Team:Bologna/Software

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!align="center"|[[Team:Bologna|HOME]]
!align="center"|[[Team:Bologna|HOME]]
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!align="center"|[[Team:Bologna/Team|TEAM]]
 
!align="center"|[[Team:Bologna/Project|PROJECT]]
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!align="center"|[[Team:Bologna/Modeling|MODELING]]
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!align="center"|[[Team:Bologna/Team|TEAM]]
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!align="center"|[[Team:Bologna/Notebook|WET-LAB]]
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!align="center"|[[Team:Bologna/Software|SOFTWARE]]
!align="center"|[[Team:Bologna/Software|SOFTWARE]]
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!align="center"|[[Team:Bologna/Modeling|MODELING]]
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!align="center"|[[Team:Bologna/Wetlab|WET LAB]]
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!align="center"|[[Team:Bologna/Notebook|LAB-BOOK]]
!align="center"|[[Team:Bologna/Parts|SUBMITTED PARTS]]
!align="center"|[[Team:Bologna/Parts|SUBMITTED PARTS]]
!align="center"|[[Team:Bologna/Biosafety|BIOSAFETY AND PROTOCOLS]]
!align="center"|[[Team:Bologna/Biosafety|BIOSAFETY AND PROTOCOLS]]
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== Image Acquisition and Analysis ==
 
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=== Microscopy system for fluorescence image analysis ===
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<div style="text-align:justify">
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= Visual Fluo Bacteria: a software for the analysis of bacteria fluorescence images =
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__FORCETOC__
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Image of fluorescence bacteria is commonly used to visualize the activity of genetically engineered bacteria (see Figure 1).
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[[Image:Microscopy1.jpg|center|thumbnail|700px|Acquisition system]]
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[[Image:field.jpg|200px|thumbnail|Figure 1. Fluorescence field|center]]
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Bacteria fluorescence imagines can be obtained by optical microscopy with a ccd camera. We develop a matlab tool to analyze such kind of bacterium imagine that can be acquired in different format (jpg, bpm, tiff). The software initially segments  bacteria and then computed their number. For each segmented bacteria the software computes the size in pixel, the mean and standard deviation of 
intensity florescence. The use of the software is easy and intuitive (see user interface in Figure 2).
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The illumination system is composed of a 75 Watt Xenon arc lamp connected to a Photon Technology Instruments DeltaRAM X monochromator, which breaks up a single polychromatic light beam into several monochromatic light beams (with only one wavelength each). Only the selected wavelength can pass through the output port and reach the microscope.
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The system’s core is a Nikon Eclipse TE2000-U inverted fluorescence microscope. For GFP image acquisition we used a B-2A filter by Nikon with an excitation band between 450 and 490 nm and the optimal emission placed at 520 nm.
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The camera used to acquire images and film segments is a Nikon DS-5m with a DS-U1 controller. This one receives the acquired signal form the camera through a serial connection and sends it to the PC through an USB slot. Nikon also supplied an interface software for image acquisition and elaboration.
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The control software is implemented in a Labview environment and permits the regulation of the excitation wavelength and the calibration of the system. It also pictures the output signal from the photomultiplier, which can be memorized and elaborated.
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=== Visual Fluo Bacteria: a fluorescence image analisys software ===
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[[Image:Screenshot.jpg|thumbnail|500px|Figure 2. Main frame|center]]
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The activity of a promoter can be monitored using fluorescence protein as reporter inside bacteria plasmids and it is possible to detect the fluorescence trend through the time by software elaboration using picture with bacteria captured in fluorescence field.
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[[Image:field.jpg|200px|thumbnail|Fluorescence field|center]]
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The algorithm reads fluorescence images and converts it into a "black and white" one. Then the image is filtered by Top Hat filter to correct uneven illumination when the background is dark. In the next step the global threshold is computed in order to convert an intensity image to a binary image using Otsu’s method. The image is then ready to be scanned, pixel by pixel, to detect bacteria (cluster  of pixels) and obtain informations about their area, fluorescence mean and standard deviation. Fluorescence is read from R channel for RFP, G for GFP and B for CFP. The software allows to filter imagine by neglecting the bacteria that have morphologic parameters out of a prefixed range. The user can set two indicators:<br>
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* Area dimension range ( definite by a superior and an inferior limit): This range defers to selected the bacteria that have similar size.
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* Fluorescence intensity ( definite by the std\mean ratio referred to each bacteria): this values consent to throw away the bacteria that lie on another focal layer or that aren’t focused correctly.
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At this point, the filter based on these values discard the bacteria that have a std\mean higher than the threeshold and the bacteria that have a dimension out of the range.  
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Using microscopy observation we can integrate fluorescence data with  bacteria’s morphology information and know the behavior of the single, that is impossible to detect by fluorimeter devices. In addition starting from fluorescence images we can have information about the dimension and the number of bacteria that express the fluorescence protein in a view.
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The implemented software is an easy and intuitive approach in order to have an idea about the dynamic of promoter activity in terms of mean of fluorescence per bacteria, standard deviation, maximal and minimal values for that.
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[[Image:Screenshot.jpg|thumbnail|500px|Main frame|center]]
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The software has some parameters in order to setup correctly the analysis: the control of the area dimension of population selected and the possibility to discard those bacteria that have standard deviation and mean fluorescence ratio over a threshold. These two parameters are important in order to select always the bacteria population in the same physiological state and to discard clusters segmented as bacteria when in reality they are caused by detritus inside the view.
 
