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This is a basic walkthrough of the some of main features of Trips-Viz using an example dataset, for detailed information on each section see the relevant heading further down this page.
Visualising a individual transcript.
From the homepage click on Saccharomyces cerevisiae, then sgd, then Single transcript plot.You will see the page shown in the screenshot below:
Enter the transcript id "YAL002W" in the Gene/Transcript input box. Leave the other settings at their default values, you can read more about them in the single transcript plot section
further down the page, or by clicking the information icon next to them.
Scroll to the bottom of the page and click the box "Uncheck All Files with Selected Seq-Type", this will unselect all Ribo-Seq files (which are turned on by default). Then
click on "Albert14", the Ribo-Seq files associated with this study will appear in the "Files box". Select each of the four files by clicking on the file name (the circle next to the file name will
be filled in blue to indicate it is selected). Click the "View plot" button at the bottom of the page.
You will see the plot shown in the screenshot above of the gene VPS8 (transcript id YAL002W). In the main part of the plot inferred A-sites of Ribo-Seq reads from the study Albert14 are displayed.
These reads are coloured red/green/blue.These correspond to the open reading frame architecture shown beneath the plot, where each of the three reading frames are represented by a horizontal bar also coloured
red/green/blue.
The annotated CDS start and CDS stop are denoted by vertical black lines on the main plot and
correspond to a start/stop codon in the first reading frame beneath the plot (red). As expected Ribo-Seq reads are mostly contained between the CDS start and CDS stop and biased toward the first
reading frame (red). However there is also a region upstream of the CDS start that also has high Ribo-Seq density and is also biased toward the first reading frame. Click on the magnifying glass beneath the
plot and draw a box around this region to zoom in.
We can see from the reading frames beneath the plot that there is an AUG codon (short white line) right where the read density increases, and a stop codon (longer black line) where the read density drops, this ORF is in the first frame (red) and as the Ribo-Seq reads in this region are also mostly red it is likely that this is the start of a translated upstream open reading frame (uORF) in the first reading frame.
The above can be repeated to view the same gene with data from other studies to see if the uORF is also translated in other studies or is perhaps specific to a certain condition. This can also be done for other genes to find other uORFs or other cases of atypical translation. The single transcript plot page is useful for exploring these specific cases when you have a gene/transcript in mind that you want to explore. If you don't have a specific gene/transcript in mind and are looking specifically for cases of translated uORFs or other ORF types then see the translated ORF detection page.
Detecting translated ORFs.
From the homepage click on Saccharomyces cerevisiae, then sgd, then Translated ORF detection. On that page you will various settings broken into the categories ORF Type, Features and Filters. Leave these settings as default, for detailed information on these see the relevant section further down this page or click on the information icon next to the setting. Scroll down to the sequence types and studies section as on the Single transcript plot page. Click the box "Uncheck All Files with Selected Seq-Type" then click on "Albert14" and select the four Ribo-Seq files as before. Then click the Generate Table button.
After a short while (Note: Wait time will increase when more data is selected, and for organisms with more annotated transcripts) a table will be shown as in the screenshot below:
Each row in the table corresponds to a different ORF, information shown for each ORF includes the start and stop position, the length the transcript id and the gene name. Each row also includes a link to the Single transcript plot page. Clicking on the link will open a separate tab where the transcript will be shown with the Albert14 data already loaded and the ORF will be highligted in yellow. Clicking on the arrow on the bottom right of the table will show the next page of the table. This table can show up to 1000 cases of ORFs, in cases where there are more than 1000 cases a csv file with all the cases can be downloaded via a link at the top left of the table.
The above can be repeated for different ORF types which can be selected at the top left of the page as well as using different studies or an aggregate of studies. Note however that ORF detection works best with studies that have strong triplet periodicity. This can be checked on the meta-information page.
Meta-Information.
From the homepage click on Saccharomyces cerevisiae, then sgd, then Meta-Information. At the top left of the page you'll see various different plot types. Click on the triplet
periodicity plot. At the top right of the page is a section for options which will change depending on the chosen plot type. Leave them on default for now. Scroll down and choose the four files from
the Albert14 as before, then click view plot. You'll see the plot shown in the screenshot below:
Read lengths are shown on the x-axis while the count is shown on the y-axis. Each read length has three bars corresponding to each of the three reading frames. If a dataset has strong periodicity one of the reading frames will be much higher than the other two. A score is also shown at the top right of the plot between 0 and 1, with 0 being the worst periodicity and 1 being the best. The better the periodicity the easier it will be to detect ORFs and the clearer it will be when visualising single transcripts.
The other plot types at the top left of the page can be used to check other aspects of datasets such as the read length distribution or metagene profiles, which can be useful when assessing a datasets quality. A description of each can be seen in the metainformation section further down this page or by clicking the information icon next to the plot name.
Differential expression/translation
From the homepage click on Saccharomyces cerevisiae, then sgd, then Differential plot. Leave the settings at the top of the page at their defaults. Scroll down and click on Albert14.
The file selection works a little bit differently for this page than the previous ones.
Only one file can be selected at a time. First click on the file SRR1177157 to select it (1. In screenshot above), the circle will turn blue to indicate which file is selected. Next click on "Add to Ribo-Seq Files 1" (2. In screenshot above), then click
on the file SRR1177159 to select that and again click "Add to Ribo-Seq Files 1". Next do the same for SRR1177162 and SRR1177163 but click the "Add to Ribo-Seq Files 2" button instead. Then click on the mRNA-Seq button in the
Seq-Types section (3. In screenshot above), the files in the file box will change. Select the files SRR1177156 and SRR1177158 and press the "Add to RNA-Seq files 1" button after each, do the same for SRR1177160 and SRR1177161 but click the
"Add to RNA-Seq files 2" button after each. When finished scroll up the page, the four boxes should look like the follwing screenshot:
Then click the View Plot button at the bottom of the page. You should see a plot like the following:
This plot shows the translation efficiency fold changes of each gene in Saccharomyces cerevisiae in the Albert14 study as calculated by the Z-score method. Average fold change is
shown on the y-axis while the geometric mean of the 8 datasets is shown on the x-axis. Points above and below the yellow line represent genes with a z-score higher/lower than 4 (this can be changed using
the inputs at the top of the page). Howevering over a specific point tells you the gene/transcript id of the point as well as the specific fold change and geometric mean. Clicking on a point will open a
seprate tab showing a comparison plot of the Ribo-Seq and mRNA-Seq for each condition. For example:
This is a comparison plot for the gene AGA1 showing the data from the Albert14 study. Ribo-Seq reads are shown in dark green/red corresponding to condition 1/2, while mRNA-Seq reads are shown in light green/red corresponding to condition 1/2. The Ribo-Seq reads in condition 1 are much higher than the RNA-Seq reads in condition 1, but for condition 2 the Ribo-Seq reads are lower, meaning the translation efficiency is lower in condition 2 than condition 1. Comparison plots such as this can also be generated manually by going to the comparison plot page directly. Any number of conditions can be plotted there which makes it useful for looking at variation between replicates or comparing more than two conditions.
Trips-viz is a (poor) acronym standing for TRanscriptome wide Information on Protein Synthesis VIZualized. This is mainly to match the naming of our sister site gwips-viz which provides Genome Wide Information on Protein Synthesis.
No. All features of Trips-viz can be used without an account. An account is only needed if you wish to change specific aspects of plots such as the background colour. An account is also needed to upload your own data, or to view private data uploaded by someone else.
These are unique codes that are generated for every plot on Trips-Viz. These allow for quickly sharing plots and record the specific files/studies used to generate the plot as well as any settings that were
changed. By following a link in the form https://trips.ucc.ie/short/
Yes. To upload you need an account, then visit the upload link at the top of the page. This is split into 3 sections called "Upload new file", "My studies"
and "My files". The "Upload new file" section allows you to choose an organism, an assembly, a study name, and a sequence type. Once those are chosen you can click the
"Browse" button to choose a file to upload and then press the "Submit" button to upload the file. After the file has been uploaded you'll see the study appear in the
"My studies" section, where the organism, study name and assembly will be listed. There will also be a text box referred to as "Access list" where you can give other
Trips-Viz users access to your data. To do this, enter a list of usernames in the text box and press the "Update" button at the bottom of the page.
The "My files" section will also be populated after uploading a file. This will list the file name, the study name and a checkbox for each file. To delete files click the
checkboxes of the files you want to delete and press the "Delete files" button at the bottom of the page.
Note that any files uploaded to Trips-Viz need to be in a specific format (sqlite) and have the extension .sqlite. In order to create a file in that format, our reccommendation
is to use RiboGalaxy, under the Trips-Viz section select the bam to sqlite tool. Note that this tool requires an annotation sqlite file which
can also be created on RiboGalaxy using the create new organism tool, or downloaded from the Trips-Viz downloads page if the organism is already available there.
Alternatively you can use the "bam_to_sqlite.py" script, which can be retrieved from the downloads page. You will also need to download a file called "transcriptomic_to_genomic.sqlite" and
"organism_name.sqlite" which can be downloaded from the same page. Then run the python 2 script as follows:
python bam_to_sqlite.py
Publicly available Ribo-Seq/mRNA-Seq data is downloaded from the ncbi sequence read archive. After which it is converted to fastq format and adapter is removed using cutadapt. Reads below 25 nucleotides in length after adapter removal are discarded. Reads are alinged to rRNA sequences using bowtie. Reads that map to rRNA are discarded while remaining reads are aligned to the transcriptome using bowtie. The resulting BAM file is parsed with a python script. Each mapped read is checked to see how many genomic positions it maps to, if only one the read is classed as unambiguous, if more than one it is classed as ambiguous. An offset is also determined for every read by choosing the highest peak near the start codon using the counts from the metagene profile.
Yes. This can potentially be done with any sequencing data that can be aligned to a transcriptome, but you will need to be somewhat familiar with python.The basic steps involve parsing a text file and placing the information in SQLite format. An example of a python script that can do this is provided on the downlods page which you can view by selecting "Scripts" under the group dropdown box and then "tsv_to_sqlite.py" in the file dropdown box. This can be easily modified to parse a csv file. The output will be in .sqlite format which you can then upload to Trips-Viz.
Yes. If you have a transcriptomic fasta file and a gtf file you can upload your own transcriptome to Trips-Viz. This can be done by first downloading the
scripts entitled "create_annotation_sqlite.py" and "create_transcriptomic_to_genomic_sqlite.py" from the downloads page (select the "Scripts" group).
Run the "create_annotation_sqlite.py" script as follows:
python create_annotation_sqlite.py <organism_name> <gtf_file_path> <fasta_file_path> <pseudo_utr_len>
where pseudo_utr_len is an integer which can be used to add a flanking region to all transcripts, which can be useful if UTR's are not annotated, set to
0 if UTR's are already annotated. This will produce a file named <organism_name>.sqlite then run the script "create_transcriptomic_to_genomic_sqlite.py" as follows:
python create_transcriptomic_to_genomic_sqlite.py <gtf_file_path> <pseudo_utr_len>
This will produce a file called "transcriptomic_to_genomic.sqlite" (this is needed for the bam_to_sqlite.py script). To upload your transcriptome go to the
uploads page, select the "Upload new transcriptome" tab. Fill in the required fields and upload the .sqlite file that was prouduced by the create_annotation_sqlite.py script.
A message will come up after a some time indicating a successful upload. Navigating back to the home page should show the newly uploaded organism
(this will only be visible by you), files can then be uploaded for that organism just like any other organism on Trips-Viz.
Previously uploaded transcriptomes can be deleted from the uploads page under the "My transcriptomes" tab.
Note the variables with labels such as "search_term_before_transcript" in the create_annotation_sqlite.py and create_transcriptomic_to_genomic_sqlite.py scripts may need to be
changed. This is because gtf files are not standardised so the scripts need to have the terms that appear immediately before and after the transcript and gene ids in the gtf file.
Here is an example of a line in a gtf file and what the corresponding variables should be set to:
gtf line:
chr1 HAVANA transcript 11869 14409 . + . gene_id "ENSG00000223972.5"; transcript_id "ENST00000456328.2"; gene_type "transcribed_unprocessed_pseudogene"; gene_status "KNOWN"; gene_name "DDX11L1"; transcript_type "processed_transcript"; transcript_status "KNOWN"; transcript_name "DDX11L1-002"; level 2; transcript_support_level "1"; tag "basic"; havana_gene "OTTHUMG00000000961.2"; havana_transcript "OTTHUMT00000362751.1";
variables:
search_term_before_transcript = 'transcript_id "'
search_term_after_transcript = '.'
search_term_before_gene_1 = 'gene_name "'
search_term_after_gene = '";'
The purpose of this plot is to allow you to see the ribosome profiling and rnaseq reads that align to an individual gene or transcript.
The ribosome profiling reads will be colour coded according to which frame the a-site is found in. If the triplet periodicity of the reads contributing
to the graph is strong then this plot should give a clear picture of which frame is being translated.
The page is split into three parts, at the top of the page you have general settings. Here you can choose settings like, the maximum triplet periodicity
score for each read, the gene you want to view, the minimum and maximum allowed readlengths and many other settings. To get a more detailed description
on any one of these settings simply click the link next to it labelled "(What's this?)", or you can view the explanations further down this page.
At the middle of the page you can select a sequence type. Then click on
one of the studies in the studies box. This will bring up a list of relavent files associated with that study in the "Files" section below. Next to each
file will be a circle. If the circle is filled in this means that reads from that file will be included in the graph. To turn on or off an individual file
simply click the circle next to the file name or the file name itself. As some studies can have many files associated with them, there are buttons in the
studies box that act as shortcuts.
There are 2 buttons in the "Studies" section to turn on/off all files currently in the "Files" section, and another 2 buttons in the "Seq type" section to turn on/off all files from
the selected sequence type. There is also a button in the studies box to "View study info". When pressed this will bring up a pop up which will display information
relevant to the selected study such as a link to the paper and a link to the raw data.
At the bottom of the page there are two buttons the first is "View on Gwips", which when clicked will display the current gene on Gwips-Viz
(note this only works if you've created a plot in Trips-Viz first) the second is the "View Plot" button which when pressed will display a loading screen for a short while after
which a plot and another button will appear below the "View Plot" button. This new button will be labelled "Direct link to this plot", by right clicking on this button and copying
the link you can send the plot to another person (for more information see
How do I share plots? ).
The main window for the plot will have a read count on the y-axis and the position in nucleotides along the x-axis. Above the main window will be the name of the gene you
are viewing followed by a short code in brackets (this can be used to re-create the plot your viewing). By default only the ribo-seq data will be shown, which is colour coded
according to the frame it aligns to. Just below the main window the orf architecture for this transcript will be shown. The three frames are colour coded red, green and blue.
Start codons are denoted by short white lines, stop codons are denoted by longer grey lines. To the right of the main window is the interactive legend. Elements in the main
window can be turned on/off by clicking the coloured boxes in the legend. Below the orf architecture lies the tools, clicking on the magnifying glass icon will allow you to draw
a bounding box in the main window to zoom into a specific area. Clicking on the four arrows icon will allow you to click and drag anywhere in the main window to move the data.
Finally clicking on the home icon will allow you to undo any affect of the move/zoom tool and reset the view to it's original settings.
Checking this box means that both ambiguously mapped reads and unambiguously mapped reads will be displayed. A read is considered ambiguous if it maps to more than one genomic position.
If this is checked then the data will be displayed, as a line graph instead of as individual peaks. This makes no difference to the actual data displayed it is purely an aestethic choice. However, the line graph is less memory intensive and can result in a smoother experience, especially when zooming.
This text box allows you to input a comma seperated list of nucleotide sequences which will be highligted in the open reading frame architecture of the resulting plot. By default these will appear as short black lines but their colour can be changed in the settings. These can be turned on/off by clicking the "Highlighted sequences" button to the right of the plot. Note: the following IUPAC codes are also recognised: R: A/G Y: C/U S: G/C W: A/U K: G/U M: A/C B: C/G/U D: A/G/U H: A/C/U V: A/C/G N: A/C/G/U
If checked, every position of every read contributes to the data displayed on the plot, if unchecked only the inferred a-site of the read
contributes to the plot.
For more information on how a-site is inferred for trips see.
This is a simple metric to include/exclude readlengths from a particular file depending on how good the triplet periodicity is.
For each readlength a score is calculated from it's triplet periodicity plot using the formula 1-(count of the second highest peak/count of the highest peak)
, thus read lengths with very good triplet periodicity will be close to 1, while readlengths with poor triplet periodicity
will be close to 0.
Any readlengths from any files with a read score below what is entered in this field will not be
displayed, thus entering a value close to 1 will result in better triplet periodicity but less data.
The gene or transcript to be plotted. If given a gene with multiple transcripts then a list will be displayed of all transcripts associated with that gene. Transcript ids are based on gencode annotations, refseq will not be recognised.
The minimum read length to be displayed on the graph. Any reads below this length will be ignored.
The maximum read length to be displayed on the graph. Any reads above this length will be ignored.
Infer the a-site of each ribosome protected fragment from either the 5' or 3' end of each read.
If this box is checked the nucleotide sequence of the transcript will be displayed below the main plot. Each nucleotide is coloured according to reading frame, such that the colour of the nucleotide corresponds to the first letter in the codon of the corresponding frame. Depending on the length of the transcript, displaying the nucleotides can add a significant amount of time to the plot generation.
The purpose of this plot is to view data from multiple datasets (or groups of datasets) for a single gene simultaneously. This is useful in the case were there are
Ribo-seq datasets generated under treatment and control conditions or as part of a time series and you would like to view what the difference looks like for a
particular gene/transcript.
At the top of the page is the General settings section. This provides the user with options to change the transcript to be plotted, the minimum and maximum readlengths,
or whether to include ambiguously mapped reads. Below General settings lies two boxes, one labelled Studies the other labelled File list. The Studies box contains a
button labelled Add, a coloured square, and a list of studies that have been aligned to the currently selected organism/transcriptome assembly.
Only files listed in the
File list box will be plotted in the graph.
In order to add files to the file list box, a user must click on one of the studies. This will populate the Ribo-Seq files and RNA-Seq files boxes below the Studies box
with a list of files relevant to that study. Clicking on one of these files and then pressing the Add button in the Studies box will place that file into the File list.
As long as the colour in the coloured box remains unchanged, multiple files can be added to the same group in the File list box by continually selecting different files
