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Database abstraction layer

A database abstraction layer (DBAL or DAL) is an application programming interface which unifies the communication between a computer application and databases such as SQL Server, IBM Db2, MySQL, PostgreSQL, Oracle or SQLite. Traditionally, all database vendors provide their own interface that is tailored to their products. It is up to the application programmer to implement code for the database interfaces that will be supported by the application. Database abstraction layers reduce the amount of work by providing a consistent API to the developer and hide the database specifics behind this interface as much as possible. There exist many abstraction layers with different interfaces in numerous programming languages. If an application has such a layer built in, it is called database-agnostic.

Physical level (lowest level)
The lowest level connects to the database and performs the actual operations required by the users. At this level the conceptual instruction has been translated into multiple instructions that the database understands. Executing the instructions in the correct order allows the DAL to perform the conceptual instruction.

Implementation of the physical layer may use database-specific APIs or use the underlying language standard database access technology and the database's version SQL.

Implementation of data types and operations are the most database-specific at this level.

Conceptual or logical level (middle or next highest level)
The conceptual level consolidates external concepts and instructions into an intermediate data structure that can be devolved into physical instructions. This layer is the most complex as it spans the external and physical levels. Additionally it needs to span all the supported databases and their quirks, APIs, and problems.

This level is aware of the differences between the databases and able to construct an execution path of operations in all cases. However the conceptual layer defers to the physical layer for the actual implementation of each individual operation.

External or view level
The external level is exposed to users and developers and supplies a consistent pattern for performing database operations. Database operations are represented only loosely as SQL or even database access at this level.

Every database should be treated equally at this level with no apparent difference despite varying physical data types and operations.

Database abstraction in the API
Libraries unify access to databases by providing a single low-level programming interface to the application developer. Their advantages are most often speed and flexibility because they are not tied to a specific query language (subset) and only have to implement a thin layer to reach their goal. As all SQL dialects are similar to one another, application developers can use all the language features, possibly providing configurable elements for database-specific cases, such as typically user-IDs and credentials. A thin-layer allows the same queries and statements to run on a variety of database products with negligible overhead.

Popular use for database abstraction layers are among object-oriented programming languages, which are similar to API-level abstraction layers. In an object-oriented language like C++ or Java, a database can be represented through an object, whose methods and members (or the equivalent thereof in other programming languages) represent various functionalities of the database. They also share advantages and disadvantages with API-level interfaces.

Language-level abstraction
An example of a database abstraction layer on the language level would be ODBC that is a platform-independent implementation of a database abstraction layer. The user installs specific driver software, through which ODBC can communicate with a database or set of databases. The user then has the ability to have programs communicate with ODBC, which then relays the results back and forth between the user programs and the database. The downside of this abstraction level is the increased overhead to transform statements into constructs understood by the target database.

Alternatively, there are thin wrappers, often described as lightweight abstraction layers, such as OpenDBX and libzdb. Finally, large projects may develop their own libraries, such as, for example, libgda for GNOME.

In favor

 * Development period: software developers only have to know the database abstraction layer's API instead of all APIs of the databases their application should support. The more databases should be supported the bigger is the time saving.
 * Wider potential install-base: using a database abstraction layer means that there is no requirement for new installations to utilise a specific database, i.e. new users who are unwilling or unable to switch databases can deploy on their existing infrastructure.
 * Future-proofing: as new database technologies emerge, software developers won't have to adapt to new interfaces.
 * Developer testing: a production database may be replaced with a desktop-level implementation of the data for developer-level unit tests.
 * Added Database Features: depending on the database and the DAL, it may be possible for the DAL to add features to the database. A DAL may use database programming facilities or other methods to create standard but unsupported functionality or completely new functionality. For instance, the DBvolution DAL implements the standard deviation function for several databases that do not support it.