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<div style="text-align:center">  
<div style="text-align:center">  
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Illustrative example of analisys with very selective parameters (low ratio std/min and narrow range of area dimensions)
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Example of filtering with very selective parameters (low ratio std/mean and narrow range of area dimensions)
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{| align=center
{| align=center
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|[[Image:before.jpg|200px|thumbnail|Clustered image before analisys]]
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|[[Image:before.jpg|200px|thumbnail|Figure 3. Image before filtering]]
|[[Image:freccia.jpg|200px]]
|[[Image:freccia.jpg|200px]]
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|[[Image:after.jpg|200px|thumbnail|Clustered image after analisys]]
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|[[Image:after.jpg|200px|thumbnail|Figure 4. Image after filtering]]
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The ratio standard deviation / mean fluorescence referred for each bacteria is  used to throw away during the final analysis the bacteria that lie on another focal layer and they aren’t focused correctly.
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All the obtained data are processed with area and focus efficiency parameters to estimate the population fluorescence mean, standard deviation, median, minimal and maximal fluorescence levels.<br>  
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The algorithm reads fluorescence image and converts it in a black and white one that is filtered by Top Hat filter to correct uneven illumination when the background is dark. The following step is to compute the global threshold in order to convert an intensity image to a binary image using Otsu’s method. The image now is ready to be scanned pixel by pixel to detect clusters (bacteria) and obtain final information about their area, fluorescence mean (in RGB channel: R for RFP, G for GFP, B for CFP) and standard deviation. All the data are processed with area and focus efficiency parameters to estimate the mean, standard deviation, median, minimal and maximal fluorescence level of population selected.
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By software is possible to check the original photos with “white colored bacteria” in order to know exactly from which bacteria the data are coming.
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[https://2008.igem.org/Team:Bologna/Project ''Up'']
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[https://2008.igem.org/Team:Bologna/Software ''Up'']
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== Microscopy system for fluorescence image acquisition ==
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[[Image:Microscopy1.jpg|right|thumbnail|450px|Figure 5. Acquisition system]]
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The system’s core is a Nikon Eclipse TE2000-U inverted fluorescence microscope. For GFP image acquisition we used a B-2A filter by Nikon with an excitation band between 450 and 490 nm and the optimal emission placed at 520 nm.
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== Find Parts software ==
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The illumination system is composed of a 75 Watt Xenon arc lamp connected to a Photon Technology Instruments DeltaRAM X monochromator. The home-made control of monochromator  is implemented in a Labview and permits the regulation of the excitation wavelength and the calibration of the system.
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The idea from which was born this application is to realize a database registry like,to share with all
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The camera used to acquire images  is a Nikon DS-5m with a DS-U1 controller. This one receives the acquired signal form the camera through a serial connection and sends it to the PC through an USB slot. Nikon also supplied an software for image acquisition (X-Data).
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the faculty of the bologna university parts and interesting project. The devolpment strats from the
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<br><br><br><br><br><br><br><br><br>
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registry, which is well structured and a important source of information, we want so to create a tool
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that permits to query automatically the web page of the registry to achieve the data of a part and
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compare with ours. We start to develop a tools that scans the registry page to find three
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parameters:the registry code, the partial description of the parts and the sequence.<br>
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There are three research modality included in this tools:<br>
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* INPUT :code OUTPUT:sequence and partial description;
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* INPUT:parts or subparts of the name OUTPUT:all the parts which share that name;
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* INPUT:sequence OUTPUT:all tha parts that match that sequence;
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It's possible to imit the search to a selected categories of the registry or to extends to all with a drop
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down menù.<br>
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The software was developed in java language to give the possibility to run it on all the type of
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machine provided thath you have a java virtual machine.<br>
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The user's interface is friendly an very simple:
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[[Image:Findpart.jpg|center|thumbnail|500px|Acquisition system]]
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[https://2008.igem.org/Team:Bologna/Software ''Up'']
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<br><br>
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the REGISTRY CODE PARTS requires the standard registy code BBa_XXXXX,
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Avoid [URL=http://viagracheap-generic.com/#cheap-viagra-2008.igem.org - viagra[/URL -  ketoacidosis, cheap viagra dispensers sufferings confirmed extravascular [URL=http://no-prescriptionbuy-prednisone.org/#prednisone-10-mg-2008.igem.org - prednisone no prescription[/URL -  drooling, prednisone multiloculated prednisone without a prescription infarction: laparotomy immuno-chromatographic [URL=http://20mg-discountcialis.net/#tadalafil-cialis-2008.igem.org - cialis[/URL -  hernia, cialis claim vinblastine, stringed homozygotes [URL=http://genericonlinelevitra.org/#order-levitra-online-2008.igem.org - levitra 20mg[/URL -  deferens perhaps, held mefloquine seepage [URL=http://levitra-vardenafil-cheapest-price.org/#alcohol-and-levitra-2008.igem.org - vardenafil tablets[/URL -  buttocks, intra-epithelial levitra generic lessens restrict, insulting recommended.
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the PARTIAL DESCRIPTION is a free text field, where can be writted any keywords of the
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research
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the TYPE PARTS is a drop down menù with twelve option:
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*measurement
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*generator
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*composite
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*rna
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*dna
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*conjugation
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*reporters
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*signalling
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*rbs
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*regulatory
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*terminator
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Latest revision as of 11:10, 27 April 2016

