X-Correlation-ID, [43] Correlation-ID [44]. If a web server responds with Cache-Control: no-cache then a web browser or other caching system intermediate proxies must not use the response to satisfy subsequent requests without first checking with the originating server this process is called validation. This header field is part of HTTP version 1. It may be simulated by setting the Expires HTTP version 1. Notice that no-cache is not instructing the browser or proxies about whether or not to cache the content.

It just tells the browser and proxies to validate the cache content with the server before using it this is done by using If-Modified-Since, If-Unmodified-Since, If-Match, If-None-Match attributes mentioned above.

Sending a no-cache value thus instructs a browser or proxy to not use the cache contents merely based on "freshness criteria" of the cache content. This instructs the user agent that the content is stale and should be validated before use. The header field Cache-Control: no-store is intended to instruct a browser application to make a best effort not to write it to disk i. e not to cache it. The request that a resource should not be cached is no guarantee that it will not be written to disk. If the user navigates back to a previous page a browser may still show you a page that has been stored on disk in the history store.

This is correct behavior according to the specification. Many user agents show different behavior in loading pages from the history store or cache depending on whether the protocol is HTTP or HTTPS. It is a means for the browser to tell the server and any intermediate caches that it wants a fresh version of the resource. It, however, is only defined for the request header. Its meaning in a response header is not specified. As of this edit , this article uses content from "What is the X-REQUEST-ID http header?

All relevant terms must be followed. As of this edit , this article uses content from "Why does ASP. NET framework add the 'X-Powered-By:ASP. NET' HTTP Header in responses? From Wikipedia, the free encyclopedia. HTTP Persistence Compression HTTPS QUIC Request methods OPTIONS GET HEAD POST PUT DELETE TRACE CONNECT PATCH Header fields Cookie ETag Location HTTP referer DNT X-Forwarded-For Response status codes Moved Permanently Found See Other Forbidden Not Found Unavailable for Legal Reasons Security access control methods Basic access authentication Digest access authentication Security vulnerabilities HTTP header injection HTTP request smuggling HTTP response splitting HTTP parameter pollution v t e.

June doi : RFC May Retrieved December 13, HTTP Semantics. Retrieved November 12, June 11, Retrieved June 12, Archived from the original on May 9, Retrieved March 13, Retrieved July 24, Retrieved January 7, Retrieved November 26, HTTP Caching. October 8, Retrieved January 14, Retrieved January 31, Retrieved March 24, Retrieved September 10, Retrieved September 30, July 27, Retrieved April 23, Retrieved October 8, Archived from the original on February 16, Retrieved January 16, Electronic Frontier Foundation.

Retrieved January 19, The Washington Post. SAP SE. Retrieved January 20, Django web framework. Archived from the original on January 20, Retrieved March 22, Rapid7 Blog. December 23, Retrieved April 13, Save-Data Request Header Field". Web Platform Incubator Community Group. June 30, Retrieved March 5, Retrieved December 24, April Retrieved April 19, Retrieved June 8, Retrieved April 17, Retrieved August 2, Retrieved April 28, Mozilla Developer Network.

Retrieved July 23, Retrieved May 18, Retrieved May 1, August 9, Retrieved January 25, May 26, Retrieved January 3, Retrieved September 28, Retrieved June 14, Archived from the original on August 26, Retrieved August 26, Retrieved May 29, April 1, Retrieved January 24, March 12, Retrieved March 14, September 22, Retrieved April 15, Retrieved March 20, Categories : Hypertext Transfer Protocol headers Internet-related lists.

Hidden categories: Articles with short description Short description is different from Wikidata Use mdy dates from June Use American English from June All Wikipedia articles written in American English Articles with imported Creative Commons Attribution-ShareAlike 3. 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. Persistence Compression HTTPS QUIC. OPTIONS GET HEAD POST PUT DELETE TRACE CONNECT PATCH. Cookie ETag Location HTTP referer DNT X-Forwarded-For.

Basic access authentication Digest access authentication. HTTP header injection HTTP request smuggling HTTP response splitting HTTP parameter pollution. Acceptable instance-manipulations for the request. See Content negotiation. List of acceptable encodings. See HTTP compression. List of acceptable human languages for response.

Access-Control-Request-Method, Access-Control-Request-Headers [12]. Initiates a request for cross-origin resource sharing with Origin below.

Authentication credentials for HTTP authentication. Used to specify directives that must be obeyed by all caching mechanisms along the request-response chain. Control options for the current connection and list of hop-by-hop request fields. Connection: keep-alive Connection: Upgrade. The type of encoding used on the data. The length of the request body in octets 8-bit bytes. A Base64 -encoded binary MD5 sum of the content of the request body.

