The menus for the main window described later in this chapter. A one line summary of the current selection see the section called The Select Menu and the section called The Selection in Chapter 1 for more. This line contains one button for each entry that has been loaded. These buttons allow the user to set the default entry see the section called The Default Entry in Chapter 1 and to set the active entries see the section called The Active Entries in Chapter 1.

For more detail on operating the buttons see the section called The Entry Button Line. This shows an overview of the sequence and the features from the active entries. see the section called The Overview and DNA Views in Chapter 3. This is called the "DNA view" to distinguish it from the overview, but in fact it operates in a very similar way.

A textual summary of the active features. See the section called The Feature List in Chapter 3. Most of the items in this menu are used to read and write entries and parts of entries, the exceptions are Clone and Close. This will open the file manager, or if it is already open will bring it to the foreground. Entries can be dragged from the file manager into the Artemis main window and dropped. When dropped the entry is then read in and displayed. Read an entry see the section called The "Entry" in Chapter 1 , but keep it separate from the others.

A new button will be created on the entry button line for the new entry. The new entry will be marked as active see the section called The Active Entries in Chapter 1 and will be the new default entry see the section called The Default Entry in Chapter 1. See the section called Sequence and Annotation File Formats in Chapter 1. This function only reads the feature section of the input file - the sequence if any is ignored. Read the features from an entry see the section called The "Entry" in Chapter 1 chosen by the user and then insert them into the entry selected by the user.

Artemis can read in and visualise BAM, CRAM, VCF and BCF files. These files need to be indexed as described below. Some examples can be found here. BAM files need to be sorted and indexed using SAMtools.

The index file should be in the same directory as the BAM file. This provides an integrated BamView panel in Artemis, displaying sequence alignment mappings to a reference sequence.

Multiple BAM files can be loaded in from here either by selecting each file individually or by selecting a file of path names to the BAM files. The BAM files can be read from a local file system or remotely from an HTTP server. BamView will look to match the length of the sequence in Artemis with the reference sequence lengths in the BAM file header. It will display a warning when it opens if it finds a matching reference sequence from these lengths and changes to displaying the reads for that.

The reference sequence for the mapped reads can be changed manually in the drop down list in the toolbar at the top of the BamView. In the case when the reference sequences are concatenated together into one e. in a multiple FASTA sequence and the sequence length matches the sum of sequence lengths given in the header of the BAM, Artemis will try to match the names e. contig or chromosome against the reference sequence names in the BAM.

It will then adjust the read positions accordingly using the start position of the feature. When open the BamView can be configured via the popup menu which is activated by clicking on the BamView panel. The 'View' menu allows the reads to be displayed in a number of views: stack, strand-stack, paired-stack, inferred size and coverage. In Artemis the BamView display can be used to calculate the number of reads mapped to the regions covered by selected features.

In addition the reads per kilobase per million mapped reads RPKM values for selected features can be calculated on the fly. Note this calculation can take a while to complete. CRAM files can be loaded in a similar fashion to BAMs. They can be created, sorted and indexed using SAMtools. Variant Call Format VCF files can also be read.

The VCF files need to be compressed and indexed using bgzip and tabix respectively see the tabix manual and download page.

The compressed file gets read in e. gz and below are the commands for generating this from a VCF file:. bgzip file. tabix -p vcf file. Alternatively a Binary VCF BCF can be indexed with BCFtools and read into Artemis or ACT. They then get displayed in separate rows in the VCF panel. For single base changes the colour represents the base it is being changed to, i. T black, G blue, A green, C red.

There are options available to filter the display by the different types of variants. Right clicking on the VCF panel will display a pop-up menu in which there is a 'Filter This opens a window with. check boxes for a number of variant types and properties that can be used to filter on. This can be used to show and hide synonymous, non-synonymous, deletion grey , insertion magenta , and multiple allele orange line with a circle at the top variants.

In this window there is a check box to hide the variants that do not overlap CDS features. There is an option to mark variants that introduce stop codons into the CDS features with a circle in the middle of the line that represents the variant.

There are also options to filter the variants by various properties such as their quality score QUAL or their depth across the samples DP. Placing the mouse over a vertical line shows an overview of the variation as a tooltip. Also right clicking over a line then gives an extra option in the pop-up menu to show the details for that variation in a separate window. There are also alternative colouring schemes.

It is possible to colour the variants by whether they are synonymous or non-synonymous or by their quality score the lower the quality the more faded the variant appears. There is an option to provide an overview of the variant types e. synonymous, non-synonymous, insertion, deletion for selected features. Also, filtered data can be exported in VCF format, or the reconstructed DNA sequences of variants can be exported in FASTA format for selected features or regions for further analyses.

These sequences can be used as input for multiple sequence alignment tools. Save the default entry to the file it came from, unless the entry has been given a new name, in which case the entry is saved to a file with that name. If the entry has no name, Artemis will prompt the user for a new name. This item will do the same as "Save Default Entry" for the chosen entry. This sub-menu contains the less frequently used save functions. Ask for the name of file to save the given entry to.

The name of entry as displayed in the entry button line will change to the new name. Note that currently the header of a GENBANK entry can't be converted to the.

equivalent EMBL header it will be discarded instead. Note that currently the header of a EMBL entry can't be converted to the equivalent GENBANK header it will be discarded instead. This saves a file in Sequin table format which is used by Sequin. Writes the features in GFF format and sequence of the entry in FASTA format to a file selected by the user. Note that if you use this function on an EMBL or GENBANK entry the header will discarded.

It will also check that the start and stop codons of each CDS are sensible, that no two features have the same key and location and that all required EMBL qualifiers are present. This acts like "Save Default Entry", but save all the entries. Prompt for a file name and then write the translation of the bases of the selected features to that file.

The file is written in FASTA format. Prompt for a file name and then write the bases of the selection to that file in the selected format. If the selection consists of features rather than a base range then the bases of each feature will be written to the file as a separate record.

