refinement / 5.3.1：
Assessing the consistency of maps with projection data / 5.3.2：
Detecting over-fitting at high resolution in maps and effect on the FSC / 5.3.3：
Local over-fitting / 5.3.4：
Estimates of alignment accuracy / 5.3.5：
Correlation and the signal-to-noise ratio (SNR) / 5.4.1：
Analysis of alignment accuracy with synthetic data / 5.4.2：
The relationship between alignment accuracy and resolution / 5.4.3：
Estimating alignment accuracy from tilt pairs / 5.4.4：
Estimating alignment accuracy from the reconstructed map / 5.4.5：
Estimating alignment accuracy from projection-matching results / 5.4.6：
Discussion / 5.5：
Acknowledgements
Model building and validation / 6：
Using known components or homologs: model building / 6.1：
Identifying known/modeled structures of individual subunits / 6.2.1：
Rigid-body fitting / 6.2.2：
Flexible fitting / 6.2.3：
Building atomistic models in cryo-EM density maps / 6.3：
Building models into cryo-EM density maps / 6.3.1：
Model refinement / 6.3.3：
Model validation / 6.3.4：
Model uncertainty / 6.3.5：
Model deposition / 6.3.6：
Revisiting the cryo-EM model challenge / 6.3.7：
Toward the future / 6.3.8：
Conclusions / 6.3.9：
Quality evaluation of cryo-EM map-derived models / 6.4：
Map-model metrics / 6.4.1：
Model-only metrics / 6.4.3：
Summary and conclusions / 6.4.4：
Acknowledgment
How algorithms from crystallography are helping electron cryo-microscopy / 6.5：
Map improvement / 6.5.1：
Map interpretation and model building / 6.5.3：
Model optimization / 6.5.4：
Validation / 6.5.5：
Validation-guided corrections / 6.5.6：
Archiving structures and data / 6.5.7：
Single-particle cryo-EM structure deposition / 6.6.1：
Preparing files for deposition / 6.6.3：
Data validation / 6.6.4：
Sample sequence and ligands / 6.6.5：
Deposition using OneDep / 6.6.6：
Post-deposition: what happens next? / 6.6.7：
Accessing cryo-EM structure data / 6.6.8：
Editor biographies
Section authors
Introduction and overview / 1：
9.

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