LEADER 08670nam 2200541 450 001 9910568257303321 005 20221123160923.0 010 $a1-4842-8042-3 024 7 $a10.1007/978-1-4842-8042-3 035 $a(MiAaPQ)EBC6965042 035 $a(Au-PeEL)EBL6965042 035 $a(CKB)21707960300041 035 $a(OCoLC)1313808273 035 $a(OCoLC-P)1313808273 035 $a(CaSebORM)9781484280423 035 $a(PPN)262173271 035 $a(EXLCZ)9921707960300041 100 $a20221123d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCreating apps with React Native $edeliver cross-platform 0 crash, 5 star apps /$fM. Holmes He 210 1$aBerkeley, California :$cApress,$d[2022] 210 4$d©2022 215 $a1 online resource (445 pages) $cillustrations 300 $aIncludes index. 311 08$aPrint version: He, M. Holmes Creating Apps with React Native Berkeley, CA : Apress L. P.,c2022 9781484280416 320 $aIncludes index. 327 $aIntro -- Table of Contents -- About the Author -- About the Technical Reviewer -- The Path to a 05 App -- Chapter 1: Start Thinking in React -- 1.1 Component -- 1.1.1 Key Takeaways -- 1.2 The "Hello World" App in Pieces -- 1.2.1 React Native Development Environment -- 1.2.2 JSX -- 1.2.3 props -- 1.2.3.1 Style -- 1.2.3.2 Children -- 1.2.4 JSX Internals -- 1.2.5 States -- 1.2.5.1 State Change on the Current Component -- 1.2.5.2 Cascading State Changes -- 1.2.6 setState() Internals -- 1.2.7 Key Takeaways -- 1.3 Summary -- Chapter 2: Foundations of React -- 2.1 Flexbox, a Practical Guide -- 2.1.1 Component Size -- 2.1.2 Case Study: Feed -- 2.1.3 Key Takeaways -- 2.2 Composition vs. Inheritance, HOC -- 2.2.1 Case Study: Multiple Photo Feeds -- 2.2.2 Key Takeaways -- 2.3 ScrollView and FlatList -- 2.3.1 Case Study: Moment -- 2.3.2 Key Takeaways -- 2.4 Error Handling -- 2.4.1 Case Study: Moment (Reinforced) -- 2.4.2 Key Takeaways -- 2.5 Summary -- Chapter 3: Animation in React Native -- 3.1 Introduction to React Native Animation -- 3.2 Layout Animation -- 3.2.1 Presets -- 3.2.2 LayoutAnimation.create(?) -- 3.2.3 Raw Animation Config -- 3.2.4 Android -- 3.2.5 Case Study, Read More -- 3.2.6 Key Takeaways -- 3.3 Value Animation -- 3.3.1 Animate the Animation -- 3.3.1.1 Animated.timing(?) -- 3.3.1.2 Animated.spring(?) -- 3.3.1.3 Animation Cohort -- 3.3.1.4 setValue(?) -- 3.3.2 Bind the Animation Value -- 3.3.2.1 The transform props.style -- 3.3.2.2 Value Interpolation -- 3.3.2.3 Value Calculation -- 3.3.3 Case Study 1, Looming Animation for Image Loading -- 3.3.4 Case Study 2, Loading Indicators -- 3.3.5 Key Takeaways -- 3.4 Gesture-Driven Animation -- 3.4.1 Native Event -- 3.4.2 Case Study, a Pull Down Load Experience -- 3.4.3 Key Takeaways -- 3.5 Summary -- Chapter 4: Native Modules and Components -- 4.1 Native Modules -- 4.1.1 iOS Native Module. 327 $a4.1.1.1 Setup -- 4.1.1.2 Implement the Native Module -- 4.1.1.3 Async Calls -- 4.1.2 Android Native Module -- 4.1.2.1 Setup -- 4.1.2.2 Implement the Native Module -- 4.1.2.3 Register the Native Module -- 4.1.2.4 Async Calls -- 4.1.3 Use the Native Module in JavaScript -- 4.1.4 Key Takeaways -- 4.2 Native Components -- 4.2.1 iOS Native Component -- 4.2.1.1 Setup -- 4.2.1.2 Implement the View Manager -- 4.2.1.3 View Property -- 4.2.2 Android Native Component -- 4.2.2.1 Setup -- 4.2.2.2 Implement the View Manager -- 4.2.2.3 View Property -- 4.2.3 Use the Native Component in JavaScript -- 4.2.3.1 The Easy Way -- 4.2.3.2 The Right Way, Abstraction on the JavaScript Layer -- 4.2.4 Children of a Native Component -- 4.2.5 Key Takeaways -- 4.3 Advanced Techniques -- 4.3.1 Event -- 4.3.1.1 Send Events from iOS -- 4.3.1.2 Send Events from Android -- 4.3.1.3 Receive Events in JavaScript -- 4.3.2 React Tag -- 4.3.2.1 React Refs -- 4.3.2.2 React Tags -- 4.3.2.3 Reconcile React Tag Implementation on JavaScript -- 4.3.3 Direct Manipulation -- 4.3.4 Synchronous Method Call -- 