Against it

 * Speed: any abstraction layer will reduce the overall speed more or less depending on the amount of additional code that has to be executed. The more a database layer abstracts from the native database interface and tries to emulate features not present on all database backends, the slower the overall performance. This is especially true for database abstraction layers that try to unify the query language as well like ODBC.
 * Dependency: a database abstraction layer provides yet another functional dependency for a software system, i.e. a given database abstraction layer, like anything else, may eventually become obsolete, outmoded or unsupported.
 * Masked operations: database abstraction layers may limit the number of available database operations to a subset of those supported by the supported database backends. In particular, database abstraction layers may not fully support database backend-specific optimizations or debugging features. These problems magnify significantly with database size, scale, and complexity.

title: Database abstraction layer


text:
Ahtanum

There are two things named Ahtanum in the U.S. state of Washington:
 * Ahtanum, Washington (town)
 * Ahtanum View Correction Center (state prison)

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: Ahtanum


text:
Sabaria

Sabaria was a semi-autonomous territory in Iberia, between the Kingdom of the Suebi and the Visigothic kingdom, in the 4th-6th centuries that extended from Benavente to Salamanca and from Sayago to Simancas.

Sabaria was autonomous enough to mint its own currency, although this coinage is sometimes confused with the Visigothic mint of Senimure-Semure in the current city of Zamora.

title: Sabaria


text:
Nickajack Expedition

The Nickajack Expedition in 1794 was a long-running battle fought from late summer to fall between American frontiersmen and the Chickamauga Cherokee. This Cherokee band had resisted the increasing American encroachment into their territory and raided American settlements in the region.

The military expedition was a decisive success for the American settlers of what was then called the Southwest Territory and surrounding regions. It eventually became known to Americans as the "Last Battle of the Cherokee". Other Cherokee defeats followed shortly, and the US forced them to agree to another treaty ceding some of their land. A total of 39 Cherokee chiefs and leaders, including Chickamauga, signed the 1798 Treaty of Tellico, ceding a large territory in East Tennessee to the United States.

Background
Following a 1777 peace treaty between Native Americans and the American settlers of the Overhill settlements west of the Appalachian Mountains during the American War of Independence, followers of the Cherokee chief Dragging Canoe (who opposed the peace), separated from the tribe and relocated to what is today southeastern Tennessee, near the borders with Georgia and the area later known as Alabama.

They were joined by bands of Shawnee and Creek at this new settlement, which had been established along Chickamauga Creek; it became their namesake. During the Revolutionary War, the Chickamauga Cherokee engaged in ongoing raids against American settlers, often with British and Spanish military aid.

Shortly after the conclusion to the war, the Cherokee moved again, this time west of Lookout Mountain, using Nickajack Cave as a stronghold. Violence between them and European Americans continued for decades.

Notwithstanding the December 1791 Treaty of Holston between Territorial Governor William Blount and most Cherokee bands, settlers in the "Cumberland Region" (especially around the Nashville area) still feared for their lives. They had been frequently raided by the Chickamauga and their allies to the south. By 1792, Blount was engaged in continuing peace negotiations with the Chickamauga. In September 1792 when the Nickajack native peoples launched their attack against the Mero District that was foiled at the Battle of Buchanan's Station those negotiations failed, and resulted in two more years of defensive engagements on the part of the American settlers. When the sons of Colonel Anthony Bledsoe (1739-1788) and Major General George Winchester were killed in 1794, however, Blount finally sanctioned military action.

The expedition
Governor Blount appointed Major James Ore to head an expedition against the Chickamauga, or "Lower Cherokee" as they had come to be known. Col. John Montgomery commanded the territorial militia, and Col. William Whitley of Kentucky (whose state had also been subject to Cherokee attack from this area ) commanded his 6th Regiment of militia. They singled out two Chickamauga villages, Nickajack Town and Running Water Town, as targets, as these had been the source of many raiding parties. In the mountainous areas, the American force struggled to find the sites and plan attacks.

The expeditions finally reached Nickajack Town in mid-August, but found only a hundred or so warriors present. Many of the villagers had heard of the army's approach and had fled to Running Water Town before Ore's men could reach the village. Warriors from Running Water Town were on their way to Nickajack to investigate the activity and encountered the fleeing villagers. The Nickajack warriors joined with those from Running Water, and together they returned to engage the Americans.

By this time the militias had begun to pursue the fleeing villagers. The soldiers and warriors met at "the Narrows" along the Tennessee River and engaged in battle. It proved to be a disaster for the Chickamauga. They were routed after wounding only three Americans, and killing none. The state militias left 70 dead and destroyed both villages, including food stores.

