Fast UniFrac is a new version of UniFrac that is specifically designed to handle very large datasets. Like UniFrac, Fast UniFrac provides a suite of tools for the com parison of m icrobial com m unities using phylogenetic inform ation. It takes as input a single phylogenetic tree that contains sequences derived from at least three different environm ental sam ples, a file m apping ids used in the tree to a set of unique sam ple ids (sam e form at as prior version 'environm ent file', and an (optional) category m apping file describing additional relationships between sam ples and subcategories for visualizations. For exam ple, in a given set of gut sam ples, you m ight define subcategories for different diets, different physical locations/dates, different species, and/or different treatm ents like antibiotics or high fat. For sam ple data click here. For citation, click here.
Both the UniFrac distance m etric and the P test can be used to m ake com parisons. Both of these techniques bypass the need to choose operational taxonom ic units (OTUs) based on sequence divergence prior to analysis.
Fast UniFrac allows you to:
Determ ine if the sam ples in the input phylogenetic tree have significantly different m icrobial com m unities.
Cluster sam ples to determ ine whether there are environm ental factors (such as tem perature, pH, or salinity) that group com m unities together.
Determ ine whether system under study was sam pled sufficiently to support cluster nodes.
Easily visualize the differences between sam ples graphically, with support for three dim ensional exploration of datasets and with m ultiple subcategory coloring.
Please enter your em ail and password to continue. After you register you will be able to analyze up to 100000 unique sequences, up to 200sam ples, and perform significance test based on up to 1000 tree perm utations.
If you wish to analyze m uch larger datasets than the defaults, please contact us and we will be happy to try to accom m odate you.
Fast UniFrac tutorial
Introduction
This tutorial takes you through the steps of analyzing data in the Fast UniFrac web application. The purpose of this tutorial is to show you how to use the interface to find the im portant variables for describing phylogenetic variation am ong your sam ples: in this case, to test what types of physical or chem ical factors are m ost im portant for structuring bacterial diversity. The dataset used in this tutorial includes 50 of the 464 sam ples analyzed in Ley, RE, Lozupone, CA, Ham ady, M, Knight, R and JI Gordon. (2008). Worlds within worlds: evolution of the vertebrate gut m icrobiota. Nat. Rev. Microbiol. 6(10): 776-88 (Pubm ed). It includes sequences from 16S ribosom al RNA surveys of diverse freeliving bacterial assem blages and the guts of diverse m am m als and term ites. At the end of this tutorial, you should be fully equipped to test hypotheses about your own sequences.
Also included in this tutorial are other exam ple files you m ay use to explore som e of the other features of Fast UniFrac.
Example data files
To use Fast UniFrac, you need three files: a tree file, a sam ple id m apping file, and a category m apping file. The tree file contains a phylogenetic tree, in Newick form at. The sam ple id m apping file contains a table showing how m any tim es each taxon (from the tree) occurred in each of your sam ples. The category m apping file contains additional m etadata about the sam ples, and is a table relating each sam ple to param eters you have m easured such as tem perature, pH, etc. In general, people usually prepare the two m apping files using Excel, although it is im portant to save them as plain text form at and not as Excel docum ents.
You can either generate your own tree file, or use one of the reference trees. The PhyloChip reference tree m atches the probes on the PhyloChip and is useful for analyzing PhyloChip data; the Greengenes reference tree is from the Greengenes core set and is a phylogenetically diverse and representative set of bacteria. These trees are built using 16S rRNA, although you can use trees built from any m olecule, not just the 16S, or even trees constructed from m orphological or other data.
The sam ple id m apping file m ust be generated m apping the sequence ids in the tree file with the sam ple ids used in your study. In other words, exactly the sam e taxon nam es m ust be used in your tree and in your sam ple id m apping file.
The category m apping file m aps your sam ple ids to additional m etadata, such as subcategories, and sam ple descriptions. This file can be autogenerated but it is highly recom m ended that you generate one that is m eaningful for the variation you plan to exam ine in your studies. For exam ple, if you were studying the effects of diet on the gut com m unities of conventional and hum anized m ice, you m ight want one colum n indicating whether the sam ple was from a conventional or a hum anized m ouse, another colum n indicating whether the m ouse was on a chow diet or a high-fat diet, another colum n containing the com bination of these two colum ns (i.e. diet and hum anized/conventional), etc.
In this section, several exam ple files are listed, not all of which are used in this tutorial.
Greengenes coreset reference datasets
This is the tree and the sequences m atching the Greengenes core set as of May 2009. These files are useful for m apping your sequences against known bacterial diversity.
1. Greengenes coreset tree (May 09)
2. Greengenes coreset fasta (May 09)
NRM data (demo subset)
These data are from the Ley et al. 2008 Nature Reviews Microbiology paper referenced above, and provide an exam ple of m apping heterogeneous reads to the Greengenes core set tree so that the com m unities can be com pared by UniFrac. The sam ple ID m apping file was generated by blasting the dataset from the paper against the Greengenes_coreset_fasta file linked above, and the category m apping file was constructed m anually to provide a range of fine- and coarse-grained representations of the environm ental data.
1. Ley et al exam ple sam ple ID m apping file
2. Ley et al exam ple category m apping file
Example PhyloChip data
Exam ple data from Sagaram et al. 2009 AEM paper (Pubm ed) for use with PhyloChip reference tree.
1. Sagaram et al PhyloChip sam ple ID m apping file
2. Sagaram et al PhyloChip category m apping file
Crump et al data
These sequences are from Crum p et al. 1999 "Phylogenetic analysis of particle-attached and free-living bacterial com m unities in the Colum bia river, its estuary, and the adjacent coastal ocean", AEM 65:3192 (Pubm ed). This dataset was used in the original online UniFrac tutorial (Pubm ed)so are provided again here with two im portant changes. We provide an exam ple category m apping file that contains additional m etadata about each of the sam ples.
1. Crum p et al exam ple tree file
2. Crum p et al exam ple sam ple ID m apping file
3. Crum p et al exam ple category m apping file
Megablast protocol and sample mapping generation script
The application of UniFrac to large sequence sets, such as those generated with pyrosequencing, is also lim ited by the com putational power needed to m ake a de novo phylogenetic tree using standard m ethods, such as neighbor joining, likelihood, or parsim ony m ethods. In order to prepare phylogenetic trees for input into UniFrac from very large datasets, we recom m end using QIIME. The best source for inform ation about QIIME are the website and the QIIME paper, which you can get at the following links:
1. Source code
2. QIIME allows analysis of high-throughput com m unity sequencing data
The quickest way to get started with QIIME is using the virtual m achine.
One potential workflow for working with ?arge datasets is to use QIIME to:
1. Preprocess sequences to handle low quality reads
2. Select OTUs
3. Generate a phylogenetic tree , and then use the QIIME script convert_otu_table_to_unifrac_sam ple_m apping.py, to generate the proper input files for
the Fast UniFrac web interface.
In the initial release of Fast UniFrac, we also described the following procedure for generating a phylogenetic tree, which is based on m apping sequences to their closest relative in a reference tree using BLAST. This functionality is now in QIIME, and we recom m end using QIIME for this step, but retain this docum entation below for those who m ay still be interested in using it
The BLAST to greengenes protocol
We illustrate that the analysis of such large sequence sets can be carried out by assigning them to their closest relative in a phylogeny of the Greengenes core set (DeSantis et al., 2006) using BLAST’s m egablast protocol (Altschul et al., 1990). Below is a detailed protocol for carrying out this analysis. Note that
a different BLAST database can be substituted for use with any reference tree.