Obsolete [15]. RFC , , The Media type of the body of the request used with POST and PUT requests. An HTTP cookie previously sent by the server with Set-Cookie below. RFC , The date and time at which the message was originated in "HTTP-date" format as defined by RFC HTTP Semantics, section 5.

Disclose original information of a client connecting to a web server through an HTTP proxy. The domain name of the server for virtual hosting , and the TCP port number on which the server is listening.

The port number may be omitted if the port is the standard port for the service requested. Host: en. org Host: en. Only perform the action if the client supplied entity matches the same entity on the server. Typically the link layer encapsulates IP packets in frames with a CRC footer that detects most errors, many transport-layer protocols carried by IP also have their own error checking.

The IPv4 packet header consists of 14 fields, of which 13 are required. The 14th field is optional and aptly named: options. The fields in the header are packed with the most significant byte first big endian , and for the diagram and discussion, the most significant bits are considered to come first MSB 0 bit numbering.

The most significant bit is numbered 0, so the version field is actually found in the four most significant bits of the first byte, for example.

The packet payload is not included in the checksum. Its contents are interpreted based on the value of the Protocol header field. List of IP protocol numbers contains a complete list of payload protocol types. Some of the common payload protocols include:.

The Internet Protocol enables traffic between networks. The design accommodates networks of diverse physical nature; it is independent of the underlying transmission technology used in the link layer. Networks with different hardware usually vary not only in transmission speed, but also in the maximum transmission unit MTU.

When one network wants to transmit datagrams to a network with a smaller MTU, it may fragment its datagrams. In IPv4, this function was placed at the Internet Layer and is performed in IPv4 routers limiting exposure to these issues by hosts. In contrast, IPv6 , the next generation of the Internet Protocol, does not allow routers to perform fragmentation; hosts must perform Path MTU Discovery before sending datagrams.

When a router receives a packet, it examines the destination address and determines the outgoing interface to use and that interface's MTU. If the packet size is bigger than the MTU, and the Do not Fragment DF bit in the packet's header is set to 0, then the router may fragment the packet.

The router divides the packet into fragments. The maximum size of each fragment is the outgoing MTU minus the IP header size 20 bytes minimum; 60 bytes maximum. The router puts each fragment into its own packet, each fragment packet having the following changes:.

It is possible that a packet is fragmented at one router, and that the fragments are further fragmented at another router. For example, a packet of 4, bytes, including a 20 bytes IP header is fragmented to two packets on a link with an MTU of 2, bytes:. When forwarded to a link with an MTU of 1, bytes, each fragment is fragmented into two fragments:.

Also in this case, the More Fragments bit remains 1 for all the fragments that came with 1 in them and for the last fragment that arrives, it works as usual, that is the MF bit is set to 0 only in the last one. And of course, the Identification field continues to have the same value in all re-fragmented fragments.

This way, even if fragments are re-fragmented, the receiver knows they have initially all started from the same packet. A receiver knows that a packet is a fragment, if at least one of the following conditions is true:.

The receiver identifies matching fragments using the source and destination addresses, the protocol ID, and the identification field. The receiver reassembles the data from fragments with the same ID using both the fragment offset and the more fragments flag.

When the receiver receives the last fragment, which has the more fragments flag set to 0, it can calculate the size of the original data payload, by multiplying the last fragment's offset by eight and adding the last fragment's data size.

When the receiver has all fragments, they can be reassembled in the correct sequence according to the offsets to form the original datagram. IP addresses are not tied in any permanent manner to networking hardware and, indeed, in modern operating systems , a network interface can have multiple IP addresses. In order to properly deliver an IP packet to the destination host on a link, hosts and routers need additional mechanisms to make an association between the hardware address [c] of network interfaces and IP addresses.

The Address Resolution Protocol ARP performs this IP-address-to-hardware-address translation for IPv4.

In addition, the reverse correlation is often necessary. For example, unless an address is preconfigured by an administrator, when an IP host is booted or connected to a network it needs to determine its IP address. Protocols for such reverse correlations include Dynamic Host Configuration Protocol DHCP , Bootstrap Protocol BOOTP and, infrequently, reverse ARP. From Wikipedia, the free encyclopedia.

Redirected from Internet Protocol Version 4. Fourth version of the Internet Protocol. Main article: Localhost. See also: IPv4 subnetting reference. Main article: Domain Name System. Main article: IPv4 address exhaustion. Main article: IP fragmentation.

Retrieved IPv4 Market Group. Archived from the original PDF on June 16, Cotton; L. Vegoda; B. Haberman April Bonica ed. Special-Purpose IP Address Registries. doi : ISSN BCP RFC Best Common Practice. Obsoletes RFC , , and Updated by RFC Rekhter; B. Moskowitz; D. Karrenberg; G. de Groot; E. Lear February Address Allocation for Private Internets.

