School of Science, Medicine & Health Michael J Macartney. Supervised By: Dr Gregory Peoples Prof Peter McLennan Mr Marc Brown

Size: px
Start display at page:

Download "School of Science, Medicine & Health Michael J Macartney. Supervised By: Dr Gregory Peoples Prof Peter McLennan Mr Marc Brown"

Transcription

1 School of Science, Medicine & Health 2013 Michael J Macartney Supervised By: Dr Gregory Peoples Prof Peter McLennan Mr Marc Brown

2 The Cardiac Branch The Vascular Branch Blood Pressure = CVD & all-cause mortality risk (Palatini P., et al 2009, Zhang & Zhang., 2009) Heart rate = CVD & allcause mortality risk (Palatini P., et al 2009, Cook & Togni., 2006, Ivanovid., et al 2012) Heart rate variability = CVD mortality (Tsuji & Larson., et al 1996) Slower heart rate recovery correlates with cardiac mortality (Jouven & Empana., et al 2005)

3 Peak HR ( bpm) Sub-Max HR (80-160bpm) Heart Rate Recovery Fish oil supplementation shows potential to improve heart rate recovery in myocardial infarction patients. (O Keefe et al., 2006) Therapeutic dose (6-8g/day) fish oil supplementation has been demonstrated to reduce sub-maximal heart rate. No effect peak heart rate (Peoples & McLennan., 2008) (Buckley et al., 2009) Resting HR (40-80bpm) Fish oil supplementation in humans resulted in a reduction of resting heart rate by 1.6beats.min -1. (Mozaffarian et al., 2005)

4 The overall aim of this study, was to investigate the role of dietary achievable fish oil on cardiac function, under resting, exercise induced stress and recovery conditions, in a subject group with a fit and healthy background. Experimental Overview: Subject Recruitment Male 18-40yrs (N=39) Baseline RBC fatty acid and resting cardiovascular (CV) measurements Repeat bout cycling protocol Split into supplement groups based on matching data (n=14)* Presupplementation Postsupplementation RBC fatty acid measurements Repeat bout cycling protocol and resting CV measures * Each group consumed 2x1g capsules/day of either soy bean oil for control group or fish oil containing a total of 560mg of DHA and 140mg of EPA Omega-3 PUFA.

5 R e la tiv e % o f E ry th r o c y te F a tty A c id s 8 c d c d c = Significant difference within group after supplementation (FO post v FO pre) (p<0.0001). d = Significant difference between groups after supplementation (FO post v Control post) (p<0.0001) 6 Fatty acid key: 20:5n-3 = Eicosapentaenoic acid (EPA) 22:6n-3 = Docosahexaenoic acid (DHA) n-3 Index = DHA + EPA 4 F is h O il P re 2 F is h O il P o s t C o n tro l P re C o n tro l P o s t 0 D H A n - 3 In d e x

6 H e a r t R a te (b e a ts.m in -1 ) H e a r t R a te (b e a ts.m in -1 ) 6 5 a 7 0 F is h O il b C o n tro l 8 0 C o n tro l F is h O il c d L a b h e a r t r a te A w a k e S u p in e h e a r t r a te M e a n h e a r t r a te S le e p in g M in im u m h e a r t r a te 0 P r e - s u p p le m e n t P o s t - s u p p le m e n t No significant differences within conditions between control and FO group following supplementation. a,b,c,d = Significant differences (p<0.05) between all baseline measured conditions.

7 C h a n g e S D 1 :S D C o n t r o l F is h O il Trending reduction for fish oil group (p=0.18). p<0.05 set as critical value.

8 T o ta l B e a t C h a n g e Reduction of 22 beats over 5 minutes is an average of 4.5beats.min -1 less C o n tro l F is h O il F ir s t 5 m in * S e c o n d 5 m in * = Significant less total beats (FO post v Control post supplementation) min 5-10 (P=0.005).

9 H e a r t R a te (b p m ) L o g H R N o d iffe r e n c e a t p e a k N e t R e c o v e r y H e a rt R a te N o d iffe r e n c e a t 1 0 t h m in P e a k R e c o v e ry T im e (s e c s ) M in u te Representative (subject 007) graph of net recovery heart rate during supine recovery. Notes: No significant differences between FO and control group at the start point (peak heart rate) or end point (10 th minute) of recovery. p<0.05 set as critical value.

10 L o g it H e a r t R a te L o g it H e a r t R a te C o n tro l P re F is h O il P re C o n tro l P o s t lo g t 1 /2 F is h O il P o s t lo g t 1 / t 1 / 2 (p r e ) : s t 1 / 2 (p r e ) : s t 1 /2 (p o s t ) : s * t 1 /2 (p o s t ) : s L o g T im e Control Logit HR vs. Log recovery time * L o g T im e Fish Oil Logit HR vs. Log recovery time. Notes: Values expressed as (means ±SE) for post-exercise (5 min work capacity trial). The value of log time at logit = 0 corresponds to log half-recovery time (log t½). Absolute time(s) of t½ inset on graph.

11 The current study demonstrated that fish oil; resulted in DHA membrane incorporation. did not true resting HR. resulted in the capacity to HR under exercise induced stress. produced a more rapid HR recovery profile. These effects are most likely explained by a direct effect of fish oil on the cardiac intrinsic rate. (Laustiola, Salo et al., 1986; McLennan., 2001; Pepe & McLennan., 2002; Harris, Gonzales et al., 2006)

12 HR & slowed HRR is a risk factor for CVD and all-cause mortality. HR results in myocardial perfusion time. Most importantly, the effects demonstrated in this study were still evident despite the fact an already very fit and healthy subject population; along with dietary achievable (1 x 250g salmon meal/week) fish oil supplementation intake was used.