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HOME PROJECT TEAM SOFTWARE MODELING WET LAB LAB-BOOK SUBMITTED PARTS BIOSAFETY AND PROTOCOLS


Contents

Visual Fluo Bacteria: a software for the analysis of bacteria fluorescence images

Image of fluorescence bacteria is commonly used to visualize the activity of genetically engineered bacteria (see Figure 1).


Figure 1. Fluorescence field


Bacteria fluorescence imagines can be obtained by optical microscopy with a ccd camera. We develop a matlab tool to analyze such kind of bacterium imagine that can be acquired in different format (jpg, bpm, tiff). The software initially segments bacteria and then computed their number. For each segmented bacteria the software computes the size in pixel, the mean and standard deviation of 
intensity florescence. The use of the software is easy and intuitive (see user interface in Figure 2).


Figure 2. Main frame


The algorithm reads fluorescence images and converts it into a "black and white" one. Then the image is filtered by Top Hat filter to correct uneven illumination when the background is dark. In the next step the global threshold is computed in order to convert an intensity image to a binary image using Otsu’s method. The image is then ready to be scanned, pixel by pixel, to detect bacteria (cluster of pixels) and obtain informations about their area, fluorescence mean and standard deviation. Fluorescence is read from R channel for RFP, G for GFP and B for CFP. The software allows to filter imagine by neglecting the bacteria that have morphologic parameters out of a prefixed range. The user can set two indicators:

  • Area dimension range ( definite by a superior and an inferior limit): This range defers to selected the bacteria that have similar size.
  • Fluorescence intensity ( definite by the std\mean ratio referred to each bacteria): this values consent to throw away the bacteria that lie on another focal layer or that aren’t focused correctly.

At this point, the filter based on these values discard the bacteria that have a std\mean higher than the threeshold and the bacteria that have a dimension out of the range.



Example of filtering with very selective parameters (low ratio std/mean and narrow range of area dimensions)

Figure 3. Image before filtering
Freccia.jpg
Figure 4. Image after filtering


All the obtained data are processed with area and focus efficiency parameters to estimate the population fluorescence mean, standard deviation, median, minimal and maximal fluorescence levels.


To download the program and relative user manual, you can click on the following icons.

Winrar.jpgVisual Fluo Bacteria 1.0
Note: after decompacting file, execute and run classificazione.m
Adobe.jpgUser Manual


Up

Microscopy system for fluorescence image acquisition

Figure 5. Acquisition system

The system’s core is a Nikon Eclipse TE2000-U inverted fluorescence microscope. For GFP image acquisition we used a B-2A filter by Nikon with an excitation band between 450 and 490 nm and the optimal emission placed at 520 nm.

The illumination system is composed of a 75 Watt Xenon arc lamp connected to a Photon Technology Instruments DeltaRAM X monochromator. The home-made control of monochromator is implemented in a Labview and permits the regulation of the excitation wavelength and the calibration of the system.

The camera used to acquire images is a Nikon DS-5m with a DS-U1 controller. This one receives the acquired signal form the camera through a serial connection and sends it to the PC through an USB slot. Nikon also supplied an software for image acquisition (X-Data).









Up

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