Aftermath
Coupled with other military victories by the Americans after the Cherokee defeat at Nickajack and Running Water towns, the Cherokee agreed to treaty. A total of 39 Cherokee chiefs and leaders, including those representing the Chickamauga band, finally signed the 1798 Treaty of Tellico, ceding a large amount of land in East Tennessee to the United States.

title: Nickajack Expedition


text: Category:Iranian Majlis Representatives

title: Category:Iranian Majlis Representatives


text:
Interactome

In molecular biology, an interactome is the whole set of molecular interactions in a particular cell. The term specifically refers to physical interactions among molecules (such as those among proteins, also known as protein–protein interactions, PPIs; or between small molecules and proteins ) but can also describe sets of indirect interactions among genes (genetic interactions). The word "interactome" was originally coined in 1999 by a group of French scientists headed by Bernard Jacq. Mathematically, interactomes are generally displayed as graphs. though interactomes may be described as biological networks, they should not be confused with other networks such as neural networks or food webs.

Molecular interaction networks
Molecular interactions can occur between molecules belonging to different biochemical families (proteins, nucleic acids, lipids, carbohydrates, etc.) and also within a given family. Whenever such molecules are connected by physical interactions, they form molecular interaction networks that are generally classified by the nature of the compounds involved. Most commonly, interactome refers to protein–protein interaction (PPI) network (PIN) or subsets thereof. For instance, the Sirt-1 protein interactome and Sirt family second order interactome is the network involving Sirt-1 and its directly interacting proteins where as second order interactome illustrates interactions up to second order of neighbors (Neighbors of neighbors). Another extensively studied type of interactome is the protein–DNA interactome, also called a gene-regulatory network, a network formed by transcription factors, chromatin regulatory proteins, and their target genes. Even metabolic networks can be considered as molecular interaction networks: metabolites, i.e. chemical compounds in a cell, are converted into each other by enzymes, which have to bind their substrates physically.

In fact, all interactome types are interconnected. For instance, protein interactomes contain many enzymes which in turn form biochemical networks. Similarly, gene regulatory networks overlap substantially with protein interaction networks and signaling networks.

Size
It has been suggested that the size of an organism's interactome correlates better than genome size with the biological complexity of the organism. Although protein–protein interaction maps containing several thousand binary interactions are now available for several species, none of them is presently complete and the size of interactomes is still a matter of debate.

Yeast
The yeast interactome, i.e. all protein–protein interactions among proteins of Saccharomyces cerevisiae, has been estimated to contain between 10,000 and 30,000 interactions. A reasonable estimate may be on the order of 20,000 interactions. Larger estimates often include indirect or predicted interactions, often from affinity purification/mass spectrometry (AP/MS) studies.

Genetic interaction networks
Genes interact in the sense that they affect each other's function. For instance, a mutation may be harmless, but when it is combined with another mutation, the combination may turn out to be lethal. Such genes are said to "interact genetically". Genes that are connected in such a way form genetic interaction networks. Some of the goals of these networks are: develop a functional map of a cell's processes, drug target identification using chemoproteomics, and to predict the function of uncharacterized genes.

In 2010, the most "complete" gene interactome produced to date was compiled from about 5.4 million two-gene comparisons to describe "the interaction profiles for ~75% of all genes in the budding yeast", with ~170,000 gene interactions. The genes were grouped based on similar function so as to build a functional map of the cell's processes. Using this method the study was able to predict known gene functions better than any other genome-scale data set as well as adding functional information for genes that hadn't been previously described. From this model genetic interactions can be observed at multiple scales which will assist in the study of concepts such as gene conservation. Some of the observations made from this study are that there were twice as many negative as positive interactions, negative interactions were more informative than positive interactions, and genes with more connections were more likely to result in lethality when disrupted.

Interactomics
Interactomics is a discipline at the intersection of bioinformatics and biology that deals with studying both the interactions and the consequences of those interactions between and among proteins, and other molecules within a cell. Interactomics thus aims to compare such networks of interactions (i.e., interactomes) between and within species in order to find how the traits of such networks are either preserved or varied.

Interactomics is an example of "top-down" systems biology, which takes an overhead view of a biosystem or organism. Large sets of genome-wide and proteomic data are collected, and correlations between different molecules are inferred. From the data new hypotheses are formulated about feedbacks between these molecules. These hypotheses can then be tested by new experiments.