Bayesian course - problem set 6 (lecture 7)

Bayesian course - problem set 6 (lecture 7) Bayesian course - problem set 6 (lecture 7) Ben Lambert December 7, 2016 1 A meta-analysis of beta blocker trials Table 1 shows the results of some of the 22 trials included in a meta-analysis of clinical

More information

Practice variation in long-term secondary stroke prevention in the Netherlands

Practice variation in long-term secondary stroke prevention in the Netherlands C H A P T E R 8 Practice variation in long-term secondary stroke prevention in the Netherlands Sander M. van Schaik Blanche S. de Vries Henry C. Weinstein Marieke C. Visser Renske M. van den Berg-Vos Journal

More information

Controlling Human Heart Rate Response During Treadmill Exercise

Controlling Human Heart Rate Response During Treadmill Exercise Controlling Human Heart Rate Response During Treadmill Exercise Frédéric Mazenc (INRIA-DISCO), Michael Malisoff (LSU), and Marcio de Queiroz (LSU) Special Session: Advances in Biomedical Mathematics 2011

More information

Multicompartment Pharmacokinetic Models. Objectives. Multicompartment Models. 26 July Chapter 30 1

Multicompartment Pharmacokinetic Models. Objectives. Multicompartment Models. 26 July Chapter 30 1 Multicompartment Pharmacokinetic Models Objectives To draw schemes and write differential equations for multicompartment models To recognize and use integrated equations to calculate dosage regimens To

More information

f (x) f (a) f (a) = lim x a f (a) x a

f (x) f (a) f (a) = lim x a f (a) x a Differentiability Revisited Recall that the function f is differentiable at a if exists and is finite. f (a) = lim x a f (x) f (a) x a Another way to say this is that the function f (x) f (a) F a (x) =

More information

Heart rate control and variability

Heart rate control and variability Heart rate control and variability Na (Lina) Li (CDS13 ) EE @ SEAS Harvard University CDS @ 20 The persistent mystery Young, fit, healthy more extreme Resting Heart Rate (bpm) 60 0 50 100 150 200 250 300

More information

Stable isotope. Relative atomic mass. Mole fraction 203 Tl Tl Thallium isotopes in Earth/planetary science

Stable isotope. Relative atomic mass. Mole fraction 203 Tl Tl Thallium isotopes in Earth/planetary science Stable isotope Relative atomic mass Mole fraction 203 Tl 202.972 345 0.2952 205 Tl 204.974 428 0.7048 Thallium isotopes in Earth/planetary science Because molecules, atoms, and ions of the stable isotopes

More information

gender mains treaming in Polis h practice

gender mains treaming in Polis h practice gender mains treaming in Polis h practice B E R L IN, 1 9-2 1 T H A P R IL, 2 O O 7 Gender mains treaming at national level Parliament 25 % of women in S ejm (Lower Chamber) 16 % of women in S enat (Upper

More information

Universal structures of normal and pathological heart rate variability (Supplemental Information)

Universal structures of normal and pathological heart rate variability (Supplemental Information) Universal structures of normal and pathological heart rate variability (Supplemental Information) Alfonso M. Gañán-Calvo 1, Juan Fajardo-López 2 1 Depto. de Ingeniería Aeroespacial y Mecánica de Fluidos,

More information

Quantitative Bivariate Data

Quantitative Bivariate Data Statistics 211 (L02) - Linear Regression Quantitative Bivariate Data Consider two quantitative variables, defined in the following way: X i - the observed value of Variable X from subject i, i = 1, 2,,

More information

Mini-Lesson 5. Section 5.1: Algebraic Equations

Mini-Lesson 5. Section 5.1: Algebraic Equations Mini-Lesson 5 Section 5.1: Algebraic Equations DEFINITION: An algebraic equation is a mathematical sentence connecting one expression to another expression with an equal sign (=). Verify that a given value

More information

Validation of the GastroPlus TM Software Tool and Applications

Validation of the GastroPlus TM Software Tool and Applications Validation of the GastroPlus TM Software Tool and Applications Fagen Zhang and Leah Luna The Dow Chemical Company FZ/MB 01.11.11 Acknowledgements Michael Bartels Barun Bhhatarai (Novartis) Tyler Auernhammer

More information

Hemodynamics II. Aslı AYKAÇ, PhD. NEU Faculty of Medicine Department of Biophysics

Hemodynamics II. Aslı AYKAÇ, PhD. NEU Faculty of Medicine Department of Biophysics Hemodynamics II Aslı AYKAÇ, PhD. NEU Faculty of Medicine Department of Biophysics Laplace s Law Relates the pressure difference across a closed elastic membrane on liquid film to the tension in the membrane

More information

Supplement of Simultaneous shifts in elemental stoichiometry and fatty acids of Emiliania huxleyi in response to environmental changes

Supplement of Simultaneous shifts in elemental stoichiometry and fatty acids of Emiliania huxleyi in response to environmental changes Supplement of Biogeosciences, 15, 1029 1045, 2018 https://doi.org/10.5194/bg-15-1029-2018-supplement Author(s) 2018. This work is distributed under the Creative Commons Attribution 3.0 License. Supplement

More information

Influence of Bacterial Growth Rate on Dose Optimization of Linezolid for Treatment of Tuberculosis

Influence of Bacterial Growth Rate on Dose Optimization of Linezolid for Treatment of Tuberculosis Influence of Bacterial Growth Rate on Dose Optimization of Linezolid for Treatment of Tuberculosis Kristina Bigelow Johns Hopkins University School of Medicine 1/15/217 Linezolid Recently added to WHOs