Network Working Group. BCP 5. Obsoletes RFC and Weil; V. Kuarsingh; C. Donley; C. Liljenstolpe; M. Azinger April IANA-Reserved IPv4 Prefix for Shared Address Space. Internet Engineering Task Force.

Updates RFC Cheshire; B. Aboba; E. Guttman May Dynamic Configuration of IPv4 Link-Local Addresses. Proposed Standard. Arkko; M. Vegoda January IPv4 Address Blocks Reserved for Documentation. Troan May Carpenter ed. Deprecating the Anycast Prefix for 6to4 Relay Routers. Huitema June An Anycast Prefix for 6to4 Relay Routers. Obsoleted by RFC Bradner; J. McQuaid March Benchmarking Methodology for Network Interconnect Devices.

Updated by: RFC and RFC Vegoda; D. Meyer March IANA Guidelines for IPv4 Multicast Address Assignments. Best Common Practice M. Venaas; R. Parekh; G. Van de Velde; T. Chown; M.

Eubanks August Multicast Addresses for Documentation. Reynolds, ed. January Assigned Numbers: RFC is Replaced by an On-line Database. Obsoletes RFC June Retrieved 15 November Special Addresses: In certain contexts, it is useful to have fixed addresses with functional significance rather than as identifiers of specific hosts. When such usage is called for, the address zero is to be interpreted as meaning "this", as in "this network".

Archived from the original on Number Resource Organization. Retrieved 3 February Archived from the original on 7 August Retrieved 15 April December March Piscataway, NJ: University of Technology, Mauritius, Institute of Electrical and Electronics Engineers. August ISBN OCLC Technical Criteria for Choosing IP The Next Generation IPng. Internet Protocol. ACM SIGCOMM Computer Communication Review.

Internet Protocol version 4 at Wikipedia's sister projects.

BMC Bioinformatics volume 9 , Article number: Cite this article. Metrics details. Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples.

Weighted correlation network analysis WGCNA can be used for finding clusters modules of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits using eigengene network methodology , and for calculating module membership measures.

Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial.

The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis.

The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software.

Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings.

The WGCNA package provides R functions for weighted correlation network analysis, e. co-expression network analysis of gene expression data. Correlation networks are increasingly being used in biology to analyze large, high-dimensional data sets. We refer to the i -th row x i as the i -th node profile across m sample measurements. Sometimes a quantitative measure referred to as sample trait is provided for the columns of X.

Abstractly speaking, we define a sample trait T as a vector with m components that correspond to the columns of the data matrix X. A sample trait can be used to define a node significance measure.

For example, a trait-based node significance measure can be defined as the absolute value of the correlation between the i -th node profile x i and the sample trait. Alternatively, a correlation test p-value [ 1 ] or a regression-based p-value for assessing the statistical significance between x i and the sample trait T can be used to define a p-value based node significance measure, for example by defining. The rationale behind correlation network methodology is to use network language to describe the pairwise relationships correlations between the rows of X Equation 1.

Although other statistical techniques exist for analyzing correlation matrices, network language is particularly intuitive to biologists and allows for simple social network analogies. Correlation networks can be used to address many analysis goals including the following. First, correlation networks can be used to find clusters modules of interconnected nodes.

Thus, a network module is a set of rows of X Equation 1 which are closely connected according to a suitably defined measure of interconnectedness. A second analysis goal is to summarize the node profiles of a given module by a representative, e. a highly connected hub node, which is centrally located in the module. Focusing the analysis on module or their representatives amounts to a network-based data reduction method.

Relating modules instead of nodes to a sample trait can alleviate the multiple testing problem. A third analysis goal is to identify 'significant' modules. Toward this end, a node significance measure can be used to identify modules with high average node significance referred to as module significance.

A fourth analysis goal is to annotate all network nodes with respect to how close they are to the identified modules. This can be accomplished by defining a fuzzy measure of module memberships that generalizes the binary module membership indicator to a quantitative measure.

Fuzzy measures of module membership can be used to identify nodes that lie intermediate between and close to two or more modules. A fifth analysis goal is to define the network neighborhood of a given seed set of nodes.

Intuitively speaking, a neighborhood is composed of nodes that are highly connected to a given set of nodes. Thus, neighborhood analysis facilitates a guilt-by-association screening strategy for finding nodes that interact with a given set of interesting nodes. A sixth analysis goal is to screen for nodes based on node screening criteria which can be based on a node significance measure, on module membership information, on network topological properties e.

high connectivity , etc. A seventh analysis goal is to contrast one network with another network. This differential network analysis can be used to identify changes in connectivity patterns or module structure between different conditions. An eighth analysis goal is to find shared modules between two or more networks consensus module analysis.

Since by definition consensus modules are building blocks in multiple networks, they may represent fundamental structural properties of the network.