Experimental methods to map interactomes
The study of interactomes is called interactomics. The basic unit of a protein network is the protein–protein interaction (PPI). While there are numerous methods to study PPIs, there are relatively few that have been used on a large scale to map whole interactomes.

The yeast two hybrid system (Y2H) is suited to explore the binary interactions among two proteins at a time. Affinity purification and subsequent mass spectrometry is suited to identify a protein complex. Both methods can be used in a high-throughput (HTP) fashion. Yeast two hybrid screens allow false positive interactions between proteins that are never expressed in the same time and place; affinity capture mass spectrometry does not have this drawback, and is the current gold standard. Yeast two-hybrid data better indicates non-specific tendencies towards sticky interactions rather while affinity capture mass spectrometry better indicates functional in vivo protein–protein interactions.

Computational methods to study interactomes
Once an interactome has been created, there are numerous ways to analyze its properties. However, there are two important goals of such analyses. First, scientists try to elucidate the systems properties of interactomes, e.g. the topology of its interactions. Second, studies may focus on individual proteins and their role in the network. Such analyses are mainly carried out using bioinformatics methods and include the following, among many others:

Validation
First, the coverage and quality of an interactome has to be evaluated. Interactomes are never complete, given the limitations of experimental methods. For instance, it has been estimated that typical Y2H screens detect only 25% or so of all interactions in an interactome. The coverage of an interactome can be assessed by comparing it to benchmarks of well-known interactions that have been found and validated by independent assays. Other methods filter out false positives calculating the similarity of known annotations of the proteins involved or define a likelihood of interaction using the subcellular localization of these proteins.

Predicting PPIs
Using experimental data as a starting point, homology transfer is one way to predict interactomes. Here, PPIs from one organism are used to predict interactions among homologous proteins in another organism ("interologs"). However, this approach has certain limitations, primarily because the source data may not be reliable (e.g. contain false positives and false negatives). In addition, proteins and their interactions change during evolution and thus may have been lost or gained. Nevertheless, numerous interactomes have been predicted, e.g. that of Bacillus licheniformis.

Some algorithms use experimental evidence on structural complexes, the atomic details of binding interfaces and produce detailed atomic models of protein–protein complexes as well as other protein–molecule interactions. Other algorithms use only sequence information, thereby creating unbiased complete networks of interaction with many mistakes.

Some methods use machine learning to distinguish how interacting protein pairs differ from non-interacting protein pairs in terms of pairwise features such as cellular colocalization, gene co-expression, how closely located on a DNA are the genes that encode the two proteins, and so on. Random Forest has been found to be most-effective machine learning method for protein interaction prediction. Such methods have been applied for discovering protein interactions on human interactome, specifically the interactome of Membrane proteins and the interactome of Schizophrenia-associated proteins.

Text mining of PPIs
Some efforts have been made to extract systematically interaction networks directly from the scientific literature. Such approaches range in terms of complexity from simple co-occurrence statistics of entities that are mentioned together in the same context (e.g. sentence) to sophisticated natural language processing and machine learning methods for detecting interaction relationships.

Protein function prediction
Protein interaction networks have been used to predict the function of proteins of unknown functions. This is usually based on the assumption that uncharacterized proteins have similar functions as their interacting proteins (guilt by association). For example, YbeB, a protein of unknown function was found to interact with ribosomal proteins and later shown to be involved in bacterial and eukaryotic (but not archaeal) translation. Although such predictions may be based on single interactions, usually several interactions are found. Thus, the whole network of interactions can be used to predict protein functions, given that certain functions are usually enriched among the interactors. The term hypothome has been used to denote an interactome wherein at least one of the genes or proteins is a hypothetical protein.

Perturbations and disease
The topology of an interactome makes certain predictions how a network reacts to the perturbation (e.g. removal) of nodes (proteins) or edges (interactions). Such perturbations can be caused by mutations of genes, and thus their proteins, and a network reaction can manifest as a disease. A network analysis can identify drug targets and biomarkers of diseases.

