Sep 16, 2018 - Assignment 1 integrates learning outcomes 1 and 2 using manual. 28 August Received the bill and paid the annual insurance on the stock. Repairs to Motor Vehicle (Mac's Garage). Professional report, using the (default) Office style for the report. Assessment 3: Practical Test 2 – Microsoft Excel.
UNITED STATES 2019 Educational Grants supported in part by: Short Courses MSACL hosts a diverse offering of Short Courses. Short Courses will occur over the first three days of MSACL (Sunday, Monday, Tuesday of March 31 - April 2). Courses are NOT replicated on different days. They are single courses that span 1 or 2 days. Level: Intermediate - Advanced Prereqs: Students who sign up for this advanced course should either have five years or more of personal LC/MS/MS experience and familiarity with the scientific literature or have taken one or more introductory courses which cover atmospheric pressure ionization (API) techniques as well as the basics involved in routine LC/MS and LC/MS/MS analyses. Location: TBA Group: TBA Instructor(s): Jack Henion, PhD Jack Henion is Professor Emeritus of Toxicology at Cornell University in the Analytical Toxicology Section of the Diagnostic Laboratory within the College of Veterinary Medicine.
He is also a co-founder, Chairman and CSO of Advion BioSciences (ABS), Inc., located in Ithaca, New York. Henion's work in LC/MS relates to the analysis of real-world samples common to pharmaceutical, environmental, and biochemical problems. The instructor has published extensively in the areas of conventional capillary GC/MS as well as LC/MS, SFC/MS, IC/MS, and CE/MS using atmospheric pressure ionization (API) technologies with quadrupole, ion trap, and time-of-flight mass spectrometers. Course Contact Hours Sunday PM Monday AM Lunch Monday PM Tuesday AM STARTS ENDS Tuesday 8:00-12:00 Overview: This course presents presents a comprehensive overview of technology and techniques of analytical mass spectrometry and from that foundation extends into exciting, disruptive recent developments.
Sample preparation. Topics: New types of extraction, Issues to consider, Isolation of proteins from biological samples Ultrafiltration, Affinity techniques, Molecularly Imprinted Polymers (MIP?s), Aptamers, Thermo's MSIA pipette tips, Electro Extraction, Quechers, SISCAPA, Micro extractions: Dried blood spots (DBS), Dried Plasma Spots (DPS). Advanced separation techniques for large molecules.
Topics: UHPLC, Hydrophobic Interaction Chromatography (HIC), Nano-UHPLC, Micro LC/MS, Size Exclusion Chromatography (SEC), ion exchange chromatography, Capillary Electrophoresis (CE), Differential Mobility Spectrometry (DMS). Ionization techniques for MS. Topics: Electrospray ionization (ESI), Nano ESI, Micro ESI, Atmospheric pressure chemical ionization (APCI), Atmospheric pressure photoionization (APPI), Matrix assisted laser desorption ionization (MALDI), LAESI, Electron Ionization (EI) and its potential for LC/MS. To Discuss: New ionization techniques which may be used without on-line separation science technology.
This area has evolved into a variety of ambient ionization techniques such as DESI, DART, ASAP, etc. Mass Analyzers. Quadrupoles, Ion traps, linear and quadrupole, Time-of-Flight (TOF), Orbitraps, Hybrid mass analyzer systems, Ion mobility spectrometers, and Differential Mobility Spectrometry (DMS). To Discuss: Developments and improvements in mass analyzers including linear ion traps, FTMS, time-of-flight (TOF), orbitraps, and accelerator mass spectrometry (AMS), the latter currently being applied to micro-dosing experiments by the pharmaceutical industry. Issues such as full-scan acquisition rates, high-resolution mass spectrometry (HRMS), the importance and usefulness of exact mass measurements for qualitative and quantitative analysis, and the analytical merits compared with modern SRM LC/MS experiments will be discussed with many practical examples and applications. The latter will include clinical chemistry issues as well as pharmaceutical, food safety, environmental and industrial examples. Imaging and profiling by MS.
Applications of recently reported ionization techniques for imaging the location of chemicals in various matrices employing MALDI, DESI, LAESI, LESA and other techniques. Topics: The technique of MALDI and its applications to tissue imaging as well as DESI, LAESI and also liquid extraction surface analysis (LESA) employing nano-electrospray. A comparison of the various classes of compounds where MALDI and nano ESI provide complimentary coverage of certain compounds found in biological and other matrices. High resolution MS. Topics: Fundamentals, Mass Defects, Isotopic patterns, Mass axis calibration, Types of HRMS systems, Qual/Quan Analysis, Data mining processes, Future directions. To Discuss: The analytical merits of HRAMS from QTOF as well as orbitraps and FTMS systems will be presented.