More information

8.4 Application to Economics/ Biology & Probability

8.4 Application to Economics/ Biology & Probability 8.4 Application to Economics/ Biology & 8.5 - Probability http://classic.hippocampus.org/course_locat or?course=general+calculus+ii&lesson=62&t opic=2&width=800&height=684&topictitle= Costs+&+probability&skinPath=http%3A%2F%

More information

Endurance, Energy, Healthy Aging

Endurance, Energy, Healthy Aging Endurance, Energy, Healthy Aging NR is being extensively researched in major universities and hospitals Effects of NR on metabolism are nothing short of astonishing. Researcher from Weill Cornell Medical

More information

People power: the physics of human performance

People power: the physics of human performance People power: the physics of human performance Blake Laing, Ph.D. and Harold Meyer, Ph.D., MPH, Southern Adventist University OBJECTIVES 1. Calculate the mechanical power required to move a mass up an

More information

DISCRETE PROBABILITY DISTRIBUTIONS

DISCRETE PROBABILITY DISTRIBUTIONS DISCRETE PROBABILITY DISTRIBUTIONS REVIEW OF KEY CONCEPTS SECTION 41 Random Variable A random variable X is a numerically valued quantity that takes on specific values with different probabilities The

More information

Supplementary Information for

Supplementary Information for Supplementary Information for Multi-Functional In Vivo Vascular Imaging Using Near-Infrared II Fluorescence Guosong Hong 1,3, Jerry C. Lee 2,3, Joshua T. Robinson 1, Uwe Raaz 2, Liming Xie 1, Ngan F. Huang

More information

Organ-on-a-chip: practical applications & challenges. Remko van Vught

Organ-on-a-chip: practical applications & challenges. Remko van Vught Organ-on-a-chip: practical applications & challenges Remko van Vught 10-4-2018 A leap forward in physiological relevance The challenge of reductionism: Make things as simple as possible, but not simpler,

More information

Entropy Change Index as a Measure of Cardio-Thermal Physiological Stress Response

Entropy Change Index as a Measure of Cardio-Thermal Physiological Stress Response Vol.2, Issue.2, Mar-Apr 2012 pp-202-208 ISSN 2249-6645 Entropy Change Index as a Measure of Cardio-Thermal Physiological Stress Response Satish C. Boregowda 1, Rodney Handy 2*, Robert E. Choate 3, and

More information

The physics of medical imaging US, CT, MRI. Prof. Peter Bogner

The physics of medical imaging US, CT, MRI. Prof. Peter Bogner The physics of medical imaging US, CT, MRI Prof. Peter Bogner Clinical radiology curriculum blocks of lectures and clinical practice (7x2) Physics of medical imaging Neuroradiology Head and neck I. Head

More information

Lesson 2: Introduction to Variables

Lesson 2: Introduction to Variables In this lesson we begin our study of algebra by introducing the concept of a variable as an unknown or varying quantity in an algebraic expression. We then take a closer look at algebraic expressions to

More information

Distributed analysis in multi-center studies

Distributed analysis in multi-center studies Distributed analysis in multi-center studies Sharing of individual-level data across health plans or healthcare delivery systems continues to be challenging due to concerns about loss of patient privacy,

More information

Author's response to reviews

Author's response to reviews Author's response to reviews Title: Diverse risks of incident cardiovascular disease and all-cause mortality in men and women with low cash margins living alone: cohort data from 60-year-olds Authors:

More information

Plant and animal cells (eukaryotic cells) have a cell membrane, cytoplasm and genetic material enclosed in a nucleus.

Plant and animal cells (eukaryotic cells) have a cell membrane, cytoplasm and genetic material enclosed in a nucleus. 4.1 Cell biology Cells are the basic unit of all forms of life. In this section we explore how structural differences between types of cells enables them to perform specific functions within the organism.

More information

BIOL 51A - Biostatistics 1 1. Lecture 1: Intro to Biostatistics. Smoking: hazardous? FEV (l) Smoke

BIOL 51A - Biostatistics 1 1. Lecture 1: Intro to Biostatistics. Smoking: hazardous? FEV (l) Smoke BIOL 51A - Biostatistics 1 1 Lecture 1: Intro to Biostatistics Smoking: hazardous? FEV (l) 1 2 3 4 5 No Yes Smoke BIOL 51A - Biostatistics 1 2 Box Plot a.k.a box-and-whisker diagram or candlestick chart

More information

NSAC Isotopes Subcommittee Meeting January 20, 2015 Erin Grady, MD Society of Nuclear Medicine and Molecular Imaging

NSAC Isotopes Subcommittee Meeting January 20, 2015 Erin Grady, MD Society of Nuclear Medicine and Molecular Imaging NSAC Isotopes Subcommittee Meeting January 20, 2015 Erin Grady, MD Society of Nuclear Medicine and Molecular Imaging 1 SNMMI Represents professionals in the field of nuclear medicine Physicians Technologists

More information

SCIE1000 Theory and Practice in Science Final Examination, Semester One 2011

SCIE1000 Theory and Practice in Science Final Examination, Semester One 2011 1. Researchers propose two models for estimating the weight of accumulated flammable material in a forest, where weight is measured in appropriate units and t is the number of years since the most recent

More information

HPLC Preparative Scaleup of Calcium Channel Blocker Pharmaceuticals Application

HPLC Preparative Scaleup of Calcium Channel Blocker Pharmaceuticals Application HPLC Preparative Scaleup of Calcium Channel Blocker Pharmaceuticals Application Pharmaceuticals Author Cliff Woodward and Ronald Majors Agilent Technologies, Inc. 2850 Centerville Road Wilmington, DE 19808

