Mediator: Christophe Zimmer (Pasteur Institute)
Image formation:
Light microscopy: principles and variants
Fluorescence
Point spread function and resolution
Noise
Basic image processing techniques
Image restoration:
denoising
deblurring
registration
Detection:
automatic thresholding
correlation
other methods
Image segmentation:
adaptive thresholding
edge filters
region based methods
Tracking
detection and association
correlation tracking
optical flow
Advanced methods: examples
Inverse deconvolution methods
Deformable models
Statistical detection and estimation
Highlights of image processing in biological research:
Single molecule dynamics
Super-resolution microscopy
Automated phenotyping and lineage tracing
Reconstruction of neuronal circuitry
Duration: 7 sessions of 3h with practice
Mediator: Jean-Luc Lebrun
Course Synopsis
This course is based on the book "Scientific Writing: A Reader and Writer's Guide". It helps identify and articulate the differences between efficient and deficient scientific writing. Through many in-class exercises, it promotes good scientific writing habits such as conciseness and clarity. The course material is mostly provided by the participants: they bring a published or unpublished paper (6 to 8 pages) to the course and learn how to evaluate and improve documents of the same type.
Good scientific writing skills open up many opportunities to the researcher: publications, confrence or seminar attendance. They also lead to better patents, better research partnerships and better funded research. Clarity and efficiency in scientific writing is a testimony to the quality of a researcher; It influences career promotion.
Course structure
- Introduction: "Write to be read" - a reader and reviewer perspective
- Module 1: The 'Why' and the 'How' of each item in a standard scientific paper structure: title, abstract, introduction, body (headings, subheadings, tables and graphs), conclusion
- Module 2: Literature review, role and rules
- Module 3: Elementary principles of composition: reaching clarity, conciseness, precision and fluidity in writing
- Module 4: Identification of writing problems: a walkthrough process to detect structural problems at the sentence, paragraph and paper level
Requirements
• A scientific paper written by the student (not a review) published, submitted, or draft
• Highlighters of various colours
• The companion book: "Scientific Writing: a Reader and Writer's Guide" World Scientific Publishing 2007 will be provided to the participants.
Class size : 20 participants
The trainer
Jean-Luc Lebrun has managed research programs while working at Apple Computer in its Advanced Technology Research group for over ten years. He subsequently invested his energy in the commercialization of research. He teaches scientific writing at the following A-Star* research Institutes: BII, BTI, CMM, DSI, GIS, I2R,IBN, ICES,IHPC,IME, IMRE, SBIC, Simtech, and SSCC as well as medical research centres: SGH, NCCS.
Duration : 2,5 days
Mediator: Konrad Hinsen (CNRS, Synchrotron Soleil)
This series of lectures and exercises provides a practical introduction to using the programming language Python for solving common problems in computational science.
Topics covered (roughly in chronological order):
• Presentation of the Python language and its use in scientific computing
• Basic language features
• Working with files
• Efficient numerical operations (NumPy library)
• Plotting (matplotlb library)
• Moving on to more complex programs: object-oriented programming and error handling
Students are expected to have experience in working with a computer: creating and editing text files, consulting documentation on the Web etc. No prior programming experience is required.
Duration: 6 sessions of 3 hours in the computer room
Mediator: Konrad Hinsen (CNRS, Synchrotron Soleil)
This series of lectures and exercises addresses advanced topics in scientific computing.
Topics covered:
• Basic parallelization using task farming
• Graphical user interfaces (Tk library)
• Managing source code (and more) with a version control system (Mercurial)
• 3D visualization and animation with VPython
Possible additional topics (chosen according to the students' preferences):
• Interfacing C and Fortran code with Python
• Debugging, profiling, optimization
• Performance boosting using Pyrex/Cython
• Handling large data sets (HDF library, Pytables library)
• Advanced visualization using MayaVi
Students are expected to have a working knowledge of the Python language. Some of the additional topics (Interfacing C and Fortran code, Performance boosting using Pyrex/Cython) also require a working knowledge of the C language.
Duration: 5 sessions of 3 hours in the computer room