If the selection is a range of bases, then those bases will be written. Prompt for a number and a file name, then write that many bases upstream of each selected feature to the file in the selected format.

For example if the selected feature has a location of " Writing upstream bases of a feature on the complementary strand will work in the expected way. Prompt for a number and a file name, then write that many bases downstream of each selected feature to the file in the selected format.

Prompt for a file name, then write the complete sequence to that file in the selected format. Prompt for a file name, then write a codon usage table for the selected features. The file in written in the same format as the data at Kazusa codon usage database site.

In the output file each codon is followed by its occurrence count per thousand and it's percentage occurrence. See the section called Add Usage Plots in Chapter 3 to find out how to plot a usage graph. Make a new main window with the same contents as the current window. All changes in the old window. will be reflected in the new window, and vice versa. The exception to this rule is the selection see the section called The Selection in Chapter 1 , which is not shared between the old and new window.

Print out the contents of the current window. All or some of the window panels can be selected for printing to an image file. SVG scalable vector graphics is an XML based vector image format. These images can be converted to a raster image e.

Therefore the SVG format can be useful for creating publication quality figures. The other formats available png , jpeg etc and are raster or bitmap images. This option can be used to print the contents of the current window to a file as PostScript or to a printer. This opens the print image in a preview window. This shows what the image will look like when printed to a file. This enables the user to define their own shortcut preferences.

Close this window. The items in this menu are used to change which entry is the default entry and which entries are active see the section called The "Entry" in Chapter 1. At the bottom of the menu there is a toggle button for each entry which controls whether the entry is active or not.

These toggle buttons work in a similar way the the buttons on the entry button line see the section called The Entry Button Line. Here is a description of the other menu items:. Set the name of an entry chosen from a sub-menu. The name of the entry is used as the name of the file when the entry is saved. Set the default entry by choosing one of the entries from the sub-menu. See the section called The Default Entry in Chapter 1. Remove an entry from Artemis by choosing one of the entries from the sub-menu.

The original file that this entry came from if any will not be removed. Remove the entries that are currently active. See the section called The Active Entries in Chapter 1.

Choosing this menu item will deactivate all entries. The items in this menu are used to modify the current selection see the section called The Selection in Chapter 1. Artemis shows a short summary of the current selection at the top of the main window see 2 for details..

Open a new Feature Selector window. The Select button will set the selection to the contain those features that match the given key, qualifier and amino acid motif combination.

The View button will create a new feature list see the section called The Feature List containing only those features that match the given key, qualifier and amino acid motif combination.

Reset the selection so that nothing is selected then select all the features in the active entries. Reset the selection so that nothing is selected then select all the bases in the sequence. Select all features that have no corresponding match in ACT. This is used to highlight regions that are different between sets of sequence. It will only take into account matches that have not been filtered out using the score, identity or length cut-off.

Clear the selection so that nothing is selected. Ask the user for a feature key, reset the selection so that nothing is selected, then select all the features with the key given by the user. Select all the features that have the same key as any of the currently selected features. Extend the current selection of bases to cover complete open reading frames. Selecting a single base or codon and then choosing this menu item has a similar effect to double clicking the middle button on a base or residue see the section called Changing the Selection from a View Window in Chapter 3 for details.

Select those and only those features that overlap the currently selected range of bases or any of the currently selected features. The current selection will be discarded. Select those and only those features that are fully contained by the currently selected range of bases or any of the currently selected features. Ask the user for a range of bases, then select those bases. The range should look something like this:. If the first number is larger than the second the bases will be selected on the forward strand, otherwise they will be selected on the reverse strand unless there is a complement around the range, in which case the sense is reversed.

Ask the user for a range of amino acids in the selected feature and select those bases. The range should look something like this: , or Invert the selection - after choosing this menu item the selection will contain only those features that were not in the selection beforehand. Open a view window for each selected feature showing it's feature table entry. Open a view window that will show the current selection. The window is updated as the selection changes, so it can be left open.

When one feature is selected the window will show the text EMBL, GenBank or GFF format of the feature, the base composition, GC percentage, correlation score see the section on Correlation Scores in Chapter 3 and the bases and translation of the sequence of the feature. When a range of bases is selected the window will show the base composition, GC content percentage and the bases and translation of the sequence of the feature.

On this sub-menu allows the user to view the results of feature searches that are launched from the run menu in Artemis see the section called the The Run Menu. This list includes pseudo genes. Each of the items in this sub-menu each allow the user to view a subset of the active features. The features are displayed in a new window that contains a menu bar with possible actions to apply to the subset, and feature list see the section called The Feature List in Chapter 3. Most of the possible actions will apply only to the features in the list.

For example "Show Overview" in the View menu see the section called Overview will include statistics only on the features in the list. Show those CDS features that have a suspicious start codon.

the first codon of the feature isn't ATG in eukaroytic mode or ATG, GTG and TTG in prokaryotic mode. This function is effected by the setting of the " Eukaroytic Mode" option in the main options menu see the section called Genetic Code Tables in Chapter 2 for more. Show those CDS features that have a suspicious stop codon.

the last codon of the feature isn't one of TAA, TAG or TGA. Show those features that are duplicated ie. features that have the same key and location as another feature.

These sort of duplicates aren't allowed by the EMBL database. Show those CDS features that overlap another CDS feature on either strand. Show those features that are missing a qualifier that is required by the EMBL database. Show those features that have a key chosen by the user. Show the currently selected features in a new feature list. The contents of the list will remain the same even if selection subsequently changes. This is useful for bookmarking a collection of features for later use.

Open a new window the will show a summary of the active entries and some statistics about the sequence such as the GC content. The overview window show the following statistics about the sequence:. the GC content percentage ignoring bases other than A,T,C and G.