The above incomplete enumeration of analysis goals shows that correlation networks can be used as a data exploratory technique similar to cluster analysis, factor analysis, or other dimensional reduction techniques and as a screening method. For example, correlation networks can be used to screen for modules and intramodular hubs that relate to a sample trait. Correlation networks allow one to generate testable hypotheses that should be validated in independent data or in designed validation experiments.

In the following, we focus on gene co-expression networks which represent a major application of correlation network methodology. Co-expression networks have been found useful for describing the pairwise relationships among gene transcripts [ 2 — 9 ].

In co-expression networks, we refer to nodes as 'genes', to the node profile x i as the gene expression profile, and to the node significance measure GS i as the gene significance measure. A glossary of important network-related terms can be found in Table 1. Here we introduce an R software package that summarizes and extends our earlier work on weighted gene co-expression network analysis WGCNA [ 5 , 10 — 12 ].

WGCNA has been used to analyze gene expression data from brain cancer [ 10 ], yeast cell cycle [ 13 ], mouse genetics [ 14 — 17 ], primate brain tissue [ 18 — 20 ], diabetes [ 21 ], chronic fatigue patients [ 22 ] and plants [ 23 ].

While these publications have made R software code available in various forms, there is a need for a comprehensive R package that summarizes and standardizes methods and functions.

To address this need, we introduce the WGCNA R package which also includes enhanced and novel functions for co-expression network analysis. Figure 1 provides an overview of typical analysis steps and the rationale behind them.

To determine whether a co-expression module is biologically meaningful, one can use functional enrichment and gene ontology information. Overview of WGCNA methodology. This flowchart presents a brief overview of the main steps of Weighted Gene Co-expression Network Analysis. The WGCNA package contains a comprehensive set of functions for performing a correlation network analysis of large, high-dimensional data sets. Functions in the WGCNA package can be divided into the following categories: 1.

network construction; 2. module detection; 3. module and gene selection; 4. calculations of topological properties; 5. data simulation; 6. visualization; 7. interfacing with external software packages. An exhaustive list of implemented functions together with detailed descriptions is provided in the R package manual posted on our web site.

Here we briefly outline the main functionality of the package and highlight new contributions. A network is fully specified by its adjacency matrix a ij , a symmetric n × n matrix with entries in [0, 1] whose component a ij encodes the network connection strength between nodes i and j.

To calculate the adjacency matrix, an intermediate quantity called the co-expression similarity s ij is first defined. The WGCNA package also implements alternative co-expression measures, e.

more robust measures of correlation the biweight midcorrelation [ 24 ] or the Spearman correlation. A signed co-expression measure can be defined to keep track of the sign of the co-expression information. For convenience, we define the co-expression similarity measure such that it takes on values in [0, 1]. Using a thresholding procedure, the co-expression similarity is transformed into the adjacency.

An unweighted network adjacency a ij between gene expression profiles x i and x j can be defined by hard thresholding the co-expression similarity s ij as. where τ is the "hard" threshold parameter. The hard-thresholding procedure is implemented in the function signumAdjacencyFunction. While unweighted networks are widely used, they do not reflect the continuous nature of the underlying co-expression information and may thus lead to an information loss.

In contrast, weighted networks allow the adjacency to take on continuous values between 0 and 1. A weighed network adjacency can be defined by raising the co-expression similarity to a power [ 5 , 10 ]:. The function adjacency calculates the adjacency matrix from expression data. Adjacency functions for both weighted and unweighted networks require the user to choose threshold parameters, for example by applying the approximate scale-free topology criterion [ 5 ].

The package provides functions pickSoftThreshold, pickHardThreshold that assist in choosing the parameters, as well as the function scaleFreePlot for evaluating whether the network exhibits a scale free topology. Figure 2A shows a plot identifying scale free topology in simulated expression data. Network visualization plots. Log-log plot of whole-network connectivity distribution. The x -axis shows the logarithm of whole network connectivity, y -axis the logarithm of the corresponding frequency distribution.

On this plot the distribution approximately follows a straight line, which is referred to as approximately scale-free topology.

Results of classical multidimensional scaling. Modules tend to form separate 'fingers' in this plot. Intramodular hub genes are located at the finger tips. Network heatmap plot. Branches in the hierarchical clustering dendrograms correspond to modules. Color-coded module membership is displayed in the color bars below and to the right of the dendrograms. In the heatmap, high co-expression interconnectedness is indicated by progressively more saturated yellow and red colors.

If the first character of expression is , the expression is read from a file titled by the remaining characters in the string. Larger values produce more visible detail. As of this edit , this article uses content from "What is the X-REQUEST-ID http header? To master this strategy and make money every 5 minutes with Binary Options , you must learn technical analysis. Therefore, when you encounter such a pattern and trend, trade your money right away as this is a favorable time.

Categories:

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