I. SEARCH PARAMETERS AND ACCEPTANCE CRITERIA

Size: px
Start display at page:

Download "I. SEARCH PARAMETERS AND ACCEPTANCE CRITERIA"

Transcription

1 Revised Publicatin Guidelines fr Dcumenting the Identificatin and Quantificatin f Peptides, Prteins, and Pst Translatinal Mdificatins by Mass Spectrmetry The identificatin f prteins r peptides is cmmnly accmplished by peptide sequencing (MS/MS analyses), peptide mass fingerprinting (PMF), r sme cmbinatin f bth. Bth methds typically depend n acquiring mass spectra, cnversin f data int a frmat fr searching, fllwed by interpretatin via matching the data against a sequence database r spectral library with an apprpriate search engine. Many parameters are cmmn t the tw appraches and sme are unique t each. Similarly, pst translatinal mdificatins (PTMs) can affect prtein identificatins; their determinatin/lcalizatin may als be the bjective f the experiment. In additin, quantificatin by istpe dependent r istpe free methds may be an additinal aspect f the data set. The fllwing guidelines describe the infrmatin required by the jurnal fr articles dealing with mass spectrmetric analyses designed fr prtein, peptide r PTM identificatin and their quantificatin. Manuscripts that use generated datasets fr sftware r algrithm develpment may prvide nly the parameters in sectin I belw prviding they d NOT reprt the identities f the peptides r prteins used. If identificatins r sequences are listed, the requirements f Sectin II (and III, if germane) must be met. I. SEARCH PARAMETERS AND ACCEPTANCE CRITERIA The fllwing supprting infrmatin shuld be included in the Experimental sectin f the manuscript fr either MS/MS r PMF analyses (recgnizing that sme sftware d nt explicitly prvide sme f these parameters): Peak Lists: The methd and/r prgram (including versin number and/r date) used t create the "peak lists" frm the riginal data and the parameters used in the creatin f this peak list, particularly any prcessing which might affect the quality f the subsequent database search. Examples include smthing, any signal t nise threshlding, charge states assignment r de istping, etc. In cases where additinal custmized prcessing f the cllectins f peak lists has been perfrmed, e.g. clustering r filtering, the methd and/r prgram (including versin number) shuld be referenced. Search Engine: The name and versin (r release date) f all prgrams used fr database searching must be prvided. Sequence Database r Spectral Library: The name and versin (r release date) f all sequence database(s) r spectral libraries used must be listed. If a database r library was cmpiled in huse, a cmplete descriptin f the surce f the Page 1 f 7

2 Revised Publicatin Guidelines fr Dcumenting the Identificatin and Quantificatin f Peptides, Prteins, and Pst Translatinal Mdificatins by Mass Spectrmetry sequences r spectra is required and the sftware used fr library generatin. The number f entries actually searched frm each database r library shuld be included. If the database r library used is very small (<1000 entries) r excludes cmmn cntaminants, justificatin must be specifically prvided since this may generate misleading assignments and an inaccurate false discvery rate estimate. Enzyme specificity: A descriptin f all enzymes used t generate peptides, including the number f missed and nn specific cleavages (e.g. semi tryptic) permitted, must be listed. Fixed mdificatin(s); A list f all mdificatins cnsidered (including residue specificity) must be given. Variable mdificatins: A list f all mdificatins cnsidered (including residue specificity) must be given. Mass tlerance fr precursr ins. Mass tlerance fr fragment ins (nt required fr PMF data). Knwn cntaminants excluded (particularly fr PMF data): All mitted peaks frm pre designated cntaminants (r if any f these fragments are used fr calibratin) must be identified. Threshld scre/expectatin value: Criteria used fr accepting individual spectra shuld be stated alng with a justificatin. False Discvery Rates at Peptide and Prtein levels: Fr large scale experiments, the results f any additinal statistical analyses that estimate a measure f identificatin certainty fr the dataset, r allw a determinatin f the false discvery rate, e.g., the results f decy searches r ther cmputatinal appraches. II. PROTEIN AND PEPTIDE IDENTIFICATION The infrmatin belw fr each prtein and peptide sequence identified shuld be specified in the Results (r Supplemental) sectin. If the identificatins are presented nly at the peptide level, then prtein level infrmatin may be mitted. All peptide sequences assigned: A list (in ne r mre Tables), nting any deviatin frm the expected enzyme cleavage specificity, must be prvided. Precursr charge and mass/charge: These parameters shuld be listed fr each peptide assignment in the same table. All mdificatins bserved. Page 2 f 7

3 Revised Publicatin Guidelines fr Dcumenting the Identificatin and Quantificatin f Peptides, Prteins, and Pst Translatinal Mdificatins by Mass Spectrmetry Number f matched and unmatched masses: Fr PMF data, the ttal number f peaks, bth matched and unmatched, shuld be listed in the identificatin table. Scre(s): The relevant scre (depending n the sftware used) and any assciated statistical infrmatin btained fr searches cnducted must be prvided fr each peptide. Prtein accessin number and sequence database r spectral library surce. Cunt f the number f distinct peptide sequences assigned t each prtein: When cmputing this number, multiple matches t peptides with the same primary sequence shuld be cunted as a single distinct peptide, including multiple matches that represent different precursr charge states r mdificatin states. Any alternative assumptins must be justified. Prtein sequence % cverage: This value shuld be expressed as the number f amin acids spanned by the assigned peptides divided by the sequence length X 100. Alternatively, a derived prtein identificatin prbability can be given. Fr all prteins identified n the basis f ne unique peptide spectrum, (a peptide mass fingerprint spectrum cunts as ne spectrum), the ability t view anntated spectra fr these identificatins must be made available. This can be achieved in ne f three ways: Submissin f all spectra and search results t a public results repsitry that is equipped with a spectral viewer prir t submissin f the manuscript t the jurnal. This infrmatin will appear as a hyperlink in the published article. Submissin (with the manuscript) f spectra and search results in a file frmat that allws visualizatin f the spectra using a freely available viewer. Submissin (with the manuscript) f anntated spectra in an ffice r PDF frmat. Nte: Files submitted thrugh the nline manuscript submissin prcess must be less than 100 MB in size. If files are greater than 100 MB in size, the jurnal recmmends depsiting the file in a suitable repsitry, such as Tranche [ then supplying the hash frm Tranche (r ther identificatin cde if a different repsitry is used) in the manuscript and in the cver letter accmpanying the manuscript submissin. Psting f results n the authr s website as the sle surce f this data des nt satisfy this requirement, as the ability t annymusly access the data is necessary fr the review prcess. Page 3 f 7