1. Create the Greengenes BLAST database:
This link is a fasta file containing the sequences from the greengenes coreset. This fasta record can be form atted into a BLAST database using the com m and:
f o r m a t d b-i G r e e n G e n e s C o r e-M a y09.r e f.f n a-p F-o F-n
g g_c o r e s e t
2. Perform the megablast search:
A fasta record of your sam ples can be BLASTed against the gg_coreset BLAST database created in step 1 using the following com m and:
b l a s t a l l-p b l a s t n-n T-d g g_
c o r e s e t-i-e1e-30-b5-m9-o b l a s t_o u t p u t.t x t
Note that the -m 9 flag is essential because it specifies the hit table output form at that the script below requires.
Also note that the sequence nam es m ust conform to the following form at:
s a m p l e N a m e D e l i m i t e r s e q u e n c e I d
For instance, if you sequenced 2 clones from each of two sam ples nam ed SA and SB, valid sequence nam es m ight be:
S A#01
S A#02
S B#01
S B#02
If you have not nam es the sequences according to this convention, it is possible to also use a m apping file describing which sequence is from which sam ple. See docum entation within the code for m ore details on this.
3. Use this python script and the BLAST output from step 2 to create an environment file that can be used with UniFrac:
Note that the PyCogent toolkit m ust be downloaded from SourceForge and the cogent directory should be on your PYTHONPATH.
You can then use the code as follows:
p y t h o n c r e a t e_u n i f r a c_e n v_f i l e_B L A S T.p y
blast_output.txt: Path to the hit tables from the BLAST searches
outfile_path.txt: Path to where the environm ent file will be saved
sam ple_nam e_delim iter: A delim iter (e.g. a #) that separates the sam ple nam e from the sequence id.
Steps
1. Create a phylogenetic tree containing sequences from samples that you would like to compare, or select a reference tree.
The tree should be rooted, and m ust have branch lengths to use Fast UniFrac. Typically, the tree is rooted by including an outgroup, e.g. an archaeal sequence to root the bacteria, but we som etim es use m idpoint rooting as well. If an unrooted tree is supplied, UniFrac will assign a root arbitrarily. If you have extra sequences in the tree that are not annotated by sam ple, they will autom atically be rem oved from the tree when you upload the file, so the outgroup will not be included in the analysis. If no sequences appear in the tree after upload, the m ost likely problem is that there was an issue with your sam ple ID m apping file (for exam ple, you m ight have used GenBank identifiers in the tree, but NCBI GIs in the sam ple ID m apping file, which wouldn't m atch each other).
There are m any different program s that you can use for sequence alignm ent and/or the phylogeny include the NAST alignm ent tool, PyNAST, FastTree, ARB, ClustalW, MUSCLE, PHYLIP, PAUP, or MrBayes. For 16S rRNA sequences, we prefer PyNAST for alignm ent. For generating trees from large dataset, we prefer FastTree for de novo tree generation trees or m apping sequences to their closest relative in a reference tree. These preferred options as well as several others can be run using QIIME. For large datasets, it is greatly preferred to select OTUs prior to the alignm ent and tree building step. This cuts down on the com putation tim e and does not have an effect on the results. Because UniFrac depends on branch lengths, it is im portant to look at your tree to ensure that you don't see long branches that result from m isalignm ent rather than from long periods of evolution. At the end of this process, you can export the tree in Newick form at for upload into the UniFrac interface.
Alternatively, you can choose one of the reference trees provided and m ap your sequences to this tree. This can be useful, particularly for large datasets, such as those produced by 454 pyrosequencing, since creating a single phylogenetic tree with all sequences m ay not be feasible with the program s listed above. One sim ple way to m ap your sequences onto their closest relatives in a reference tree is use m egablast. In this tutorial, the original sequences from the NRM paper were assigned to their closest hit in the 11-Aug_2007 version of the greengenes coreset (can be downloaded from https://www.doczj.com/doc/559166253.html,/Download/Sequence_Data/Fasta_data_files/). Sequences with no hit or that m atch with an e-value greater than e-50 were dropped from this exam ple dataset.
For the purpose of this tutorial, we provide the greengenes coreset tree in Newick form at that we exported from an arb database that is available for download at https://www.doczj.com/doc/559166253.html,/Download/Sequence_Data/Arb_databases/greengenes236469.arb.gz. A sm all num ber of sequences were added to this tree using parsim ony insertion in arb so that the fasta data files and tree for the core set were in sync. The resulting tree (Greengenes coreset tree (May 09)) and corresponding sequences (Greengenes coreset fasta (May 09)) can downloaded, but please note that this tree can be im ported to your history and does not need to be re-uploaded. In order to im port the GreenGenes reference tree to your history follow these steps:
1. In the upper m enu, go to Shared Data - Data Libraries:
2. Then, select 'GreenGenes coreset tree (May 09):
3. Click on the checkbox next to 'GreenGenesCore-May09.ref.tre' and, finally, on the 'Go' button:
4. The reference tree is now in your history and you can use it.
2. Create a sample ID mapping file.
This file m aps each sequence ID in the tree to the sam ple ID that it cam e from. This m ust be done m anually (or via a script): for each sequence, type the sequence ID used in the tree, then a tab, then the sam ple ID that it com es from, then optionally, another tab and then the num ber of tim es each sequence was observed (sequence abundance).
The sequence abundance colum n is im portant if you have dereplicated the sequence data in any way (e.g. choosing OTUs and only including a representative sequence in the tree, rem oving exact duplicate sequences, or pre-screening clones using RFLP patterns prior to sequencing), and you are planning on using tools in the interface that consider differences in relative abundance (e.g. weighted UniFrac). It is fine to use a tree and sam ple ID m apping file with all of the sequences (e.g. 5 duplicate sequences in the tree each with a weight of 1 rather than 1 representative sequence with a weight of 5) and to perform abundance-based analyses, although dereplicating the data will allow you to process larger datasets.
For PCoA analysis, it is m ost convenient to nam e each environm ent so that sam ples of the sam e type have nam es that start with the sam e first 1, 3, or 5 letters or that have sam ple types followed by a period, hash, or plus character (this allows you to apply colors in the PCoA scatterplots later).
In this exam ple, there are 50 bacterial sam ples from the following sam ple types: Surface and subsurface saline water (Sws, and Swb respectively), Nonsaline water (Nw), Saline sedim ents (Sse), Nonsaline sedim ents (Nsa), Soils (Nso), the Vertebrate gut (Vg) and the Term ite gut (Tg). We'll label each sam ple with its 2-3 letter sam ple code, followed by a hash, and a unique num ber because our hypothesis is that the organism s from the sam e overall environm ent should be m ore sim ilar to one another.
The following is a short snippet of a sam ple ID m apping file. The first colum n is the sequence ID, the second colum n is sam ple ID, and the last colum n is the num ber of tim es the sequence was observed.
150394T g#12491
150394T g#12512
215260N s o#651
215260N s o#1294
16073V g#h#111
...
For the purpose of this tutorial, we provide a sam ple ID m apping file called fastunifrac_Ley_et_al_NRM_2_sam ple_id_m ap.txt.zip sam ple ID m apping file.
3.Create a category mapping file.
The category m apping file relates sam ple nam es in the sam ple ID m apping file to their related m eta data (defined via subcategory colum ns) and descriptions of where the sam ples cam e from. The descriptions can be accessed throughout the results interface in order to m ake them easier to interpret. The subcategory colum ns allow for dynam ic coloring of PCoA results in the 3d viewer to determ ine which categories are related to which principal coordinate axes.
