Download 400 HITS Txt
What are some ways to download a spool files to the PC. Is there an easy way to get ASCII .txt versus PDF? Is there any easy way to locate spool files for a specific job? My spool files are HUGE - what are my options to download this data?
Download 400 HITS txt
Support may request joblogs, dumps, etc for debugging issues or you may have a joblog or report you simply need to share with a colleague. This TechNote will outline some ways to download small, medium, and large spool files to your PC.
Before we get started - you may or may not have the iACS client and need to download it. You could be still using the legacy Operations Navigator / iSeries Navigator. The following URLs will address both of these situations.
Click "Ok" to set the filter - this display looks a lot better. I see the different spool files I created. Notice how they are not in the same OUTQ. The next question - how to download them and in what format?
This screen is important - you can tell iACS where to download the data and in what format. In this case I have un-checked the "Use PDF format if available" in order to get the plain / ASCII .txt that support typically needs.
In Windows Explorer - I navigate to where I download the spoolfiles: ==> C:\temp\SpoolFiles I can see I have a directory by the name of my system "RCH730A" - within that directory I see the spool files.
There may be times when the spool file data is too large and not very practical to download to your PC. In these situations you may consider creating an OUTQ for this data, moving the spool files to this new OUTQ, and then saving that OUTQ to a SAVF.
Alternatively you can also use a docker / singularity container to run OMA standalone in any environment that allows running containers. You need to bind mount the folder with your dataset into the docker's /oma path, i.e. assuming you want to run the ToyExample with the genomes in /tmp/OMA/ToyExample/DB/*fa, you would need to execute the following command to download and execute OMA standalone:
Additionally it is possible to export the precomputed all-against-all for any of the >2000 genomes currently in the oma database. This can result in a massive speedup of time to run omastandalone. To export genomes, go to and select the genomes you wish to include in your omastandalone run. The resulting compressed tar file should be downloaded and uncompressed in the root directory of your analysis. Then simply run omastandalone as normal.
ESPRIT stores its output files in a directory calledEspritOutput in your working directory. The output consists of three text files and one tarball. In the tarball, FASTA files with the MSAs of the hits ESPRIT found are stored. The other three files are explained in detail in Table 2.
All hits found by ESPRIT are listed in this file. It is a list of contigs, ordered according to their position relative to the putative ortholog. Each line describes one contig, the fields are separated by tabs. In the first field, the fragment pair ID is printed; the next two fields contain the labels of the first and second fragments found in this hit. The forth and fifth fields contain the label of the corresponding full gene and its genome name. Then follows the distance difference between the two fragments and the number of positions between them (i.e. the gap); at last, an array is listed containing the IDs of all s3 genes corresponding to this hit.
ESPRIT often detects more candidate pairs than it will list in the hits.txt file, but not all of them survive the quality check. Still, if you want to see which triplets have been filtered out, have a look at dubious.txt where they are still listed. The file format is the same as for hits.txt.
If the local file size is larger than the remote file size, then the local file is overwritten andthe entire remote file is re-downloaded. This behavior is the same as using OutFile withoutResume.
If the remote server doesn't support download resuming, then the local file is overwritten and theentire remote file is re-downloaded. This behavior is the same as using OutFile withoutResume.
Google's automated crawlers support the Robots Exclusion Protocol (REP). This means that before crawling a site, Google's crawlers download and parse the site's robots.txt file to extract information about which parts of the site may be crawled. The REP isn't applicable to Google's crawlers that are controlled by users (for example, feed subscriptions), or crawlers that are used to increase user safety (for example, malware analysis).
Google ignores invalid lines in robots.txt files, including the Unicode Byte Order Mark (BOM) at the beginning of the robots.txt file, and use only valid lines. For example, if the content downloaded is HTML instead of robots.txt rules, Google will try to parse the content and extract rules, and ignore everything else.
Set Content Handling to Passthrough in the integration request (for upload) and in a integration response (for download). Make sure that no mapping template is defined for the affected content type. For more information, see Integration Passthrough Behaviors and Select VTL Mapping Templates.
On Windows: Windows Attachment Manager could have removed the file that you tried to download. To see what files you can download, or why your file was blocked, check your Windows Internet security settings.