4 Revised Publicatin Guidelines fr Dcumenting the Identificatin and Quantificatin f Peptides, Prteins, and Pst Translatinal Mdificatins by Mass Spectrmetry III. POST TRANSLATIONAL MODIFICATIONS Studies fcusing n psttranslatinal mdificatins (PTMs) require specialized methdlgy and dcumentatin t assign the type(s) and site(s) f the mdificatin(s). The guidelines in this sectin apply t PTMs that ccur under physilgical cnditins and t which bilgical significance may be assigned, such as phsphrylatin, glycsylatin, etc. as well as purpsefully induced chemical mdificatins f central imprtance t the results f the study, such as chemical crss linking. These guidelines d nt apply t cmmn mdificatins arising frm sample handling r preparatin such as xidatin f Met r alkylatin f Cys. In additin t the tabular presentatin(s) f the data described in guideline II, the fllwing infrmatin is required: The site(s) f mdificatin: Within each peptide sequence, all mdificatins must be clearly lcated (unless ambiguus; see belw) and the manner in which this was accmplished (thrugh cmputatin r manual inspectin) must be described. A justificatin fr any lcalizatin scre threshld emplyed. Ambiguus assignments: Peptides cntaining ambiguus PTM site lcalizatins must be listed in a separate table frm thse with unambiguus site lcalizatins. In cases where there are multiple mdificatin sites and at least ne is ambiguus, then these peptides shuld be listed with the ambiguus assignments. Ambiguus assignments must be clearly labeled as such. Examples f ambiguities include: Mdified peptides in which ne r mre mdificatin sites are ambiguus. Instances where the peptide sequence is repeated in the same prtein s the specific mdificatin site cannt be assigned. Instances in which the same peptide is repeated in multiple prteins, e.g. splice variants and paralgs (See als Sectin IV). Isbaric mdificatins (e.g., acetylatin vs. trimethylatin, phsphrylatin vs. sulfnatin etc), where the pssibilities may nt be distinguished. Examples f methds able t distinguish between these include mass spectrmetric appraches such as accurate mass determinatin, bservatin f signature fragment ins (e.g. m/z 79 vs. m/z 80 in negative in mde fr assignment f phsphrylatin ver sulfnatin), r bilgical r chemical strategies. Page 4 f 7

5 Revised Publicatin Guidelines fr Dcumenting the Identificatin and Quantificatin f Peptides, Prteins, and Pst Translatinal Mdificatins by Mass Spectrmetry Anntated, mass labeled spectra: Spectra fr all mdified peptides must be either submitted t a public repsitry r accmpany the manuscript as described in guideline II. IV. PROTEIN INFERENCE FROM PEPTIDE ASSIGNMENTS Since prtein identificatin experiments that are based n prtelytic digestin and subsequent characterizatin f the resulting peptides result in the lss f cnnectivity between these peptides and their prtein precursrs, identificatins based n the assignment f peptide sequences can result in a cmbinatin f tw pssible utcmes: distinct peptides that map t nly ne prtein sequence r peptides that are cmmn t mre than ne prtein sequence (prtein grup) arising, fr example, frm alternative splicing. When identificatins are f the latter type, authrs are required (in additin t the tabular presentatin(s) f the data described in guideline II) t: Prvide accessin numbers (r ther identifiers) fr all prteins that were cmbined int the grup. Authrs shuld justify any cases where a single prtein frm a prtein grup has been singled ut r when asserting that mre than ne indistinguishable member f a prtein grup is actually present. Prvide a summary list f cmmn peptides belnging t each prtein grup and thse distinct t a specific prtein. State (and justify) if prteins are identified frm a different species than the ne being studied. Fr example, identificatin f a muse r human prtein in a hamster study. V. QUANTIFICATION Manuscripts presenting quantitative prtemic results must prvide the fllwing infrmatin: All relevant quantificatin data (as part f the peptide and prtein identificatin tables), alng with a descriptin f hw the raw data were prcessed t prduce these measurements. A descriptin f hw the analytical reliability f measurements was validated using technical replicates and statistical methds. Citatin f standard methds r specialized sftware may be used. Hwever, it is essential t demnstrate that the data cntained in the manuscript actually cnfrm t the same mdels. Page 5 f 7

6 Revised Publicatin Guidelines fr Dcumenting the Identificatin and Quantificatin f Peptides, Prteins, and Pst Translatinal Mdificatins by Mass Spectrmetry A descriptin f hw the bilgical reliability f measurements was validated using bilgical replicates, statistical methds, independent experiments, etc. Studies based n a single bilgical experiment are generally nt acceptable (except as a dataset t test biinfrmatic systems). If a bilgical replicate frm the same surce cannt be perfrmed (e.g. patient sample), a large enugh number f similar bilgical samples, apprpriately justified, must be perfrmed in rder t enable sund cnclusins. A descriptin f the treatment f relevant systematic errr effects such as interference frm verlapping precursr ins, incmplete istpe labeling, bias crrectin fr pipetting errr, etc. A descriptin f the treatment f randm errr issues such as utlier rejectin and the categrical exclusin f data by threshlds; fr example, based n signal t nise r minimum in cunts. Prper estimates f uncertainty and the methds used fr the errr analysis. Quantificatin f many prteins r peptides generally results in the need t use sme frm f multiple hypthesis testing crrectin. Whenever pssible, cnfidence in prtein quantificatin shuld be prvided fr each individual prtein rather than the glbal dataset. Any cnclusins drawn r hypthesis generated frm the quantitative data in the manuscript must be in cncert with the determined estimate f uncertainty. If a cmpnent is nt being identified by database searching in a particular experiment, assurance f the identity f the analyte being measured and the specificity (e.g. presence/absence f interference) with which it is measured must be prvided. This particularly applies t intensity based methds such as SELDI, selected reactin mnitring / multiple reactin mnitring (SRM/MRM) and accurate mass and retentin time (AMT) based methds. A descriptin f the way multiple isfrms in a prtein grup were quantified. Fr spectral cunting measurements, in additin t the abve guidelines, additinal details shuld be prvided such as whether numbers f peptides r spectra were cunted, whether mdified peptides, semi tryptic peptides r shared peptides were cunted, and whether r nt dynamic exclusin was used, etc. VI. RAW DATA SUBMISSION Page 6 f 7