For the purpose of this tutorial, we provide a category m apping file called Ley et al exam ple category m apping file with 4 subcategory colum ns that define for each sam ple (1) which sam ple type it is from(EnvType), (2) whether the sam ple cam e from a freeliving bacterial assem blage or from the gut (FreelivingGut), (3) whether the freeliving com m unities were saline or nonsaline (SalineNon), and whether they were from aquatic (Water) or "Particulate" sam ples such as soils and sedim ents (WaterPartic). There is also a short description of each sam ple in the final colum n.
The file form at is tab-delim ited text. The first line is a header line that m ust start with a "#" character.
Optionally, a general description of the input files can be included in the lines im m ediately following the header line that start with a "#". This description will be included in the upload and results screens so that relevant inform ation can be easily accessed.
The first colum n m ust be nam ed Sam pleID, m ust contain unique (short, m eaningful) sam ple IDs containing only alphanum eric characters. (With the exception of ".", "+", and "#" characters.)
The second colum n to "n-1 th" colum n are subcategories. These can be anything (random assignm ent if you want) but each subcategory should a sm all num ber of distinct values <= num ber of sam ples. There m ust be at least two unique values for each category.
The last colum n m ust be nam ed "Description" and contains the short descriptions for the sam ples.
#S a m p l e I D E n v T y p e F r e e l i v i n g G u t...D e s c r i p t i o n
#G e n e r a l d e s c r i p t i o n o f a n a l y s i s l i n e1(o p t i o n a l)
#G e n e r a l d e s c r i p t i o n o f a n a l y s i s l i n e2(o p t i o n a l)
#...
T g#1249T e r m i t e G u t G u t...W h o l e g u t o f t h e w o o d-f e e d i n g t e r m i t e
T g#1251T e r m i t e G u t G u t...W h o l e g u t o f t h e f u n g u s-g r o w i n g t e r m i t e M a c r o t e r m e s g i l v u s
N s o#65S o i l F r e e l i v i n g...U n c u l t i v a t e d a g r i c u l t u r a l s o i l i n W i s c o n s i n
N s o#1209S o i l F r e e l i v i n g...S o i l f r o m a f e r t i l i z e d S w i t z e r l a n d p l o t i n t h e D O K.
V g#h#111V e r t e b r a t e G u t G u t...F e c e s f r o m A n g o l a n C o l o b u s M o n k e y f r o m t h e S t L o u i s Z o o.
...
For the purpose of this tutorial, we provide a category m apping file called fastunifrac_Ley_et_al_NRM_3_category_m ap.txt.zip sam ple ID m apping file.
4. Go to the Fast UniFrac web site.
If you're reading this tutorial, you already know how to get here. You will need to register and log in to com plete the tutorial, because we restrict the num ber of sequences that unregistered users can analyze. The reason for this is that m any of the analyses are com putationally expensive, so we need to keep track of which groups are using a lot of resources to ensure fair access for everyone. Please note that if you have previously registered for the original UniFrac interface, you will have to contact m icrobiom ehelp@https://www.doczj.com/doc/559166253.html, to register for FastUniFrac. We apologize for this inconvenience.
5. The Fast UniFrac upload screen
After you have logged in, you have to upload your sam ple ID m apping file and your category m apping file. To get to the upload page, click 'Get data' on the Tools panel and then 'Upload file':
Then, the upload page will appear:
First, upload your sam ple ID m apping file. Click 'Browse' below where it says File, and navigate to your sam ple ID m apping file (in this case, fastunifrac_Ley_et_al_NRM_2_sample_id_map.txt). One com m on problem is that you m ight have your sam ple ID m apping file saved as a Word docum ent: this will NOT work, because Word uses a proprietary file form at that is difficult for other program s to read. If you are saving your sam ple ID m apping file from Word, rem em ber to save it as Plain Text, NOT as Microsoft Word. If you are using Excel, save as Tab-delim ited Text. At the end of this
process, your screen should look like this:
state - blue color) will appear in the history panel:
While the sam ple ID m apping is uploading, you can start with the category m apping file upload. In order to upload your category m apping file follow the above steps, but now navigate to your category m apping file (in this case, fastunifrac_Ley_et_al_NRM_3_category_map.txt). This file is m ost easily created in Excel, rem em ber to save as Tab-delim ited text.
If you have your own tree file, you can upload it following these sam e steps. In this tutorial, we will use the 'GreenGenes Core - May 2009' tree, which is already on the system.
Once all the files are uploaded (the datasets in the history panel are in green color) you can start any of the available analysis in Fast UniFrac.
6. Measuring the overall difference between each pair of samples.
In order to generate the raw distances between each pair of sam ples using the UniFrac m etric, first choose the Sample Distance Matrix option from the Tool panel, under the 'Fast UniFrac' section.
On the Sam ple Distance Matrix page you can select the reference tree, sam ple ID m apping file and the category m apping file you want to use to perform the analysis. First, select the 'GreenGenes Core - May 2009' tree using the drop-down m enu below 'Select reference tree'. Next, select the '1: fastunifrac_Ley_et_al_NRM_2_sam ple_id_m ap.txt' file and the '2: fastunifrac_Ley_et_al_NRM_3_category_m ap.txt' file using the drop-down m enus below 'Select sam ple ID m apping file' and 'Select category m apping file', respectively. If you then click the 'Execute' button, you will get a m essage saying that your job has been subm itted to the queue, and two new datasets will appear in the History panel. When the datasets are green (tim e depending on server load) you can view them clicking on the eye icon. The first dataset will display a screen like the following. containing the distance m atrix that relates each pair of environm ents:
Moving your m ouse over the m atrix cells will display the raw score for that pair of sam ples and the sam ple description, to help you to m ore easily interpret the data.
At the bottom of the m atrix, you will see a color key and a link to download the im age.
This distance m atrix is colored by quartile: the sm allest distances (m ost sim ilar pairs) are colored blue, and the largest distances (m ost different pairs) are colored gray. In this exam ple, all of the distances are fairly sim ilar (num erical values ranging from0.68 to 0.86), indicating that any given pair of environm ents shares less than one third, and as little as 15%, of the total phylogenetic diversity contained by both together. However, the distance m atrix by itself is often difficult to interpret: the pattern of sim ilarities is not very clear.
The second dataset that appears in the History panel is a tab-delim ited file with the raw distance m atrix. You can download it by clicking on the disk icon.
7. Clustering the samples.
It can be useful to see how the environm ents cluster together since there are often patterns in the clustering that could not have been determ ined from the pattern of significant differences alone.
Select the Cluster Samples option from the Tool panel, choose the reference tree, sam ple ID m apping and category m apping files following the steps
described above and then click the 'Execute' button.
As in the Distance Matrix case, this analysis also generates two new datasets in the History panel. The result is a tree relating the different environm ental sam ples. The first dataset is a graphic representation of the tree and the second one is the tree in Newick form at, so you can use it in other program s such as TreeView.
In the graphic representation of the tree, if you m ove your m ouse over each sam ple ID, it will display the sam ple description, helping you m ore easily interpret the clustering patterns of your sam ples.
In this case it is easy to see that the different sam ple types cluster together: for exam ple, the term ite gut sequences (Tg) form a cluster at the upper part of the plot, below them all the vertebrate gut sequences (Vg) are a clum p, etc.