4.3.5 Export Constants -- 4.3.5.1 iOS -- 4.3.5.2 Android -- 4.3.5.3 Access Constants in JavaScript -- 4.3.6 Initial Properties -- 4.3.7 Dependency Injection -- 4.3.8 Key Takeaways -- 4.4 Exception Handling -- 4.5 Case Study - a Video Component -- 4.5.1 iOS Implementation of a Video Component -- 4.5.2 Android Implementation of a Video Component -- 4.5.3 JavaScript Layer -- 4.5.3.1 Native Component Wrapper -- 4.5.3.2 View Manager Wrapper -- 4.5.3.3 Video Feed -- 4.5.3.4 Ref Forwarding -- 4.5.3.5 Video Feed in Moment -- 4.5.4 Reinforced Video Component -- 4.5.4.1 Protect the iOS Component -- 4.5.4.2 Protect the Android Component -- 4.5.4.3 JavaScript Layer -- 4.6 Summary -- Chapter 5: Network Programming -- 5.1 A Very Brief Introduction to TCP/IP -- 5.1.1 TCP. 327 $a5.1.1.1 Three-Way Handshake (Opening Connection) -- 5.1.1.2 Sliding Window -- 5.1.1.3 Congestion Control -- 5.1.1.4 Four-Way Handshake (Closing Connection) -- 5.1.1.5 Miscellanies -- 5.1.2 HTTP/1.1 -- 5.1.2.1 HTTP Is Text Based -- 5.1.2.2 Common Request Headers -- 5.1.2.3 Common Response Headers -- 5.1.2.4 HTTP Status Code -- 5.1.2.5 Cache Control -- 5.1.2.6 HTTP API Design -- 5.1.3 DNS -- 5.1.4 TLS -- 5.1.4.1 Pinning -- 5.1.5 The Modern Internet -- 5.1.6 Key Takeaway -- 5.2 Network Programming on the JavaScript Layer -- 5.2.1 Asynchronous Operations -- 5.2.1.1 Promise -- 5.2.1.2 Await -- 5.2.2 fetch(?) -- 5.2.3 Case Study, Move Everything Online -- 5.3 Network Programming on the Native Layer -- 5.3.1 Case Study, Enable Local Caching -- 5.4 Exception Handling -- 5.4.1 Case Study, Reinforce the Network Components -- 5.4.2 Case Study, Offline Mode -- 5.5 Summary -- Chapter 6: Advanced Topics -- 6.1 Revisit Rendering -- 6.2 Redux -- 6.2.1 Case Study, Like -- 6.2.1.1 Reduxfy Feeds -- 6.2.1.2 Implement Like -- 6.3 Long List -- 6.3.1 Case Study, Apply Basic Heuristics -- 6.4 0 Crash, Design Exception Flow -- 6.4.1 Robustness Built in Software Architecture -- 6.4.1.1 Entry Points -- 6.4.1.2 Crash Points -- 6.4.2 Last Resort, Global Error Handler -- 6.4.3 Wrap Up -- 6.5 Native Modules Inside Out -- 6.5.1 Phase 0, Prior Bootstrap -- 6.5.2 Phase 1, Bootstrap -- 6.5.2.1 requiresMainQueueSetup -- 6.5.2.2 Threads and Locks -- 6.5.3 Phase 2, Native Module on the JavaScript Layer -- 6.5.3.1 The Nature of a Native Call -- 6.5.4 Execute the Bundle -- 6.5.5 The Two-Way Communication -- 6.5.6 The Native Module Metadata -- 6.5.7 Wrap Up -- 6.6 Animation Inside Out -- 6.6.1 Establish the Animated Node Graph -- 6.6.1.1 JavaScript Pass -- 6.6.1.2 Native Pass -- 6.6.2 Bind the Event Receiver -- 6.6.3 Attach the Event Source -- 6.6.4 Native Event Transmission. 327 $a6.6.4.1 Identify Receivers -- 6.6.4.2 Update -- 6.7 Adaptive to All Screens, Layout Design -- 6.8 Time to Say Goodbye -- Index. 330 $aProduce high-quality, cross-platform apps with user experiences almost identical to pure native apps. When evaluating cross-platform frameworks, developers make an assumption that quality will be compromised. But that doesn't have to be true. The principles in this book will show you how to meet quality expectations both from engineering and consumer standpoints. Youll also realize the ideal of a greater front end. That means your whole front-end team, including app side and web side, will be optimized. The shared knowledge base as well as mobilization potential give more flexibility and strength in all front-end facets without the need of increasing team sizes. The market has seen a large amount of high quality React Native apps and successful stories about them. Nevertheless, under optimized apps and unsuccessful stories shadow. The fundamental difference between the two opposing groups is understanding. Discover the critical points in the React and React Native architecture, and develop general best practices that can lead to consistently developing 0 crash, 5 star apps based on an understanding of fundamentals. You will: Measure and define successful app design Create animation based on user need Reduce performance bottleneck throughout your apps. 606 $aMobile apps$xDevelopment 606 $aCross-platform software development 615 0$aMobile apps$xDevelopment. 