7 Revised Publicatin Guidelines fr Dcumenting the Identificatin and Quantificatin f Peptides, Prteins, and Pst Translatinal Mdificatins by Mass Spectrmetry If a manuscript is accepted by the jurnal, all mass spectra cntributing t the described wrk must be depsited in electrnic frm by the time f publicatin at a publicly accessible site that is independent f the authrs' cntrl. Submissin f all mass spectrmetric utput files in the riginal instrument vendr file frmat is the preferred and mst direct means f meeting this requirement. Data cnversin t an pen frmat such as mzml is encuraged if sftware capable f reading the instrument vendr file frmat is nt widely available. In all cases, the spectra are expected t be prvided in a frm prir t any prcessing that might affect the quality f subsequent interpretatin as described in the peak list guideline (See sectin I). The editrs f MCP recgnize that uplading large datasets can smetimes engender unfreseen difficulties and authrs encuntering prblems shuld cntact the Bethesda ffice fr advice and/r assistance. Authrs will nt be penalized fr delays resulting frm such difficulties. Requests fr exemptins (r delays nt related t technical prblems) frm this requirement must be made in writing t ne f the c editrs at the time f submissin. Upn acceptance f the manuscript (and by the time f publicatin), authrs shuld prvide a URL and passwrd, if apprpriate, fr accessing the data. This will be listed as part f the published article. e.g. The data assciated with this manuscript may be dwnladed frm PrtemeCmmns.rg Tranche, using the fllwing hash: JyU4hPRjRHPMtNMpO1DziH5R5KzLAXJ8MDwDe4mqL07AslL5imsCyjcYwt2eSZKTEpiKF7 qbc+ldijersjfeddz5fiaaaaaaaab0g== Further infrmatin regarding this requirement can be btained by cntacting mcpnline@asbmb.rg. Page 7 f 7

Tutorial 3: Building a spectral library in Skyline

Tutorial 3: Building a spectral library in Skyline SRM Curse 2013 Tutrial 3 Spectral Library Tutrial 3: Building a spectral library in Skyline Spectral libraries fr SRM methd design and fr data analysis can be either directly added t a Skyline dcument

More information

CS 477/677 Analysis of Algorithms Fall 2007 Dr. George Bebis Course Project Due Date: 11/29/2007

CS 477/677 Analysis of Algorithms Fall 2007 Dr. George Bebis Course Project Due Date: 11/29/2007 CS 477/677 Analysis f Algrithms Fall 2007 Dr. Gerge Bebis Curse Prject Due Date: 11/29/2007 Part1: Cmparisn f Srting Algrithms (70% f the prject grade) The bjective f the first part f the assignment is

More information

Appropriate Documentation for Phase I and II History/Architecture Reports

Appropriate Documentation for Phase I and II History/Architecture Reports APPENDIX D: HISTORY/ARCHITECTURE REPORT GUIDELINES Apprpriate Dcumentatin fr Phase I and II Histry/Architecture Reprts The results f the secndary surce review and field survey dictate the reprting frmat

More information

Hypothesis Tests for One Population Mean

Hypothesis Tests for One Population Mean Hypthesis Tests fr One Ppulatin Mean Chapter 9 Ala Abdelbaki Objective Objective: T estimate the value f ne ppulatin mean Inferential statistics using statistics in rder t estimate parameters We will be

More information

Standard Title: Frequency Response and Frequency Bias Setting. Andrew Dressel Holly Hawkins Maureen Long Scott Miller

Standard Title: Frequency Response and Frequency Bias Setting. Andrew Dressel Holly Hawkins Maureen Long Scott Miller Template fr Quality Review f NERC Reliability Standard BAL-003-1 Frequency Respnse and Frequency Bias Setting Basic Infrmatin: Prject number: 2007-12 Standard number: BAL-003-1 Prject title: Frequency

More information

DEFENSE OCCUPATIONAL AND ENVIRONMENTAL HEALTH READINESS SYSTEM (DOEHRS) ENVIRONMENTAL HEALTH SAMPLING ELECTRONIC DATA DELIVERABLE (EDD) GUIDE

DEFENSE OCCUPATIONAL AND ENVIRONMENTAL HEALTH READINESS SYSTEM (DOEHRS) ENVIRONMENTAL HEALTH SAMPLING ELECTRONIC DATA DELIVERABLE (EDD) GUIDE DEFENSE OCCUPATIOL AND ENVIRONMENTAL HEALTH READINESS SYSTEM (DOEHRS) ENVIRONMENTAL HEALTH SAMPLING ELECTRONIC DATA DELIVERABLE (EDD) GUIDE 20 JUNE 2017 V1.0 i TABLE OF CONTENTS 1 INTRODUCTION... 1 2 CONCEPT

More information

ENSC Discrete Time Systems. Project Outline. Semester

ENSC Discrete Time Systems. Project Outline. Semester ENSC 49 - iscrete Time Systems Prject Outline Semester 006-1. Objectives The gal f the prject is t design a channel fading simulatr. Upn successful cmpletin f the prject, yu will reinfrce yur understanding

More information

CHAPTER 24: INFERENCE IN REGRESSION. Chapter 24: Make inferences about the population from which the sample data came.

CHAPTER 24: INFERENCE IN REGRESSION. Chapter 24: Make inferences about the population from which the sample data came. MATH 1342 Ch. 24 April 25 and 27, 2013 Page 1 f 5 CHAPTER 24: INFERENCE IN REGRESSION Chapters 4 and 5: Relatinships between tw quantitative variables. Be able t Make a graph (scatterplt) Summarize the

More information

WRITING THE REPORT. Organizing the report. Title Page. Table of Contents

WRITING THE REPORT. Organizing the report. Title Page. Table of Contents WRITING THE REPORT Organizing the reprt Mst reprts shuld be rganized in the fllwing manner. Smetime there is a valid reasn t include extra chapters in within the bdy f the reprt. 1. Title page 2. Executive

More information

SUPPLEMENTARY MATERIAL GaGa: a simple and flexible hierarchical model for microarray data analysis

SUPPLEMENTARY MATERIAL GaGa: a simple and flexible hierarchical model for microarray data analysis SUPPLEMENTARY MATERIAL GaGa: a simple and flexible hierarchical mdel fr micrarray data analysis David Rssell Department f Bistatistics M.D. Andersn Cancer Center, Hustn, TX 77030, USA rsselldavid@gmail.cm

More information

Checking the resolved resonance region in EXFOR database

Checking the resolved resonance region in EXFOR database Checking the reslved resnance regin in EXFOR database Gttfried Bertn Sciété de Calcul Mathématique (SCM) Oscar Cabells OECD/NEA Data Bank JEFF Meetings - Sessin JEFF Experiments Nvember 0-4, 017 Bulgne-Billancurt,

More information

Determining the Accuracy of Modal Parameter Estimation Methods

Determining the Accuracy of Modal Parameter Estimation Methods Determining the Accuracy f Mdal Parameter Estimatin Methds by Michael Lee Ph.D., P.E. & Mar Richardsn Ph.D. Structural Measurement Systems Milpitas, CA Abstract The mst cmmn type f mdal testing system

More information

Technical Bulletin. Generation Interconnection Procedures. Revisions to Cluster 4, Phase 1 Study Methodology

Technical Bulletin. Generation Interconnection Procedures. Revisions to Cluster 4, Phase 1 Study Methodology Technical Bulletin Generatin Intercnnectin Prcedures Revisins t Cluster 4, Phase 1 Study Methdlgy Release Date: Octber 20, 2011 (Finalizatin f the Draft Technical Bulletin released n September 19, 2011)