This figure shows the power of UniFrac: although m any sam ples are not significantly different from one another when corrected for m ultiple com parisons, biologically interesting patterns still com e out of the analysis.
However, to be confident that the results are correct, it is necessary to perform the Jackknife Sample Clusters analysis, which will sam ple a sm aller num ber of sequences from each environm ent and tell you whether the clusters are well-supported.
When we perform this analysis with default param eters, the nodes are colored like this:
This m eans that if 37 sequences are chosen from each sam ple (the m axim um am ount that can be resam pled without dropping the sm allest sam ple from the analysis), the best nodes are recovered between 79% and 96% of the tim e (colored green and yellow respectively), which m eans that the support for them is relatively strong.
If instead we increase the num ber of sequences required per sam ple (by changing the Minim um sequences to keep on the Jackknife Sam ple Clusters page), som e of the sam ples will be discarded, and the support for the rem aining clusters m ay change.
The m ost reasonable value to use for Minim um sequences to keep is about 75% of the sm allest sam ple you want to include in the analysis, but in practice people usually just use the sm allest sam ple (despite the fact that there is then no resam pling perform ed on that sam ple).
8. Perform Principal Coordinates Analysis (PCoA).
The cluster diagram s are useful for showing which environm ents are m ost closely related to one another, but it is also im portant to see if the environm ents are distributed along any axes of variation that can be interpreted easily (e.g. a pH or tem perature gradient).
Select the PCoA option from the tool panel, choose the reference tree, sam ple ID m apping and category m apping files following the steps described above
and then click the 'Execute' button.
form ats of the PCoA analysis:
The first two links show the 2D PCoA plots (but using a different color palette), the following two link shows the 3D PCoA plots using the KiNG applet and the last three links allows you to download the raw data.
The 2D PCoA plots looks as follows:
Each point is a sam ple, you can m ove your m ouse over each point to get the full nam e and description of the sam ple.
In each set of three plots, the points are colored by one category of the category m apping file, which is shown as the title. Hence, it is easy to see how the different sam ples cluster following a given category.
In the last row you can see the scree plot (fraction of variance explained by each axis in red, cum ulative variance in blue). This plot can tell you how m any axes are likely to be im portant and help determ ine how m any "real" underlying gradients there m ight be in your data as well as their relative "strength".
The 3D PCoA plots are opened through the KiNG applet, and looks som ething like this:
You'll notice several things. On the left is the m ain window that plots three selected axes (by default it plots the 1st, 2nd, and 3rd PC axes). You can change which axes are displayed by selecting the Choose view axes... option under the Views m enu.
On the right you'll see two panels. The top panel changes how the sam ples are colored and how the axes are scaled. By default, the sam ple displayed here are colored by environm ent type, defined in the category m apping file as EnvType. Also by default, the axes are unscaled (this com bination of coloring and scaling is indicated by the highlighted EnvType: UniFrac Unscaled label in the upper right panel. When the axes are scaled, the points along that axis are m ultiplied by the fraction of variance explained by that axis. So in this exam ple, you'll see that PC 1 explains about 14% of the variance along this axis, while PC 2 explains about 8%. Thus if we scale the axes by clicking the EnvType: UniFrac Scaled option in the upper right, PC 1 would appear roughly
twice as long as PC 2, as shown below.
If you want to color the sam ples differently, for exam ple by whether the sam ples are from the gut or free living, we can choose the FreelivingGut: UniFrac Unscaled option to change the view to this:
In the lower right panel, you'll see a num ber of item s with check boxes next to them that change when you change views in the upper right. Change back to the original view by clicking the EntType: UniFrac Unscaled option in the upper right hand box. The lower right hand panel should now have four groups of check boxes: (1) Multidim ensional Axes which should be off by default, (2) Prim ary (PC1-3 Offset Axes) which control the display of the lines and text of the first three axes (offset from the origin), (3) EnvType (49 pts) which controls the 49 sam ple points, and (4) EnvType Labels (49 pts) which controls the 49 sam ple labels. Click the group check box toggles all group m em bers (the item s listed below each) on or off.
Let's start by turning off all of the sam ple labels. After you click the check box next to the EnvType Labels (49 pts) group, you should see som ething like this:
Notice that the labels are rem oved from the m ain display on the left and the EnvType Labels (49 pts) group collapses, hiding the group m em bers.
Sim ilarly we can control group m em bers in the sam e way. If we wanted to view just the sam ples that belong to the Soil and VertebrateGut subgroups that we defined in the category m apping file, we can uncheck the boxes under the EnvType (49 pts) group that correspond with the other subgroups we want
to hide. Once you've done that, you should see som ething like this:
the left panel. Click and drag som ewhere near the m iddle of the cloud of points dragging to the left and down. You should now see som ething like this:
Here you can now see the separation of the sam ples along the third axis (PC 3) as well as PC 1 and PC 2. Next let's try viewing a different set of axes, specifically PC 1, PC 3, and PC 4. First select the Choose viewing axes... option from the Views m enu at top left of the screen. You should see dialog box -
choose PC 3 and PC 4 for the Y and Z axes then click the Set axes button.
In the m ain screen, you'll see that the prim ary axes are no longer relevant since we are not plotting PC 1, PC 3, and PC 4. Turn them off by clicking the
Prim ary (PC 1-3) Offset Axes group off in the lower right panel. Then click the check box next to Multidim ensional Axes group. You should then see a list of
axes (PC 1 through PC 10) appear. Click the check boxes next to the PC 1 Line, PC 3 Line, and the PC 4 Line subgroups. You should see orthogonal axes
appear in the m ain screen on the left, looking som ething like this:
Now you can see the clustering of the sam ples along this set of PC axes. Finally, if you want to get a quick look at the general patterns of the sam ples along the first 10 axes, you can sim ply type the forward slash character "/", or select the Parallel coordinates option under the Views m enu, which will
change the view to som ething like this:
Notice that the Term iteGut (orange) and VertebrateGut (blue) separate along PC 4 (the group of orange lines at the top of PC 4). If we type the forward slash again, "/", it will toggle the m ain view back to our the three dim ensional view. If we rotate around PC 4, we can see this separation in the view
below.
There are m any other useful features in the KiNG viewer that you'll have to discover on your own. Please refer to the Help m enu for the User m anual and additional inform ation about the viewer.
Congratulations!
After working through this tutorial, you are now ready to use Fast UniFrac for your own analyses and discover the rest of the features not described in this tutorial. There are additional exam ple files in the Exam ple Data Files section above that you m ay use to explore som e of the other features of Fast UniFrac. Have fun!
About
Principal investigator:
Rob Knight - rob@https://www.doczj.com/doc/559166253.html,
University of Colorado at Boulder
596 UCB
Boulder, CO 80309-0596
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Rob Knight - rob@https://www.doczj.com/doc/559166253.html,
Catherine Lozupone - catherine.lozupone@https://www.doczj.com/doc/559166253.html,
Jose A. Navas Molina - Jose.NavasMolina@https://www.doczj.com/doc/559166253.html,
We welcom e your feedback/sponsorship. Please send your com m ents/suggestions to Microbiom eHelp@https://www.doczj.com/doc/559166253.html,.
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Download:
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Other software:
UniFrac provides a suite of tools for the com parison of m icrobial com m unities using phylogenetic inform ation.
DivergentSet uses a phyogenetic tree to guide the construction of divergent sets of sequences, and, even on a single CPU, is up to two orders of m agnitude faster than the naive m ethod of using a full distance m atrix. The DivergentSet software m akes it easier to perform a wide range of bioinform atics analyses, including finding significant m otifs and covariations.