615 0$aCross-platform software development. 676 $a005.35 700 $aHe$b M. Holmes$01228945 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910568257303321 996 $aCreating Apps with React Native$92852958 997 $aUNINA LEADER 14337nam 22006855 450 001 9910437878003321 005 20200629222809.0 010 $a1-4614-8423-5 024 7 $a10.1007/978-1-4614-8423-3 035 $a(CKB)3710000000031183 035 $a(SSID)ssj0001067179 035 $a(PQKBManifestationID)11669854 035 $a(PQKBTitleCode)TC0001067179 035 $a(PQKBWorkID)11080075 035 $a(PQKB)10070054 035 $a(DE-He213)978-1-4614-8423-3 035 $a(MiAaPQ)EBC6314507 035 $a(MiAaPQ)EBC1591894 035 $a(Au-PeEL)EBL1591894 035 $a(CaPaEBR)ebr10969092 035 $a(OCoLC)864999751 035 $a(PPN)176098771 035 $a(EXLCZ)993710000000031183 100 $a20131125d2013 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aApplied Statistics for Business and Management using Microsoft Excel /$fby Linda Herkenhoff, John Fogli 205 $a1st ed. 2013. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2013. 215 $a1 online resource (XIV, 417 p. 620 illus., 569 illus. in color.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-4614-8422-7 327 $aIntro -- Preface -- Acknowledgments -- Contents -- Chapter 1: Data and Statistics -- Key Concepts -- Discussion -- Common Pitfalls -- Final Thoughts and Activities -- Practice Problems -- Discussion Boards -- Group Activity -- Parting Thought -- Problem Solutions -- Chapter 2: Introduction to Excel and Basic Charts -- Key Concepts -- Discussion -- Basic Concepts -- Start Up -- Adding Data Analysis Toolpak -- Excel Elements -- Entering Formulas -- Cell References -- Sorting Data -- Filtering Data -- Getting Excel Help -- Statistical Tools -- Predefined or Built-In Formulas -- Formatting Data -- Chart Wizard -- Formatting of Graphs -- Bar and Column Charts -- Pie Charts -- Line Charts and Area Charts -- Line Graph Example -- Area Chart Example -- Other Charts -- Bubble Chart -- Radar Chart -- PivotTables (Aka Crosstabs) -- Excel -- Common Pitfalls -- Final Thoughts and Activities -- Practice Problems -- Discussion Boards -- Group Activity -- Parting Thought -- Problem Solutions -- Chapter 3: Summarizing Data: Descriptive Statistics and Histograms -- Key Concepts -- Discussion -- Symbols -- The Histogram -- Excel -- Descriptive Statistics -- Descriptive Output Results -- Changing the Width of the Column Output -- Using Excel Functions -- Histograms -- Example Problem -- Setting Up the Bin Ranges -- Creating the Histogram Chart -- Histogram Clean Up -- Closing Gaps Between Bars -- Change Labels on x-axis -- Removing More from the Chart and Labeling the Last Column -- Remove Legend -- Axes Labels -- Moving Axes Labels -- Changing the Bar Color -- Changing Chart Title -- Changing Chart Background Fill -- Rotating the y-axis Label from Vertical to Horizontal -- Common Pitfalls -- Final Thoughts and Activities -- Practice Problems -- Discussion Boards -- Group Activity -- Parting Thought -- Problem Solutions -- Chapter 4: Normal Distributions. 