This should be the same as the "GC percentage" above. The overview window also shows the following statistics about the features in the active entries if there are any features. Note that the "genes" are the non-pseudo CDS features. the number of coding bases excluding introns. Open a new window the will show a summary of the features and bases of the forward strand. Open a new window the will show a summary of the features and bases of the reverse strand.

Create a view window for each selected feature, which shows bases of the feature. Create a view window for each selected feature, which shows bases of the feature in FASTA format. Feature Amino Acids. Create a view window for each selected feature, which shows amino acids of the feature.

Create a view window for each selected feature, which shows amino acids of the feature in FASTA format. Show some statistics about each selected feature. On the left on the feature information window is the amino acid composition of the feature. On the right is the codon composition of the feature. For more detail about the coiled coils algorithm see "Predicting Coiled Coils from Protein Sequences", Science Vol. Some general information about graphs and plots in Artemis can be found in the section called Graphs and Plots in Chapter 3.

Configuration options for graphs are described Artemis Option Descriptions in Chapter 6. The items in this menu allow the user to navigate around the sequence and features. Open a new navigation window. This window allows the user to perform five different tasks:. Scroll all the views so that a particular base is in the centre of the display.

To use this function, type a base position into the box to the right of the " Goto Base:" label then press the goto button at the bottom of the window. The requested base will be selected and then the overview display and the DNA display will scroll so that the base is as near as possible to the middle of the main window. Find the next feature that has the given gene name.

To use this function, type a gene name into the box to the right of the " Goto Feature With This Gene Name:" label and then press the goto button. Artemis will select the first feature with the given text in any of it's qualifiers and will then scroll the display so that feature is in view. Find the next feature that has a qualifier containing a particular string.

To use this function, type a string into the box to the right of the " Goto Feature With This Qualifier Value:" label and then press the goto button. Find the next feature that has a particular key.

To use this function, type a key into the box to the right of the " Goto Feature With This Key:" label and then press the goto button. Artemis will select the first feature with the given key and will then scroll the display so that feature is in view. Find the next occurrence of a particular base pattern in the sequence. To use this function, type a base pattern into the box to the right of the "Find Base Pattern:" label and then press the goto button.

Artemis will select the first contiguous group of bases on either strand that match the given base pattern and will then scroll the display so that those bases are in view. Any IUB base code can be used in the pattern, so for example searching for aanntt will match any six bases that start with "aa" and ends with " tt ". See Table for a list of the available base codes. Find the next occurrence of a particular residue pattern in the sequence. To use this function, type an amino acid pattern into the box to the right of the " Goto Amino Acid String:" label and then press the goto button.

Artemis will select the first contiguous group of bases on either strand that translate to the given amino acids and will then scroll the display so that those bases are in view. The letter 'X' can be used as an ambiguity code, hence 'AAXXXDD' will match 'AALRTDD' or 'AATTTDD' etc.

Note that for all the functions above except the first " Goto Base" , if the "Start search at beginning" option is set or if there is nothing selected the search will start at the beginning of the sequence. Otherwise the search will start at the selected base or feature. This means that the user can step through the matching bases or features by pressing the goto button repeatedly. If the "Ignore Case" toggle is on which is the default Artemis will ignore the difference between upper and lower case letters when searching for a gene name, a qualifier value or a feature key.

The "Allow Substring Matches" toggle affects 2 and 3. If on Artemis will search for qualifier values that contain the given characters. For example searching for the genename CDC will find CDC1, CDC2, ABCDC etc. Scroll all the views so that the first base of the selection is as close to the centre as possible. If the a range of bases is selected the views will move to the first base of the range. If one or more features are selected, then the first base of the first selected feature will be centred.

Otherwise, if one or more segments see the section called Feature Segments in Chapter 1 is selected then the first base of the first selected segment will be centred. This does the same as " Goto Start of Selection", but uses the last base of the selected range or the last base of the last selected feature or segment.

Scroll the views to the start of the first selected feature. Scroll the views to the end of the first selected feature. Scroll the views so that the start of the sequence is visible. Scroll the views so that the end of the sequence is visible. Ask the user for a base position within the first selected feature, then scroll the views so that the base position is centred.

Ask the user for an amino acid position within the first selected feature, then scroll the views so that that position is centred. This menu contains most of the functions that change the entries. Note that the changes will not be saved back to the original files until one of the save functions in the File menu is used see the section called Save An Entry in Chapter 3. This function will undo the last change that was made using the Edit or Create menus.

Up to 20 changes can be undone. This menu item is only enabled when there is something to undo. This function will redo the last undo operation. Open an edit window for each selected feature. From the top down the edit window has these parts:. At the top left is a selector for choosing the key of the feature. This only contains a subset of the legal keys. At the top right of the edit window is a selector for adding a qualifier. Just below the key and qualifier selector is the location entry field.

Artemis understands most of the EMBL location syntax, including joins, complements, ranges with non-exact ends eg. join Below the location is a row of buttons:. This is normally used to create an intron in a feature. See the section called Start of Selection for more.

In the intial screen see below you are invited to import your qualifier list from the import options in the "File" menu. These lists can be optionally saved between sessions in the file '. qualifiers ' in the home directory. When a qualifier list or OBO file has been added then it is possible to search for keywords within a list.

The qualifier selected in the drop down list under the SEARCH button can then be added to the current feature annotation or added to selected features in Artemis. If the keywords text field is left blank then all qualifiers are available from the drop down list of qualifier values. The centre of the edit window contains the qualifier entry section. The qualifiers should be entered in same way they appear in the feature table part of an EMBL entry, but without the leading FT and spaces.

The bottom of the window contains three buttons. The OK button will update the feature with the changes that have been made by the user and will then close the edit window. The Cancel button will discard the changes and then close the window. The Apply will make the changes, but will not close the window. Before any changes are made the location and the qualifiers are checked for formatting errors.