Instances where either SRM LC/MS or LC HRAMS may be preferred for optimal selectivity due to chemical background or other interference issues. Miniaturization in MS. Topics: Purdue University 'Mini 11', Torion, Microsaic, Advion expression CMS, Waters QDa.
To Discuss: The benefits and limitations of smaller analytical instrumentation systems will be compared. This includes miniaturization of HPLC systems as well as the mass spectrometers themselves. The commercial introduction of chip-based HPLC systems closely integrated with mass spectrometers offers a glimpse of future directions in analytical chemistry. Synergistic Integration. A systematic overview via specific examples with applications highlighting noted examples of innovative novel and non-standard technologies which demonstrate the analytical potential of new analytical technologies. Developing instrumentation and technologies will be important aspects of future mass spectrometry techniques and its expansion to important new applications. An extremely important example is the need for LC/MS bioanalysis (quantitation) of biologics (ADC?s, large molecules, RNA, etc.) in biological samples employing both bottom up and top down methods.
HRAMS coupled with „protein friendly? Chromatography will significantly expand our present analytical capabilities. Ion mobility spectrometry (IMS) and transportable mass spectrometers could lead to point-of-care applications and other far reaching applications of mass spectrometry beyond what we are doing today. The future is very exciting! Level: Beginner - Intermediate Prereqs: - knowledge of Excel - able to bring a laptop - able to pre-install software on their laptop.Namely: R and R-studio - willingness to break up with Excel Location: TBA Group: TBA Instructor(s): Daniel Holmes, MD, Shannon Haymond, PhD & Stephen Master, MD, PhD Co-Instructor: Daniel Holmes, MD Clinical Associate Professor, Department of Pathology and Laboratory Medicine, University of British Columbia Daniel Holmes did his undergraduate degree in Chemical Physics from the University of Toronto with a focus on Quantum Mechanics.
He went to medical school at the University of British Columbia (UBC) where he also did his residency in Medical Biochemistry. He is a Clinical Associate Professor of Pathology and Laboratory Medicine at UBC and Division Head of Clinical Chemistry at St. Paul's Hospital in Vancouver. Interests include laboratory medicine statistics, clinical endocrinology, clinical lipidology and clinical mass spectrometry.
Assay development efforts in the last two years have focused on assays specialized endocrine testing. Co-Instructor: Stephen Master, MD PhD Associate Professor of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia Stephen Master received his undergraduate degree in Molecular Biology from Princeton University, and subsequently obtained his MD and PhD from the University of Pennsylvania School of Medicine. After residency in Clinical Pathology at Penn, he stayed on as a faculty member with a research focus in mass spectrometry-based proteomics as well as extensive course development experience in bioinformatics.
He is currently Associate Professor of Pathology and Laboratory Medicine at Children’s Hospital of Philadelphia. One of his current interests is in the applications of bioinformatics and machine learning for the development of clinical laboratory assays. He would play with R for fun even if he weren't getting paid, but he would appreciate it if you didn't tell that to his department chair. Course Contact Hours Sunday PM Monday AM Lunch Monday PM Tuesday AM STARTS ENDS Tuesday 8:00-12:00 Overview: Have you ever tried to do Deming regression in Excel only to discover that it is not available? Have you had your figure rejected by a journal because the resolution was not good enough? Have you wished that you could figure out a way to stop manually transcribing your LC-MS/MS results into the LIS?
Well, your wait is over because this year at MSACL we will be offering a course for complete programming newbies that will help you get going analyzing real data related to LC-MS/MS assay development, validation, implementation and publication. The only background expected is the ability to use a spreadsheet program. The skills that you will acquire will allow you to take advantage of the many tools already available in the R language and thereafter, when you see that your spreadsheet program does not have the capabilities to do what you need, you will no longer have to burst into tears. You will be empowe-R-ed. The course will be run over two days and time will be evenly split between didactic sessions and hands on problem solving with real data sets. Drs Holmes and Master will adopt a “no student left behind policy”. Students will be given ample time to solve mini problems taken from real life laboratory work and focused on common laboratory tasks.
All attendees will need to bring a laptop with the R language installed R Studio interface installed. Students may use Windows, Mac OSX or Linux environments. Both R and R studio are free and open-source. No cash required.
Students should be prepared for learning what computer programming is really like. This may involve some personal frustration but it will be worth it. Obtaining the Software Instructions for installing the R language are here: Instructions for installing R Studio are here: Course Description The course will cover:. The major types of R variables: vectors (numerical, character, logical), matrices, data frames and lists.
The important classes: numeric, character, list and changing between them. Importing data from Excel. Dealing with non-numeric instrument data: the “1000”s.
Manipulating your data: sub-setting, which, match, sort, unique, cut. Simple statistical tests: mean, median, quantiles (normal ranges), t-tests, ANOVA, Wilcoxon. Data merges: matching rows between sets. Exporting data to Excel-like format. Regressions: Ordinary Least Squares,Passing Bablok, Deming, weighted regressions.