Network structure and topology
Interaction networks can be analyzed using the tools of graph theory. Network properties include the degree distribution, clustering coefficients, betweenness centrality, and many others. The distribution of properties among the proteins of an interactome has revealed that the interactome networks often have scale-free topology where functional modules within a network indicate specialized subnetworks. Such modules can be functional, as in a signaling pathway, or structural, as in a protein complex. In fact, it is a formidable task to identify protein complexes in an interactome, given that a network on its own does not directly reveal the presence of a stable complex.

Viral interactomes
Viral protein interactomes consist of interactions among viral or phage proteins. They were among the first interactome projects as their genomes are small and all proteins can be analyzed with limited resources. Viral interactomes are connected to their host interactomes, forming virus-host interaction networks. Some published virus interactomes include

Bacteriophage
 * Escherichia coli bacteriophage lambda
 * Escherichia coli bacteriophage T7
 * Streptococcus pneumoniae bacteriophage Dp-1
 * Streptococcus pneumoniae bacteriophage Cp-1

The lambda and VZV interactomes are not only relevant for the biology of these viruses but also for technical reasons: they were the first interactomes that were mapped with multiple Y2H vectors, proving an improved strategy to investigate interactomes more completely than previous attempts have shown.

Human (mammalian) viruses
 * Human varicella zoster virus (VZV)
 * Chandipura virus
 * Epstein-Barr virus (EBV)
 * Hepatitis C virus (HPC), Human-HCV interactions
 * Hepatitis E virus (HEV)
 * Herpes simplex virus 1 (HSV-1)
 * Kaposi's sarcoma-associated herpesvirus (KSHV)
 * Murine cytomegalovirus (mCMV)

Bacterial interactomes
Relatively few bacteria have been comprehensively studied for their protein–protein interactions. However, none of these interactomes are complete in the sense that they captured all interactions. In fact, it has been estimated that none of them covers more than 20% or 30% of all interactions, primarily because most of these studies have only employed a single method, all of which discover only a subset of interactions. Among the published bacterial interactomes (including partial ones) are

The E. coli and Mycoplasma interactomes have been analyzed using large-scale protein complex affinity purification and mass spectrometry (AP/MS), hence it is not easily possible to infer direct interactions. The others have used extensive yeast two-hybrid (Y2H) screens. The Mycobacterium tuberculosis interactome has been analyzed using a bacterial two-hybrid screen (B2H).

Note that numerous additional interactomes have been predicted using computational methods (see section above).

Eukaryotic interactomes
There have been several efforts to map eukaryotic interactomes through HTP methods. While no biological interactomes have been fully characterized, over 90% of proteins in Saccharomyces cerevisiae have been screened and their interactions characterized, making it the best-characterized interactome. Species whose interactomes have been studied in some detail include Recently, the pathogen-host interactomes of Hepatitis C Virus/Human (2008), Epstein Barr virus/Human (2008), Influenza virus/Human (2009) were delineated through HTP to identify essential molecular components for pathogens and for their host's immune system.
 * Schizosaccharomyces pombe
 * Caenorhabditis elegans
 * Drosophila melanogaster
 * Homo sapiens

Predicted interactomes
As described above, PPIs and thus whole interactomes can be predicted. While the reliability of these predictions is debatable, they are providing hypotheses that can be tested experimentally. Interactomes have been predicted for a number of species, e.g.
 * Human (Homo sapiens)
 * Rice (Oryza sativa)
 * Xanthomonas oryzae
 * Arabidopsis thaliana
 * Tomato (Solanum lycopersicum)
 * Field mustard (Brassica rapa)
 * Maize, corn (Zea mays)
 * Poplar (Populus trichocarpa)
 * SARS-CoV-2

Network properties
Protein interaction networks can be analyzed with the same tool as other networks. In fact, they share many properties with biological or social networks. Some of the main characteristics are as follows.

Degree distribution
The degree distribution describes the number of proteins that have a certain number of connections. Most protein interaction networks show a scale-free (power law) degree distribution where the connectivity distribution P(k) ~ k−γ with k being the degree. This relationship can also be seen as a straight line on a log-log plot since, the above equation is equal to log(P(k)) ~ —y•log(k). One characteristic of such distributions is that there are many proteins with few interactions and few proteins that have many interactions, the latter being called "hubs".

