代做CSC2020 Coding for Medical Scientists代做留学生R程序
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Module Title: |
Coding for Medical Scientists |
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Module Code: |
CSC2020 |
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Module credits |
15 |
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% of Module Mark |
60% |
1. Required Task
You will be provided with a data file containing electrophysiology experiments carried out as part of a neuroscience practical class. During this experiment, students evoked paired synaptic responses from hippocampal brain slices and then applied various cocktails of drugs to monitor how these responses were changed by the drugs.
Your task is to write a Matlab function to analyse these data and write a short report on your findings.
More details on the experiment carried out and what to analyse can be found on the module ELE page.
Materials provided (on the ELE page):
• One dataStruct.mat file, which is a struct with 10 fields. Each field contains a replicate of the experiment.
• An Excel file called notebook.xlsx which contains information about when each drug cocktail was applied to the slice in each experiment.
• A file containing important information about the experimental details which you will need to carryout your analysis and some additional information about how to process the data
The report:
The main body of the text should not exceed 2 page of A4. As guidance this is equivalent to about 1000 words.
It should be formatted as follows:
• Font: Calibri (body) 11pt
• Line spacing: 1.15
• Margins: ‘Normal’ for a Word document: 2.54cm all sides (top, bottom, left and right)
The page count does not include any figures or tables and associated legends. Nor does it include the code appendix (see below).
Each figure and table, with associated legend, should be placed on a separate page at the end of the document.
The text should be appropriately formatted to make it easily readable – i.e. break up text by including subheadings and avoiding long paragraphs.
Your report should contain the following:
• a very short introduction, setting out what is in the report. Probably no more than a paragraph or two.
• a description of the data analysis methods and explanation of the code.
• a results section containing:
o A multi-panel figure showing details of a single example experiment.
a. You should include example traces in each of the 4 conditions (i.e. control and the 3 drug conditions).
b. A time course plot of a single experiment (e.g. a plot of time vs 1st fEPSP amplitude)
c. As an example of the sort of thing required, see Fig 1A from Brown & Randall 2005 (link).
o A figure summarising the average peak amplitude of the 1st response in the 4 conditions across all 10 of the experimental replicates
o An appropriate statistical analysis to determine the effects of the various drug treatments on the peak amplitude of the 1st response.
• a brief summary conclusion. NB for the purposes of this report, it is NOT important to know what the various drugs do ata pharmacological level – your conclusions should simply summarise the results.
• an appendix containing the ‘published code’ (see attached file for details on how to do this).
How to publish
code. pdf The function :
The function should:
• have one input argument: the filename
• have at least one output argument: a table array containing the results of the analyses of all the experiments. The table array should contain fields with the following information in them (at a minimum):
o the filename of each recording (you can find this information in each field of dataStruct)
o the average peak amplitude of the 1st fEPSP in each of the 3 drug conditions and in the baseline condition.
The precise way in which this table is arranged is up to you but it should be sensibly organised with obvious but simple field names, so that it is clear what the output from the function is.
• Be self-contained: i.e. if you write additional functions to make this run, they should be included as local subfunctions within the same m-file.
• Be well-annotated with comments, including a brief ‘help’ description explaining how the function should be used (i.e. explaining what the inputs and outputs are).
Use of Generative AI:
This assessment falls under the category of AI-supported, which means that ethical and
responsible use of Generative AI tools in the development of this assessment is supported. This may include using Generative AI tools to summarise literature, improve the structure of your work or quality of English language. It is not permitted to use Generative AI to help with writing any of your code or doing any of the data analysis (including generating figures, tables or other outputs). Please see the module specific Generative AI Statement template in the Assessment section (you can find it also at the end of this document) of the module ELE page for more details. Please keep a record of the tools, prompts and outputs used so you can produce these if necessary at aviva voce examination and demonstrate how you have built on this content to ensure your work is original.
Submission
For the coursework on this module you must submit both your report and the .m file containing your code. The report should be in either MS Word (.doc or .docx) or .pdf format. Please ensure that your name DOES NOT appear anywhere in the document, but that your personal ID number is included.
The naming of your report file must follow the format below: ID number_ModuleCode_assessment name_AC year
E.g. 91000000_CSC2020_report1_2022-23
The .m file and the report should be combined in a single .ZIP file and submitted to ELE by the stated deadline. For instructions on how to submit, please see the Medical Sciences
Sharepoint
(https://www.exeter.ac.uk/students/infopoints/yourinfopointservices/assessments/)
If you submit your work late, but are within one hour of the deadline, a penalty of 5% will be deducted from your mark (0% if below the passmark). For any work submitted more than an hour late your mark will be capped at 40% (after 24 hours from the deadline a score of zero will be awarded). This is unless you have valid mitigation (please visit thePersonal & Pastoral support ELE pagefor more information.)
In submitting this you are declaring the work is your own independent piece of work, not produced in collusion with a fellow student or plagiarized from a fellow student, or a web site, or a textbook, or any other information source. You must demonstrate good referencing practise and ensure you have sufficiently paraphrased all sources of information.
Failure to do so may result in being referred to an academic misconduct panel which has the power to grant penalties based on specific criteria. For more details please revisit the
Academic Honesty and Plagiarisminformation on ELE.
3.Acquired skills
• Importing and analysing data in Matlab
• Statistical analysis
• Data interpretation
• Data presentation
• Report writing
4. Sources of support
• Matlab help function, notes and reference book.
• Assessment documentation
• Computer lab practical classes
• International students and those who have English as a second language can make use of theEnglish Language Skills Development programme. This programme provides face-to- face workshops and courses as well as one-to-one assignment writing tutorials and an
extensive range of online resources via theirGuided Independent Learning site. Find out
more by browsing the timetables:
https://www.exeter.ac.uk/into/englishlanguage/howtoparticipate/timetablessupport/
or emailinginsessional@exeter.ac.uk