More information

Subject description processes

Subject description processes Subject representatin 6.1.2. Subject descriptin prcesses Overview Fur majr prcesses r areas f practice fr representing subjects are classificatin, subject catalging, indexing, and abstracting. The prcesses

More information

LDS emarket. Section 11 - Catalog Load Process

LDS emarket. Section 11 - Catalog Load Process LDS emarket Sectin 11 - Catalg Lad Prcess Catalg Lad Prcess There are three types f catalgs in LDS emarket: Supplier Direct Web Site An nline catalg f items hsted by the supplier using HTML cding PRG A

More information

Biocomputers. [edit]scientific Background

Biocomputers. [edit]scientific Background Bicmputers Frm Wikipedia, the free encyclpedia Bicmputers use systems f bilgically derived mlecules, such as DNA and prteins, t perfrm cmputatinal calculatins invlving string, retrieving, and prcessing

More information

Lab 1 The Scientific Method

Lab 1 The Scientific Method INTRODUCTION The fllwing labratry exercise is designed t give yu, the student, an pprtunity t explre unknwn systems, r universes, and hypthesize pssible rules which may gvern the behavir within them. Scientific

More information

Writing Guidelines. (Updated: November 25, 2009) Forwards

Writing Guidelines. (Updated: November 25, 2009) Forwards Writing Guidelines (Updated: Nvember 25, 2009) Frwards I have fund in my review f the manuscripts frm ur students and research assciates, as well as thse submitted t varius jurnals by thers that the majr

More information

Chapter 3: Cluster Analysis

Chapter 3: Cluster Analysis Chapter 3: Cluster Analysis } 3.1 Basic Cncepts f Clustering 3.1.1 Cluster Analysis 3.1. Clustering Categries } 3. Partitining Methds 3..1 The principle 3.. K-Means Methd 3..3 K-Medids Methd 3..4 CLARA

More information

Bootstrap Method > # Purpose: understand how bootstrap method works > obs=c(11.96, 5.03, 67.40, 16.07, 31.50, 7.73, 11.10, 22.38) > n=length(obs) >

Bootstrap Method > # Purpose: understand how bootstrap method works > obs=c(11.96, 5.03, 67.40, 16.07, 31.50, 7.73, 11.10, 22.38) > n=length(obs) > Btstrap Methd > # Purpse: understand hw btstrap methd wrks > bs=c(11.96, 5.03, 67.40, 16.07, 31.50, 7.73, 11.10, 22.38) > n=length(bs) > mean(bs) [1] 21.64625 > # estimate f lambda > lambda = 1/mean(bs);

More information

NUROP CONGRESS PAPER CHINESE PINYIN TO CHINESE CHARACTER CONVERSION

NUROP CONGRESS PAPER CHINESE PINYIN TO CHINESE CHARACTER CONVERSION NUROP Chinese Pinyin T Chinese Character Cnversin NUROP CONGRESS PAPER CHINESE PINYIN TO CHINESE CHARACTER CONVERSION CHIA LI SHI 1 AND LUA KIM TENG 2 Schl f Cmputing, Natinal University f Singapre 3 Science

More information

ECE 545 Project Deliverables

ECE 545 Project Deliverables ECE 545 Prject Deliverables Tp-level flder: _ Secnd-level flders: 1_assumptins 2_blck_diagrams 3_interface 4_ASM_charts 5_surce_cde 6_verificatin 7_timing_analysis 8_results

More information

Name: Block: Date: Science 10: The Great Geyser Experiment A controlled experiment

Name: Block: Date: Science 10: The Great Geyser Experiment A controlled experiment Science 10: The Great Geyser Experiment A cntrlled experiment Yu will prduce a GEYSER by drpping Ments int a bttle f diet pp Sme questins t think abut are: What are yu ging t test? What are yu ging t measure?

More information

Guide to Using the Rubric to Score the Klf4 PREBUILD Model for Science Olympiad National Competitions

Guide to Using the Rubric to Score the Klf4 PREBUILD Model for Science Olympiad National Competitions Guide t Using the Rubric t Scre the Klf4 PREBUILD Mdel fr Science Olympiad 2010-2011 Natinal Cmpetitins These instructins are t help the event supervisr and scring judges use the rubric develped by the

More information

Kinetic Model Completeness

Kinetic Model Completeness 5.68J/10.652J Spring 2003 Lecture Ntes Tuesday April 15, 2003 Kinetic Mdel Cmpleteness We say a chemical kinetic mdel is cmplete fr a particular reactin cnditin when it cntains all the species and reactins

More information

CAUSAL INFERENCE. Technical Track Session I. Phillippe Leite. The World Bank

CAUSAL INFERENCE. Technical Track Session I. Phillippe Leite. The World Bank CAUSAL INFERENCE Technical Track Sessin I Phillippe Leite The Wrld Bank These slides were develped by Christel Vermeersch and mdified by Phillippe Leite fr the purpse f this wrkshp Plicy questins are causal

More information

Cells though to send feedback signals from the medulla back to the lamina o L: Lamina Monopolar cells

Cells though to send feedback signals from the medulla back to the lamina o L: Lamina Monopolar cells Classificatin Rules (and Exceptins) Name: Cell type fllwed by either a clumn ID (determined by the visual lcatin f the cell) r a numeric identifier t separate ut different examples f a given cell type

More information

TRAINING GUIDE. Overview of Lucity Spatial

TRAINING GUIDE. Overview of Lucity Spatial TRAINING GUIDE Overview f Lucity Spatial Overview f Lucity Spatial In this sessin, we ll cver the key cmpnents f Lucity Spatial. Table f Cntents Lucity Spatial... 2 Requirements... 2 Supprted Mdules...

More information

Perfrmance f Sensitizing Rules n Shewhart Cntrl Charts with Autcrrelated Data Key Wrds: Autregressive, Mving Average, Runs Tests, Shewhart Cntrl Chart

Perfrmance f Sensitizing Rules n Shewhart Cntrl Charts with Autcrrelated Data Key Wrds: Autregressive, Mving Average, Runs Tests, Shewhart Cntrl Chart Perfrmance f Sensitizing Rules n Shewhart Cntrl Charts with Autcrrelated Data Sandy D. Balkin Dennis K. J. Lin y Pennsylvania State University, University Park, PA 16802 Sandy Balkin is a graduate student

More information

BASD HIGH SCHOOL FORMAL LAB REPORT

BASD HIGH SCHOOL FORMAL LAB REPORT BASD HIGH SCHOOL FORMAL LAB REPORT *WARNING: After an explanatin f what t include in each sectin, there is an example f hw the sectin might lk using a sample experiment Keep in mind, the sample lab used

More information

Math Foundations 20 Work Plan

Math Foundations 20 Work Plan Math Fundatins 20 Wrk Plan Units / Tpics 20.8 Demnstrate understanding f systems f linear inequalities in tw variables. Time Frame December 1-3 weeks 6-10 Majr Learning Indicatrs Identify situatins relevant

More information

Tutorial 4: Parameter optimization

Tutorial 4: Parameter optimization SRM Curse 2013 Tutrial 4 Parameters Tutrial 4: Parameter ptimizatin The aim f this tutrial is t prvide yu with a feeling f hw a few f the parameters that can be set n a QQQ instrument affect SRM results.