Any errors will brought to the attention of the user through the use of annoying pop-up boxes. No changes will be performed until there all errors have been fixed. Make a copy in a new edit window of the selected bases and the features in that range.

Any features that overlap the end of the range will be truncated. This opens a search window with options to find or replace qualifier text. Clicking on the Show Boolean Search Options displays 4 options. The Match any string i. x OR y means that the words in the Find text field will be separated by an OR condition. So that it finds those features with qualifiers that contain any of the words. The Match all string i. x AND y means that the words in the Find text field will be separated by an AND condition.

So that it finds those features with qualifiers that contain all of the words. The No boolean search option is the default. This means it searches for those features with. qualifiers that contain the complete text from the Find text field. In addition selecting the Duplicate Qualifiers tab provides options to search for or delete duplicate qualifiers.

This function allows the user to add or change qualifiers on all the selected features in one operation. The main part of the window acts like the qualifier editing field of the feature edit window see the section called Selected Features in Editor. This function allows the user to remove all qualifiers with a particular name from all the selected features. This function allows the user to convert all qualifiers of a particular type to another qualifier for all the selected features.

Make a copy of each selected feature. Each new feature will be added just after the original in the same entry as the original. Create a new feature that contains all the exons and qualifiers of the selected features. The selected features must all have the same key. If the selection contains exactly two exons and those exons are adjacent in the same feature, split the feature into two pieces between the exons.

The original feature is truncated and a new feature is created. The qualifiers of the old feature are copied to new feature. All exons in a feature are unmerged. Remove each selected feature from its entry. Delete the selected exons. The last exon of a feature can't be deleted delete the whole feature instead. Delete the selected introns. Move the selected features to another entry. Choose the destination entry from the sub-menu. Copy the selected features to another entry.

For each of the selected features this function will attempt to move the start position to the first ATG in the feature if the feature does not already start on a ATG codon. If there is no ATG in the first thirty percent of the bases of the feature the start position will be unchanged. The search will stop at the end of the first segment of a multi-segment feature. This works in the same way as "Trim Selected Features To Met ", but will attempt to move the start position of the feature to the first TTG, ATG or GTG in the feature if it does not already start on one of those codons.

As above it will only search the first thirty percent of the feature bases and will only search the first segment of a multi-segment feature. For each of the selected features this function will attempt to move the start position to the next ATG in the feature the first codon is skipped.

This works in the same way as "Trim Selected Features To Next Met", but will attempt to move the start position of the feature to the next TTG, ATG or GTG in the feature the first codon is skipped.

Extend each of the selected features which do not start on a stop codon so that each feature starts just after the previous stop codon in this reading frame. Extend each of the selected features which do not end on a stop codon so that each feature ends just before the next stop codon in this reading frame.

Same as above but in addition this fixes the stop codons. Check and fix the stop codons to all the selected features. For each feature if the last codon is a stop codon, then all is well, nothing further is done to the feature.

If the last codon is not a stop codon, but the very next codon is a stop codon, then the end of the feature is moved forward by three bases.

If both the last codon and the very next codon after the feature are not stop codons, the feature is selected, an error message is displayed and processing stops immediately.

Ask for a gene name prefix using a text requester , and then give a unique gene name to each CDS. feature in the active entries using that prefix. For example if there are four CDS features with locations:.

SPBC16A3 will give the four features these names: SPBC16A3. and SPBC16A3. Warn about inconsistencies such as overlapping CDSs. Reverse and complement the sequence and all the features in all the entries active and inactive. Reverse and complement the sequence and all the features in a selected contig feature.

If this option is used in ACT then all the matches within the contig are also reversed. Any matches extending past the boundary of the contig are deleted. The changes to the comparison file can be saved by right clicking in the comparison window and selecting "Save Comparison File However, ideally the comparison between the two sequences should be recalculated.

Deletes the selected range of bases if any from both strands. The deletion will not proceed if the selected range overlaps any features. Prompt the user for some bases to insert just before the selected bases. The operation will not proceed if there is no selected range, but bases can be inserted anywhere in the sequence, including inside a feature.

The same bases, reversed and complemented, will be inserted at the corresponding place on the opposite strand. Prompt the user for the name of a file containing the bases to insert just before the selected bases.

Prompt the user for some bases to replace the selected bases. The following table shows other difficulties or limitations caused by using. INI files that are overcome by using the Registry. Microsoft Press. ISBN Retrieved August 28, August 20, Retrieved April 8, Retrieved April 10, Retrieved September 25, Archived from the original on May 9, The Old New Thing.

Retrieved July 29, Retrieved December 2, Retrieved December 28, Cranfield University. Archived from the original PDF on May 29, ico Files". November 15, Retrieved March 31, August 26, October 9, This page tells the user to edit the registry when resolving the issue.

reg file". Windows PowerShell in Action Third ed. Manning Publications. May Windows PowerShell in 24 Hours, Sams Teach Yourself. Sams Publishing. Retrieved December 14, Retrieved June 4, March 7, Retrieved March 22, January Microsoft Windows security : essentials. Indianapolis, Ind. Retrieved August 8, November 1, March 23, Microsoft Support page. exe ". Retrieved September 9, Retrieved July 1, September 2, Retrieved August 13, Retrieved March 3, Retrieved July 26, Solomon, Alex Windows internals 6th ed.

Redmond, Wash. Archived from the original on May 27, Retrieved May 17, Retrieved April 3, Page from the riscos. com website. Mentioned in points 82 and November 2, Archived from the original on February 19, Retrieved April 1, Using the Registry and Regedit Wine User Guide.

Hipson, Peter Mastering Windows XP Registry. Russinovich, Mark E. Microsoft Windows Internals Fourth ed. Wikibooks has a book on the topic of: Windows registry hacks. Microsoft Windows components. App Installer Command Prompt Control Panel Applets Device Manager Disk Cleanup Drive Optimizer Driver Verifier DxDiag Event Viewer IExpress Management Console Netsh Performance Monitor Recovery Console Resource Monitor Settings Sysprep System Configuration System File Checker System Information System Policy Editor System Restore Task Manager Windows Error Reporting Windows Ink Windows Installer PowerShell Windows Update Windows Insider WinRE WMI.