and pressing the Add button after each selection. If the user does change the colour in the coloured box (a pop up appears when clicking on the coloured box) all
subsequently added files will be added to the File list under the same group.
There is no limit to the number of files that can be added to each group or the total
number of groups. Clicking the View plot button at the bottom of the page will create a plot below the View plot button. Each group of files in the File list will be
plotted on a single line with its corresponding colour. By default the legend will show the filename of the first file in the group, but this can be changed by clicking
on the label in the File list box and typing in a custom label.
The gene or transcript to be plotted. If giving a gene with multiple transcripts then a list will be displayed of all transcripts associated with that gene. Transcript ids are based on gencode annotations, refseq will not be recognised.
The minimum read length to be displayed on the graph. Any reads below this length will be ignored.
The maximum read length to be displayed on the graph. Any reads above this length will be ignored.
These values can be used to highlight a specific region of a transcript. Filling in a value for highlight start and highlight stop will colour in that region of the plot.
Checking this box means that both ambiguously mapped reads and unambiguously mapped reads will be displayed. A read is considered ambiguous if it maps to more than one genomic position.
When looking at the difference between two groups of files there may be a large difference in the number of mapped reads in each group. Checking this option aims to mitigate the effect of mapped reads differences by determining the factor difference between the groups and then decreasing the counts of the higher groups by that factor. For example if there is one dataset in condition 1 with 1,000,000 mapped reads and one dataset in condition 2 with 500,000 mapped reads, the count for every position in condition 1 would be divided by 2.
The purpose of these plots is to allow you to gain insight into specific features of a particular dataset, such as wether or not it has good triplet periodicity.
The top left section of the page allows you to choose a plot type by clicking on it. Some plots have settings that you can change, if these are applicable to this
plot the settings will appear in the options section in the top right of the page.
At the middle of the page is the "Studies" section and "Seq types" section. To choose which files are contributing to the plot, first click on a sequence type and then on on of the
studies in the "Studies" section. This will bring up a list of relavent files in the "Files" section below. Next to each
file will be a circle. If the circle is filled in this means that reads from that file will be included in the graph. To turn on or off an individual file
simply click the circle next to the file name or the file name itself. As some studies can have many files associated with them, there are buttons in the
studies box that act as shortcuts. There are 4 buttons to turn on/off all riboseq/rnaseq files, and another 4 buttons to turn on/off all riboseq/rnaseq files
in the selected study. There is also a button in the studies box to "View study info", when pressed this will bring up a pop up which will display information
relevant to the selected study such as a link to the paper and a link to the raw data. Once you have selected all the relevant files you wish to include in the
plot click the "View Plot" button below the riboseq/rnaseq boxes or press the enter button on your keyboard. The different plots are explained under seperate
headings below.
This plot shows different read lengths on the x-axis and their counts on the y-axis. All mapped reads in a file contribute to this plot (wether they map ambiguously or not). This plot can be used to help determine if a file contains genuine riboseq reads or not. In general we would expect the readlength distribution to have a relatively narrow peak centered around ~30 nucleotides for genuine riboseq data.
This plot shows the nucleotide composition across all selected reads in a given range. The minimum and maximum readlengths allowed to contribute to this plot can be controlled in the settings for this plot. Other settings you can change for this plot are the ability to inlcude mapped/unmapped reads. Wether the nucleotides are taken from the 5' end or 3' end of all reads, and wether the absolute counts are shown or percentages at each position. This plot can be used to assess if there is an unusually high number of nucleotides at a certain position in the read. For example if you see a high percentage of A's at the 3' end of unmapped reads it may indicate that polyA tails were not correctly removed from these reads, or a high percentage of T at the first position may indicate a problem with untemplated addition. In frame or out of frame (relative to the CDS) reads can also be selected. Unlike the "All mapped reads" or "All unmapped reads" options these are not precomputed. This means they will take longer to return a plot, it also means any sequence mismatches such as untemplated additions or untrimmed adapter will not be apparent. This may be useful to assess if nuclease bias has an affect on triplet periodicity.
This plot shows the triplet periodicity across a given range of readlengths. The user may decide the minimum and maximum readlengths in the settings for this plot. Each readlength will have three bars associated with it corresponding to one of three frames the reads align to. A particular read length is considered to have strong triplet periodicity if the count in one of the frames is much higher than the other two frames. The legend in the top right will also show a score based on all readlengths in this particular plot. This is done by using the formula: 1-(count of the second highest peak/count of the highest peak), if more than one readlength is shown the highest peaks and second highest peaks of each readlength is summed and then the formula is applied. This will result in readlengths with strong periodicity receiving a score close to 1, while readlengths with weak periodicity receive a score close to 0. The better the triplet periodicity for a given study the clearer it will be which frame is being translated when viewing reads from this dataset on the single transcript plot.
This plot shows a breakdown of reads into multiple categories for each file. Categories are as follows
"cutadapt removed": the number of reads removed by cutadapt because they
were too short after trimming.
"rRNA mapped": Number of reads removed because they mapped to ribosomal rRNA.
"Unmapped": Number of reads which could not be mapped to the transcriptome.
"Ambiguous": the number of reads which mapped to more than one genomic location (see "How is the Ribo-Seq/mRNA-Seq data processed?").
"Mapped noncoding": Number of reads
mapped to regions not annotated as coding.
"Mapped coding": Number of reads mapped to annotated coding regions.
The reasons for using this plot would be to assess problems with the mapping, for instance if the majority
of reads were unmapped and the number of reads removed by cutadapt was very low it may indicate a problem with adapter removal. It could also be used to assess wether a file was genuinely
riboseq or not as we would expect a higher proportion of reads to align the the coding than noncoding regions compared to rnaseq data.
This plot shows counts on the y-axis and a position relative to some sequence feature on the x-axis, this can be the annotated start or stop codon, or a custom sequence.
All reads from any gene that lie near the chosen position will be plotted, this can be based off the 5' end of reads, 3' end of reads, or the inferred A-site.
Genuine Ribo-Seq data should have a much higher density of reads 3' of the start codon than 5' of the start codon (and vice versa for the stop codon).
The following settings can be changed in this plot:
Include 5' ends/Include 3' ends: Choose which end of the read the counts are based on, only one option can be chosen at a time.
Apply offsets: If checked the offsets used to infer the A-site will be applied to the read counts.
Aggregate: If checked, the counts from all selected files will be grouped together, if unchecked files will be plotted with different colours seperately. A maximum of 6 files can be selected if not aggregating.
Normalise: If checked, counts will be normalised based on the number of mapped reads in each file. This is based on a factor difference between the file with
lowest number of mapped reads and all other files. For example if plotting three files A,B and C which have 100,200,300 mapped reads respectively. The counts
from file A would be unchanged (as it has the lowest number of mapped reads), all counts from file B woudld be multiplied by a factor of 0.5 (100/200 = 0.5) and all counts from File C would be multiplied
by a factor of 0.33 (100/300 = 0.33).
Min/Max readlen: Readlengths outside this range will not contribute to the plot.
Custom sequence list: Rather than centering the plot around annotated start or stop codons multiple custom sequences (seperated via comma) can be entered in this box.
The search limits below restrict where the custom sequences should be searched for, this can be across the whole gene, the 5' leader (5' UTR), the annotated
coding region (CDS) or the 3' trailer (3' UTR). This can be further restricted by the boxes below to include or exclude sections of the chosen region.
For example to recreate the stop codon plot, UAG,UAA,UGA would be entered into the custom sequence list, CDS should be chosen as the region and
"Include last" option should be checked with the number of nucleotides set to 3.
Custom sequence search is done on the fly so will take longer than the annotated start/stop codon plots which are pre-computed.
Note: the following IUPAC codes are also recognised in the custom sequence search:
R: A/G
Y: C/U
S: G/C
W: A/U
K: G/U
M: A/C
B: C/G/U
D: A/G/U
H: A/C/U
V: A/C/G
N: A/C/G/U
This plot shows different readlengths on the y-axis and a position relative to start/stop codon on the x-axis. The data itself represents count data of reads (based on 5'/3' end of reads depending on start codon. Genuine Ribo-Seq data should have a much higher density of reads 3' of the start codon than 5' of the start codon (and vice versa for the stop codon). Other settings the user can choose are the colour palette of the heatmap, the minimum and maximum readlengths and minimum/maximum position relative to the start/stop codon, wether to reverse the colour scale, wether to use log values on the scale, and wether to use a fixed value for maximum scale value (useful if comparing different files and you don't want the scale to change).
This plot simply maps the counts for every transcript from two different files. This is useful for assessing technical/biological replicates. Ideally there should be a good correllation between replicates, showing a tight linear relationship between the two files. If this is not the case it could indicate an issue with one of the files.
This plot shows the counts of each dinucleotide across all reads in the selected files. If there is a particular dinucleotide much higher than the others it could indicate a bias for that file.
This shows a sorted table of the top 100 most frequently unmapped reads in the files selected. Each read shows the number of times it appeared in the file and a link to the blast resource, where the read will be automatically filled into the search box. This table can be useful to detect contamination in samples, for example if there was a high number of unmapped reads in a particular human sample and some of the most frequent unmapped reads gave hits to mycoplasma genomes you could investigate if the samples have been conataminated.
This is used to get Ribo-Seq or RNA-Seq counts for every gene. Counts can be taken from just Ribo-Seq files or just RNA-Seq files, or both. For performance reasons
only the top 1000 genes are shown, but clicking the link just above and to the left of the table allows you to download all rows in csv format.
The "View Plot" link in the table will bring you to the Single gene/transcript plot page of Trips-Viz and plot the data from the selected files.
By default the 1000 genes will be sorted by highest count first (or highest translation efficiency if both Ribo-Seq and RNA-Seq files are chosen), but clicking on the
headers in the table can change the sorting order. All values shown are raw counts.
This plot uses the Z-score approach as described in Andreev et al. 2015. to determine which
transcripts are up/down regulated. The z-score here represents how many standard deviations from the mean a particular transcript is. However as we expect more variance in
lowly expressed transcripts, than highly expressed ones, so transcripts are first placed into groups of similiar expression levels. The mean and standard deviation of
each group is then calculated which in turn can be used to calculate the z-score of each transcript. Z-scores can be either positive or negative (which represents up/down regulation).
To create one of these plots choose the settings at the top of the page (see the below headings for more info or click the "what's this" link next to them for more info). Then you need
to place your files of interest in the boxes labelled "Ribo-Seq files Condition 1","Ribo-Seq files Condition 2", "mRNA-Seq files Condition 1","mRNA-Seq files Condition 2".
To do this choose a sequence type below the boxes. Then click on a study in the studies box. A list of files will appear in the Files section below the Studies box.
Click on one of these files and then press one of the buttons in the studies box labelled "Add to (Ribo-Seq/mRNA-Seq) files (1/2)". The file will then appear in the relevant box.
At least two groups need to have files in them, either both Ribo-Seq groups, or both mRNA-Seq groups. All four groups can also have files in them in which case the fold change
will be based on translation efficiency (Ribo-Seq/mRNA-Seq). If there is more than one file in a group then they will be treated in a pairwise manner, i.e the first file
Condition 1 will be compared to the first file in Condition 2, the second file in Condition 1 will be compared to the second file in Condition 2, etc. The resulting z-score plot will
then be based on the average fold change and average expression from each pair. The z-scores for each pair can also be viewed by downloading the csv file.
Once you've selected your desired files press the view plot button at the bottom of the page. This will create a plot where the fold-change of the files is represented on the
y-axis and the expression (here expression is determined by the geometric mean of the counts in all files) on the x-axis. The yellow lines on the graph indicate wether the fold
change is above or below the chosen z-score. Transcripts that fall above the upper yellow line are above the z-score and are coloured green (upregulated), while transcripts that
fall below the lower yellow line are below the z-score*(-1) and are coloured red (downregulated). Clicking on any one of the points will open a seperate tab showing the comparison
plot for that transcript with the conditions coloured green/red.
Note: In human and mouse only one principal isoform is used for each gene as determined by appris.
In order to see which transcript is associated with a given point on the plot just hover over the point. This will show a pop up with information such as transcript, gene,
minimum expression and fold change. Clicking on one of these points will open another tab showing the comparison plot page on Trips-viz, which shows the plot for the transcript
you clicked and the files contributing to the plot colour coded red/green.
Choose the method used for identifying differentially expressed/translated genes. If correlation is selected only fold changes of transcripts will be displayed with no attempt to identify significant genes. Significantly differentially expressed/translated genes can be found using the z-score approach, DESeq2 or Anota2seq. If DEseq2 or Anota2seq is used a link above the plot allows you to download the inputs/outputs of the relevant software.
Carry out the search using coding transcripts (default), non-coding transcripts or all transcripts. If this is not set to coding then region has to be set to "all".
If using the z-score plot this option allows you to set the minimum Z-score required before a transcript is considered significantly up/downregulated. Z-score represents the number of standard deviations a particular transcript is from the mean. So for example given a minimum z-score of 4, only transcripts 4 standard deviations above or below the mean would be considered significantly up/down regulated. If using the DESeq2 or Anota2seq plot types this values represents the max p-adjusted value that will be used to consider genes significant. The values is a percentage so entering 5 in this box will count all genes with a p-adjusted values below 5% as significant. The default value for the DESeq2 plot is 5 while for Anota2seq it is 15.
This determines which region the counts for a particular transcript will be taken from.
In some cases you may wish to compare datasets with a very different number of mapped reads. Ticking this box finds the difference between the groups based on the number of unambiguously mapped reads. The counts for one of the groups will then be reduced to mitigate the difference. For example if there is one dataset in condition 1 with 1,000,000 mapped reads and one dataset in condition 2 with 500,000 mapped reads, the count for every transcript in condition 1 would be divided by 2. This is only applicable when not using the DESeq2 or Anota2seq plot types.
Any transcripts with less than this number of reads in any group will be ignored. If normalisation is used the minimum applies to the normalised value. This is only applicable when not using the DESeq2 or Anota2seq plot types.
Enter a comma seperated list of gene names, these will then be highlighted red/green on the z-score plots and black on other plot types.
Enter a comma seperated list of transcript ids, search will then be carried out only on these transcripts.
The purpose of these plots is to explore any information inherit to the transcriptome annotations, not related to Ribo-Seq/RNA-Seq or other data types, for example transcript lengths or GC content. Many of the plots have multiple inputs for seperate transcript lists allowing for easy comparisons between groups.
Used to explore the nucleotide composition of an individual transcript. Enter a transcript ID in the input box and press "View plot", a plot will be displayed showing a sliding window across the entire transcript of individual nucleotide compositions as well as GC content and minimum free energy (MFE). These can be turned on/off using the legend on the top right of the plot.
Used to investigate the nucleotide composition of multiple groups of transcripts. Between 1 and 4 lists of transcripts (comma seperated) can be entered in the boxes at the top right. The nucleotide and region that is taken into consideration can also be chosen here, as well as the plot type (scatter or box).
Used to investigate the lengths of multiple groups of transcripts. Between 1 and 4 lists of transcripts (comma seperated) can be entered in the boxes at the top right. The region that is taken into consideration can also be chosen here, as well as the plot type (scatter or box).
Displays the number of annotated genes and transcripts in a transcriptome. These are broken down further by the number of annotated coding (green) and non-coding (red) genes/transcripts.
Displays the codon counts of coding regions of all principal isoforms if the supplied list is empty, a custom list of transcrips (comma seperated) can also be entered to only get codon counts from those transcripts.
Build a nucleotide frequence plot centered around the TSS (transcription start site), CDS start, CDS stop, or TTS (transcription termination site). A comma seperated list of transcripts needs to be entered.
Generate a table of uORFs or other ORF types with information such as start/stop positions, length and start codon. Results can also be downloaded as a csv file.
Used to detect translated ORFs in various categories, which can be selected at the top left of the page. Several features will be calculated derived from the selected ribosome profiling data. These features are then ranked, individual ranks are then aggregated to create a global rank. A table will be returned showing ORFs ranked from highest to lowest. Each ORF will have a link to view the ORF on the single transcript plot page with the selected ribosome profiling data where the ORF in question will be highlighted. Only the top 1000 ranked ORFs will be shown on the page but all ranked ORFs can be downloaded in csv format by clicking the link above and to the left of the table. If proteomics data is also selected a column in the csv file will indicate the number of peptides that overlap the ORF in question. If all files in a particular study are selected an aggregated file for that study will be used which will speed up the search significantly.
This feature compares the number of reads 5' of the start codon to the number of reads 3' of the start codon, with the assumption that there should be a large increase in reads at the start if the ORF is translated and thus a large difference between these two values. Only inframe reads are considered from the 5 codons before and after the start.
This feature compares the number of reads 5' of the stop codon to the number of reads 3' of the stop codon, with the assumption that there should be a large decrease in reads at the stop if the ORF is translated and thus a large difference between these two values. Only inframe reads are considered from the 5 codons before and after the stop.
This feature is dependent on the frame difference feature (highest or lowest). If the frame difference feature (highest) is used, this feature counts the number of positions in the ORF where the inframe count is higher than either of the other two frames, if frame difference (lowest) is used it counts the number of positions where the inframe reads are not the lowest of the three frames.
Takes the number of inframe reads and substracts the number of out of frame reads (from whichever of the two is highest).
Takes the number of inframe reads and substracts the number of out of frame reads (from whichever of the two is lowest).
Limit the search to only ORFs that minimally or maximally overlap a CDS region, values entered are between 0 and 1. If for example the min limit is set to 0 and max limit set to 0.5, the only ORFs that are returned will overlap CDS regions by 50% or less. Values will be changed automatically to the suggested values when switching ORF types, for example setting the max limit to 1 (100%) if searching for nested or CDS ORF types.
Limit the search to only ORFs with a length between the min and max limits (in nucleotides).
Search for ORFs only on principal transcripts, all transcripts or a custom list (enter a comma seperated list in the input box).
Search for ORFs begining with either AUG, CUG or GUG codons.