More information

MATH 122A FINAL EXAM STUDY GUIDE (Fall 2017-Spring 2018)

MATH 122A FINAL EXAM STUDY GUIDE (Fall 2017-Spring 2018) MATH A FINAL EXAM STUDY GUIDE (Fall 07-Spring 08) The questions on the Math A final exam have a multiple choice format while the questions in this study guide are not multiple-choice in order to encourage

More information

CONDITIONAL JOINT TRANSFER ENTROPY OF CARDIOVASCULAR AND CEREBROVASCULAR CONTROL SYSTEMS IN SUBJECTS PRONE TO POSTURAL SYNCOPE

CONDITIONAL JOINT TRANSFER ENTROPY OF CARDIOVASCULAR AND CEREBROVASCULAR CONTROL SYSTEMS IN SUBJECTS PRONE TO POSTURAL SYNCOPE CONDITIONAL JOINT TRANSFER ENTROPY OF CARDIOVASCULAR AND CEREBROVASCULAR CONTROL SYSTEMS IN SUBJECTS PRONE TO POSTURAL SYNCOPE Vlasta Bari 1, Andrea Marchi 2,3, Beatrice De Maria 2,4, Gianluca Rossato

More information

Chemical Exchange. Spin-interactions External interactions Magnetic field Bo, RF field B1

Chemical Exchange. Spin-interactions External interactions Magnetic field Bo, RF field B1 Chemical Exchange Spin-interactions External interactions Magnetic field Bo, RF field B1 Internal Interactions Molecular motions Chemical shifts J-coupling Chemical Exchange 1 Outline Motional time scales

More information

Mathematical models for vulnerable plaques: MPI Workshop Delaware 2009

Mathematical models for vulnerable plaques: MPI Workshop Delaware 2009 Mathematical models for vulnerable plaques: MPI Workshop Delaware 29 J. Bell University of Maryland Baltimore County jbell@math.umbc.edu P.-W. Fok University of Delaware pakwing@udel.edu C. Breward Oxford

More information

MATH 122A FINAL EXAM STUDY GUIDE (Spring 2014)

MATH 122A FINAL EXAM STUDY GUIDE (Spring 2014) MATH A FINAL EXAM STUDY GUIDE (Spring 0) The final eam for spring 0 will have a multiple choice format. This will allow us to offer the final eam as late in the course as possible, giving more in-class

More information

Prediction of heart rate response to conclusion of spontaneous breathing trial by fluctuation dissipation theory

Prediction of heart rate response to conclusion of spontaneous breathing trial by fluctuation dissipation theory Prediction of heart rate response to conclusion of spontaneous breathing trial by fluctuation dissipation theory 1 Man Chen, 1 Liang Ren Niestemski, 2 Robert Prevost, 2 Michael McRae, 3 Sharath Cholleti,

More information

Objective SWBAT find distance traveled, use rectangular approximation method (RAM), volume of a sphere, and cardiac output.

Objective SWBAT find distance traveled, use rectangular approximation method (RAM), volume of a sphere, and cardiac output. 5.1 Estimating with Finite Sums Objective SWBAT find distance traveled, use rectangular approximation method (RAM), volume of a sphere, and cardiac output. Distance Traveled We know that pondering motion

More information

Metabolism. -Chemical reactions in the body -Temperature-dependent rates -Not 100% efficient, energy lost as heat (not lost if used to maintain Tb)

Metabolism. -Chemical reactions in the body -Temperature-dependent rates -Not 100% efficient, energy lost as heat (not lost if used to maintain Tb) Metabolism 58 Metabolism -Chemical reactions in the body -Temperature-dependent rates -Not 100% efficient, energy lost as heat (not lost if used to maintain Tb) 1. Anabolic -creation, assembly, repair,

More information

ADVANCED STATISTICAL ANALYSIS OF EPIDEMIOLOGICAL STUDIES. Cox s regression analysis Time dependent explanatory variables

ADVANCED STATISTICAL ANALYSIS OF EPIDEMIOLOGICAL STUDIES. Cox s regression analysis Time dependent explanatory variables ADVANCED STATISTICAL ANALYSIS OF EPIDEMIOLOGICAL STUDIES Cox s regression analysis Time dependent explanatory variables Henrik Ravn Bandim Health Project, Statens Serum Institut 4 November 2011 1 / 53

More information

Thermodynamic Assessment of Multiple Physiological Stress Responses Using Maxwell Relations

Thermodynamic Assessment of Multiple Physiological Stress Responses Using Maxwell Relations Vol.2, Issue.2, Mar-Apr 2012 pp-297-302 ISSN: 2249-6645 Thermodynamic Assessment of Multiple Physiological Stress Responses Using Maxwell Relations Satish C. Boregowda 1, Robert E. Choate 2, Rodney Handy

More information

HEAT ACCLIMATIZATION GUIDE

HEAT ACCLIMATIZATION GUIDE HEAT ACCLIMATIZATION GUIDE H E AT A C C L I M AT I Z AT I O N G U I D E 2003 RANGER & AIRBORNE SCHOOL STUDENTS u Should you be concerned about hot weather? u How fast can you become heat acclimatized?

More information

Multi-state Models: An Overview

Multi-state Models: An Overview Multi-state Models: An Overview Andrew Titman Lancaster University 14 April 2016 Overview Introduction to multi-state modelling Examples of applications Continuously observed processes Intermittently observed

More information

Plant and animal cells (eukaryotic cells) have a cell membrane, cytoplasm and genetic material enclosed in a nucleus.