Action Center Aero AutoPlay AutoRun ClearType Explorer Search Indexing Service IFilter Saved search Namespace Special folder Start menu Taskbar Task View Windows Spotlight Windows XP visual styles. Service Control Manager BITS CLFS Multimedia Class Scheduler Shadow Copy Task Scheduler Error Reporting Wireless Zero Configuration. CDFS DFS exFAT IFS FAT NTFS Hard link links Mount Point Reparse point TxF EFS ReFS UDF.

Domains Active Directory DNS Group Policy Roaming user profiles Folder redirection Distributed Transaction Coordinator MSMQ Windows Media Services Active DRM Services IIS WSUS SharePoint Network Access Protection PWS DFS Replication Print Services for UNIX Remote Desktop Services Remote Differential Compression Remote Installation Services Windows Deployment Services System Resource Manager Hyper-V Server Core. Architecture of Windows NT Startup process NT NT 6 CSRSS Desktop Window Manager Portable Executable EXE DLL Enhanced Write Filter Graphics Device Interface hal.

exe Object Manager Open XML Paper Specification Registry Resource Protection Security Account Manager Server Message Block Shadow Copy SMSS System Idle Process USER WHEA Win32 console Winlogon WinUSB. Security and Maintenance AppLocker BitLocker Credential Guard Data Execution Prevention Family Safety Kernel Patch Protection Mandatory Integrity Control Protected Media Path User Account Control User Interface Privilege Isolation Windows Defender Windows Firewall. COM WoW64 Windows Subsystem for Linux.

Active Scripting WSH VBScript JScript COM ActiveX ActiveX Document COM Structured storage DCOM OLE OLE Automation Transaction Server DirectX. NET Universal Windows Platform Windows Mixed Reality Windows Runtime WinUSB. Solitaire Collection Surf. ScanDisk File Protection Media Control Interface Next-Generation Secure Computing Base POSIX subsystem HPFS Interix Video for Windows Virtual DOS machine Windows on Windows Windows SideShow Windows Services for UNIX Windows System Assessment Tool Windows To Go WinFS.

DVD Player File Manager Hover! Mahjong Minesweeper. Category List. Categories : Windows components Configuration files. Hidden categories: CS1 errors: missing periodical Articles with short description Short description matches Wikidata Use mdy dates from August All articles with unsourced statements Articles with unsourced statements from October Articles with unsourced statements from August Articles needing additional references from November All articles needing additional references.

Navigation menu Personal tools Not logged in Talk Contributions Create account Log in. Namespaces Article Talk. Views Read Edit View history. Main page Contents Current events Random article About Wikipedia Contact us Donate. Help Learn to edit Community portal Recent changes Upload file. What links here Related changes Upload file Special pages Permanent link Page information Cite this page Wikidata item.

Download as PDF Printable version. Wikimedia Commons Wikibooks. Registry Editor, the user interface for the registry, in Windows April 6, ; 30 years ago with Windows 3.

Microsoft Windows. IA , x and ARM and historically DEC Alpha , Itanium , MIPS , and PowerPC. Hierarchical database. A string value, normally stored and exposed in UTF LE when using the Unicode version of Win32 API functions , usually terminated by a NUL character. An "expandable" string value that can contain environment variables , normally stored and exposed in UTFLE, usually terminated by a NUL character. A DWORD value, a bit unsigned integer numbers between 0 and 4,,, [2 32 — 1] little- endian.

A DWORD value, a bit unsigned integer numbers between 0 and 4,,, [2 32 — 1] big- endian. A symbolic link UNICODE to another registry key, specifying a root key and the path to the target key. A multi-string value, which is an ordered list of non-empty strings , normally stored and exposed in Unicode, each one terminated by a null character, the list being normally terminated by a second null character. Spots are still available for this hybrid event, and you can RSVP here to save your seat.

Join us as we discuss how to shape the future of finance. In its broadest sense, Open Banking has created a secure and connected ecosystem that has led to an explosion of new and innovative solutions that benefit the customer, rapidly revolutionizing not just the banking industry but the way all companies do business.

Target benefits are delivered through speed, transparency, and security, and their impact can be seen across a diverse range of use cases. Sharing financial data across providers can enable a customer individual or business to have real-time access to multiple bank accounts across multiple institutions all in one platform, saving time and helping consumers get a more accurate picture of their own finances before taking on debt, providing a more reliable indication than most lending guidelines currently do.

Companies can also create carefully refined marketing profiles and therefore, finely tune their services to the specific need. Open Banking platforms like Klarna Kosma also provide a unique opportunity for businesses to overlay additional tools that add real value for users and deepen their customer relationships. The increased transparency brought about by Open Banking brings a vast array of additional benefits, such as helping fraud detection companies better monitor customer accounts and identify problems much earlier.

The list of new value-add solutions continues to grow. The speed of business has never been faster than it is today. For small business owners, time is at a premium as they are wearing multiple hats every day. Macroeconomic challenges like inflation and supply chain issues are making successful money and cash flow management even more challenging. This presents a tremendous opportunity that innovation in fintech can solve by speeding up money movement, increasing access to capital, and making it easier to manage business operations in a central place.

Fintech offers innovative products and services where outdated practices and processes offer limited options. For example, fintech is enabling increased access to capital for business owners from diverse and varying backgrounds by leveraging alternative data to evaluate creditworthiness and risk models. This can positively impact all types of business owners, but especially those underserved by traditional financial service models. When we look across the Intuit QuickBooks platform and the overall fintech ecosystem, we see a variety of innovations fueled by AI and data science that are helping small businesses succeed.