Hubs
Highly connected nodes (proteins) are called hubs. Han et al. have coined the term "party hub" for hubs whose expression is correlated with its interaction partners. Party hubs also connect proteins within functional modules such as protein complexes. In contrast, "date hubs" do not exhibit such a correlation and appear to connect different functional modules. Party hubs are found predominantly in AP/MS data sets, whereas date hubs are found predominantly in binary interactome network maps. Note that the validity of the date hub/party hub distinction was disputed. Party hubs generally consist of multi-interface proteins whereas date hubs are more frequently single-interaction interface proteins. Consistent with a role for date-hubs in connecting different processes, in yeast the number of binary interactions of a given protein is correlated to the number of phenotypes observed for the corresponding mutant gene in different physiological conditions.

Modules
Nodes involved in the same biochemical process are highly interconnected.

Evolution
The evolution of interactome complexity is delineated in a study published in Nature. In this study it is first noted that the boundaries between prokaryotes, unicellular eukaryotes and multicellular eukaryotes are accompanied by orders-of-magnitude reductions in effective population size, with concurrent amplifications of the effects of random genetic drift. The resultant decline in the efficiency of selection seems to be sufficient to influence a wide range of attributes at the genomic level in a nonadaptive manner. The Nature study shows that the variation in the power of random genetic drift is also capable of influencing phylogenetic diversity at the subcellular and cellular levels. Thus, population size would have to be considered as a potential determinant of the mechanistic pathways underlying long-term phenotypic evolution. In the study it is further shown that a phylogenetically broad inverse relation exists between the power of drift and the structural integrity of protein subunits. Thus, the accumulation of mildly deleterious mutations in populations of small size induces secondary selection for protein–protein interactions that stabilize key gene functions, mitigating the structural degradation promoted by inefficient selection. By this means, the complex protein architectures and interactions essential to the genesis of phenotypic diversity may initially emerge by non-adaptive mechanisms.

Criticisms, challenges, and responses
Kiemer and Cesareni raise the following concerns with the state (circa 2007) of the field especially with the comparative interactomic: The experimental procedures associated with the field are error prone leading to "noisy results". This leads to 30% of all reported interactions being artifacts. In fact, two groups using the same techniques on the same organism found less than 30% interactions in common. However, some authors have argued that such non-reproducibility results from the extraordinary sensitivity of various methods to small experimental variation. For instance, identical conditions in Y2H assays result in very different interactions when different Y2H vectors are used.

Techniques may be biased, i.e. the technique determines which interactions are found. In fact, any method has built in biases, especially protein methods. Because every protein is different no method can capture the properties of each protein. For instance, most analytical methods that work fine with soluble proteins deal poorly with membrane proteins. This is also true for Y2H and AP/MS technologies.

Interactomes are not nearly complete with perhaps the exception of S. cerevisiae. This is not really a criticism as any scientific area is "incomplete" initially until the methodologies have been improved. Interactomics in 2015 is where genome sequencing was in the late 1990s, given that only a few interactome datasets are available (see table above).

While genomes are stable, interactomes may vary between tissues, cell types, and developmental stages. Again, this is not a criticism, but rather a description of the challenges in the field.

It is difficult to match evolutionarily related proteins in distantly related species. While homologous DNA sequences can be found relatively easily, it is much more difficult to predict homologous interactions ("interologs") because the homologs of two interacting proteins do not need to interact. For instance, even within a proteome two proteins may interact but their paralogs may not.

Each protein–protein interactome may represent only a partial sample of potential interactions, even when a supposedly definitive version is published in a scientific journal. Additional factors may have roles in protein interactions that have yet to be incorporated in interactomes. The binding strength of the various protein interactors, microenvironmental factors, sensitivity to various procedures, and the physiological state of the cell all impact protein–protein interactions, yet are usually not accounted for in interactome studies.

Interactome web servers

 * Protinfo PPC predicts the atomic 3D structure of protein protein complexes.
 * IBIS (server) reports, predicts and integrates multiple types of conserved interactions for proteins.

Interactome visualization tools

 * GPS-Prot Web-based data visualization for protein interactions
 * PINV - Protein Interaction Network Visualizer

Interactome databases

 * BioGRID database
 * mentha the interactome browser
 * IntAct: The Molecular Interaction Database
 * Interactome.org — a dedicated interactome web site.

title: Interactome


text:
Hyperbolic

Hyperbolic is an adjective describing something that resembles or pertains to a hyperbola (a curve), to hyperbole (an overstatement or exaggeration), or to hyperbolic geometry.