Plant and animal cells (eukaryotic cells) have a cell membrane, cytoplasm and genetic material enclosed in a nucleus. 4.1 Cell biology Cells are the basic unit of all forms of life. In this section we explore how structural differences between types of cells enables them to perform specific functions within the organism.

More information

ESRI 2008 Health GIS Conference

ESRI 2008 Health GIS Conference ESRI 2008 Health GIS Conference An Exploration of Geographically Weighted Regression on Spatial Non- Stationarity and Principal Component Extraction of Determinative Information from Robust Datasets A

More information

ON THE PROBLEM OF COMPARING THE MEANS AND MEDIANS OF TWO INDEPENDENT LOGNORMAL DISTRIBUTIONS

ON THE PROBLEM OF COMPARING THE MEANS AND MEDIANS OF TWO INDEPENDENT LOGNORMAL DISTRIBUTIONS Journal of Data Science 16(2017), 631-656 ON THE PROBLEM OF COMPARING THE MEANS AND MEDIANS OF TWO INDEPENDENT LOGNORMAL DISTRIBUTIONS José A. Montoya 1, Gudelia Figueroa 1, Gerardo Navarro 2 1 Department

More information

ST3241 Categorical Data Analysis I Two-way Contingency Tables. 2 2 Tables, Relative Risks and Odds Ratios

ST3241 Categorical Data Analysis I Two-way Contingency Tables. 2 2 Tables, Relative Risks and Odds Ratios ST3241 Categorical Data Analysis I Two-way Contingency Tables 2 2 Tables, Relative Risks and Odds Ratios 1 What Is A Contingency Table (p.16) Suppose X and Y are two categorical variables X has I categories

More information

F.IF.C.7: Graphing Trigonometric Functions 4

F.IF.C.7: Graphing Trigonometric Functions 4 Regents Exam Questions www.jmap.org Name: 1 In the interval 0 x 2π, in how many points will the graphs of the equations y = sin x and y = 1 2 intersect? 1) 1 2) 2 3) 3 4) 4 3 A radio wave has an amplitude

More information

Velocity Images. Phase Contrast Technique. G. Reiter 1,2, U. Reiter 1, R. Rienmüller 1

Velocity Images. Phase Contrast Technique. G. Reiter 1,2, U. Reiter 1, R. Rienmüller 1 Velocity Images - the MR Phase Contrast Technique G. Reiter 1,2, U. Reiter 1, R. Rienmüller 1 SSIP 2004 12 th Summer School in Image Processing, Graz, Austria 1 Interdisciplinary Cardiac Imaging Center,

More information

Internal dose assessment of 177 Lu-DOTA-SP for quantification of arginine renal protection effect

Internal dose assessment of 177 Lu-DOTA-SP for quantification of arginine renal protection effect Internal dose assessment of 177 Lu-DOTA-SP for quantification of arginine renal protection effect 1 Puerta N., 1 Rojo A., 2 Crudo J., 2 Zapata A., 2 Nevares N., 2 López Bularte A., 2 Perez J., 2 Zaretzky

More information

Science One Math. October 23, 2018

Science One Math. October 23, 2018 Science One Math October 23, 2018 Today A general discussion about mathematical modelling A simple growth model Mathematical Modelling A mathematical model is an attempt to describe a natural phenomenon

More information

MR Advance Techniques. Flow Phenomena. Class I

MR Advance Techniques. Flow Phenomena. Class I MR Advance Techniques Flow Phenomena Class I Flow Phenomena In this class we will explore different phenomenona produced from nuclei that move during the acquisition of data. Flowing nuclei exhibit different

More information

Understanding the contribution of space on the spread of Influenza using an Individual-based model approach

Understanding the contribution of space on the spread of Influenza using an Individual-based model approach Understanding the contribution of space on the spread of Influenza using an Individual-based model approach Shrupa Shah Joint PhD Candidate School of Mathematics and Statistics School of Population and

More information

Contrast Mechanisms in MRI. Michael Jay Schillaci

Contrast Mechanisms in MRI. Michael Jay Schillaci Contrast Mechanisms in MRI Michael Jay Schillaci Overview Image Acquisition Basic Pulse Sequences Unwrapping K-Space Image Optimization Contrast Mechanisms Static and Motion Contrasts T1 & T2 Weighting,

More information

Analysis of Longitudinal Data: Comparison between PROC GLM and PROC MIXED.

Analysis of Longitudinal Data: Comparison between PROC GLM and PROC MIXED. Analysis of Longitudinal Data: Comparison between PROC GLM and PROC MIXED. Maribeth Johnson, Medical College of Georgia, Augusta, GA ABSTRACT Longitudinal data refers to datasets with multiple measurements

More information

Applications of the definite integral to rates, velocities and densities

Applications of the definite integral to rates, velocities and densities Chapter 4 Applications of the definite integral to rates, velocities and densities 4.1 Two cars, labeled 1 and 2 start side by side and accelerate from rest. Figure 4.1 shows a graph of their velocity

More information

Sediment impacts on coral communities: gametogenesis, spawning, recruitment and early post-recruitment survival Dr Luke Smith

Sediment impacts on coral communities: gametogenesis, spawning, recruitment and early post-recruitment survival Dr Luke Smith Sediment impacts on coral communities: gametogenesis, spawning, recruitment and early post-recruitment survival Dr Luke Smith AIMS, Fremantle, Western Australia 83 Overview Survival of the different early

More information

Levels of Organization. Monday, December 5, 16

Levels of Organization. Monday, December 5, 16 Levels of Organization The human body is structured into systems. Cells are the smallest unit of life. Calls similar in shape and function work together as tissues. Different types of tissues form organs

More information

EKOLOGIE EN SYSTEMATIEK. T h is p a p e r n o t to be c i t e d w ith o u t p r i o r r e f e r e n c e to th e a u th o r. PRIMARY PRODUCTIVITY.