By efficiently embedding and connecting financial services like banking, payments, and lending to help small businesses, we can reinvent how SMBs get paid and enable greater access to the vital funds they need at critical points in their journey. Overall, we see fintech as empowering people who have been left behind by antiquated financial systems, giving them real-time insights, tips, and tools they need to turn their financial dreams into a reality. Innovations in payments and financial technologies have helped transform daily life for millions of people.

People who are unbanked often rely on more expensive alternative financial products AFPs such as payday loans, money orders, and other expensive credit facilities that typically charge higher fees and interest rates, making it more likely that people have to dip into their savings to stay afloat.

A few examples include:. Mobile wallets - The unbanked may not have traditional bank accounts but can have verified mobile wallet accounts for shopping and bill payments. Their mobile wallet identity can be used to open a virtual bank account for secure and convenient online banking. Minimal to no-fee banking services - Fintech companies typically have much lower acquisition and operating costs than traditional financial institutions.

They are then able to pass on these savings in the form of no-fee or no-minimum-balance products to their customers. This enables immigrants and other populations that may be underbanked to move up the credit lifecycle to get additional forms of credit such as auto, home and education loans, etc. Entrepreneurs from every background, in every part of the world, should be empowered to start and scale global businesses.

Most businesses still face daunting challenges with very basic matters. These are still very manually intensive processes, and they are barriers to entrepreneurship in the form of paperwork, PDFs, faxes, and forms. Stripe is working to solve these rather mundane and boring challenges, almost always with an application programming interface that simplifies complex processes into a few clicks. Stripe powers nearly half a million businesses in rural America.

The internet economy is just beginning to make a real difference for businesses of all sizes in all kinds of places. We are excited about this future. The way we make decisions on credit should be fair and inclusive and done in a way that takes into account a greater picture of a person. Lenders can better serve their borrowers with more data and better math. Zest AI has successfully built a compliant, consistent, and equitable AI-automated underwriting technology that lenders can utilize to help make their credit decisions.

While artificial intelligence AI systems have been a tool historically used by sophisticated investors to maximize their returns, newer and more advanced AI systems will be the key innovation to democratize access to financial systems in the future. D espite privacy, ethics, and bias issues that remain to be resolved with AI systems, the good news is that as large r datasets become progressively easier to interconnect, AI and related natural language processing NLP technology innovations are increasingly able to equalize access.

T he even better news is that this democratization is taking multiple forms. AI can be used to provide risk assessments necessary to bank those under-served or denied access. AI systems can also retrieve troves of data not used in traditional credit reports, including personal cash flow, payment applications usage, on-time utility payments, and other data buried within large datasets, to create fair and more accurate risk assessments essential to obtain credit and other financial services.

By expanding credit availability to historically underserved communities, AI enables them to gain credit and build wealth. Additionally, personalized portfolio management will become available to more people with the implementation and advancement of AI. Sophisticated financial advice and routine oversight, typically reserved for traditional investors, will allow individuals, including marginalized and low-income people, to maximize the value of their financial portfolios.

Moreover, when coupled with NLP technologies, even greater democratization can result as inexperienced investors can interact with AI systems in plain English, while providing an easier interface to financial markets than existing execution tools. Open finance technology enables millions of people to use the apps and services that they rely on to manage their financial lives — from overdraft protection, to money management, investing for retirement, or building credit.

More than 8 in 10 Americans are now using digital finance tools powered by open finance. This is because consumers see something they like or want — a new choice, more options, or lower costs.

What is open finance? At its core, it is about putting consumers in control of their own data and allowing them to use it to get a better deal.

When people can easily switch to another company and bring their financial history with them, that presents real competition to legacy services and forces everyone to improve, with positive results for consumers. For example, we see the impact this is having on large players being forced to drop overdraft fees or to compete to deliver products consumers want.

We see the benefits of open finance first hand at Plaid, as we support thousands of companies, from the biggest fintechs, to startups, to large and small banks. All are building products that depend on one thing - consumers' ability to securely share their data to use different services.

Open finance has supported more inclusive, competitive financial systems for consumers and small businesses in the U. and across the globe — and there is room to do much more.

As an example, the National Consumer Law Consumer recently put out a new report that looked at consumers providing access to their bank account data so their rent payments could inform their mortgage underwriting and help build credit. This is part of the promise of open finance. At Plaid, we believe a consumer should have a right to their own data, and agency over that data, no matter where it sits. This will be essential to securing benefits of open finance for consumers for many years to come.

As AWS preps for its annual re:Invent conference, Adam Selipsky talks product strategy, support for hybrid environments, and the value of the cloud in uncertain economic times. Donna Goodison dgoodison is Protocol's senior reporter focusing on enterprise infrastructure technology, from the 'Big 3' cloud computing providers to data centers. She previously covered the public cloud at CRN after 15 years as a business reporter for the Boston Herald.

AWS is gearing up for re:Invent, its annual cloud computing conference where announcements this year are expected to focus on its end-to-end data strategy and delivering new industry-specific services. Both prongs of that are important. But cost-cutting is a reality for many customers given the worldwide economic turmoil, and AWS has seen an increase in customers looking to control their cloud spending. By the way, they should be doing that all the time.

The motivation's just a little bit higher in the current economic situation. This interview has been edited and condensed for clarity. Besides the sheer growth of AWS, what do you think has changed the most while you were at Tableau? Were you surprised by anything? The number of customers who are now deeply deployed on AWS, deployed in the cloud, in a way that's fundamental to their business and fundamental to their success surprised me. There was a time years ago where there were not that many enterprise CEOs who were well-versed in the cloud.

It's not just about deploying technology. The conversation that I most end up having with CEOs is about organizational transformation. It is about how they can put data at the center of their decision-making in a way that most organizations have never actually done in their history. And it's about using the cloud to innovate more quickly and to drive speed into their organizations.