The following phenomena are described as hyperbolic because they manifest hyperbolas, not because something about them is exaggerated.

 * Hyperbolic angle, an unbounded variable referring to a hyperbola instead of a circle
 * Hyperbolic coordinates, location by geometric mean and hyperbolic angle in quadrant I

 * Hyperbolic distribution, a probability distribution characterized by the logarithm of the probability density function being a hyperbola
 * Hyperbolic equilibrium point, a fixed point that does not have any center manifolds
 * Hyperbolic function, an analog of an ordinary trigonometric or circular function
 * Hyperbolic geometric graph, a random network generated by connecting nearby points sprinkled in a hyperbolic space
 * Hyperbolic geometry, a non-Euclidean geometry
 * Hyperbolic group, a finitely generated group equipped with a word metric satisfying certain properties characteristic of hyperbolic geometry
 * Hyperbolic growth, growth of a quantity toward a finite-time singularity
 * Hyperbolic logarithm, original designation of natural logarithm (1647–1748) before Euler's formulation with e
 * Hyperbolic manifold, a complete Riemannian n-manifold of constant sectional curvature −1
 * Hyperbolic motion, an isometry in a hyperbolic space
 * Hyperbolic navigation, a class of radio navigation systems based on the difference in timing between the reception of two signals, without reference to a common clock
 * Hyperbolic number, a synonym for split-complex number
 * Hyperbolic orthogonality, an orthogonality found in pseudo-Euclidean space
 * Hyperbolic paraboloid, a doubly ruled surface shaped like a saddle
 * Hyperbolic partial differential equation, a partial differential equation (PDE) of order n that has a well-posed initial value problem for the first n−1 derivatives
 * Hyperbolic plane can refer to:
 * The 2-dimensional plane in hyperbolic geometry (a non-Euclidean geometry)
 * The hyperbolic plane as isotropic quadratic form
 * Hyperbolic quaternions, a non-associative algebra, precursor to Minkowski space
 * Hyperbolic rotation, a synonym for squeeze mapping
 * Hyperbolic sector, a planar region demarcated by radial lines and a hyperbola
 * Hyperbolic soccerball, a tessellation of the hyperbolic plane
 * Hyperbolic space, hyperbolic spatial geometry in which every point is a saddle point
 * Hyperbolic trajectory, a Kepler orbit with eccentricity greater than 1
 * Hyperbolic versor, a versor parameterized by a hyperbolic angle
 * Hyperbolic unit, a non-real quantity with square equal to +1
 * Hyperbolic (album), a 2024 album by Pnau

title: Hyperbolic


text:
Hugh McCrae

Hugh Raymond McCrae OBE (4 October 1876 – 17 February 1958) was an Australian writer, noted for his poetry.

Life and career
McCrae was born in Melbourne, the son of the Australian author George Gordon McCrae and grandson of the painter and diarist Georgiana McCrae. Originally he trained as an architect, but later took up drawing, writing and acting, settling eventually in Sydney and later in the New South Wales town of Camden. His works are notable for a sense of lightness and delicacy, and he produced, in addition to a volume of memoirs, a considerable body of verse, and a light operetta, an edition of his grandmother's journal, and a volume of prose pieces.

McCrae starred as Australian poet Adam Lindsay Gordon in W. J. Lincoln's 1916 feature film The Life's Romance of Adam Lindsay Gordon, shot in and around Melbourne. In the 1920s, Australian-born composer John Gough set McCrae's poem "Song of the Rain" (from the collection Colombine) to music. McCrae wrote a fantasy play, The Ship of Heaven, which was produced by the Independent Theatre in 1933, for which Alfred Hill composed and conducted the music.

McCrae was well known to a number of distinguished figures in Australian artistic and literary circles. He is remembered for his friendships with Norman Lindsay and Kenneth Slessor, but he was also friendly with such figures as Christopher Brennan and Shaw Neilson. At one time he shared an apartment in New York with Pat Sullivan, the creator of Felix the Cat. When a film about Felix the Cat was being planned, "Sullivan suggested that McCrae should do the drawings while he (Sullivan) supplied the ideas. McCrae refused and has regretted it ever since."

