myAriel

The course is meant to be an introduction to biotechnologically relevant techniques that requires a particular knowledge of their physical, computational and technological basis.

The course aims at giving describing the principles and the potentials of a selection of advanced optical microscopy techniques and of nanotechnologies. The course also aims at providing a basic understanding and training of quantitative image analysis.

The topics included in the course can be divided into three main sections:

ADVANCED OPTICAL MICROSCOPY 

Introduction on the resolution of optical microscopes

Effects of the finite resolution on images

Introduction to confocal microscopy

Optical processes and techniques that can overcome the resolution limit, such as: non linear microscopy, STED,TIRF,PALM, SNOM.

Introduction to time resolved fluorescence and FLIM microscopy

NANOTCHNOLOGY

Aims and uses of nanoparticles in biomedicine.

Nanoparticles: quantum dots, nanomag, metallic nanoparticles, polymer particles, liposomes.

General concepts, stability, bioconiugation, cell internalization.

Optical tweezers for micro-manipulation.

Atomic force microscopy.

Later-free optical  biosensors. Introduction to SPR-based techniques.

Micromechanical devices.

 

QUANTITATIVE ANALYSIS OF IMAGES

Introduction to the fundamentals of computer graphics aimed to the understanding and elaboroation of the informations contained in images.

Colorimetry: color spectrum, Gamut, chromatic coordinates, gamma value of displays, RGB, CMYK.

Digital image types: (es. BMP,TIF,GIF,JPG)

Lossy and lossless compression

ImageJ interface introduction, Image visualization (Look Up Tables, Brightness and Contrast), Pixel Statistics.

Processing examples (FFT and filtering)

An example of a quantitative analysis: electrophoretic gel

Object recognition I : image segmentation and particle analysis

Multidimensional images from color channels to image stacks

Video processing

Best Fitting procedures. Fitting a model into quantitative data extracted from a digital image. Extraction of experimental parameters and confidence levels.

Object Recognition II: Training a convolutional Netural Networks for the automatic recognition of objects in image. Image annotation.