MotifCluster allows you to analyze m otifs in a set of protein sequences, relating them to the alignm ent, phylogeny, and (where available) the 3D structures. It is useful for detecting rem ote hom ologies between protein fam ilies, for understanding which proteins are m ost likely to share functions, and for identifying
residue changes that m ight be im portant for the evolution of new enzym e activities.
能谱仪技术指标 1、技术指标： 1)*可靠性：可以配合各主流品牌的场发射扫描电镜使用，且在北京的地质行业有配合先 例，提供用户名单和联系方式； 2)探测器：硅漂移晶体，超薄窗口，完全独立真空；晶体有效面积不小于60 mm2，探头 整体有效采集面积不小于50mm2；适合低电压或小束流分析； 3)*探测器制冷和定位：采用三级帕尔贴制冷，最低工作温度可达零下80摄氏度；探头采 用马达控制的自动伸缩设计，可以在软件里实现控制，确保针对不同尺寸样品的定位精度； 4)元素分析范围Be4—U92； 5)免维护性：探头不包含冗余的前置放大电路板，随时可以断电，无需重新校正； 6)分辨率MnKa优于127eV，CKa优于56eV，F Ka优于64eV（20000CPS）；在不同计数 率下谱峰稳定，分辨率衰减小于1eV； 7)输出最大计数率：大于500,000CPS谱峰无畸变，可处理最大计数率优于750,000CP S； 8)软件：64位能谱应用软件，操作简便界面清楚，直接读出电镜参数和仪器状态，结果 输出方便，适合于不同层次的用户尽快掌握； 9)谱定性分析：具备点、线、面扫描分析功能，高帽法扣除背景避免人为误差； 10)*谱定量分析:可对抛光表面或粗糙表面进行点、线和面的分析；具有虚拟标样法（间接 标样法）以及有标样法（直接标样法）；可以方便的得到归一化和非归一化定量结果； 11)*谱峰稳定性：具备零峰设计，相对峰位稳定，无需铝铜双峰校准，保证数据重现性； 12)图像输出：支持BMP,TIFF, JPEG等流行的图像格式，对视场上任选区域进行能谱分析 和线、面扫描，可得到元素的线分布、常规面分布、快速面分布和定量面分布等，所支持电镜数字图像最大清晰度优于8192*8192，全息X射线成分图最大清晰度（live Spectrum Mapping）优于4096*4096. 13)*高级应用软件：针对地质领域，可以提供多视场自动叠加的数据拼接功能，实现大范 围面扫描和特征元素富集区域的自动分析； 14)图形处理器配置不低于：知名品牌，Intel Core i7-2600 处理器，8G以上内存，1TB硬 盘，DVD/RW 刻录光驱，24”平板液晶显示器，专用实验台等； 2、培训 要求卖方在用户现场进行技术培训，一年以后免费提供深入的技术培训课程，终生提供免费的应用咨询以及技术帮助 3、售后服务 3.1 安装：要求卖方到用户现场进行免费安装、调试、试运行。 3.2保修期1年 *3.3 国内有生产厂家独资建立的全套技术中心和演示实验室，探头返修或其它部件更换所无需返回原厂，节省时间和费用； *5.4 国内地质矿物行业近三年内有5台以上相同配置的销售业绩，需提供用户名单和联系方式
S-4800扫描电镜附件Horiba X射线能谱仪操作规程 一、启动能谱计算机及EMAX软件 确认EMAX液氮灌中有充足的液氮；打开地上接线板上的红色开关；打开能谱计算机；打开EMAX软件；预热三十分钟后才能开始能谱操作。 二、启动S-4800及PC-SEM软件 同S-4800扫描电镜操作。 三、加载样品 同S-4800扫描电镜操作。 四、插入能谱探头 慢慢摇入能谱探头，用力不要过大。 五、S-4800参数设定 （1）加速电压：一般设为元素激发能量的2－3倍，常用范围15－20kv，原子序数越大电压越高； （2）工作距离：WD=15mm； （3）Probe current：High； （4）Focus mode：HR； （5）聚光镜C1电流：选大一些，数字越小，电流越大。 六、调整、观察样品 同S-4800扫描电镜操作：调节电子光学系统（合轴，消像散），观察样品，记录图像。 七、EMAX软件中参数设定 电镜控制：<菜单>—<选项>—<电镜控制>：放大倍数、加速电压、工作距离都要与S-4800的设定一致。当SEM的参数变化时，要随时调整电镜控制。 八、能谱操作 （1）选择所需要的功能： Analyzer——对SEM所扫描的整幅图像进行定性和定量分析； Point & ID——对SEM图像中指定的感兴趣区域进行定性和定量分析； Mapping——元素分布图。 （2）Analyzer分析操作： (a)项目：输入项目名称，也可以输入注释及文件检索时所需的关键字。 (b)样品：输入样品名称及相关信息，表面是否经过喷镀等，如表面喷镀物质，需选则喷镀元素名称并设定镀层厚度，程序在定量分析时扣除该元素。 (c)电镜设置：调节电镜电子束的电流，选择处理时间（5或6），来调节死时间（20-30%），以相对较好的条件进行分析。 (d)采集谱图。 (e)定性分析：确定元素。 (f)定量分析设定：设定分析条件。
数据处理与能谱分析 随着计算机技术的发展，利用计算机处理实验数据也越来越常见，随之如来就产生了许多的软件如：Matlab 、Excel、CAD等等，但一般这些软件在处理物质放射性衰变时都比较繁琐，因此在处理放射性物质衰变时的数据时，就必须自己依照其规律制作数据处理软件来探究该物质的各种性质，从而来确定该物质的类型以及其运用。 我们要使用C++平台和C语言来制作软件及编写对应的响应函数，在进行实验时我们把我们利用专业仪器测得的数据按照要求记录并保存在一个TXT文件中，使用C语言来编写程序对文件里的数据进行读写和操作；例如：调用文件打开函数(fopen)和关闭函数(fclose)语句来对文件进行打开和关闭处理，调用读写语句对文件内的数据进行察看和编写fscanf(fp,”%d %d”,&I,&t)、fprintf(fp,”%d %d”,I,t)。 制作EXE软件来对数据进行图谱显示： 在相应的操作界面放置相应的显示框图和对应的按钮，如下图
然后给框图和按钮赋地址，并添加相应的变量和相应函数。最后组建编译运行。软件运行后先点击数据读写按钮在点击原始图谱按钮最后点击五点平滑按钮就得到了该物质数据图谱。如下图所示： 注：横坐标为道址、纵坐标为计数率 添加相应的数据输出框和按钮如：峰值、道值、面积。最后再添加相应的响应函数，再编译运行。得到如下图所示的图谱：
至此我们利用C语言对数据处理和能谱分析已经结束，最后我们在根据图谱的形状、峰值、道值以及各个部分的面积来确定物质衰变的 性质和物质本身的性质，最终来确定放射性物质的用途。