WITNESS LIST Energy Resources Committee January 18, 2006-9:00A Interim Charges For: Patterson, Jerry (General Land Office) On: Ames Jones, Elizabeth (Railroad Commission of Texas) Burns, Morris (Permian Basin Petroleum Association) Carrillo, Victor (Railroad Commission of Texas) Haynes, Adam (Texas Independent Producers and Royalty Owners) Hudson, Paul (Public Utility Commission) Kitchens, Ronald (Railroad Commission of Texas) Nugent, Patrick (Texas Pipeline Association) Parsley, Julie (Public Utility Commission) Sebree, Ben (Texas Oil and Gas Association) Smitherman, Barry (Public Utility Commission) Welder, Roger (Texas Land and Minerals Association)


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12/6/2018
Music lovers are expected to pack the house at Casa Nueva or The Union Bar and Grill during busy weekends like Boogie on The Bricks, but what keeps people coming back are the local bands who have built their legacy in Athens.
For Any Colour, a Pink Floyd tribute band that got its name from a track on the album The Dark Side of the Moon, Athens has become a place for them to perfect their sound and share their love for music with people of all ages.
“They continue to invite us back because they like what we do and we do it well,” said John Patrick Corliss, bass and vocals for Any Colour. “If you don’t play it right, people are going to know. Your attention to detail has to be impeccable.”
Inspired by jazz and rock music, members of Any Colour grew up playing acoustic versions of Pink Floyd songs and began to hone in on their philosophical lyrics, experimentation with sound and extended compositions.
Any Colour has performed more than 10 times in Athens during the past academic year, specifically at Casa Nueva, The Union and Jackie O’s Brewery. Any Colour has also performed in Columbus, Ohio, where they have an upcoming show on Dec. 21 at Woodlands Tavern.
Nate Swanson| FOR THE POST
Colin Richards from "Spare Change" performs nostalgic 'late 60s songs at Casa Nueva for Benefest on Nov. 29, 2018.
“I love the music so much so that I think the one thing you can do to keep the music going is to pay tribute,” said Nick Dzuban, guitar and vocals for Any Colour. “If we play well enough that people go home and still play Pink Floyd, then I think we’re doing our job right.”
Whether the crowd ranges from students to parents to Athens residents, all generations appreciate live music done well. The members of Any Colour like to transform the raw recordings of Pink Floyd into their interpretation of a live show.
“One of the biggest compliments we can receive when we play at something like Dad’s Weekend is ‘Man, I saw Floyd in ’77 and you guys knocked it right out of the park’,” Corliss said.
Bartenders and employees at Casa Nueva agree Any Colour has mastered the psychedelic rock sound that made Pink Floyd one of the most influential groups in music history.
“They’re a super group of dudes that have a dedication to Floyd and can really play a tune,” said Mike Lannen, a bartender at Casa Nueva and frontman of his own band Death To The State. “That’s pretty cool in my book.”
What to expect at a show at Casa Nueva is not extravagant lights or overbearing fog machine effects, rather, the atmosphere is what makes shows so popular. Athens’ small-town vibe allows the crowd to be immersed with the music along with other enthusiasts from the area.
“The great thing about Casa is there’s always a positive atmosphere,” said Lannen. “Athens is very much a town of musicians supporting other musicians with a wide spectrum of different music.”
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Dubai, the most populous city in the United Emirates, entered its modern era shortly after the conclusion of the First Gulf War. While many agree the war initially had a negative economical impact on the city it was very quick to recover. The recovery is attributed to several factors but in particular the healthy value of oil following both Iraqi conflicts. This reliable source of income allowed the country's leadership to direct their focus on free trade and tourism. Dubai found a great deal of success moving in this direction and today is easily one of the most recognizable modern cities. With their success the ruler of Dubai H.H.S. Mohammed bin Rashid Al Maktoum, foresaw the impending need to equip the city with an efficient and modern rapid transit system and so story of one of the world’s most innovative and interesting metro infrastructure begins.
Our region is home to more than 200 million young people. We have the opportunity to inspire them with hope and to direct their energies towards improving their lives and the lives of those around them. If we fail, we will abandon them to emptiness, unemployment and the malicious ideologies of terrorism.