More information

Department: MATHEMATICS

Department: MATHEMATICS Cde: MATH 022 Title: ALGEBRA SKILLS Institute: STEM Department: MATHEMATICS Curse Descriptin: This curse prvides students wh have cmpleted MATH 021 with the necessary skills and cncepts t cntinue the study

More information

PSU GISPOPSCI June 2011 Ordinary Least Squares & Spatial Linear Regression in GeoDa

PSU GISPOPSCI June 2011 Ordinary Least Squares & Spatial Linear Regression in GeoDa There are tw parts t this lab. The first is intended t demnstrate hw t request and interpret the spatial diagnstics f a standard OLS regressin mdel using GeDa. The diagnstics prvide infrmatin abut the

More information

Document for ENES5 meeting

Document for ENES5 meeting HARMONISATION OF EXPOSURE SCENARIO SHORT TITLES Dcument fr ENES5 meeting Paper jintly prepared by ECHA Cefic DUCC ESCOM ES Shrt Titles Grup 13 Nvember 2013 OBJECTIVES FOR ENES5 The bjective f this dcument

More information

Assessment Primer: Writing Instructional Objectives

Assessment Primer: Writing Instructional Objectives Assessment Primer: Writing Instructinal Objectives (Based n Preparing Instructinal Objectives by Mager 1962 and Preparing Instructinal Objectives: A critical tl in the develpment f effective instructin

More information

Accreditation Information

Accreditation Information Accreditatin Infrmatin The ISSP urges members wh have achieved significant success in the field t apply fr higher levels f membership in rder t enjy the fllwing benefits: - Bth Prfessinal members and Fellws

More information

2004 AP CHEMISTRY FREE-RESPONSE QUESTIONS

2004 AP CHEMISTRY FREE-RESPONSE QUESTIONS 2004 AP CHEMISTRY FREE-RESPONSE QUESTIONS 6. An electrchemical cell is cnstructed with an pen switch, as shwn in the diagram abve. A strip f Sn and a strip f an unknwn metal, X, are used as electrdes.

More information

Unit 1: Introduction to Biology

Unit 1: Introduction to Biology Name: Unit 1: Intrductin t Bilgy Theme: Frm mlecules t rganisms Students will be able t: 1.1 Plan and cnduct an investigatin: Define the questin, develp a hypthesis, design an experiment and cllect infrmatin,

More information

, which yields. where z1. and z2

, which yields. where z1. and z2 The Gaussian r Nrmal PDF, Page 1 The Gaussian r Nrmal Prbability Density Functin Authr: Jhn M Cimbala, Penn State University Latest revisin: 11 September 13 The Gaussian r Nrmal Prbability Density Functin

More information

Pipetting 101 Developed by BSU CityLab

Pipetting 101 Developed by BSU CityLab Discver the Micrbes Within: The Wlbachia Prject Pipetting 101 Develped by BSU CityLab Clr Cmparisns Pipetting Exercise #1 STUDENT OBJECTIVES Students will be able t: Chse the crrect size micrpipette fr

More information

Mathematics and Computer Sciences Department. o Work Experience, General. o Open Entry/Exit. Distance (Hybrid Online) for online supported courses

Mathematics and Computer Sciences Department. o Work Experience, General. o Open Entry/Exit. Distance (Hybrid Online) for online supported courses SECTION A - Curse Infrmatin 1. Curse ID: 2. Curse Title: 3. Divisin: 4. Department: 5. Subject: 6. Shrt Curse Title: 7. Effective Term:: MATH 70S Integrated Intermediate Algebra Natural Sciences Divisin

More information

CHM112 Lab Graphing with Excel Grading Rubric

CHM112 Lab Graphing with Excel Grading Rubric Name CHM112 Lab Graphing with Excel Grading Rubric Criteria Pints pssible Pints earned Graphs crrectly pltted and adhere t all guidelines (including descriptive title, prperly frmatted axes, trendline

More information

Verification of NIMs Baseline Data Reports and Methodology Reports

Verification of NIMs Baseline Data Reports and Methodology Reports hzkwekdd/^^/ke /ZdKZd 'EZ> >/Ddd/KE / D ' d h d^ dddd Verificatin f NIMs Baseline Data Reprts and Methdlgy Reprts & dd dddd d d /EdZKhd/KE d > Z d / d K d d ZK'E/d/KEK&sZ/&/Z^ d d e d d,sz/&/d/kewzk^^

More information

READING STATECHART DIAGRAMS

READING STATECHART DIAGRAMS READING STATECHART DIAGRAMS Figure 4.48 A Statechart diagram with events The diagram in Figure 4.48 shws all states that the bject plane can be in during the curse f its life. Furthermre, it shws the pssible

More information

ALE 21. Gibbs Free Energy. At what temperature does the spontaneity of a reaction change?

ALE 21. Gibbs Free Energy. At what temperature does the spontaneity of a reaction change? Name Chem 163 Sectin: Team Number: ALE 21. Gibbs Free Energy (Reference: 20.3 Silberberg 5 th editin) At what temperature des the spntaneity f a reactin change? The Mdel: The Definitin f Free Energy S

More information

THE LIFE OF AN OBJECT IT SYSTEMS

THE LIFE OF AN OBJECT IT SYSTEMS THE LIFE OF AN OBJECT IT SYSTEMS Persns, bjects, r cncepts frm the real wrld, which we mdel as bjects in the IT system, have "lives". Actually, they have tw lives; the riginal in the real wrld has a life,

More information

A New Evaluation Measure. J. Joiner and L. Werner. The problems of evaluation and the needed criteria of evaluation

A New Evaluation Measure. J. Joiner and L. Werner. The problems of evaluation and the needed criteria of evaluation III-l III. A New Evaluatin Measure J. Jiner and L. Werner Abstract The prblems f evaluatin and the needed criteria f evaluatin measures in the SMART system f infrmatin retrieval are reviewed and discussed.