附件：部分响应函数 数据读写程序： void CShiyanDlg::OnReadfile() { // TODO: Add your control notification handler code here FILE *fp; int datanum=0; int i; int data1,data2; if((fp=fopen("090623.txt","r"))==NULL) { printf("Cannot open the file.\n"); exit(0); } while(!feof(fp)) { fscanf(fp,"%d %d",&data1,&data2); data[datanum++]=data2; fscanf(fp,"\n"); } for(i=0;i<2048;i++) { if( i<2||i>2045) data_ph[i]=data[i]; else data_ph[i]=(data[i-2]+4*data[i-1]+6*data[i]+4*data[i+1]+data[i+2])*1.0/16.0; printf("%d %f\n",i+1,data_ph[i]); } fclose(fp); if((fp=fopen("out.txt","w"))==NULL) { printf("file open error.\n"); exit(0); } for(i=0;i<2048;i++) { fprintf(fp,"%d %f\n",i+1,data_ph[i]); } fclose(fp); } 原始图谱响应函数： void CShiyanDlg::OnYuantu() { // TODO: Add your control notification handler code here double xViewport,yViewport;
自动寻峰由于谱结构的复杂和统计涨落的影响，从谱中正确地找到全部存在的峰是比较困难的。 尤其是找到位于很高本底上的弱峰，分辨出相互靠得很近的重峰更为困难。谱分析对寻峰方法的基本要求如下： (1) 比较高的重峰分辨能力。能确定相互距离很近的峰的峰位。 (2) 能识别弱峰，特别是位于高本底上的弱峰。 (3) 假峰出现的几率要小。 (4) 不仅能计算出峰位的整数道址，还能计算出峰位的精确值，某些情况下要求峰位的误差小于0.2 道。 很多作者对寻峰方法进行了研究，提出了很多有效的寻峰方法。 目的： 判断有没有峰存在 确定峰位(高斯分布的数学期望) ，以便把峰位对应的道址，转换成能量确定峰边界——为计算峰面积服务(峰边界道的确定，直接影响峰面积的计算) 分为两个步骤：谱变换和峰判定 要求：支持手动/自动寻峰，参数输入，同时计算并显示峰半高宽、精确峰位、峰宽等信息，能够区分康普顿边沿和假峰 感兴区内寻峰 人工设置感兴趣大小，然后在感兴区内采用简单方法寻峰重点研究：对感兴区内的弱峰寻峰、重峰的分解对于一个单峰区，当峰形在峰位两侧比较对称时，可以由峰的FWHM 计算峰区的左、 右边界道址。峰区的宽度取为3FWHM ，FWHM 的值可以根据峰位m p 由测量系统的FWHM 刻度公式计算。由于峰形对称，左、右边界道和峰位的距离都是 1.5FWHNM mi L INT(m p 1.5FWHM 0.5) m R INT(m p1.5FWHM 0.5)
式中m p是峰位，INT的含义是取整数。 对于存在有低能尾部的峰，其峰形函数描述(参见图)。 y m HEXP[ (m m p)2/2 2] , m> mp_ j y m HEXP[J(2m 2m p J)/2 2] , m< mp_ J 式中H为峰高，mp为峰位，是高斯函数的标准偏差，J为接点的道址和峰位之间的距离。在峰位的左侧，有一个接点，其道址为mp-J。在接点的右侧，峰函数是高斯函数。在接点的左侧，峰函数用指数曲线来描述。这时峰区的左、右边界道址为 m L INT(m p1.12FWHM 2/ J 0.5J 0.5) m R INT(m p 1.5FWHM 0.5) 全谱自动寻峰 基于核素库法：能量刻度完成后，根据核素库中的能量计算对应的道址，在各个道址附近(左右10道附近)采用简单的寻峰方法(导数法) 方法： 根据仪器选择开发 IF函数法/简单比较法(适于寻找强单峰，速度快)
自动寻峰 由于谱结构的复杂和统计涨落的影响，从谱中正确地找到全部存在的峰是比较困难的。尤其是找到位于很高本底上的弱峰，分辨出相互靠得很近的重峰更为困难。 谱分析对寻峰方法的基本要求如下： (1) 比较高的重峰分辨能力。能确定相互距离很近的峰的峰位。 (2) 能识别弱峰，特别是位于高本底上的弱峰。 (3) 假峰出现的几率要小。 (4) 不仅能计算出峰位的整数道址，还能计算出峰位的精确值，某些情况下要求峰位的误差小于0.2道。 很多作者对寻峰方法进行了研究，提出了很多有效的寻峰方法。 目的： 判断有没有峰存在 确定峰位（高斯分布的数学期望），以便把峰位对应的道址，转换成能量 确定峰边界——为计算峰面积服务（峰边界道的确定，直接影响峰面积的计算） 分为两个步骤：谱变换和峰判定 要求：支持手动/自动寻峰，参数输入，同时计算并显示峰半高宽、精确峰位、峰宽等信息，能够区分康普顿边沿和假峰 感兴区内寻峰 人工设置感兴趣大小，然后在感兴区内采用简单方法寻峰 重点研究：对感兴区内的弱峰寻峰、重峰的分解 对于一个单峰区，当峰形在峰位两侧比较对称时，可以由峰的FWHM计算峰区的左、右边界道址。峰区的宽度取为3FWHM，FWHM的值可以根据峰位m p由测量系统的FWHM刻度公式
计算。由于峰形对称，左、右边界道和峰位的距离都是1.5FWHNM 。 )5.0FWHM 5.1(INT p L +-=m m ) 5.0FWHM 5.1(INT p R ++=m m 式中m p 是峰位，INT 的含义是取整数。 对于存在有低能尾部的峰，其峰形函数描述（参见图）。 ] 2/)([H 22p m σ--=m m EXP y ，m ≥mp －J ] 2/)22([HEXP 2p m σ+-=J m m J y ，m ≤mp －J 式中H 为峰高，mp 为峰位，σ是高斯函数的标准偏差，J 为接点的道址和峰位之间的距离。在峰位的左侧，有一个接点，其道址为mp －J 。在接点的右侧，峰函数是高斯函数。在接点的左侧，峰函数用指数曲线来描述。这时峰区的左、右边界道址为 ) 5.05.0/FWHM 12.1(INT 2p L +--=J J m m ) 5.0FWHM 5.1(INT p R ++=m m 带有低能尾部的峰函数的图形 全谱自动寻峰 基于核素库法：能量刻度完成后，根据核素库中的能量计算对应的道址，在各个道址附近（左右10道附近）采用简单的寻峰方法（导数法） 方法： 根据仪器选择开发 IF 函数法/简单比较法(适于寻找强单峰，速度快)