327 $aKey Concepts -- Discussion -- Excel -- Outline placeholder -- 1. Percentile Calculation Problems (NORM.DIST) -- (a) Calculating the area to the left of a value -- What % of the Time Did You Deliver to Less Than () 90 Stores During Last December and January? In Other Words How Much Data Is... -- (b) Calculating the area between 2 values -- What % of the Time Did You Deliver Fire Logs to Between 90 and 120 Stores During Last December and January? -- (c) Calculating the area to the right of a value -- What % of Time Did You Deliver to 130 or More Stores ( \geq ) During Last December and January? In Other Words How Much Da... -- (d) Graphing a normal distribution (Area Graph) -- Step 1 -- Step 2 -- Step 3 -- 2. Converting Percentiles to Measured Units (NORM.INV) -- Calculate the Number of Stores Corresponding with the 99th Percentile -- 3. Converting Measured Units to z-Scores (STANDARDIZE) -- Convert the Measured Value of 135 Stores to a z-Score -- 4. Calculate Rank and Percentile (Rank and Percentile) -- Outline placeholder -- Output -- 5. Non-normal Distributions -- Calculate What Percentage of Rents Fall Between 409 and 573 -- Step 1: Convert the Measured Values to Standard Units -- Step 2: Use Chebyshev Approximation=1-(1/(k)2) where k is the boundary value in standard units -- Common Pitfalls -- Final Thoughts and Activities -- Practice Problems -- Discussion Boards -- Group Activity -- Parting Thought -- Problem Solutions -- Chapter 5: Survey Design -- Key Concepts -- Discussion -- Basic Concepts -- Survey Design -- Scale -- Types of Questions -- Single Response/Select -- Multiple Response/Select -- Structured Questions -- Ranking and Rating -- Non-structured (Open-Ended) Questions -- Data -- Labels -- Demographic Data -- Response Rates -- Editing: Data Quality -- Coding -- Errors in Survey Question Creation -- Loaded Questions. 327 $aLeading Questions -- Double-Barreled Questions -- Errors in Survey Data Collection -- Random Sampling Error -- Systematic Error -- Response Bias -- Checklist -- Excel -- Final Thoughts and Activities -- Practice Problems and Case Studies -- Discussion Boards -- Group Activity -- Parting Thought -- Problem Solutions -- Chapter 6: Sampling -- Key Concepts -- Discussion -- Types of Problems -- Mean versus proportion problems require slightly different treatment -- Finite versus infinite population size is another important factor in determining the appropriate sample size -- Rules of thumb -- Excel -- Problem Type: Infinite Mean -- Practice Problem for Infinite Mean -- Problem Type: Infinite Proportion -- Practice Problem for Infinite Proportion -- Finite Population Correction Factor (fpc) -- Final Thoughts and Activities -- Practice Problems -- Discussion Boards -- Group Activity -- Parting Thought -- Problem Solutions -- Chapter 7: Inference -- Key Concepts -- Discussion -- Inferring Proportions -- Example Problem -- Excel -- Inferring Averages -- Example Problem -- Excel -- Confidence Intervals with Proportion Inference -- Example Problem -- Excel -- Final Thoughts and Activities -- Practice Problems and Case Studies -- Discussion Boards -- Group Activity -- Parting Thought -- Problem Solutions -- Chapter 8: Probability -- Key Concepts -- Discussion -- Example 1 -- Example 2 -- Excel -- Finding Probabilities Using Normal Distributions -- What Is the Probability That a Dealership Will Sell 90 Cars or Less (x90) per Week? -- What Is the Probability That a Car Dealership Will Sell at least 130 (x130) Cars per Week? -- What Is the Probability That a Car Dealership Will Sell Between 90 and 120 Cars per Week? -- Calculating Combinations and Permutations -- Permutation -- Combination -- Finding Probabilities Using the Binomial Distribution. 