More information

Heat Management Methodology for Successful UV Processing on Heat Sensitive Substrates

Heat Management Methodology for Successful UV Processing on Heat Sensitive Substrates Heat Management Methdlgy fr Successful UV Prcessing n Heat Sensitive Substrates Juliet Midlik Prime UV Systems Abstract: Nw in 2005, UV systems pssess heat management cntrls that fine tune the exthermic

More information

Computational modeling techniques

Computational modeling techniques Cmputatinal mdeling techniques Lecture 4: Mdel checing fr ODE mdels In Petre Department f IT, Åb Aademi http://www.users.ab.fi/ipetre/cmpmd/ Cntent Stichimetric matrix Calculating the mass cnservatin relatins

More information

Tutorial 7: Automated SRM data analysis using mprophet

Tutorial 7: Automated SRM data analysis using mprophet Tutrial 7: Autmated SRM data analysis using mprphet mprphet is a statistical tl which can be emplyed t achieve autmated high-cnfidence identificatin f peptides. It has three functinalities: First, mmap

More information

AMERICAN PETROLEUM INSTITUTE API RP 581 RISK BASED INSPECTION BASE RESOURCE DOCUMENT BALLOT COVER PAGE

AMERICAN PETROLEUM INSTITUTE API RP 581 RISK BASED INSPECTION BASE RESOURCE DOCUMENT BALLOT COVER PAGE Ballt ID: Title: USING LIFE EXTENSION FACTOR (LEF) TO INCREASE BUNDLE INSPECTION INTERVAL Purpse: 1. Prvides a methd t increase a bundle s inspectin interval t accunt fr LEF. 2. Clarifies Table 8.6.5 Als

More information

Evaluating enterprise support: state of the art and future challenges. Dirk Czarnitzki KU Leuven, Belgium, and ZEW Mannheim, Germany

Evaluating enterprise support: state of the art and future challenges. Dirk Czarnitzki KU Leuven, Belgium, and ZEW Mannheim, Germany Evaluating enterprise supprt: state f the art and future challenges Dirk Czarnitzki KU Leuven, Belgium, and ZEW Mannheim, Germany Intrductin During the last decade, mircecnmetric ecnmetric cunterfactual

More information

Department of Electrical Engineering, University of Waterloo. Introduction

Department of Electrical Engineering, University of Waterloo. Introduction Sectin 4: Sequential Circuits Majr Tpics Types f sequential circuits Flip-flps Analysis f clcked sequential circuits Mre and Mealy machines Design f clcked sequential circuits State transitin design methd

More information

Biology 479 Biology Portfolio Checklist Version F18 For Students Matriculating in AY

Biology 479 Biology Portfolio Checklist Version F18 For Students Matriculating in AY Bilgy 479 Bilgy Prtfli Checklist Versin F18 Fr Students Matriculating in AY 2018-19 Student s Name: Student s Ryal ID: Student s Academic Advisr: Intrductin While classrms prvide an essential site fr the

More information

o o IMPORTANT REMINDERS Reports will be graded largely on their ability to clearly communicate results and important conclusions.

o o IMPORTANT REMINDERS Reports will be graded largely on their ability to clearly communicate results and important conclusions. BASD High Schl Frmal Lab Reprt GENERAL INFORMATION 12 pt Times New Rman fnt Duble-spaced, if required by yur teacher 1 inch margins n all sides (tp, bttm, left, and right) Always write in third persn (avid

More information

A B C. 2. Some genes are not regulated by gene switches. These genes are expressed constantly. What kinds of genes would be expressed constantly?

A B C. 2. Some genes are not regulated by gene switches. These genes are expressed constantly. What kinds of genes would be expressed constantly? STO-143 Gene Switches Intrductin Bacteria need t be very efficient and nly prduce specific prteins when they are needed. Making prteins that are nt needed fr everyday cell metablism wastes energy and raw

More information

Appropriate Documentation for Phase I and II Archaeological Reports

Appropriate Documentation for Phase I and II Archaeological Reports APPENDIX E: ARCHAEOLOGY REPORT GUIDELINES Apprpriate Dcumentatin fr Phase I and II Archaelgical Reprts The terminlgy standard reprt is the same dcument type referred t as a Phase I r Phase II reprt as

More information

Web-based GIS Systems for Radionuclides Monitoring. Dr. Todd Pierce Locus Technologies

Web-based GIS Systems for Radionuclides Monitoring. Dr. Todd Pierce Locus Technologies Web-based GIS Systems fr Radinuclides Mnitring Dr. Tdd Pierce Lcus Technlgies Lcus Technlgies 2014 Overview What is the prblem? Nuclear pwer plant peratrs need t mnitr radinuclides t safeguard the envirnment

More information

Editorial Calendar User Guide

Editorial Calendar User Guide Editrial Calendar User Guide Table f Cntents Intrductin... 1 Permissins... 1 Navigatin & Views... 1 Navigatin Menu... 2 Mini Calendar... 2 Calendars in View... 3 Cntent Filtering... 4 Mnthly Calendar...

More information

Ballot for Revised Annex R - API Guidelines for Use of Single Technology Matrix

Ballot for Revised Annex R - API Guidelines for Use of Single Technology Matrix T: Cc: API Lubricants Grup Lubricants Grup Mailing List API Ballt fr Revised Annex R - API Guidelines fr Use f Single Technlgy Matrix On Nv. 14, 2018 the Lubricants Standards Grup (LSG) discussed the BOI/VGRA,

More information

Experiment #3. Graphing with Excel

Experiment #3. Graphing with Excel Experiment #3. Graphing with Excel Study the "Graphing with Excel" instructins that have been prvided. Additinal help with learning t use Excel can be fund n several web sites, including http://www.ncsu.edu/labwrite/res/gt/gt-

More information

BLAST / HIDDEN MARKOV MODELS

BLAST / HIDDEN MARKOV MODELS CS262 (Winter 2015) Lecture 5 (January 20) Scribe: Kat Gregry BLAST / HIDDEN MARKOV MODELS BLAST CONTINUED HEURISTIC LOCAL ALIGNMENT Use Cmmnly used t search vast bilgical databases (n the rder f terabases/tetrabases)

More information

AIP Logic Chapter 4 Notes

AIP Logic Chapter 4 Notes AIP Lgic Chapter 4 Ntes Sectin 4.1 Sectin 4.2 Sectin 4.3 Sectin 4.4 Sectin 4.5 Sectin 4.6 Sectin 4.7 4.1 The Cmpnents f Categrical Prpsitins There are fur types f categrical prpsitins. Prpsitin Letter

More information

How do scientists measure trees? What is DBH?