Those are cultural characteristics, not technology characteristics, and those have organizational implications about how they organize and what teams they need to have. It turns out that while the technology is sophisticated, deploying the technology is arguably the lesser challenge compared with how do you mold and shape the organization to best take advantage of all the benefits that the cloud is providing. How has your experience at Tableau affected AWS and how you think about putting your stamp on AWS?

I, personally, have just spent almost five years deeply immersed in the world of data and analytics and business intelligence, and hopefully I learned something during that time about those topics.

I'm able to bring back a real insider's view, if you will, about where that world is heading — data, analytics, databases, machine learning, and how all those things come together, and how you really need to view what's happening with data as an end-to-end story. It's not about having a point solution for a database or an analytic service, it's really about understanding the flow of data from when it comes into your organization all the way through the other end, where people are collaborating and sharing and making decisions based on that data.

AWS has tremendous resources devoted in all these areas. Can you talk about the intersection of data and machine learning and how you see that playing out in the next couple of years? What we're seeing is three areas really coming together: You've got databases, analytics capabilities, and machine learning, and it's sort of like a Venn diagram with a partial overlap of those three circles.

There are areas of each which are arguably still independent from each other, but there's a very large and a very powerful intersection of the three — to the point where we've actually organized inside of AWS around that and have a single leader for all of those areas to really help bring those together. There's so much data in the world, and the amount of it continues to explode. We were saying that five years ago, and it's even more true today.

The rate of growth is only accelerating. It's a huge opportunity and a huge problem. A lot of people are drowning in their data and don't know how to use it to make decisions. Other organizations have figured out how to use these very powerful technologies to really gain insights rapidly from their data. What we're really trying to do is to look at that end-to-end journey of data and to build really compelling, powerful capabilities and services at each stop in that data journey and then…knit all that together with strong concepts like governance.

By putting good governance in place about who has access to what data and where you want to be careful within those guardrails that you set up, you can then set people free to be creative and to explore all the data that's available to them.

AWS has more than services now. Have you hit the peak for that or can you sustain that growth? We're not done building yet, and I don't know when we ever will be. We continue to both release new services because customers need them and they ask us for them and, at the same time, we've put tremendous effort into adding new capabilities inside of the existing services that we've already built.

We don't just build a service and move on. Inside of each of our services — you can pick any example — we're just adding new capabilities all the time. One of our focuses now is to make sure that we're really helping customers to connect and integrate between our different services.

So those kinds of capabilities — both building new services, deepening our feature set within existing services, and integrating across our services — are all really important areas that we'll continue to invest in. Do customers still want those fundamental building blocks and to piece them together themselves, or do they just want AWS to take care of all that? There's no one-size-fits-all solution to what customers want. It is interesting, and I will say somewhat surprising to me, how much basic capabilities, such as price performance of compute, are still absolutely vital to our customers.

But it's absolutely vital. Part of that is because of the size of datasets and because of the machine learning capabilities which are now being created. They require vast amounts of compute, but nobody will be able to do that compute unless we keep dramatically improving the price performance. We also absolutely have more and more customers who want to interact with AWS at a higher level of abstraction…more at the application layer or broader solutions, and we're putting a lot of energy, a lot of resources, into a number of higher-level solutions.

One of the biggest of those … is Amazon Connect, which is our contact center solution. In minutes or hours or days, you can be up and running with a contact center in the cloud.

At the beginning of the pandemic, Barclays … sent all their agents home. In something like 10 days, they got 6, agents up and running on Amazon Connect so they could continue servicing their end customers with customer service. We've built a lot of sophisticated capabilities that are machine learning-based inside of Connect. We can do call transcription, so that supervisors can help with training agents and services that extract meaning and themes out of those calls.

We don't talk about the primitive capabilities that power that, we just talk about the capabilities to transcribe calls and to extract meaning from the calls. It's really important that we provide solutions for customers at all levels of the stack. Given the economic challenges that customers are facing, how is AWS ensuring that enterprises are getting better returns on their cloud investments?

Now's the time to lean into the cloud more than ever, precisely because of the uncertainty. We saw it during the pandemic in early , and we're seeing it again now, which is, the benefits of the cloud only magnify in times of uncertainty. For example, the one thing which many companies do in challenging economic times is to cut capital expense.

Many encryption and compression functions return strings for which the result might contain arbitrary byte values. If you want to store these results, use a column with a VARBINARY or BLOB binary string data type.

This avoids potential problems with trailing space removal or character set conversion that would change data values, such as may occur if you use a nonbinary string data type CHAR , VARCHAR , TEXT. This is a nonbinary string unless the character set is binary. If an application stores values from a function such as MD5 or SHA1 that returns a string of hex digits, more efficient storage and comparisons can be obtained by converting the hex representation to binary using UNHEX and storing the result in a BINARY N column.

Each pair of hexadecimal digits requires one byte in binary form, so the value of N depends on the length of the hex string. N is 16 for an MD5 value and 20 for a SHA1 value. For SHA2 , N ranges from 28 to 32 depending on the argument specifying the desired bit length of the result. The size penalty for storing the hex string in a CHAR column is at least two times, up to eight times if the value is stored in a column that uses the utf8mb4 character set where each character uses 4 bytes.

Storing the string also results in slower comparisons because of the larger values and the need to take character set collation rules into account.

Suppose that an application stores MD5 string values in a CHAR 32 column:. To convert hex strings to more compact form, modify the application to use UNHEX and BINARY 16 instead as follows:. Applications should be prepared to handle the very rare case that a hashing function produces the same value for two different input values. One way to make collisions detectable is to make the hash column a primary key.

Exploits for the MD5 and SHA-1 algorithms have become known. You may wish to consider using another one-way encryption function described in this section instead, such as SHA2. Passwords or other sensitive values supplied as arguments to encryption functions are sent as cleartext to the MySQL server unless an SSL connection is used. Also, such values appear in any MySQL logs to which they are written. To avoid these types of exposure, applications can encrypt sensitive values on the client side before sending them to the server.