牛津仪器Inca X-act能谱仪详细配置及功能 1.专利的分析型SDD硅漂移探测器 ?SuperATW窗口，10mm2有效面积； ?在MnKα处的分辨率: 优于127eV ?稳定性: 1,000cps—100,000cps 谱峰漂移<1eV，分辨率变化<1eV ? 48小时内谱峰漂移<1eV (Mn Ka) ?峰背比20,000: 1 (Fe 55, Mn Ka) ?分析元素范围：Be4-Pu94 2.INCA 系统 --系统计算机 ?HP DC8000 --系统桌 显微分析处理器(分立式设计) --Inca X-strea mⅡ显微分析处理器 ?探测器高压偏压电源。 ?6个程序可选时间常数和4个能量范围(10, 20, 40, 80KeV)的数字信号处理器 ?计算机控制的数字脉冲处理器，输出最大计数率350,000CPS, 可处理最大计数率850,000CPS, ?活时间校正。三个鉴别器覆盖全范围的反脉冲堆积，直至下限铍。 ?数字零点稳定器。 ?探测器控制系统。 --Inca Mics显微分析处理器 ?带有存储器和辅助电路的高速微控制器，用以收集和处理X射线信号。 ?IEEE1394 数据接口，用以高速传输数据到系统计算机。 ?二个RS232串口或一个RS232串口和一个LASERBUS口。 ?线性电源 ?符合美国和欧洲电磁规定，并执行CE标记。 ?SUPERSCAN – 先进的超级数字扫描系统。 ?包括Kalman噪声限制程序，在快速扫描和限制图像噪声之间兼顾和控制。 ?同步图像收集和数据传输到PC(零等待)。 ?电镜图像接口电缆。 INCA软件导航器 ?真正的32位软件 ?独一无二的导航器界面, 非常友好，全中文操作界面, 引导用户从启动分析项目到打印实验报告的全部显微分析过程。 ?用户可容易地在导航器之间切换，直接面对工作流程和IMS，以便直接看到自动分析过程的进展。
能谱分析EDX 1.开始菜单----
数字化多道伽马能谱仪 技术要求 一、设备名称：数字化多道伽马能谱仪，数量：1套 二、交货期：合同生效后1个月内 交货地点：北京1套 三、主要用途： 应用领域：放射性矿产勘查、地质找矿、工程地质及水文地质研究、评估、放射性地质调查；辐射环境评价及核应急中放射性监测；建材、装饰材料、岩矿、岩芯样品中放射性元素含量的定量分析。 使用专业方向：野外地质勘察、室内样品分析； 解决的问题：大幅提高地质勘查工作的管理水平，提高勘查密度，每天可勘查测试数百个勘查点，现场决策，一般测试时间为50to200s；可现场检测U、Th、K等放射性元素含量及辐射总量。 四、技术指标： 1、仪器配置： 1.1主机； 1.2主机充电器； 1.3智能手机 1.4伽马能谱仪控制分析软件 1.5 智能手机充电器； 1.6 数据线； 1.7 防震手提箱； 2、主要技术参数： 2.1探测器:φ75×75㎜3 NaI(Tl)+PMT； 2.2测量范围：30～3000 keV全谱+总道； 2.3脉冲处理器：数字化多道分析器(可选1024/512/256道模式)； 2.4系统分辨率：FWHM≤8.0％@662keV； 2.5非线性：积分≤0.05%；微分＜0.1%； 2.6极限敏度(最低检出限)： U：0.2ppm (或226Ra：0.2Bq/kg)； Th：0.5ppm (或232Th：0.2Bq/kg)； K：0.2% (或40K：0.5Bq/kg)； 2.6系统稳定性：谱漂<0.1%/八小时；
2.7无放射源：仪器自动稳谱，无需放射源稳谱，避免放射性污染 2.8功耗：≤1.9W(电池连续供电≥15h)； 2.9体积：φ10×50㎝3 3.0量:3.5 kg； 3.1 使用环境：-10～+50℃(≤95％RH)。 五、配置要求： 1.1主机； 1.2主机充电器； 1.3智能手机 1.4伽马能谱仪控制分析软件 1.5 智能手机充电器； 1.6 数据线； 1.7 U盘（附仪器资料）； 1.8 说明书； 1.9 防震手提箱； 六、服务要求 1、拟提供售后服务的项目； 1.1 整机保修，免费保修年限：1年； 1.2 软件终身免费维护、升级。 3、售后服务响应及到达现场的时间； 卖方承诺在买方电话报修后，3小时内给出电话服务支持或技术响应，48小时内免费上门维修。维修人员须由合同签署公司提供或选派，维修人员必须有此类设备丰富的维修经验，并具备从业资格。 4、维修技术人员及设备方面的保证措施及收费标准； 售后服务由卖方负责，并通过公司及技术服务中心在保修期内执行免费保修。 新机器的开机调试及人员培训由卖方负责，其中培训目标为：使用户指定受训人员能独立操作此仪器。 卖方提供免费的技术咨询、技术培训以及软件升级服务。 用户拥有该软件的使用及升级的权利。
扫描电镜和能谱仪 一、实验目的 1.了解能谱仪的原理、结构。 2.运用扫描电子显微镜/能谱仪进行样品微观形貌观察及微区成分的分析。 3.掌握扫描电镜及能谱仪的样品制备方法。 二、实验原理 能谱仪（EDS）是利用X光量子有不同的能量，由Si(li)探测器接收后给出电脉冲讯号，经放大器放大整形后送入多道脉冲分析器，然后在显像管上把脉冲数－脉冲高度曲线显示出来，这就是X光量子的能谱曲线。 1.简介 特征X射线分析法是一种显微分析和成分分析相结合的微区分析，特别适用于分析试样中微小区域的化学成分。其原理是用电子探针照射在试样表面待测的微小区域上，来激发试样中各元素的不同波长(或能量)的特征X射线(或荧光X射线)。然后根据射线的波长或能量进行元素定性分析，根据射线强度进行元素的定量分析。 2.了解EX-250能谱仪的原理及构造 X 射线的产生是由于入射电子于样品发生非弹性碰撞的结果，当高能电子与原子作用时, 它可能使原子内层电子被激发，原子处于激发状态，内层出现空位，此时，可能有外层电子向内层跃迁，外层和内层电子的能量差就以光子的形式释放出来，它就是元素的特征X射线。 1）分析原理高能电子束与样品原子相互作用，可引起一个内层电子的发射，使原子处于高能激发态。在原子随后的去激过程中，即外层的电子发生跃迁时，会发射出某个能量的特征X-射线或俄歇电子，使原子降低能量。若以辐射特征X-射线的形式释放能量，则 λ=hc/E λ=hc/E K -E L2 式中，λ－特征X射线的波长；E －特征X射线的能量；h —普朗克常数；c —光子。元素的特征 X 射线能量和波长各有其特征值。莫塞莱定律确定了特征 X-射线波长与元素的原子序数Z之间的关系： λ＝ P（Z ?σ）-2 式中，P —对特定始、终态的跃迁过程P为常数；σ—核屏蔽系数，K系谱线时σ＝1。2）能谱仪构造
Bruker能谱仪 日常维护： Quantax系列电制冷能谱仪为免维护系统，开机30秒即可正常工作。 开关机程序及注意事项： ★开启电脑工作站，正常运行后，登陆Windows XP系统； ★登陆桌面的Esprit软件，如Esprit 1.8，Esprit 1.9，然后输入User: edx；Password: edx ★建议实验室温度不要超过35°C；不用时能谱仪处理器仍然保持开启状态，电脑、显示器和打印机关掉。 ★仪器主机房间在满足电镜的前提下，能谱仪的脉冲处理器的物理放置位置尽量远离大功率仪器和机械振动等干扰源。 \ 2.4 谱图采集的简易步骤与方法 步骤例子/提示 1：准备分析用的样品调试好SEM 2：调节电子显微镜进入EDS分析状态加速电压、放大倍数、工作距离 3：进入ESPRIT软件User：edx；Password：edx 4：重置或检查系统设定图像捕捉：单一、连续、平均叠加 分析方法：Automatic P/B-ZAF Interactive P/B ZAF 采集时间：自动，精确或手动 谱图坐标：cps/eV，keV 自动分析：采集完成后 5：检查设备状态、设置以及电镜参数QUANTAX系统屏幕上的参数必须和电 镜设置的参数一致；使用自动的谱仪设 置；所有可用设备的状态指示器必须是 绿色。 6：选择Objects、Point功能区域。谱图功能区域也可用于简单的采集谱图 任务。 7：开始谱图预览并检查谱图通过已知的元素谱图特征线检查能量校 准的情况。 8：调节电镜束流以得到需要的计数 率，并调节图像亮度、对比度，重新 聚焦图像等。 谱图分析计数率： 计数率：5…50kcps 面分布：计数率指示器为绿色或红色