EKOLOGIE EN SYSTEMATIEK. T h is p a p e r n o t to be c i t e d w ith o u t p r i o r r e f e r e n c e to th e a u th o r. PRIMARY PRODUCTIVITY. EKOLOGIE EN SYSTEMATIEK Ç.I.P.S. MATHEMATICAL MODEL OF THE POLLUTION IN NORT H SEA. TECHNICAL REPORT 1971/O : B i o l. I T h is p a p e r n o t to be c i t e d w ith o u t p r i o r r e f e r e n c e to

More information

TECHNICAL APPENDIX WITH ADDITIONAL INFORMATION ON METHODS AND APPENDIX EXHIBITS. Ten health risks in this and the previous study were

TECHNICAL APPENDIX WITH ADDITIONAL INFORMATION ON METHODS AND APPENDIX EXHIBITS. Ten health risks in this and the previous study were Goetzel RZ, Pei X, Tabrizi MJ, Henke RM, Kowlessar N, Nelson CF, Metz RD. Ten modifiable health risk factors are linked to more than one-fifth of employer-employee health care spending. Health Aff (Millwood).

More information

Isotope Production for Nuclear Medicine

Isotope Production for Nuclear Medicine Isotope Production for Nuclear Medicine Eva Birnbaum Isotope Program Manager February 26 th, 2016 LA-UR-16-21119 Isotopes for Nuclear Medicine More than 20 million nuclear medicine procedures are performed

More information

Zebrafish as a tool to study mechanisms of developmental toxicology of environmental chemicals. Jessica Legradi, P. Cenijn, R. Carvalho, J.

Zebrafish as a tool to study mechanisms of developmental toxicology of environmental chemicals. Jessica Legradi, P. Cenijn, R. Carvalho, J. Zebrafish as a tool to study mechanisms of developmental toxicology of environmental chemicals Jessica Legradi, P. Cenijn, R. Carvalho, J. Legler Overview Introduce zebrafish as model organism Zebrafish

More information

Lesson 3: Working With Linear Relations Day 3 Unit 1 Linear Relations

Lesson 3: Working With Linear Relations Day 3 Unit 1 Linear Relations (A) Lesson Context BIG PICTURE of this UNIT: CONTEXT of this LESSON: mastery with algebraic manipulations/calculations involving linear relations proficiency in working with graphic and numeric representations

More information

Improving a Heart Rate Controller for a Cardiac Pacemaker. Connor Morrow

Improving a Heart Rate Controller for a Cardiac Pacemaker. Connor Morrow Improving a Heart Rate Controller for a Cardiac Pacemaker Connor Morrow 03/13/2018 1 In the paper Intelligent Heart Rate Controller for a Cardiac Pacemaker, J. Yadav, A. Rani, and G. Garg detail different

More information

Lesson 5: Solving Equations

Lesson 5: Solving Equations Up to this point, our study of algebra has involved a deep look at algebraic expressions and operations on those expressions. We ve learned how to characterize, write, and simplify algebraic expressions,

More information

Frequency (Hz) Amplitude (pa) D1 WT D1 KO D2 WT D2 KO D1 WT D1 KO D2 WT D2 KO

Frequency (Hz) Amplitude (pa) D1 WT D1 KO D2 WT D2 KO D1 WT D1 KO D2 WT D2 KO A D1 MSNs B D2 MSNs C Frequency (Hz) 4 3 2 1 D Amplitude (pa) 5 4 3 2 1 D1 D1 D2 D2 D1 D1 D2 D2 Supplemental Figure 1. B deletion did not alter GABA-mIPSCs in D1 or D2 MSNs. (A,B) Representative recording

More information

A Discreet Compartmental Model for Lead Metabolism in the Human Body

A Discreet Compartmental Model for Lead Metabolism in the Human Body A Discreet ompartmental Model for Lead Metabolism in the Human ody Frederika E. Steyn, Stephan V. Joubert and harlotta E. oetzee Tshwane University of Technology Abstract A real-life example of a mathematical

More information

Exponential growth and decay models have the form y = A e bt, t 0 for constants A and b, where independent variable t usually represents time.

Exponential growth and decay models have the form y = A e bt, t 0 for constants A and b, where independent variable t usually represents time. Chapter 3 Mathematical models 3. Introduction A mathematical model is an equation which is intended to match or model the behavior of some natural quantities. Eponential functions are found in many mathematical

More information

Risk Assessment of Staphylococcus aureus and Clostridium perfringens in ready to eat Egg Products

Risk Assessment of Staphylococcus aureus and Clostridium perfringens in ready to eat Egg Products Risk Assessment of Staphylococcus aureus and Clostridium perfringens in ready to eat Egg Products Introduction Egg products refer to products made by adding other types of food or food additives to eggs

More information

Chapter 24 MRA and Flow quantification. Yongquan Ye, Ph.D. Assist. Prof. Radiology, SOM Wayne State University

Chapter 24 MRA and Flow quantification. Yongquan Ye, Ph.D. Assist. Prof. Radiology, SOM Wayne State University Chapter 24 MRA and Flow quantification Yongquan Ye, Ph.D. Assist. Prof. Radiology, SOM Wayne State University Previous classes Flow and flow compensation (Chap. 23) Steady state signal (Cha. 18) Today

More information

An Trinh, Todd Williams, and Daniel Vitkuske Supelco, 595 N. Harrison Rd., Bellefonte, PA, T GID