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|
XPMP2
X-Plane multiplayer library 2 - using instancing
|
A/C animation dataRef changes. More...
#include <XPMPRemote.h>
Classes | |
| struct | DataRefValTy |
| dataRef animation types and value More... | |
Public Member Functions | |
| RemoteAcAnimTy (XPMPPlaneID _id) | |
| Constructor. More... | |
| size_t | msgSize () const |
| current message size More... | |
Static Public Member Functions | |
| static constexpr size_t | msgSize (std::uint8_t num) |
message size assuming num array elements More... | |
Public Attributes | |
| std::uint32_t | modeS_id = 0 |
| plane's unique id at the sender side (might differ remotely in case of duplicates) More... | |
| std::uint8_t | numVals = 0 |
| number of dataRef values in the following array More... | |
| std::uint8_t | filler = 0 |
| not yet used More... | |
| struct XPMP2::RemoteAcAnimTy::DataRefValTy | v [1] |
| array of dataRef animation types and value More... | |
A/C animation dataRef changes.
This structure has variable length depending on the number of actual dataRef values to carry. And several of these variable length structures are added into one variable length network message.
| struct XPMP2::RemoteAcAnimTy::DataRefValTy |
dataRef animation types and value
| Class Members | ||
|---|---|---|
| DR_VALS | idx | index into XPMP2::Aircraft::v |
| uint8_t | v | dataRef animation value |
|
inline |
Constructor.
|
inline |
current message size
|
inlinestaticconstexpr |
message size assuming num
array elements
| std::uint8_t XPMP2::RemoteAcAnimTy::filler = 0 |
not yet used
| std::uint32_t XPMP2::RemoteAcAnimTy::modeS_id = 0 |
plane's unique id at the sender side (might differ remotely in case of duplicates)
| std::uint8_t XPMP2::RemoteAcAnimTy::numVals = 0 |
number of dataRef values in the following array
| struct XPMP2::RemoteAcAnimTy::DataRefValTy XPMP2::RemoteAcAnimTy::v[1] |
array of dataRef animation types and value