1.项目情况简介 CIT-3000F 便携式γ能谱仪软件是四川新先达测控技术有限公司专门为便携式伽玛能谱仪定制的配套的嵌入式软件。该软件集γ能谱数据采集和数据处理分析于一体，用户可以完成γ能谱的原始数据采集，数据的高级处理、显示以及建立数据分析模型并自动分析计算放射性核素含量与相应比活度。 同时，仪器分析参数、数据计算模型及用户数据均以文件方式存储于SD卡。 图1 数据采集部分结构框图 图2 CIT-3000F主控系统框图 2.开发环境 硬件平台：Windows平台计算机，C8051F060 单片机，C8051F500 单片机软件平台：Keil uVision Keil C51是美国Keil Software公司出品的51系列兼容单片机C语言软件开发系统，与汇编相比，C语言在功能上、结构性、可读性、可维护性上有
明显的优势，因而易学易用。Keil提供了包括C编译器、宏汇编、连接器、库管理和一个功能强大的仿真调试器等在内的完整开发方案，通过一个集成开发环境（uVision）将这些部分组合在一起。运行Keil软件需要WIN98、NT、WIN2000、WINXP等操作系统。Keil C51生成的目标代码效率非常之高，多数语句生成的汇编代码很紧凑，容易理解。在开发大型软件时更能体现高级语言的优势；与汇编相比，C语言在功能上、结构性、可读性、可维护性上有明显的优势，因而易学易用。用过汇编语言后再使用C来开发，体会更加深刻。Keil C51软件提供丰富的库函数和功能强大的集成开发调试工具，全Windows界面。 3.软件架构 CIT-3000F便携式伽玛能谱仪嵌入式软件是以C8051F500嵌入式处理器为处理器硬件平台，基于Keil C51编程实现的软件，软件主要功能包括：数据采集通讯模块，数据处理模块，数据模型模块，参数设置模块，数据显示模块、数据存储模块以及数据分析模块等。 软件架构如图1所示。 图3 CIT-3000F便携式伽玛能谱仪软件架构图 4.软件功能实现 CIT-3000F便携式伽玛能谱仪嵌入式软件的功能主要分为数据采集、数据处理、数据显示、模型建立、参数分析等功能模块。数据处理模块包括谱线
能谱仪技术参数 （带*部分是必须满足的条件） 1主要参数： 1.1*该能谱仪与JEM-2100F场发射透射电镜配套使用； 1.2*探测器：电制冷探测器，窗口有效面积≧30mm2； 1.3*能量分辨率：在100,000CPS条件下Mn Ka保证优于129eV； 1.4 *元素分析范围优于Be4～Am95； 1.5 谱峰稳定性：1,000cps到100,000cps，Mn Ka峰谱峰漂移小于1eV，48小时内峰位漂移小于1.5eV； 1.6 具备零峰修正功能，快速稳定谱峰，开机后无需重新修正峰位,10min内可达到稳定状态； 1.7 *能谱仪处理单元与计算机采用分立式设计，单探测器输出最大计数率600,000CPS，可处理最大计数率优于1,500,000CPS； 1.8*可进行谱定性和定量分析。要求配备完善而精准的原子数据库，包含所有的分析线系(K, L, M 和N线系)，具备较为准确的定量方法； 1.9可将电镜图像传输到能谱仪的显示器上，支持最大像素4096*4096，并以该图为中心做微区分析； 1.10*所有校准数据均保存于信号处理器中，重装或者升级电脑可由用户自己完成，无需工程师上门重新校准系统；数据需能够复查，便于进行重新提取和分析； 1.11通过处理器自动扣除和峰（非软件扣除），自动选择最佳处理时间 1.12 *具有STEM模式点、线、面分析功能，且配备高分辨电镜专用的漂移矫正功能。 1.13计算机配置：22寸显示器，处理器intel 4核/内存8GB/硬盘1TB/1G独立显卡/win7 pro系统。 1.14 *实验报告:多种输出格式, 单键可生成Word文档, 及HTML格式。 2保修、培训及交货 2.1. 免费保修期：至少1年。保修期内，任何由制造商选材和制造不当引起的质量问题，厂家负责免费维修。保修期自验收签字之日起计算。保修期满前1个月内卖方应负责一次免费全面检查，并写出正式报告，如发现潜在问题，应负责排除。 2.2. 维修响应时间：卖方应在24小时内对用户的服务要求做出响应，一般问题在48小时内解决，重大问题或其它无法立刻解决的问题应在一周内解决或提出明确的解决方案，
EDAX GENESIS能谱仪技术参数说明 1.PVSEM/SUTW探头：锂漂移硅Si(Li) CDU型或标准STD型SEM Sapphire? 蓝 宝石X射线探头。带超薄窗口，能够探测低至铍(包括)的所有元素。有效探测晶体面积为10mm2。晶体被自动保护以抵御探头变暖。探测器多次热循环不会影响其性能。配置包括前置放大器、放大器、电缆和2.5 或10升杜瓦瓶。探头分辨率小于132eV。 2.PV8200/01Acquisition Electronic Kit 带有32位数字信号处理器(DSP)的能谱信 号接收与处理系统，包括信号采集板、高压源以及直流电源板。独立运行式多道分析器、9个可用软件选择的时间常数（0.4微秒--102.4微秒）、4个通道宽度可选用（2.5、5、10、20 ev/ch）、由于输入计数率导致峰漂移的修正以及三级快速鉴别器等。最大输入计数率可达500,000cps、输出计数率可达100,000cps。 3.PV8604/11 英特尔奔腾IV 2.0 GHz 处理器：40 GB硬盘，256 MB内存，CD-RW 读写式光驱，3.5” 1.44 MB软驱，以太网卡，图形加速卡，键盘和鼠标，Windows XP操作系统，MS Windows Office专业版。 4.PV8350/00GENESIS SEM Quant ZAF software定性与定量分析软件，用于扫描电 镜块状样品定性定量能谱分析，使用EDAX增强的ZAF修正算法，包括： (1)可让用户选择的屏幕色彩 (2)谱线的收集和显示 (3)谱线自动连续变幅 (4)鼠标拖动谱线变幅 (5)自动或手动能量定标和分辨率计 算 (6)KLM线标记和峰的标注(含逃逸峰 和吸收边) (7)自动和手动峰鉴别 (8)在峰标定中的可见峰剥离 (9)可进行重叠图形显示 (10)用户自定义谱峰通道和感兴趣 ROI区 (11)全数字化的速率计功能 (12)具有归一化、加、减和乘功能的谱 线比较 (13)谱线的平滑 (14)逃逸峰扣除 (15)峰的生成(16)手动和自动选取背底点并按 Kramers方法建立背底模型(17)E DAX独特的可见的重叠峰剥离 技术 (18)具有用户定义报告格式的完全无 标样定量计算（误差水平接近有标样定量计算的水平） (19)对轻元素进行定量计算时专设轻 元素调整因子 (20)定量计算具有纯元素标样法、混合 物标样法或部分标样法 (21)改进后的ZAF修正算法, 含有 SEC因子修正功能，配以更完善的元素周期表 (22)具备全自动EDX分析功能－自动 按顺序分析的JOB方式 5.PV8005/10电子束控制单元：数字化的慢速扫描发生器单元。它提供对电子显微 镜电子束的数字化控制，以便获取最多4个有顺序的视频信号和最多15个X射线信号。它将每个得到的信号数字化为16位的结果。需要专门的电镜接口。