How do scientists measure trees? What is DBH? Hw d scientists measure trees? What is DBH? Purpse Students develp an understanding f tree size and hw scientists measure trees. Students bserve and measure tree ckies and explre the relatinship between

More information

BIOLOGY 101. CHAPTER 17: Gene Expression: From Gene to Protein. The Flow of Genetic Information

BIOLOGY 101. CHAPTER 17: Gene Expression: From Gene to Protein. The Flow of Genetic Information BIOLOGY 101 CHAPTER 17: Gene Expressin: Frm Gene t Prtein Gene Expressin: Frm Gene t Prtein: CONCEPTS: 17.1 Genes specify prteins via transcriptin and translatin 17.2 Transcriptin is the DNA-directed synthesis

More information

CHAPTER 3 INEQUALITIES. Copyright -The Institute of Chartered Accountants of India

CHAPTER 3 INEQUALITIES. Copyright -The Institute of Chartered Accountants of India CHAPTER 3 INEQUALITIES Cpyright -The Institute f Chartered Accuntants f India INEQUALITIES LEARNING OBJECTIVES One f the widely used decisin making prblems, nwadays, is t decide n the ptimal mix f scarce

More information

MACHINE LEARNING FOR CLUSTER- GALAXY CLASSIFICATION

MACHINE LEARNING FOR CLUSTER- GALAXY CLASSIFICATION MACHINE LEARNING FOR CLUSTER- GALAXY CLASSIFICATION Silvia de Castr García Directres: Dr. Ricard Pérez Martínez, Dra. Ana María Pérez García 16/03/2018 Machine Learning fr cluster-galaxy classificatin

More information

A Matrix Representation of Panel Data

A Matrix Representation of Panel Data web Extensin 6 Appendix 6.A A Matrix Representatin f Panel Data Panel data mdels cme in tw brad varieties, distinct intercept DGPs and errr cmpnent DGPs. his appendix presents matrix algebra representatins

More information

MODULE FOUR. This module addresses functions. SC Academic Elementary Algebra Standards:

MODULE FOUR. This module addresses functions. SC Academic Elementary Algebra Standards: MODULE FOUR This mdule addresses functins SC Academic Standards: EA-3.1 Classify a relatinship as being either a functin r nt a functin when given data as a table, set f rdered pairs, r graph. EA-3.2 Use

More information

Multiple Source Multiple. using Network Coding

Multiple Source Multiple. using Network Coding Multiple Surce Multiple Destinatin Tplgy Inference using Netwrk Cding Pegah Sattari EECS, UC Irvine Jint wrk with Athina Markpulu, at UCI, Christina Fraguli, at EPFL, Lausanne Outline Netwrk Tmgraphy Gal,

More information

Purpose: Use this reference guide to effectively communicate the new process customers will use for creating a TWC ID. Mobile Manager Call History

Purpose: Use this reference guide to effectively communicate the new process customers will use for creating a TWC ID. Mobile Manager Call History Purpse: Use this reference guide t effectively cmmunicate the new prcess custmers will use fr creating a TWC ID. Overview Beginning n January 28, 2014 (Refer t yur Knwledge Management System fr specific

More information

GENESIS Structural Optimization for ANSYS Mechanical

GENESIS Structural Optimization for ANSYS Mechanical P3 STRUCTURAL OPTIMIZATION (Vl. II) GENESIS Structural Optimizatin fr ANSYS Mechanical An Integrated Extensin that adds Structural Optimizatin t ANSYS Envirnment New Features and Enhancements Release 2017.03

More information

Comprehensive Exam Guidelines Department of Chemical and Biomolecular Engineering, Ohio University

Comprehensive Exam Guidelines Department of Chemical and Biomolecular Engineering, Ohio University Cmprehensive Exam Guidelines Department f Chemical and Bimlecular Engineering, Ohi University Purpse In the Cmprehensive Exam, the student prepares an ral and a written research prpsal. The Cmprehensive

More information

The steps of the engineering design process are to:

The steps of the engineering design process are to: The engineering design prcess is a series f steps that engineers fllw t cme up with a slutin t a prblem. Many times the slutin invlves designing a prduct (like a machine r cmputer cde) that meets certain

More information

The standards are taught in the following sequence.

The standards are taught in the following sequence. B L U E V A L L E Y D I S T R I C T C U R R I C U L U M MATHEMATICS Third Grade In grade 3, instructinal time shuld fcus n fur critical areas: (1) develping understanding f multiplicatin and divisin and

More information

Competition and Invasion in a Microcosmic Setting

Competition and Invasion in a Microcosmic Setting University f Tennessee, Knxville Trace: Tennessee Research and Creative Exchange University f Tennessee Hnrs Thesis Prjects University f Tennessee Hnrs Prgram 5-2004 Cmpetitin and Invasin in a Micrcsmic

More information

13. PO TREATMENT OF DEPT (DISTORTIONLESS ENHANCEMENT POLARIZATION TRANSFER)

13. PO TREATMENT OF DEPT (DISTORTIONLESS ENHANCEMENT POLARIZATION TRANSFER) 94 Prduct Operatr Treatment 3. PO TREATMENT OF DEPT (DISTORTIONLESS ENHANCEMENT POLARIZATION TRANSFER) DEPT is a ne-dimensinal sequence used as a tl fr unambiguus identificatin f the CH, CH, and CH 3 peaks

More information

EASTERN ARIZONA COLLEGE Introduction to Statistics

EASTERN ARIZONA COLLEGE Introduction to Statistics EASTERN ARIZONA COLLEGE Intrductin t Statistics Curse Design 2014-2015 Curse Infrmatin Divisin Scial Sciences Curse Number PSY 220 Title Intrductin t Statistics Credits 3 Develped by Adam Stinchcmbe Lecture/Lab

More information

Part 3 Introduction to statistical classification techniques

Part 3 Introduction to statistical classification techniques Part 3 Intrductin t statistical classificatin techniques Machine Learning, Part 3, March 07 Fabi Rli Preamble ØIn Part we have seen that if we knw: Psterir prbabilities P(ω i / ) Or the equivalent terms

More information

Credits: 4 Lecture Hours: 4 Lab/Studio Hours: 0

Credits: 4 Lecture Hours: 4 Lab/Studio Hours: 0 Cde: MATH 025 Title: ELEMENTARY ALGEBRA Divisin: MATHEMATICS Department: MATHEMATICS Curse Descriptin: This curse is a review f elementary algebra and requires previus experience in algebra. The curse

More information

T Algorithmic methods for data mining. Slide set 6: dimensionality reduction

T Algorithmic methods for data mining. Slide set 6: dimensionality reduction T-61.5060 Algrithmic methds fr data mining Slide set 6: dimensinality reductin reading assignment LRU bk: 11.1 11.3 PCA tutrial in mycurses (ptinal) ptinal: An Elementary Prf f a Therem f Jhnsn and Lindenstrauss,

More information

Interference is when two (or more) sets of waves meet and combine to produce a new pattern.