An Trinh, Todd Williams, and Daniel Vitkuske Supelco, 595 N. Harrison Rd., Bellefonte, PA, T GID An Trinh, Todd Williams, and Daniel Vitkuske Supelco, 595 N. Harrison Rd., Bellefonte, PA, 16823 T403159 GID Medicinal chemistry is a branch within the pharmaceutical industry that merges expertise in

More information

i-stat ctni Performance Verification vs. ABBOTT AxSYM

i-stat ctni Performance Verification vs. ABBOTT AxSYM Report #1 i-stat ctni Performance Verification vs. ABBOTT AxSYM i-stat Corporation - 1 Summary of Evaluation Study Results Correlation Slope Note 1 with comparative assay for [ctni]

More information

Lecture 7 Time-dependent Covariates in Cox Regression

Lecture 7 Time-dependent Covariates in Cox Regression Lecture 7 Time-dependent Covariates in Cox Regression So far, we ve been considering the following Cox PH model: λ(t Z) = λ 0 (t) exp(β Z) = λ 0 (t) exp( β j Z j ) where β j is the parameter for the the

More information

Assessing Human Health Through Real-time Data Collection at the Grand Canyon

Assessing Human Health Through Real-time Data Collection at the Grand Canyon Assessing Human Health Through Real-time Data Collection at the Grand Canyon. Principal Investigator: Glory Aviña, PhD MBA Sandia National Laboratories Sandia National Laboratories is a multi-program laboratory

More information

REGRESSION ANALYSIS FOR TIME-TO-EVENT DATA THE PROPORTIONAL HAZARDS (COX) MODEL ST520

REGRESSION ANALYSIS FOR TIME-TO-EVENT DATA THE PROPORTIONAL HAZARDS (COX) MODEL ST520 REGRESSION ANALYSIS FOR TIME-TO-EVENT DATA THE PROPORTIONAL HAZARDS (COX) MODEL ST520 Department of Statistics North Carolina State University Presented by: Butch Tsiatis, Department of Statistics, NCSU

More information

Mitochondrial dynamics in ischemia and reperfusion

Mitochondrial dynamics in ischemia and reperfusion Mitochondrial dynamics in ischemia and reperfusion Derek J Hausenloy Reader in Cardiovascular Medicine, British Heart Foundation Senior Clinical Research Fellow, The Hatter Cardiovascular Institute, University

More information

STEEL PIPE NIPPLE BLACK AND GALVANIZED

STEEL PIPE NIPPLE BLACK AND GALVANIZED Price Sheet Effective August 09, 2018 Supersedes CWN-218 A Member of The Phoenix Forge Group CapProducts LTD. Phone: 519-482-5000 Fax: 519-482-7728 Toll Free: 800-265-5586 www.capproducts.com www.capitolcamco.com

More information

c. What is the average rate of change of f on the interval [, ]? Answer: d. What is a local minimum value of f? Answer: 5 e. On what interval(s) is f

c. What is the average rate of change of f on the interval [, ]? Answer: d. What is a local minimum value of f? Answer: 5 e. On what interval(s) is f Essential Skills Chapter f ( x + h) f ( x ). Simplifying the difference quotient Section. h f ( x + h) f ( x ) Example: For f ( x) = 4x 4 x, find and simplify completely. h Answer: 4 8x 4 h. Finding the

More information

Introduction. Math Calculus 1 section 2.1 and 2.2. Julian Chan. Department of Mathematics Weber State University

Introduction. Math Calculus 1 section 2.1 and 2.2. Julian Chan. Department of Mathematics Weber State University Math 1210 Calculus 1 section 2.1 and 2.2 Julian Chan Department of Mathematics Weber State University 2013 Objectives Objectives: to tangent lines to limits What is velocity and how to obtain it from the

More information

ECLT 5810 Linear Regression and Logistic Regression for Classification. Prof. Wai Lam

ECLT 5810 Linear Regression and Logistic Regression for Classification. Prof. Wai Lam ECLT 5810 Linear Regression and Logistic Regression for Classification Prof. Wai Lam Linear Regression Models Least Squares Input vectors is an attribute / feature / predictor (independent variable) The

More information

Practical Considerations Surrounding Normality

Practical Considerations Surrounding Normality Practical Considerations Surrounding Normality Prof. Kevin E. Thorpe Dalla Lana School of Public Health University of Toronto KE Thorpe (U of T) Normality 1 / 16 Objectives Objectives 1. Understand the

More information

i-stat ctni Performance Verification

i-stat ctni Performance Verification Report #1 i-stat ctni Performance Verification vs. IMMULITE TURBO DPC i-stat Corporation - 1 Summary of Evaluation Study Results Correlation Slope Note 1 with comparative assay for all ctni data 0.32 Critical

More information

CLINICALLY USEFUL RADIONUCLIDES:

CLINICALLY USEFUL RADIONUCLIDES: INTRODUCTION It is important that Nuclear Medicine Technologists be familiar with the imaging properties of all commonly used radionuclides to insure correct choice of isotope for a particular study as

More information

RADIOPHARMACEUTICALS

RADIOPHARMACEUTICALS RADIOPHARMACEUTICALS Samy Sadek, Ph.D. Associate Professor, New York Medical College. Radiopharmaceutical Chemist, St. Vincent's Hospital- Manhattan. 1 X-Ray Discovery: Roentgen Wilhelm Roentgen ca. 1895.