Currently, the only way to download everything, is to open each link, then I use the "DownThemAll!" Firefox plugin which selects all the images (or any file type) on the page and downloads them. This works page by page, but I need something similar that works a whole list of URLs.
In my own experience, I prefer Chrono Download Manager because I needed to change automatically the name of the downloaded file in a BATCH-way (a list of VIDEOS from a hmm hmm... online courses) and crawling in the html code all the different videos have the same filename. So downloading it with TabSave just gives you the same name videos and you have to guess wich is the content (somewhat like "543543.mp4", "543543(1).mp4", "543543(2).mp4" and so and so).Imagine how much extra work you need to do to achieve this kind of task.
In order to compile the ecat7 output module of Gate, the ecat library written at the PET Unit of the Catholic University of Louvain-la-Neuve (UCL, Belgium) is required. It can be downloaded from theirweb site: _Clib.html
Ignore maximum number of features when calculating hits - When calculating the total number of hits, ignore the Maximum number of features setting. This can be used to get the count of matching features, even if they would not be made available for download because they exceed the maximum count specified. On very large data sets, this can slow down the response.
Generally speaking , users can select annotation tracks that are already provided by the UCSC Genome Browser annotation databases. Most of these annotation tracks have similar file formats, but sometimes they differ (for example, different number of columns in the file). ANNOVAR will try to be smart in guessing the correct column headers, and usually it works well. However, ANNOVAR may also provide built-in region annotation databases, which can be downloaded by -downdb -webfrom annovar. Finally, users can supply your own region annotation databases in generic, BED or GFF formats.
Here ANNOVAR uses phastCons 46-way alignments to annotate variants that fall within conserved genomic regions. Here the --regionanno argument need to be supplied so that the program knows what to do. In addition, the --dbtype need to be specified so that the program knows which annotation database file to interrogate. Make sure that the annotation database is already downloaded (the command is annotate_variation.pl -downdb phastConsElements46way humandb/).
Similarly, annotation of TFBS can be done by the commands below. Download the database if it is not already downloaded. Users should be aware that there are many different types of TFBS annotations that ANNOVAR can use. See FAQ entry for more explanation. The example below uses the tfbsConsSites region annotation, which contains the location and score of transcription factor binding sites conserved in the human/mouse/rat alignment, where score and threshold are computed with the Transfac Matrix Database. See details here.
Genetic variants that are mapped to segmental duplications are most likely sequence alignment errors and should be treated with extreme caution. Sometimes they manifest as SNPs with high fold coverage and probably high confidence score, but they may actually represent two non-polymorphic sites in the genomes that happen to have the same flanking sequence. To identify variants in these regions, use the command below. Again the first command download annotation databases, yet the second command identify variants in segmental duplications.
As we can see from the results above, adding a -minqueryfrac 0.5 argument reduces the number of database hits (now only esv2751132 is shown in the Name field for the 342kb deletion). To understand this more, check the genome browser shots for this region:
The goal of this work is to provide an empirical basis forresearch on image segmentation and boundary detection. To this end, wehave collected 12,000 hand-labeled segmentations of 1,000 Corel dataset images from30 human subjects. Half of the segmentations were obtained from presenting thesubject with a color image; the other half from presenting a grayscaleimage. The public benchmark based on this data consists of all of the grayscaleand color segmentations for 300 images. The images are divided into a training setof 200 images, and a test set of 100 images.We have also generated figure-ground labelings for a subset of these images whichmay be found hereWe have used this data for bothdeveloping new boundary detection algorithms, and for developing a benchmark forthat task. You may download a MATLAB implementation of our boundarydetector below, along with code for running the benchmark. We arecommitted to maintaining a public repository of benchmark results in the spiritof cooperative scientific progress. On-Line Browsing DatasetBy Image -- This page contains the list of all the images. Clicking on an image leads youto a page showing all the segmentations of that image.By Human Subject -- Clicking on a subject's ID leads you to a page showing all of the segmentations performed by that subject.Benchmark Results By Algorithm -- This page shows the list of tested algorithms, ordered as they perform on the benchmark.
By Image -- This page shows the test images. The images are ordered by how well any algorithm can find boundaries, so that it is easy to see which images are "easy" and which are "hard" for the machine.
On all of these pages, there are many cross-links between images, subjects,and algorithms. Note that many of the smaller images are linked tofull-size versions. 041b061a72