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Creates a three-dimensional texture from raw data, with parameters to divide it into width, height, and depth. This type of texture can only be used with a WebGL 2 rendering context.
[page:Object data] --
[link:https://developer.mozilla.org/en-US/docs/Web/API/ArrayBufferView ArrayBufferView] of the texture.
[page:Number width] -- width of the texture.
[page:Number height] -- height of the texture.
[page:Number depth] -- depth of the texture.
This creates a [name] with repeating data, 0 to 255
// create a buffer with some data
const sizeX = 64;
const sizeY = 64;
const sizeZ = 64;
const data = new Uint8Array( sizeX * sizeY * sizeZ );
let i = 0;
for ( let z = 0; z < sizeZ; z ++ ) {
for ( let y = 0; y < sizeY; y ++ ) {
for ( let x = 0; x < sizeX; x ++ ) {
data[ i ] = i % 256;
i ++;
}
}
}
// use the buffer to create the texture
const texture = new THREE.Data3DTexture( data, sizeX, sizeY, sizeZ );
texture.needsUpdate = true;
[example:webgl2_materials_texture3d WebGL2 / materials / texture3d]
[example:webgl2_materials_texture3d_partialupdate WebGL2 / materials / texture3d / partialupdate]
[example:webgl2_volume_cloud WebGL2 / volume / cloud]
[example:webgl2_volume_perlin WebGL2 / volume / perlin]
See the base [page:Texture Texture] class for common properties.
Whether the texture is flipped along the Y axis when uploaded to the GPU. Default is `false`.
Whether to generate mipmaps (if possible) for the texture. Default is `false`.
Overridden with a record type holding data, width and height and depth.
Read-only flag to check if a given object is of type [name].
How the texture is sampled when a texel covers more than one pixel. The
default is [page:Textures THREE.NearestFilter], which uses the value of
the closest texel.
See the [page:Textures texture constants] page for details.
How the texture is sampled when a texel covers less than one pixel. The
default is [page:Textures THREE.NearestFilter], which uses the value of
the closest texel.
See the [page:Textures texture constants] page for details.
`1` by default. Specifies the alignment requirements for the start of each pixel row in memory. The allowable values are 1 (byte-alignment), 2 (rows aligned to even-numbered bytes), 4 (word-alignment), and 8 (rows start on double-word boundaries). See [link:https://registry.khronos.org/OpenGL-Refpages/es3.0/html/glPixelStorei.xhtml glPixelStorei] for more information.
This defines how the texture is wrapped in the depth direction.
The default is [page:Textures THREE.ClampToEdgeWrapping], where the edge
is clamped to the outer edge texels. The other two choices are
[page:Textures THREE.RepeatWrapping] and [page:Textures THREE.MirroredRepeatWrapping].
See the [page:Textures texture constants] page for details.
See the base [page:Texture Texture] class for common methods.
[link:https://github.com/mrdoob/three.js/blob/master/src/[path].js src/[path].js]


# Java Properties Sample File # This is a commment ! This is also a comment # comment ! comment # keys and values can be separated by '=', ':' or by a series of spaces: key = value key : value key value # spaces, '=', or ':' can be used in the key by escaping them with a backslash: key\:continued = value key\=continued = value key\ continued = value # the key starts with the first non blank character on the line: key = value # there can be an arbitrary amount of spaces between the key, the value, and the ':' or '=', if present: key=value key = value key:value key : value key value key value # values can be continued on another line by escaping the line end character with a backslash: key = value \ value continued # This works also over more than one line: key = value\ value continued 1\ value continued 2\ value continued 3 # But if the line ends with an even number of backslashes, the value is not continued: key = value\\ newkey = value key = value\\\\ newkey = value # both key and value can be ommitted, in which case either the key or the value or both equal the empty string: key = key = value = :


--- title: "The start of the New Year is a perfect time to start a stop doing list and to make this the cornerstone of your New Year resolutions, be it for your company, your family or yourself. It also is a perfect time to clarify your three circles, mirroring at a personal level the three questions asked by Smith: \n\n1) What are you deeply passionate about?\n2) What are you are genetically encoded for — what activities do you feel just “made to do”?\n3) What makes economic sense — what can you make a living at?" date: '2009-12-05T16:25:34+05:30' tags: [] tumblr_url: https://sureshshanmugam.com/post/270213011/the-start-of-the-new-year-is-a-perfect-time-to ---
—Jim Collins - Articles - Best New Year’s Resolution?The start of the New Year is a perfect time to start a stop doing list and to make this the cornerstone of your New Year resolutions, be it for your company, your family or yourself. It also is a perfect time to clarify your three circles, mirroring at a personal level the three questions asked by Smith:
1) What are you deeply passionate about?
2) What are you are genetically encoded for — what activities do you feel just “made to do”?
3) What makes economic sense — what can you make a living at?


I am a huge Automotive enthusiast and I am planning on taking the skills I learn from coding and applying it to the Automotive Industry. My dream job is to work with BMW coding their user interface or working on their MPerformace team in Germany.


Sherlock Holmes : Secure your Identity
If answer is "A", pass through Green door. If answer is "B", pass through Red door. If answer is "C", pass through Orange door.
Question will be displayed here
If answer is "True", pass through Green door.
If answer is "False", pass through Red door.
If answer is "True", pass through Orange door.


{% extends "base.html" %} {% block login %}
Since November 1, 2017, the Beijing pet diagnosis and treatment center of heating paws has officially opened 24 hours a day to protect the health of pet. After the decoration and upgrading, the hospital now has all kinds of professional diagnosis and treatment instruments: Lycra microscope, digital image system, respiratory anesthesia machine, Mindray B-ultrasound machine, blood pressure meter, biochemical analyzer and other conventional detection equipment. In addition, the hospital implements information-based management, and will establish pet's life-long health records for all pets, and regularly carry out health return visit to pet.
Heating paws Chengdu pet diagnosis and treatment center is the first small animal general diagnosis and treatment center and referral center in Southwest China with international advanced medical equipment and professional service standards. The hospital process is scientific and rigorous, the diagnosis and treatment environment is comfortable and safe, and the dogs and cats are managed and treated in different areas, focusing on providing brand-new animal diagnosis and treatment experience and large-scale clinical application management. At the same time, it also undertakes the training of students' practice and practicing veterinary doctors in some agricultural colleges. The diagnosis and treatment center gathers a number of senior small animal doctors for consultation, provides domestic professional clinical diagnosis and treatment services for small animals, and redefines the dignity and value of each small life.
Healing paws Shanghai Pet diagnosis and treatment center not only provides all-round pet health and diagnosis and treatment services, but also focuses on the development of animal bone surgery, neurosurgery and other characteristic departments. In order to better treat orthopedic diseases, we have introduced various kinds of diagnosis and treatment equipment, such as C-arm, ultrasonic bone knife, tplo bone plate system, PrCl advanced locking bone plate system, etc. At the same time, we also equipped with the rare MRI equipment in Shanghai pet hospital. With MRI, doctors can check the pathological organs in pets more accurately and improve the treatment accuracy.


[gecode-users] large Instance
Amin Alah Mousavi Nogholi
a.mousavinogholi at qut.edu.au
Mon Sep 2 02:30:35 CEST 2013
Hi,
I need to solve a problem with millions of decision variables and constraints. I could not solve this problem by CPlex Cp optimiser. I am just wondering that Gecode can solve or not? Does anybody has such an instance?
The second question is that I have a 4-dimensional decision variable. Can Gecode handle this decision variable.
Thanks in advance,
Amin
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CENTRAL BANK OF NIGERIA I want to assure you that there is light at the end of the tunnel after all the disappointment and frustrations that you have gone through in the hands of the fraudsters and impostors. The good news is that you can still receive your contract payment if you do the right thing.Please do not allow anybody to deceive and defraud you again. I am the only person with the constitutional power and authority to transfer your contract payment. Without my signature no beneficiary will ever receive one cent from CBN. All the powers to transfer any contract payment belongs to me and to no one else. There is only one genuine CBN Governor and that is me and no other person.. The only way I can prove myself is to transfer your funds into your account within 24hours that you have fulfilled CBN transfer condition. You have suffered enough in the hands of the fraudsters It is now time for you to see the light and receive your contractpayment. Stop listening to all thestupid,baseless stories from the fraudsters as to the reasons why your contract payment is being delayed. They are not telling you the truth. The plain truth is that they do not have the power to effect your contract payment.I am responsible for withholding your contract payment file,so if you like pay,pay, pay to the fraudsters until you are tired and frustrated of paying, you will still not receive one dollar until you adhere strictly to this genuine CBN transfer conditions. Contact me immediately you receive this letter through email.if you are serious about receiving your contract payment. I will effect your transfer within 72hours that you have contacted me.