More information

A homo-dimer of annexin V protects against ischemia reperfusion injury in lung transplantation

A homo-dimer of annexin V protects against ischemia reperfusion injury in lung transplantation A homo-dimer of annexin V protects against ischemia reperfusion injury in lung transplantation K Hashimoto, H Kim, H Oishi, M Chen, I Iskender, J Sakamoto, A Ohsumi, Z Guan, DM Hwang, TK Waddell, M Cypel,

More information

Topic 2 notes Organisms and energy

Topic 2 notes Organisms and energy Topic 2 notes Organisms and energy AEROBIC RESPIRATION All cells in the body need energy - this energy is released in a process known as respiration Cells that are more active need more energy - e.g during

More information

J Pharm Sci Bioscientific Res (4): ISSN NO

J Pharm Sci Bioscientific Res (4): ISSN NO Development and Validation of Stability Indicating Analytical Method for Simultaneous Estimation of Perindopril and Potassium in Their Combined Marketed Dosage Form ABSTRACT: Gurjeet Kaur*, Nikhil Patel

More information

PROBLEM SET 3. SOLUTIONS February 26, 2004

PROBLEM SET 3. SOLUTIONS February 26, 2004 Harvard-MIT Division of Health Sciences and Technology HST.542J: Quantitative Physiology: Organ Transport Systems Instructors: Roger Mark and Jose Venegas MASSACHUSETTS INSTITUTE OF TECHNOLOGY Departments

More information

ECLT 5810 Linear Regression and Logistic Regression for Classification. Prof. Wai Lam

ECLT 5810 Linear Regression and Logistic Regression for Classification. Prof. Wai Lam ECLT 5810 Linear Regression and Logistic Regression for Classification Prof. Wai Lam Linear Regression Models Least Squares Input vectors is an attribute / feature / predictor (independent variable) The

More information

REDUCED ORDER METHODS

REDUCED ORDER METHODS REDUCED ORDER METHODS Elisa SCHENONE Legato Team Computational Mechanics Elisa SCHENONE RUES Seminar April, 6th 5 team at and of Numerical simulation of biological flows Respiration modelling Blood flow

More information

DIETARY PREEN OIL: AN ANTI-STRESS FEED ADDITIVE FOR AQUACULTURE SPECIES

DIETARY PREEN OIL: AN ANTI-STRESS FEED ADDITIVE FOR AQUACULTURE SPECIES DIETARY PREEN OIL: AN ANTI-STRESS FEED ADDITIVE FOR AQUACULTURE SPECIES UW-Madison Animal Sciences Dept. Animal Biologics Initiative UW Co-products laboratory, Aquaculture Research Lab, Meat Sciences Program

More information

i-stat ctni Performance Verification vs. J&J Vitros ECi

i-stat ctni Performance Verification vs. J&J Vitros ECi Report #1 i-stat ctni Performance Verification vs. J&J Vitros ECi i-stat Corporation - 1 - Summary of Evaluation Study Results Correlation Slope Note 1 with comparative assay for [ctni]

More information

A GENERIC FRAMEWORK FOR THE DEVELOPMENT OF THE SIGNAL SIMULATOR

A GENERIC FRAMEWORK FOR THE DEVELOPMENT OF THE SIGNAL SIMULATOR A GENERIC FRAMEWORK FOR THE DEVELOPMENT OF THE SIGNAL SIMULATOR 1 SUDHAKAR S DUBEY, 2 SHAMI TRIPATHI M.E. Students Electronics and Telecommunication Engineering, Thakur College of Engineering and Technology,

More information

NARAYANA IIT ACADEMY INDIA

NARAYANA IIT ACADEMY INDIA NARAYANA IIT ACADEMY INDIA Sec: XI-IC SPARK CPT-5 Date: 6-0-07 TIME: 3 HOURS MAX MARKS:86 JEE-ADVANCED-06-P-Model KEY SHEET PHYSICS 3 4 5 6 7 8 9 0 d d b b d a, c d c, d b, d a,b,c,d 3 4 5 6 7 8 a c a,

More information

Shape Analysis of Heart Rate Lorenz Plots

Shape Analysis of Heart Rate Lorenz Plots Shape Analysis of Heart Rate Lorenz Plots France Sevšek University of Ljubljana, University College of Health Studies, Poljanska 26a, 1000 Ljubljana, Slovenia, france.sevsek@vsz.uni-lj.si A quantitative

More information

Chapter 6 Part 4. Confidence Intervals

Chapter 6 Part 4. Confidence Intervals Chapter 6 Part 4 Confidence Intervals October 1, 008 Goal: To clearly understand the link between probability distributions and confidence intervals. Skills: Be able to calculate (1 - α)% confidence interval

More information

Maths tutorial booklet for. M2: Algebra. Name: Target grade: Quiz scores: M2.1 Understand and use symbols =,,,,, α, ~ =

Maths tutorial booklet for. M2: Algebra. Name: Target grade: Quiz scores: M2.1 Understand and use symbols =,,,,, α, ~ = Maths tutorial booklet for M2: Algebra Name: Target grade: Quiz scores: M2.1 Understand and use symbols =,,,,, α, ~ = M2.2 = Change the subject of an equation M2.3 Substitute numerical values into algebraic

More information

What is Statistics? Statistics is the science of understanding data and of making decisions in the face of variability and uncertainty.

What is Statistics? Statistics is the science of understanding data and of making decisions in the face of variability and uncertainty. What is Statistics? Statistics is the science of understanding data and of making decisions in the face of variability and uncertainty. Statistics is a field of study concerned with the data collection,

More information

The physics US and MRI. Prof. Peter Bogner

The physics US and MRI. Prof. Peter Bogner The physics US and MRI Prof. Peter Bogner Sound waves mechanical disturbance, a pressure wave moves along longitudinal wave compression rarefaction zones c = nl, (c: velocity, n: frequency, l: wavelength

More information