327 $aRoyal Bank Retention Problem -- Common Excel Pitfalls -- Final Thoughts and Activities -- Practice Problems -- Discussion Boards -- Group Activity -- Parting Thought -- Problem Solutions -- Chapter 9: Correlation -- Key Concepts -- Discussion -- Nonlinear data caution -- Average data caution -- Excel -- Correlation: One r Value or Correlation Matrix -- Method 1: Two or More Data Sets (Matrix) -- Method 2: Only 2 Data Sets -- Common Excel Pitfalls -- Final Thoughts and Activities -- Practice Problems -- Discussion Boards -- Group Activity -- Parting Thought -- Problem Solutions -- Chapter 10: Simple Linear Regression -- Key Concepts -- Discussion -- Residuals and Tests for Linearity -- Standardized Residuals and Outliers -- Excel -- Scatterplot: Compute the Regression Line and the Coefficient of Determination -- Regression Function: Compute the Regression Model -- Compute Residual Plots Using the Regression Function -- Using Excel´s Regression Tool to Test for Normality of the Distribution of Residuals -- Method 1: Normal Probability Plot -- Method 2: Normal Distribution of Residuals -- Using Excel´s Regression Tool to Test for Constant Variance of Residuals -- Summary of Regression Analysis Process -- Common Excel Pitfalls -- Final Thoughts and Activities -- Practice Problems -- Discussion Boards -- Group Activities -- Parting Thought -- Problem Solutions -- Chapter 11: Significance Tests Part 1 -- Key Concepts -- Discussion -- Basic Concepts -- Choosing the Appropriate Significance Test -- One-Tailed Tests -- Two-Tailed Tests -- Significance Tests -- F-test -- Basic Descriptions of F-Test Applications -- Example 1: One-Way Repeated Measures Using ANOVA -- Example 2: Regression Problems -- Example 3: F-Test for Equality of Two Variances -- Example 4: Between Group ANOVA -- Excel -- Example 1: One-Way Repeated Measures Using ANOVA. 327 $aExample 2: Regression -- Example 3: Two Sample for Variances -- One-Tailed F-Test for Two Sample for Variances -- Two-Tailed F-Test for Equality of Two Variances -- Example 4: Between Group ANOVA -- One-Tail F-Test Between Group ANOVA -- Two-Tail F-Test Between Group ANOVA -- t-Test -- Basic Descriptions of t-Test Applications -- Example 1: Regression Problems -- Example 2: t-Test for Equality of Means -- Example 3: t-TEST Paired Samples -- Excel -- Example 1: Regression Problems -- Example 2: t-Test for Equality of Means -- One-Tailed Test -- Two-Tailed Test -- Example 3: Before-After Models -- One-Tailed Test -- Two-Tailed Test -- T.TEST -- Common Excel Pitfalls -- Final Thoughts and Activities -- Practice Problems and Case Studies -- Discussion Boards -- Group Activity -- Parting Thought -- Problem Solutions -- Chapter 12: Significance Tests Part 2 -- Key Concepts -- Discussion -- Significance Tests -- X2 Test -- Example 1: Goodness-of-Fit Test -- Example 2: Independence of Two Variables -- Excel -- Example 1: Goodness of Fit -- Example 2: Testing Independence -- z-Test -- Excel -- Example 1: Z-Test One Sample Mean Versus a Standard -- One-Tailed Results -- Two-Tailed Results -- Example 2: Testing the Means of Two Populations -- Z.TEST Tool for Comparing a Mean or Proportion with a Standard -- Example Problem -- Common Excel Pitfalls -- Final Thoughts and Activities -- Practice Problems and Case Studies -- Discussion Boards -- Group Activity -- Parting Thought -- Problem Solutions -- Chapter 13: Multiple Regression -- Key Concepts -- Discussion -- Excel -- Step 1: Fit the Model with Selected Independent Variables -- Step 2: Does Multicollinearity Exist? Run a Correlation Matrix -- Step 3: Run Regression Model -- Step 4: Are the Assumptions of Regression Satisfied? -- Step 5: Test Overall Model Significance (F-Test). 327 $aStep 6: Check p-Values for Independent Variables Meet Significance Criteria (t-Test). 330 $aApplied Business Statistics for Business and Management using Microsoft Exel is the first book to illustrate the capabilities of Microsoft Excel to teach applied statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical statistical problems in industry. If understanding statistics isn?t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you. Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in statistics courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Applied Business Statistics for Business and Management capitalizes on these improvements by teaching students and practitioners how to apply Excel to statistical techniques necessary in their courses and workplace. Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand business problems. Practice problems are provided at the end of each chapter with their solutions.  Linda Herkenhoff is currently a full professor and director of the Transglobal MBA program at Saint Mary?s College in Moraga, California, where she teaches Quantitative Analysis and Statistics. She is the former Executive Director of Human Resources for Stanford University. The first sixteen years of her career included various responsibilities within Chevron Corporation, primarily as a geophysicist. She has lived/worked/conducted research in over 30 countries and has spent time on all 7 continents. John Fogli is the Founder and President of Sentenium, Inc.  John's business research methods have helped public and private industries better understand the involvement necessary to lead consensus solutions. He has facilitated over 500 survey projects in the areas of consumer, employee, political, and operation(s) research. He is a member of the Market Research Association and holds a Professional Research Certificate. He is currently a part-time faculty member with the Department of Business at Diablo Valley College and sits on the Executive Council for The Pacific Chapter of American Association for Public Opinion Research. He earned his B.S. from University of California, Berkeley and an MBA from the University of San Francisco. 606 $aStatistics 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 606 $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010 606 $aStatistics, general$3https://scigraph.springernature.com/ontologies/product-market-codes/S0000X 615 0$aStatistics. 615 14$aStatistics and Computing/Statistics Programs. 615 24$aStatistics for Business, Management, Economics, Finance, Insurance. 615 24$aStatistics, general. 676 $a330.015195 700 $aHerkenhoff$b Linda$4aut$4http://id.loc.gov/vocabulary/relators/aut$0928211 702 $aFogli$b John$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437878003321 996 $aApplied Statistics for Business and Management using Microsoft Excel$92503227 997 $aUNINA LEADER 05190nam 2200493z- 450 001 9910220046903321 005 20210211 035 $a(CKB)3800000000216308 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/40766 035 $a(oapen)doab40766 035 $a(EXLCZ)993800000000216308 100 $a20202102d2016 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAmino Acids of the Glutamate Family: Functions beyond Primary Metabolism 210 $cFrontiers Media SA$d2016 215 $a1 online resource (206 p.) 225 1 $aFrontiers Research Topics 311 08$a2-88919-936-3 330 $aThe life of proteins starts and ends as amino acids. In addition to the primary function as protein building blocks, amino acids serve multiple other purposes to make a plant's life worth living. This is true especially for the amino acids of the glutamate family, namely glutamate (Glu), glutamine (Gln), proline (Pro) and arginine (Arg), as well as the product of Glu decarboxylation, ?