Utilities¶
Custom Datasets¶
-
class
trust.utils.custom_dataset.
DataHandler_CIFAR10
(*args, **kwds)[source]¶ Data Handler to load CIFAR10 dataset. This class extends
torch.utils.data.Dataset
to handle loading data even without labels- Parameters
X (numpy array) – Data to be loaded
y (numpy array, optional) – Labels to be loaded (default: None)
select (bool) – True if loading data without labels, False otherwise
-
class
trust.utils.custom_dataset.
DataHandler_MNIST
(*args, **kwds)[source]¶ Data Handler to load MNIST dataset. This class extends
torch.utils.data.Dataset
to handle loading data even without labels- Parameters
X (numpy array) – Data to be loaded
y (numpy array, optional) – Labels to be loaded (default: None)
select (bool) – True if loading data without labels, False otherwise
-
class
trust.utils.custom_dataset.
DataHandler_SVHN
(*args, **kwds)[source]¶ Data Handler to load SVHN dataset. This class extends
torch.utils.data.Dataset
to handle loading data even without labels- Parameters
X (numpy array) – Data to be loaded
y (numpy array, optional) – Labels to be loaded (default: None)
select (bool) – True if loading data without labels, False otherwise
-
class
trust.utils.custom_dataset.
DataHandler_UTKFace
(*args, **kwds)[source]¶ Data Handler to load UTKFace dataset. This class extends
torch.utils.data.Dataset
to handle loading data even without labels- Parameters
X (numpy array) – Data to be loaded
y (numpy array, optional) – Labels to be loaded (default: None)
select (bool) – True if loading data without labels, False otherwise
-
trust.utils.custom_dataset.
load_dataset_custom
(datadir, dset_name, feature, split_cfg, augVal=False, dataAug=True)[source]¶ Loads a common dataset with additional options to create class imbalances, out-of-distribution classes, and redundancies.
- Parameters
datadir (string) – The root directory in which the data is stored (or should be downloaded)
dset_name (string) – The name of the dataset. This should be one of ‘cifar10’, ‘mnist’, ‘svhn’, ‘cifar100’, ‘breast-density’.
feature (string) – The modification that should be applied to the dataset. This should be one of ‘classimb’, ‘ood’, ‘duplicate’, ‘vanilla’
split_cfg (dict) –
- Contains information relating to the dataset splits that should be created. Some of the keys for this dictionary are as follows:
- ’per_imbclass_train’: int
The number of examples in the train set for each imbalanced class (classimb)
- ’per_imbclass_val’: int
The number of examples in the validation set for each imbalanced class (classimb)
- ’per_imbclass_lake’: int
The number of examples in the lake set for each imbalanced class (classimb)
- ’per_class_train’: int
The number of examples in the train set for each balanced class (classimb)
- ’per_class_val’: int
The number of examples in the validation set for each balanced class (classimb)
- ’per_class_lake’: int
The number of examples in the lake set for each balanced class (classimb)
- ’sel_cls_idx’: list
A list of classes that are affected by class imbalance. (classimb)
- ’train_size’: int
The size of the train set (vanilla, duplicate)
- ’val_size’: int
The size of the validation set (vanilla, duplicate)
- ’lake_size’: int
The size of the lake set (vanilla, duplicate)
- ’num_rep’: int
The number of times to repeat a selection in the lake set (duplicate)
- ’lake_subset_repeat_size’: int
The size of the repeated selection in the lake set (duplicate)
- ’num_cls_imbalance’: int
The number of classes to randomly affect by class imbalance. (classimb)
- ’num_cls_idc’: int
The number of in-distribution classes to keep (ood)
- ’per_idc_train’: int
The number of in-distribution examples to keep in the train set per class (ood)
- ’per_idc_val’: int
The number of in-distribution examples to keep in the validation set per class (ood)
- ’per_idc_lake’: int
The number of in-distribution examples to keep in the lake set per class (ood)
- ’per_ood_train’: int
The number of OOD examples to keep in the train set per class (ood)
- ’per_ood_val’: int
The number of OOD examples to keep in the validation set per class (ood)
- ’per_ood_lake’: int
The number of OOD examples to keep in the lake set per class (ood)
augVal (bool, optional) – If True, the train set will also contain affected classes from the validation set. The default is False.
dataAug (bool, optional) – If True, the all but the test set will be affected by random cropping and random horizontal flip. The default is True.
- Returns
Returns a train set, validation set, test set, lake set, and number of classes. Amount of returned items depends on specific configuration. Each set is an instance of torch.utils.data.Dataset
- Return type
tuple
Other Utilities¶
-
class
trust.utils.utils.
ConcatWithTargets
(*args, **kwds)[source]¶ Provides a convenience torch.utils.data.Dataset subclass that allows one to access a targets field while creating concatenations of two datasets.
- Parameters
dataset1 (torch.utils.data.Dataset) – The first dataset to concatenate. Must have a targets field.
dataset2 (torch.utils.data.Dataset) – The second dataset to concatenate. Must have a targets field.
-
class
trust.utils.utils.
LabeledToUnlabeledDataset
(*args, **kwds)[source]¶ Provides a convenience torch.utils.data.Dataset subclass that allows one to ignore the labels in a labeled dataset, thereby making it unlabeled.
- Parameters
wrapped_dataset (torch.utils.data.Dataset) – The labeled dataset in which only the data will be returned.
-
class
trust.utils.utils.
SubsetWithTargets
(*args, **kwds)[source]¶ Provides a convenience torch.utils.data.Dataset subclass that allows one to access a targets field while creating subsets of the dataset.
- Parameters
dataset (torch.utils.data.Dataset) – The dataset from which to pull a subset
indices (sequence) – A sequence of indices of the passed dataset from which to select a subset
labels (sequence) – A sequence of labels for each of the elements drawn for the subset. This sequence should be the same length as indices.
-
class
trust.utils.utils.
SubsetWithTargetsSingleChannel
(*args, **kwds)[source]¶ Provides a convenience torch.utils.data.Dataset subclass that allows one to access a targets field while creating subsets of the dataset. Additionally, the single-channel images from the wrapped dataset are expanded to three channels for compatibility with three-chanel model input.
- Parameters
dataset (torch.utils.data.Dataset) – The dataset from which to pull a subset
indices (sequence) – A sequence of indices of the passed dataset from which to select a subset
labels (sequence) – A sequence of labels for each of the elements drawn for the subset. This sequence should be the same length as indices.
Models¶
- We have incorporated several neural network architectures in TRUST. Below is a list of neural network architectures:
densenet
dla
dla_simple
dpn
efficientnet
googlenet
lenet
mobilenet
mobilenetv2
pnasnet
preact_resnet
regnet
resnet
resnext
senet
shufflenet
shufflenetv2
vgg
To use a custom model architecture, ensure the model architecture has the following:
The forward method should have two more variables:
A boolean variable last which -
If *true: returns the model output and the output of the second last layer
If *false: Returns the model output.
A boolean variable ‘freeze’ which -
If *true: disables the tracking of any calculations required to later calculate a gradient i.e skips gradient calculation over the weights
If *false: otherwise
get_embedding_dim() method which returns the number of hidden units in the last layer.


USOPC to add athlete reps in wake of Nassar sex abuse
by EDDIE PELLSThe U.S. Olympic and Paralympic Committee will add athletes to its board and enhance its oversight of individual sports organizations in a package of reforms stemming from the Larry Nassar sex-abuse scandal.
The reforms were approved Thursday and go into effect in January. They come with Congress proposing its own, harsher, set of changes , after the scandal exposed the USOPC as deficient when it came to protecting and ensuring the safety of Olympic athletes.
Athletes will hold 33 percent of the board seats. The U.S. Olympians and Paralympians Association and Athletes Advisory Council will collaborate to choose the leaders.
The USOPC will have more power of policing national governing bodies.
An issue in the Nassar case was whether the USOPC was directly responsible for gymnasts or if responsibility was solely within USA Gymnastics, which is where Nassar worked as a volunteer doctor.
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Richard Phillips Feynman was a well known American physicist and a recipient of the Nobel Prize in Physics. He worked in theoretical physics and also pioneered the field of quantum computing. He visited South America for ten months, giving lectures and enjoying life in the tropics. He is also known for his books "Surely You're Joking, Mr. Feynman!" and "What Do You Care What Other People Think?", which include some of his adventures below the equator.
His life-long addiction was solving and making puzzles, locks, and cyphers. Recently, an old farmer in South America, who was a host to the young physicist in 1949, found some papers and notes that is believed to have belonged to Feynman. Among notes about mesons and electromagnetism, there was a napkin where he wrote a simple puzzle: "how many different squares are there in a grid of N ×N squares?".
In the same napkin there was a drawing which is reproduced below, showing that, for N=2, the answer is 5.
The input contains several test cases. Each test case is composed of a single line, containing only one integer N, representing the number of squares in each side of the grid (1 ≤ N ≤ 100).
The end of input is indicated by a line containing only one zero.
For each test case in the input, your program must print a single line, containing the number of different squares for the corresponding input.
Input: 2 1 8 0 Output: 5 1 204


The Journalists Directory identifies those in the news media reporting on speech on college and university campuses in the United States.
To suggest a journalist be added to the directory, please fill out and submit this form.