The same considerations apply to encryption keys. To avoid exposing these, applications can use stored procedures to encrypt and decrypt values on the server side. This function decrypts data using the official AES Advanced Encryption Standard algorithm.

By default these functions implement AES with a bit key length. Key lengths of or bits can be used, as described later. The key length is a trade off between performance and security. If either function argument is NULL , the function returns NULL. From MySQL 8. The derived key is used to encrypt and decrypt the data, and it remains in the MySQL Server instance and is not accessible to users. Using a KDF is highly recommended, as it provides better security than specifying your own premade key or deriving it by a simpler method as you use the function.

The functions support HKDF available from OpenSSL 1. Its default value is aesecb , which signifies encryption using a key length of bits and ECB mode. For a description of the permitted values of this variable, see Section 5.

For more information about that option, see Section 4. The string can be any length. Padding is automatically added to str so it is a multiple of a block as required by block-based algorithms such as AES. The encryption key, or the input keying material that is used as the basis for deriving a key using a key derivation function KDF.

If you are using a KDF, which you can from MySQL 8. In the further arguments for the function, you specify the KDF name, then add further options to increase the security as appropriate for the KDF.

For example:. A passphrase can be used to generate an AES key by hashing the passphrase. If you exceed the maximum key length of bits, a warning is returned. Previous versions of this documentation suggested the former approach, but it is no longer recommended as the examples shown here are more secure.

An initialization vector, for block encryption modes that require it. If you are using a KDF, you must specify an initialization vector or a null string for this argument, in order to access the later arguments to define the KDF. For modes that require an initialization vector, it must be 16 bytes or longer bytes in excess of 16 are ignored. The alternative permitted block encryption modes CBC, CFB1, CFB8, CFB, and OFB all require an initialization vector.

This optional argument is available from MySQL 8. HKDF, which is available from OpenSSL 1. HKDF extracts a pseudorandom key from the keying material then expands it into additional keys. With HKDF, you can specify an optional salt salt and context-specific information such as application details info to include in the keying material. PBKDF2, which is available from OpenSSL 1. PBKDF2 applies a pseudorandom function to the keying material, and repeats this process a large number of times to produce the key.

With PBKDF2, you can specify an optional salt salt to include in the keying material, and set the number of iterations used to produce the key iterations.

In this example, HKDF is specified as the key derivation function, and a salt and context information are provided. The argument for the initialization vector is included but is the empty string:. In this example, PBKDF2 is specified as the key derivation function, a salt is provided, and the number of iterations is doubled from the recommended minimum:.

A salt to be passed to the key derivation function KDF. Both HKDF and PBKDF2 can use salts, and their use is recommended to help prevent attacks based on dictionaries of common passwords or rainbow tables. A salt consists of random data, which for security must be different for each encryption operation. This example produces a bit salt:.

The salt can safely be stored along with the encrypted data. Context-specific information for HKDF to include in the keying material, such as information about the application. The iteration count for PBKDF2 to use when producing the key. A higher count gives greater resistance to brute-force attacks because it has a greater computational cost for the attacker, but the same is necessarily true for the key derivation process. The default if you do not specify this argument is , which is the minimum recommended by the OpenSSL standard.

Compresses a string and returns the result as a binary string. This function requires MySQL to have been compiled with a compression library such as zlib. Otherwise, the return value is always NULL. Nonempty strings are stored as a 4-byte length of the uncompressed string low byte first , followed by the compressed string.

If the string ends with space, an extra. character is added to avoid problems with endspace trimming should the result be stored in a CHAR or VARCHAR column. However, use of nonbinary string data types such as CHAR or VARCHAR to store compressed strings is not recommended anyway because character set conversion may occur. Use a VARBINARY or BLOB binary string column instead. If COMPRESS is invoked from within the mysql client, binary strings display using hexadecimal notation, depending on the value of the --binary-as-hex.

MD5 str. Calculates an MD5 bit checksum for the string. The value is returned as a string of 32 hexadecimal digits, or NULL if the argument was NULL. The return value can, for example, be used as a hash key. See the notes at the beginning of this section about storing hash values efficiently. If FIPS mode is enabled, MD5 returns NULL. See Section 6. MD5 Message-Digest Algorithm. This function returns a binary string of len random bytes generated using the random number generator of the SSL library.

Permitted values of len range from 1 to For values outside that range, an error occurs. Returns NULL if len is NULL. For use in that context, len must be at least Larger values are permitted, but bytes in excess of 16 are ignored. Consequently, statements that use this function are unsafe for statement-based replication.

SHA1 str , SHA str. Calculates an SHA-1 bit checksum for the string, as described in RFC Secure Hash Algorithm. The value is returned as a string of 40 hexadecimal digits, or NULL if the argument is NULL. One of the possible uses for this function is as a hash key. SHA is synonymous with SHA1.

SHA1 can be considered a cryptographically more secure equivalent of MD5. However, see the note regarding the MD5 and SHA-1 algorithms at the beginning this section. Calculates the SHA-2 family of hash functions SHA, SHA, SHA, and SHA The first argument is the plaintext string to be hashed. The second argument indicates the desired bit length of the result, which must have a value of , , , , or 0 which is equivalent to

Is there a paved road toward cloud native resiliency? The 5th Circuit ruling can have a major impact on the Consumer Financial Protection Bureau. Set the default entry by choosing one of the entries from the sub-menu. Previously based on Gentoo, it has been based on Arch Linux since version 6. We're a big enough business, if you asked me have you ever seen X, I could probably find one of anything, but the absolute dominant trend is customers dramatically accelerating their move to the cloud. That adoption speaks louder than any other voice.

Categories:

• Binary Options online
• Forex
• Forex Bitcoin
• Forex Brokers
• Binary Options