Non-linear regressions. Looping: Doing things repeatedly. Writing your own functions. Making highly customized graphs: scatter plots, regression lines, histograms, box plots, qq-plots. Putting it all together projects:. Preparing method comparison regression and Bland Altman plots. Preparing mass spectrometry data for upload to LIS.
Level: Intermediate Prereqs: Completed “Breaking Up With Excel” and/or familiar with basic R concepts. Able to bring a laptop. Able to pre-install software on laptop.Namely: R and R-studio. Location: TBA Group: TBA Instructor(s): Patrick Mathias, MD, PhD & Randall Julian, PhD Co-Instructor: Patrick Mathias, PhD Assistant Professor, Department of Laboratory Medicine, University of Washington Patrick Mathias completed his undergraduate degree in Electrical Engineering at Duke University, followed by a master’s degree in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. He then completed a MD degree and a PhD in Bioengineering from the University of Illinois, with a focus on nanophotonics and label-free biosensors.
He completed residency training in Clinical Pathology as well as a Clinical Informatics fellowship at the University of Washington. He is currently the Associate Director of the Informatics division in the Department of Laboratory Medicine at the University of Washington. His clinical and research interests lie in improving electronic health record systems to improve ordering and interpretation of laboratory tests, developing infrastructure for novel analytical technologies in the clinical laboratory, and applying analytics to improve laboratory operations and clinical care at a population level. Co-Instructor: Randy Julian, PhD CEO, Indigo BioAutomation Randy Julian is Founder and CEO of Indigo BioAutomation located in Indianapolis, Indiana.
Randy earned a Ph.D. In Chemistry from Purdue University in 1993 and then worked in Discovery Chemistry at Eli Lilly for 14 years. Julian worked on natural product discovery, high throughput screening for RNA anti-viral compounds and researched methods for using proteomics to optimize drug candidates in animal models. Randy founded Indigo based on informatics technology developed during his time with Lilly. Indigo now provides laboratory data analysis software which uses machine intelligence to automatically analyze over 100 million sample results per month for every major clinical laboratory in the US. Julian is an active member of the clinical mass spectrometry community, teaches short courses in statistics, informatics and analytics. Randy is the past Chairman of the Human Proteome Organization’s Standards Initiative.
He is the coauthor of two international standards for analytical data. He was also the chairman of the ASTM committee on mass spectrometry data standards. Julian maintains an active research relationship with the faculty at Purdue University where he is an Adjunct Professor of Chemistry. Course Contact Hours Sunday PM Monday AM Lunch Monday PM Tuesday AM STARTS ENDS Tuesday 8:00-12:00 Overview: Having completed your first steps into the wonderful world of data analysis with R, would you like to go further? You’ve learned the basics of R, so now it’s time to put that knowledge to work and tackle some interesting clinical applications. Along the way you will also be introduced to even more of capabilities of R and the tools developed by the amazing R community.
The course will be run over two days and time will be split between lecture sessions, individual problem solving, and a highly interactive group-level data mining of real data sets (there may even be prizes). Like the introductory course, this class will maintain the “no student left behind policy”. Students will be given time to solve problems taken from real life laboratory work and to do some more advanced analysis on large scale data sets. All attendees will need to bring a laptop with the R language installed and R Studio interface installed. Students may use Windows, Mac OSX or Linux environments. Both R and R studio are free (as in “Free Beer”) and open-source.
Students should be prepared continue to expand their skill in programming – which, as you learned in the introductory course can be a little frustrating, but not as frustrating as not being able to get the computer to do what you want at all!
On excel for PC there is a function in the data tab called 'data analysis'. This fuction allows you to select cells on your worksheet and compare 2 samples against one another using a confidence interval, and gives you a z, or t stat. Where is this function on mac? (A) The add-in for the Excel data analysis tools is called 'Analysis ToolPak,' and it is not available for Excel 2011 for Mac.
Instead, Microsoft recommends a third-party alternative. In Excel 2011 for Mac, choose Help from the topmost menu bar, type 'Analysis ToolPak' (without the quotes) into the Search box, and select the 'I can't find the Analysis ToolPak' item. You'll be directed to download the free StatPlus:mac LE from the AnalystSoft web site StatPlus:mac LE has a 'Comparing Means (T-Test)' feature.
(B) If you prefer to use only the Excel worksheet function (that is used by the Analysis ToolPak dialog box user interface), the function is available in Mac Excel 2011, and it is named TTEST. There is also a newer version named T.TEST.
Both are described in Excel Help. Choose Help, type 'ttest' or 't.test' (without the quotes) into the Search box, and select the item from the list. Mike Middleton, www.MikeMiddleton.com.