Introduction
Welcome to the NextAcademy Blog API! You can use our API to access The Blog API endpoints
Blogs
Get All Blogs
The above command returns JSON structured like this:
[
{
"id": 1,
"title": "Time to write bloggsszzzzzzz",
"body": "One day along long time ago....",
"created_at": "2016-07-15T09:16:13.702Z",
"updated_at": "2016-07-15T09:16:13.702Z"
},
{
"id": 6,
"title": "Landing Page",
"body": "Dunno",
"created_at": "2016-07-15T16:24:15.324Z",
"updated_at": "2016-07-15T16:24:15.324Z"
},
{
"id": 3,
"title": "Love hate",
"body": "Hahahaha",
"created_at": "2016-07-15T14:45:25.106Z",
"updated_at": "2016-07-15T16:28:08.224Z"
},
{
"id": 4,
"title": "Respect my taste",
"body": "Muahaha abc abc asdfasfadsf\nAdds did adds fldsf also\n\nSell falls faked\n\nSadly ka;add \n\nSelf las;do kf\n\n\nSaddle ka;fall fads\n\n\nAsks kf ask;sick a\nDad\nAds flossed\n AIDS false kids;said a;leakage\nAlaskan;dollars;dsf",
"created_at": "2016-07-15T15:41:17.802Z",
"updated_at": "2016-07-15T22:41:27.051Z"
}
]
This endpoint retrieves all blogs.
HTTP Request
GET https://nextacademy-ios-blog.herokuapp.com/api/v1/blogs
Get a Specific blog
The above command returns JSON structured like this:
{
"id": 1,
"title": "Time to write bloggsszzzzzzz",
"body": "One day along long time ago....",
"created_at": "2016-07-15T09:16:13.702Z",
"updated_at": "2016-07-15T09:16:13.702Z"
}
This endpoint retrieves a specific blog.
HTTP Request
GET https://nextacademy-ios-blog.herokuapp.com/api/v1/blogs/<ID>
URL Parameters
| Parameter | Description |
|---|---|
| ID | The ID of the blog to retrieve |
Create a Specific blog
Example Request:
curl https://nextacademy-ios-blog.herokuapp.com/api/v1/blogs \
-X POST
-H 'Content-Type: application/json'
-d '{"blog":
{
"title": "New blog",
"body": "Blog content"
}
}'
The above command returns JSON structured like this:
{
"id": 1,
"title": "Time to write bloggsszzxzzzzz",
"body": "One day along long time ago....",
"created_at": "2016-07-15T09:16:13.702Z",
"updated_at": "2016-07-15T09:16:13.702Z"
}
This endpoint creates a specific blog.
HTTP Request
POST https://nextacademy-ios-blog.herokuapp.com/api/v1/blogs
URL Parameters
| Parameter | Description |
|---|---|
| blog | The blog json including title and body |
Update a Specific blog
Example Request:
curl https://nextacademy-ios-blog.herokuapp.com/api/v1/blogs \
-X PUT
-H 'Content-Type: application/json'
-d '{"blog":
{
"title": "New blog",
"body": "Blog content"
}
}'
The above command returns JSON structured like this:
{
"id": 1,
"title": "New blog",
"body": "Blog content",
"created_at": "2016-07-15T09:16:13.702Z",
"updated_at": "2016-07-15T09:16:13.702Z"
}
This endpoint updates a specific blog.
HTTP Request
PUT https://nextacademy-ios-blog.herokuapp.com/api/v1/blogs/<ID>
URL Parameters
| Parameter | Description |
|---|---|
| ID | The ID of the blog to update |
| blog | The blog json including title and body |
Delete a Specific blog
Example Request:
curl https://nextacademy-ios-blog.herokuapp.com/api/v1/blogs/1\
-X DELETE
-H 'Content-Type: application/json'
The above returns a status 200 if successful:
This endpoint deletes a specific blog.
HTTP Request
DELETE https://nextacademy-ios-blog.herokuapp.com/api/v1/blogs/<ID>
URL Parameters
| Parameter | Description |
|---|---|
| ID | The blog ID to destroy |
Comments
Get All Comments
The above command returns JSON structured like this:
[
{
"id": 1,
"author": "You Jing",
"body": "edited comment",
"blog_id": 1,
"created_at": "2016-07-16T01:20:17.884Z",
"updated_at": "2016-07-16T01:20:17.884Z"
},
{
"id": 2,
"author": "You Jing",
"body": "new one comment",
"blog_id": 1,
"created_at": "2016-07-16T01:20:22.559Z",
"updated_at": "2016-07-16T01:20:22.559Z"
}
]
This endpoint retrieves all comments.
| Parameter | Description |
|---|---|
| ID | The ID of the blog to retrieve comments for |
HTTP Request
GET https://nextacademy-ios-blog.herokuapp.com/api/v1/blogs/<ID>/comments
Get a Specific comment
The above command returns JSON structured like this:
{
"id": 1,
"author": "You Jing",
"body": "edited comment",
"blog_id": 1,
"created_at": "2016-07-16T01:20:17.884Z",
"updated_at": "2016-07-16T01:20:17.884Z"
}
This endpoint retrieves a specific comment.
HTTP Request
GET https://nextacademy-ios-blog.herokuapp.com/api/v1/comments/<ID>
URL Parameters
| Parameter | Description |
|---|---|
| ID | The ID of the comment to retrieve |
Create a Specific comment
Example Request:
curl https://nextacademy-ios-blog.herokuapp.com/api/v1/blogs/1/comments \
-X POST
-H 'Content-Type: application/json'
-d '{"comment":
{ "author": "You Jing",
"body": "new one comment"
}
}
'
The above command returns JSON structured like this:
{
"id": 4,
"author": "You Jing",
"body": "see response",
"blog_id": 1,
"created_at": "2016-07-16T01:24:28.740Z",
"updated_at": "2016-07-16T01:24:28.740Z"
}
This endpoint creates a specific comment.
HTTP Request
POST https://nextacademy-ios-blog.herokuapp.com/api/v1/blogs/<ID>/comments
URL Parameters
| Parameter | Description |
|---|---|
| ID | The blog id to create a comment for |
| comment | The comment json including author and body |
Update a Specific comment
Example Request:
curl https://nextacademy-ios-blog.herokuapp.com/api/v1/comments/1 \
-X POST
-H 'Content-Type: application/json'
-d '{"comment":
{ "author": "You Jing",
"body": "edit a comment"
}
}
'
The above command returns JSON structured like this:
{
"id": 1,
"author": "You Jing",
"body": "edit a comment",
"blog_id": 1,
"created_at": "2016-07-16T01:20:17.884Z",
"updated_at": "2016-07-16T01:26:39.404Z"
}
This endpoint updates a specific comment.
HTTP Request
PUT https://nextacademy-ios-blog.herokuapp.com/api/v1/comments/<ID>
URL Parameters
| Parameter | Description |
|---|---|
| ID | The comment id to update |
| comment | The comment json including author and body |
Delete a Specific Comment
Example Request:
curl https://nextacademy-ios-blog.herokuapp.com/api/v1/comments/1\
-X DELETE
-H 'Content-Type: application/json'
The above returns a status 200 if successful:
This endpoint deletes a specific comment.
HTTP Request
DELETE https://nextacademy-ios-blog.herokuapp.com/api/v1/comments/<ID>
URL Parameters
| Parameter | Description |
|---|---|
| ID | The comment id to delete |
Errors
The Kittn API uses the following error codes:
| Error Code | Meaning |
|---|---|
| 400 | Bad Request – Your request sucks |
| 401 | Unauthorized – Your API key is wrong |
| 403 | Forbidden – The resource requested is hidden for administrators only |
| 404 | Not Found – The specified resource could not be found |
| 405 | Method Not Allowed – You tried to access a resource with an invalid method |
| 406 | Not Acceptable – You requested a format that isn’t json |
| 410 | Gone – The resource requested has been removed from our servers |
| 418 | I’m a teapot |
| 429 | Too Many Requests – You’re requesting too many resources! Slow down! |
| 500 | Internal Server Error – We had a problem with our server. Try again later. |
| 503 | Service Unavailable – We’re temporarially offline for maintanance. Please try again later. |


Vince Staples releases new song ‘Sheet Music’ and second episode of web series
‘The Vince Staples Show’ episode 2 features Ray J
Vince Staples has released the second episode of his YouTube series The Vince Staples Show, as well as the new song ‘Sheet Music’.
This new episode – also titled ‘Sheet Music’ – follows The Vince Staples Show’s debut episode and previous song ‘So What?’, which were both released in August. These are the California rapper’s first releases since his 2018 album, ‘FM!’
The new ‘Sheet Music’ episode begins with Staples’ girlfriend, who discovers via Instagram that he’s been cheating on her. After she storms into the house he shares with fellow California rapper Buddy, he leaps out of the window and runs to Ray J’s house to take refuge.
Ray J then introduces Staples to newfangled technology like invisible earbuds, and agrees to let him stay at his home if he signs a non-disclosure agreement (NDA) – or in this case, an “ND Ray”. Watch the second episode of The Vince Staples Show below:
Staples’ new song ‘Sheet Music’ plays over the credits of the new episode. On the LeKen Taylor-produced track, he raps in AutoTune: “Back when I only had a dollar and a dream / Spent that dollar on a 22 revolver / Man, if I only had a beam / Man, if lil shorty was my queen.”
Listen to ‘Sheet Music’ here:
Later this month, Staples will have a new original song out as part of the soundtrack of the movie Queen & Slim, which stars Daniel Kaluuya and Jodie Turner-Smith as a “black Bonnie & Clyde”.


Enum RecognitionConfig.AudioEncoding (2.0.0)
public enum RecognitionConfig.AudioEncoding extends Enum<RecognitionConfig.AudioEncoding> implements ProtocolMessageEnum
Audio encoding of the data sent in the audio message. All encodings support
only 1 channel (mono) audio. Only FLAC
includes a header that describes
the bytes of audio that follow the header. The other encodings are raw
audio bytes with no header.
For best results, the audio source should be captured and transmitted using
a lossless encoding (FLAC
or LINEAR16
). Recognition accuracy may be
reduced if lossy codecs (such as AMR, AMR_WB and MULAW) are used to capture
or transmit the audio, particularly if background noise is present.
Protobuf enum google.cloud.speech.v1beta1.RecognitionConfig.AudioEncoding
Implements
ProtocolMessageEnumStatic Fields
| Name | Description |
| AMR | Adaptive Multi-Rate Narrowband codec. |
| AMR_VALUE | Adaptive Multi-Rate Narrowband codec. |
| AMR_WB | Adaptive Multi-Rate Wideband codec. |
| AMR_WB_VALUE | Adaptive Multi-Rate Wideband codec. |
| ENCODING_UNSPECIFIED | Not specified. Will return result google.rpc.Code.INVALID_ARGUMENT. |
| ENCODING_UNSPECIFIED_VALUE | Not specified. Will return result google.rpc.Code.INVALID_ARGUMENT. |
| FLAC | This is the recommended encoding for |
| FLAC_VALUE | This is the recommended encoding for |
| LINEAR16 | Uncompressed 16-bit signed little-endian samples (Linear PCM).
This is the only encoding that may be used by |
| LINEAR16_VALUE | Uncompressed 16-bit signed little-endian samples (Linear PCM).
This is the only encoding that may be used by |
| MULAW | 8-bit samples that compand 14-bit audio samples using G.711 PCMU/mu-law. |
| MULAW_VALUE | 8-bit samples that compand 14-bit audio samples using G.711 PCMU/mu-law. |
| UNRECOGNIZED |
Static Methods
| Name | Description |
| forNumber(int value) | |
| getDescriptor() | |
| internalGetValueMap() | |
| valueOf(Descriptors.EnumValueDescriptor desc) | |
| valueOf(int value) | Deprecated. Use #forNumber(int) instead. |
| valueOf(String name) | |
| values() |
Methods
| Name | Description |
| getDescriptorForType() | |
| getNumber() | |
| getValueDescriptor() |


{% extends "_layouts/default.html" %} {% block custom_head %} {% endblock %} {% block content %} {% from "macros.html" import render_field %}
This web-based tool is designed to compare any two wild C. elegans strains for insertion-deletion (indel) variants that can be genotyped using PCR. These molecular markers can be used to follow each respective genetic background in crosses.
Enter a specific genomic region. The browser will show indel variants between these two strains. The table will show regions to search for primers. Primers might not be found flanking some indel sites. Click on additional indel sites to find one that has good quality primers. For each of these indels, you can search for primers to genotype them in the two wild strains using differential PCR product sizes. Primers are designed to avoid natural variants in either strain to ensure that the PCR works in both genetic backgrounds.
The browser also shows divergent regions, where the reference genome and some wild isolates have sequences with many variants. These regions should be avoided because indel calls are less reliable and high levels of variation will make primer searches more error-prone.