-aminobutyric acid (GABA). Synthesis, accumulation, interconversion and degradation of these five compounds contribute in many ways to the regulation of plant development and to responses to environmental challenges. Glu and Gln hold key positions as entry points and master regulators of nitrogen metabolism in plants, and have a pivotal role in the regulatory interplay between carbon and nitrogen metabolism. Pro and GABA are among the best-studied compatible osmolytes that accumulate in response to water deficit, yet the full range of protective functions is still to be revealed. Arg, with its exceptionally high nitrogen-to-carbon ratio, has long been recognized as a major storage form of organic nitrogen. Most of the enzymes involved in metabolism of the amino acids of the glutamate family in plants have been identified or can be predicted according to similarity with animal or microbial homologues. However, for some of these enzymes the detailed biochemical properties still remain to be determined in order to understand activities in vivo. Additionally, uncertainties regarding the subcellular localization of proteins and especially the lack of knowledge about intracellular transport proteins leave significant gaps in our understanding of the metabolic network connecting Glu, Gln, Pro, GABA and Arg. While anabolic reactions are distributed between the cytosol and chloroplasts, catabolism of the amino acids of the glutamate family takes place in mitochondria and has been implicated in fueling energy-demanding physiological processes such as root elongation, recovery from stress, bolting and pollen tube elongation. Exceeding the metabolic functions, the amino acids of the glutamate family were recently identified as important signaling molecules in plants. Extracellular Glu, GABA and a range of other metabolites trigger responses in plant cells that resemble the actions of Glu and GABA as neurotransmitters in animals. Plant homologues of the Glu-gated ion channels from mammals and protein kinase signaling cascades have been implicated in these responses. Pollen tube growth and guidance depend on GABA signaling and the root architecture is specifically regulated by Glu. GABA and Pro signaling or metabolism were shown to contribute to the orchestration of defense and programmed cell death in response to pathogen attacks. Pro signaling was additionally proposed to regulate developmental processes and especially sexual reproduction. Arg is tightly linked to nitric oxide (NO) production and signaling in plants, although Arg-dependent NO-synthases could still not be identified. Potentially Arg-derived polyamines constitute the missing link between Arg and NO signaling in response to stress. Taken together, the amino acids of the glutamate family emerge as important signaling molecules that orchestrate plant growth and development by integrating the metabolic status of the plant with environmental signals, especially in stressful conditions. This research topic collects contributions from different facets of glutamate family amino acid signaling or metabolism to bring together, and integrate in a comprehensive view the latest advances in our understanding of the multiple functions of Glu-derived amino acids in plants. 517 $aAmino Acids of the Glutamate Family 606 $aBotany & plant sciences$2bicssc 610 $aamino acid transport 610 $aArginine 610 $abiochemical pathways 610 $aEnzyme properties 610 $aGABA 610 $aglutamine synthetase 610 $ametabolite signaling 610 $aProline 610 $aRegulation of development 610 $aStress tolerance mechanisms 615 7$aBotany & plant sciences 700 $aSakiko Okumoto$4auth$01296304 702 $aMaurizio Trovato$4auth 702 $aDietmar Funck$4auth 702 $aGiuseppe Forlani$4auth 906 $aBOOK 912 $a9910220046903321 996 $aAmino Acids of the Glutamate Family: Functions beyond Primary Metabolism$93023979 997 $aUNINA LEADER 04580nam 22007695 450 001 9910299991003321 005 20200701072516.0 010 $a3-0348-0853-4 024 7 $a10.1007/978-3-0348-0853-8 035 $a(CKB)3710000000306086 035 $a(SSID)ssj0001386344 035 $a(PQKBManifestationID)11759668 035 $a(PQKBTitleCode)TC0001386344 035 $a(PQKBWorkID)11374068 035 $a(PQKB)11631387 035 $a(DE-He213)978-3-0348-0853-8 035 $a(MiAaPQ)EBC6314779 035 $a(MiAaPQ)EBC5587036 035 $a(Au-PeEL)EBL5587036 035 $a(OCoLC)1066193731 035 $a(PPN)183095464 035 $a(EXLCZ)993710000000306086 100 $a20141113d2014 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aArithmetic Geometry over Global Function Fields /$fby Gebhard Böckle, David Burns, David Goss, Dinesh Thakur, Fabien Trihan, Douglas Ulmer ; edited by Francesc Bars, Ignazio Longhi, Fabien Trihan 205 $a1st ed. 2014. 210 1$aBasel :$cSpringer Basel :$cImprint: Birkhäuser,$d2014. 215 $a1 online resource (XIV, 337 p.) 225 1 $aAdvanced Courses in Mathematics - CRM Barcelona,$x2297-0304 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-0348-0852-6 327 $aCohomological Theory of Crystals over Function Fields and Applications -- On Geometric Iwasawa Theory and Special Values of Zeta Functions -- The Ongoing Binomial Revolution -- Arithmetic of Gamma, Zeta and Multizeta Values for Function Fields -- Curves and Jacobians over Function Fields. 330 $aThis volume collects the texts of five courses given in the Arithmetic Geometry Research Programme 2009?2010 at the CRM Barcelona. All of them deal with characteristic p global fields; the common theme around which they are centered is the arithmetic of L-functions (and other special functions), investigated in various aspects. Three courses examine some of the most important recent ideas in the positive characteristic theory discovered by Goss (a field in tumultuous development, which is seeing a number of spectacular advances): they cover respectively crystals over function fields (with a number of applications to L-functions of t-motives), gamma and zeta functions in characteristic p, and the binomial theorem. The other two are focused on topics closer to the classical theory of abelian varieties over number fields: they give respectively a thorough introduction to the arithmetic of Jacobians over function fields (including the current status of the BSD conjecture and its geometric analogues, and the construction of Mordell?Weil groups of high rank) and a state of the art survey of Geometric Iwasawa Theory explaining the recent proofs of various versions of the Main Conjecture, in the commutative and non-commutative settings. 410 0$aAdvanced Courses in Mathematics - CRM Barcelona,$x2297-0304 606 $aNumber theory 606 $aAlgebra 606 $aGeometry, Algebraic 606 $aNumber Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/M25001 606 $aGeneral Algebraic Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/M1106X 606 $aAlgebraic Geometry$3https://scigraph.springernature.com/ontologies/product-market-codes/M11019 615 0$aNumber theory. 615 0$aAlgebra. 615 0$aGeometry, Algebraic. 615 14$aNumber Theory. 615 24$aGeneral Algebraic Systems. 615 24$aAlgebraic Geometry. 676 $a512.7 700 $aBöckle$b Gebhard$4aut$4http://id.loc.gov/vocabulary/relators/aut$01065141 702 $aBurns$b David$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aGoss$b David$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aThakur$b Dinesh$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aTrihan$b Fabien$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aUlmer$b Douglas$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aBars$b Francesc$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLonghi$b Ignazio$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTrihan$b Fabien$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299991003321 996 $aArithmetic Geometry over Global Function Fields$92543320 997 $aUNINA