Interference is when two (or more) sets of waves meet and combine to produce a new pattern. Interference Interference is when tw (r mre) sets f waves meet and cmbine t prduce a new pattern. This pattern can vary depending n the riginal wave directin, wavelength, amplitude, etc. The tw mst extreme

More information

Tree Structured Classifier

Tree Structured Classifier Tree Structured Classifier Reference: Classificatin and Regressin Trees by L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stne, Chapman & Hall, 98. A Medical Eample (CART): Predict high risk patients

More information

NUMBERS, MATHEMATICS AND EQUATIONS

NUMBERS, MATHEMATICS AND EQUATIONS AUSTRALIAN CURRICULUM PHYSICS GETTING STARTED WITH PHYSICS NUMBERS, MATHEMATICS AND EQUATIONS An integral part t the understanding f ur physical wrld is the use f mathematical mdels which can be used t

More information

Lesson Plan. Recode: They will do a graphic organizer to sequence the steps of scientific method.

Lesson Plan. Recode: They will do a graphic organizer to sequence the steps of scientific method. Lessn Plan Reach: Ask the students if they ever ppped a bag f micrwave ppcrn and nticed hw many kernels were unppped at the bttm f the bag which made yu wnder if ther brands pp better than the ne yu are

More information

QUESTIONNAIRE. for RECOGNITION of GOOD LABORATORY PRACTICE. SRI LANKA ACCREDITATION BOARD for CONFORMITY ASSESSMENT. Instructions to the Applicant:

QUESTIONNAIRE. for RECOGNITION of GOOD LABORATORY PRACTICE. SRI LANKA ACCREDITATION BOARD for CONFORMITY ASSESSMENT. Instructions to the Applicant: SRI LANKA ACCREDITATION BOARD fr CONFORMITY ASSESSMENT QUESTIONNAIRE fr RECOGNITION f GOOD LABORATORY PRACTICE Instructins t the Applicant: 1. Please fill the questinnaire n yur wn judgment f activities

More information

Differentiation Applications 1: Related Rates

Differentiation Applications 1: Related Rates Differentiatin Applicatins 1: Related Rates 151 Differentiatin Applicatins 1: Related Rates Mdel 1: Sliding Ladder 10 ladder y 10 ladder 10 ladder A 10 ft ladder is leaning against a wall when the bttm

More information

Resampling Methods. Chapter 5. Chapter 5 1 / 52

Resampling Methods. Chapter 5. Chapter 5 1 / 52 Resampling Methds Chapter 5 Chapter 5 1 / 52 1 51 Validatin set apprach 2 52 Crss validatin 3 53 Btstrap Chapter 5 2 / 52 Abut Resampling An imprtant statistical tl Pretending the data as ppulatin and

More information

THERMAL TEST LEVELS & DURATIONS

THERMAL TEST LEVELS & DURATIONS PREFERRED RELIABILITY PAGE 1 OF 7 PRACTICES PRACTICE NO. PT-TE-144 Practice: 1 Perfrm thermal dwell test n prtflight hardware ver the temperature range f +75 C/-2 C (applied at the thermal cntrl/munting

More information

NAME: Prof. Ruiz. 1. [5 points] What is the difference between simple random sampling and stratified random sampling?

NAME: Prof. Ruiz. 1. [5 points] What is the difference between simple random sampling and stratified random sampling? CS4445 ata Mining and Kwledge iscery in atabases. B Term 2014 Exam 1 Nember 24, 2014 Prf. Carlina Ruiz epartment f Cmputer Science Wrcester Plytechnic Institute NAME: Prf. Ruiz Prblem I: Prblem II: Prblem

More information

MATCHING TECHNIQUES. Technical Track Session VI. Emanuela Galasso. The World Bank

MATCHING TECHNIQUES. Technical Track Session VI. Emanuela Galasso. The World Bank MATCHING TECHNIQUES Technical Track Sessin VI Emanuela Galass The Wrld Bank These slides were develped by Christel Vermeersch and mdified by Emanuela Galass fr the purpse f this wrkshp When can we use

More information

IB Sports, Exercise and Health Science Summer Assignment. Mrs. Christina Doyle Seneca Valley High School

IB Sports, Exercise and Health Science Summer Assignment. Mrs. Christina Doyle Seneca Valley High School IB Sprts, Exercise and Health Science Summer Assignment Mrs. Christina Dyle Seneca Valley High Schl Welcme t IB Sprts, Exercise and Health Science! This curse incrprates the traditinal disciplines f anatmy

More information

Comparing Several Means: ANOVA. Group Means and Grand Mean

Comparing Several Means: ANOVA. Group Means and Grand Mean STAT 511 ANOVA and Regressin 1 Cmparing Several Means: ANOVA Slide 1 Blue Lake snap beans were grwn in 12 pen-tp chambers which are subject t 4 treatments 3 each with O 3 and SO 2 present/absent. The ttal

More information

Effect of Conductivity Between Fasteners and Aluminum Skin On Eddy Current Specimens. Abstract

Effect of Conductivity Between Fasteners and Aluminum Skin On Eddy Current Specimens. Abstract Effect f Cnductivity Between Fasteners and Aluminum Skin n Eddy Current Specimens David G. Mre Sandia Natinal Labratries Federal Aviatin Administratin Airwrthiness Assurance ND Validatin Center Albuquerque,

More information

Open Meteorological Data with OGC and INSPIRE

Open Meteorological Data with OGC and INSPIRE Open Meterlgical Data with OGC and INSPIRE INSPIRE 2014 Aalbrg Finnish Meterlgical Institute Rpe Terv, Mikk Visa, Mikk Rauhala, Tarja Riihisaari FMI Open Data Finnish Meterlgical Institute pened its data

More information

Radioactive MARC Records Specifications

Radioactive MARC Records Specifications Draft January 1, 2005 [Supersedes December 7, 2004 Draft] Prepared by William E. Men Cntents 1. Intrductin...1 2. Types f Recrds...2 3. Recrd Identificatin and Versin Infrmatin... 3 3.1 Use f the 001...3

More information

Pattern Recognition 2014 Support Vector Machines

Pattern Recognition 2014 Support Vector Machines Pattern Recgnitin 2014 Supprt Vectr Machines Ad Feelders Universiteit Utrecht Ad Feelders ( Universiteit Utrecht ) Pattern Recgnitin 1 / 55 Overview 1 Separable Case 2 Kernel Functins 3 Allwing Errrs (Sft

More information

CEE3430 Engineering Hydrology HEC HMS Bare Essentials Tutorial and Example

CEE3430 Engineering Hydrology HEC HMS Bare Essentials Tutorial and Example CEE3430 Engineering Hydrlgy HEC HMS Bare Essentials Tutrial and Example Margaret Matter and David Tarbtn February 2010 This tutrial prvides sme bare essentials step by step guidance n starting t use HEC

More information

SAP Note Missing documentation on enhancement MDR10001

SAP Note Missing documentation on enhancement MDR10001 SAP Nte 303613 - Missing dcumentatin n enhancement Nte Language: English Versin: 2 Validity: Valid Since 31.07.2000 Summary Symptm Missing dcumentatin n SAP enhancement Additinal key wrds CMOD, rder quantity

More information