Player 1
Player 2
Player 3
Player 4
| Card Rank | Card Name | Card Amount | Card Description |
|---|---|---|---|
| 8 | Princess | 1 | Knocked out if discarded |
| 7 | Countess | 1 | Discard if the other card you have is King or Prince |
| 6 | King | 1 | Trade hands with another player |
| 5 | Prince | 2 | Choose one player to discards his or her hand |
| 4 | Handmaid | 2 | Protect yourself from other players until next turn |
| 3 | Baron | 2 | Compare your other card with another player, the person with the lower rank card is knocked out |
| 2 | Priest | 2 | Secretly look at another player's hand |
| 1 | Guard | 5 | Choose another player and guess his/her hand, the person is knocked out if you guessed right |


--- title: Data layout: data ---
For a ~10 x 11mm tissue FFPE specimen under 20X objective, each cycle of t-CyCIF generates around 160 individual image tiles. The assembly process involves stitching sequential image tiles from a single t-CyCIF cycle into one large image panel, flat-fielding to correct for uneven illumination and registration of images from successive t-CyCIF cycles to each other; these procedures were performed using ImageJ, ASHLAR, and BaSiC software.
These sharable, guided analyses are created by users of all levels of coding experience using the Minerva suite. This includes Minerva Author, a tool that enables the user to easily create a guided analysis and Minerva Story, a tool that enables users to turn their story into a narrative, web-based image for viewing and sharing.
Unpublished t-CyCIF images of melanoma and precursor fields from two patients. The specimen from patient 1 illustrates different regions of melanoma progression from relatively normal melanocytes to precursor melanocytic dysplasia to invasive melanoma. In addition, the specimen shows different regions representing immune responses to early melanoma (inflammatory regression) as well as to invasive melanoma in the form of a brisk immune infiltrative immune response (tumor infiltrating lymphocytes “TILs”). The excision specimen from patient 2 illustrates the histologic evolution of melanoma from a precursor field to melanoma in situ, and ultimately to polypoidal invasive melanoma.
The unpublished 13-plex t-CyCIF images of tissue specimens from four patients with BRAF-mutant metastatic melanoma resected before (left) and after (right) treatment with BRAF and MEK inhibitors (dabrafenib/trametinib). Each image is composed of 150-200 image tiles at a nominal resolution of ~0.9 μm.
Tissue-based cyclic immunofluorescent microscopy (t-CyCIF) is a simple method for generating highly multiplexed optical images from formalin-fixed paraffin-embedded (FFPE) tissue samples routinely used for histopathological diagnosis of human disease. The method is based on previously described single-cell imaging approaches and readily implemented on existing instruments (Gerdes et al. 2013, Lin et al. 2015, 2016).
Selective inhibition of the negative cell cycle regulator WEE1 may enhance the efficacy of DNA-damaging agents by reducing DNA damage repair. These are multiplexed cyclic immunofluorescence on paired pre- and post-WEE1 inhibitor tumor biopsies, from the first phase II study assessing the efficacy of the WEE1 inhibitor adavosertib with cisplatin in metastatic triple-negative breast cancer (mTNBC). Among patients with mTNBC treated with 0-1 prior lines, adavosertib combined with cisplatin narrowly missed the prespecified ORR cutoff of > 30%. The finding of immune infiltrated tumors in patients with clinical benefit to therapy requires validation in future studies.
In a collaboration with Dr. Tyler Jacks at Ludwig MIT, we used t-CyCIF to characterize tumor intrinsic and extrinsic states in genetically engineered cancer models of lung and pancreas samples treated with clinical trial agents such as HSP90 inhibitors, classic cytotoxic agents, and immune checkpoint blockade modifiers. We are interested in understanding the effects of genetic perturbations, for example targeted deletion or over-expression of individual or panels of cancer, and immune-related genes in these samples.


1. Why is the web app so amazing?
- Because it was made with love by the most amazing people ever.
2. Who should I email if something is not working?
- Anyone but Adam.
3. What was this web app made for?
- It was made for Queen's Hackathon 2014.
4. Will you be adding other campus food locations?
- Maybe in the future but honestly probably not.


For distributed execution, Flink chains operator subtasks together into tasks. Each task is executed by one thread. Chaining operators together into tasks is a useful optimization: it reduces the overhead of thread-to-thread handover and buffering, and increases overall throughput while decreasing latency. The chaining behavior can be configured; see the chaining docs for details.
The sample dataflow in the figure below is executed with five subtasks, and hence with five parallel threads.
The Flink runtime consists of two types of processes:
The JobManagers (also called masters) coordinate the distributed execution. They schedule tasks, coordinate checkpoints, coordinate recovery on failures, etc.
There is always at least one Job Manager. A high-availability setup will have multiple JobManagers, one of which one is always the leader, and the others are standby.
The TaskManagers (also called workers) execute the tasks (or more specifically, the subtasks) of a dataflow, and buffer and exchange the data streams.
There must always be at least one TaskManager.
The JobManagers and TaskManagers can be started in various ways: directly on the machines as a standalone cluster, in containers, or managed by resource frameworks like YARN or Mesos. TaskManagers connect to JobManagers, announcing themselves as available, and are assigned work.
The client is not part of the runtime and program execution, but is used to prepare and send a dataflow to the JobManager.
After that, the client can disconnect, or stay connected to receive progress reports. The client runs either as part of the
Java/Scala program that triggers the execution, or in the command line process ./bin/flink run ...
.
Each worker (TaskManager) is a JVM process, and may execute one or more subtasks in separate threads. To control how many tasks a worker accepts, a worker has so called task slots (at least one).
Each task slot represents a fixed subset of resources of the TaskManager. A TaskManager with three slots, for example, will dedicate 1/3 of its managed memory to each slot. Slotting the resources means that a subtask will not compete with subtasks from other jobs for managed memory, but instead has a certain amount of reserved managed memory. Note that no CPU isolation happens here; currently slots only separate the managed memory of tasks.
By adjusting the number of task slots, users can define how subtasks are isolated from each other. Having one slot per TaskManager means each task group runs in a separate JVM (which can be started in a separate container, for example). Having multiple slots means more subtasks share the same JVM. Tasks in the same JVM share TCP connections (via multiplexing) and heartbeat messages. They may also share data sets and data structures, thus reducing the per-task overhead.
By default, Flink allows subtasks to share slots even if they are subtasks of different tasks, so long as they are from the same job. The result is that one slot may hold an entire pipeline of the job. Allowing this slot sharing has two main benefits:
A Flink cluster needs exactly as many task slots as the highest parallelism used in the job. No need to calculate how many tasks (with varying parallelism) a program contains in total.
It is easier to get better resource utilization. Without slot sharing, the non-intensive source/map() subtasks would block as many resources as the resource intensive window subtasks. With slot sharing, increasing the base parallelism in our example from two to six yields full utilization of the slotted resources, while making sure that the heavy subtasks are fairly distributed among the TaskManagers.
The APIs also include a resource group mechanism which can be used to prevent undesirable slot sharing.
As a rule-of-thumb, a good default number of task slots would be the number of CPU cores. With hyper-threading, each slot then takes 2 or more hardware thread contexts.
The exact data structures in which the key/values indexes are stored depends on the chosen state backend. One state backend stores data in an in-memory hash map, another state backend uses RocksDB as the key/value store. In addition to defining the data structure that holds the state, the state backends also implement the logic to take a point-in-time snapshot of the key/value state and store that snapshot as part of a checkpoint.
Programs written in the Data Stream API can resume execution from a savepoint. Savepoints allow both updating your programs and your Flink cluster without losing any state.
Savepoints are manually triggered checkpoints, which take a snapshot of the program and write it out to a state backend. They rely on the regular checkpointing mechanism for this. During execution programs are periodically snapshotted on the worker nodes and produce checkpoints. For recovery only the last completed checkpoint is needed and older checkpoints can be safely discarded as soon as a new one is completed.
Savepoints are similar to these periodic checkpoints except that they are triggered by the user and don’t automatically expire when newer checkpoints are completed. Savepoints can be created from the command line or when cancelling a job via the REST API.


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Meet the Wizard; Harry Pothead; our master prognosticator of your future vacation travels. He will be using his powerful mystical seer abilities to pick your perfect vacation local. Please tell him your name; he may be a wizard; but he ain't psychic!
Hello, and welcome ! Thanks for helping me out. I am pretty good seeing peoples futures; but I am terrible with names. Selectively psychic I guess.
I will need a little more help from you, . Its simple really. I am going to ask you three destination questions; and then a special fourth question that allows me to peer into the fabric of the universe to discern your traveling future.


Click on the pictures to learn more about our league streamers who interested
you.
My name is Louis, also known as Intendant. I'm from Switzerland, but all
my streams are in English. I'm streaming all sorts of leagues in Clash
Royale almost every evening. Sometimes chaos, but always fun.
My name is Marcin "pOwP" Lepczyński. My work is based on Clash Royale -
I do tournaments, record various trivia and information and create
cyclical episodes.
I am an esports caster and event entertainer based in Seville (Spain). I
am specialized in Clash Royale and League of Legends, with more than 2
years of experience.
Hi, my name is xDelta and I'm one of the English-speaking casters for
this league! I try to have a little more in-depth analysis on the match
as it is going on, and I hope I'll get to provide some great streams for
everyone interested. See you on stream!
Hi, my name is Kacper, I'm 16 years old. On YouTube I am known as
"RepsaK". Streams and creates tournaments. I have the most skills in
Clash Royale among Polish streamers.
I am 19 years old, I am from Mexico and I love to cast Clash Royale
leagues. I am passionate about casters.
Hi, I am Vodje. I am the only Flemmish streamer in Belgium. I speak
English, Dutch and French. I try to keep the streams as funny as
possible! Find me on twitter for more info.