McCrae was awarded the OBE in 1953. Writing after his death in 1958, Mary Gilmore declared that he was Australia's "most outstanding poet", that his poetry "came diamond-like in its perfection of form". "Nothing was too small or too great to attract him," she wrote, adding that "no matter how swallow-like he skims a surface, the deeps are below; he never wrote the shallow." However, he has not retained his critical standing, and is now esteemed mostly as the poet who first offered "an alternative to the balladry that had dominated Australian poetry". Judith Wright called him "a singer, not a thinker, [who] freed the notion of poetry from the portentousness of the Nationalist and radical schools".

After McCrae married Nancy Adams in Melbourne in May 1901, they moved at once to Sydney. They had three daughters. She died in 1943. He married Janet Le Brun in July 1946, but their marriage was dissolved in 1948.

title: Hugh McCrae


text:
Fuller

Fuller or Fuller's may refer to:

People

 * Fuller (surname)
 * A fuller, a worker who cleanses wool through the process of fulling
 * Fuller (artist), a British artist known for making map art and intricate drawings

Places

 * Fuller, Kansas, an unincorporated community in Crawford County, United States
 * Fuller, Michigan, a former community in Michigan
 * Fuller, Pennsylvania, an unincorporated community, United States
 * Fuller Lake, a body of water in Pennsylvania, United States

Companies

 * Fuller's Brewery in London, England, United Kingdom
 * Fuller Brush Company
 * Fuller's Coffee Shop, a coffee shop in Oregon, United States
 * Fuller Manufacturing, a subsidiary of Eaton Corporation acquired in 1958
 * Fuller Theological Seminary, in Pasadena, California, United States

Other uses

 * Fuller calculator, an advanced cylindrical slide rule with a helical scale
 * Fuller's earth, clay used for filtering and purifying
 * Fuller (metalworking), a tool used to form metal when hot
 * Fuller (weapon), a groove in a knife or sword blade to lighten and stiffen the blade
 * USS Fuller, the name of two ships of the U.S. Navy

title: Fuller


text: Talk:Times Square

title: Talk:Times Square


text: Category:Iranian Vice Ministers

title: Category:Iranian Vice Ministers


text:
File talk:Distribution of Races on the Balkans in 1923.jpg

This map was taken from the Perry-Castañeda Library http://www.lib.utexas.edu/maps/historical/history_europe.html http://www.lib.utexas.edu/maps/historical/shepherd/races_balkan_shepherd_1923.jpg

Proper credit should be given http://www.lib.utexas.edu/usage_statement.html --Igor 01:10, 17 Oct 2004 (UTC)


text:
Kathy Whitworth

Kathrynne Ann Whitworth (September 27, 1939 – December 24, 2022) was an American professional golfer. During her playing career she won 88 LPGA Tour tournaments, more than anyone else on the LPGA or PGA Tours. Whitworth was also a runner-up 93 times, giving her 181 top-two finishes. In 1981, she became the first woman to reach career earnings of $1 million on the LPGA Tour. She is a member of the World Golf Hall of Fame.

Early life and amateur career
Whitworth was born on September 27, 1939, in Monahans, Texas, the youngest of three daughters of Morris Whitworth, a hardware store owner and later mayor in Jal, New Mexico, where she grew up. She attended Odessa College.

Initially a tennis player, Whitworth began playing golf at 14. After working with Hardy Loudermilk, she won the 1957 and 1958 New Mexico State Amateur Championships. At 19, she changed coaches to Harvey Penick and turned pro, joining the LPGA in December 1958.

Professional career
In 1962, Whitworth won her first tournament, the Kelly Girls Open. Between that and her victory in the United Virginia Bank Classic in 1985, she won 88 tournaments on the LPGA Tour, the highest number of any player in the history of professional golf, including on the men's PGA Tour. Six were major championships. In 1974, she won the Orange Blossom Classic for the fifth time, one of only four LPGA golfers have won the same tournament five times. She was the U.S. team captain at the inaugural Solheim Cup match in 1990. She was the first LPGA player to earn $1 million, in 1981.

Whitworth was LPGA Player of the Year seven times between 1966 and 1973, won the Vare Trophy for best scoring average by an LPGA Tour player a record seven times between 1965 and 1972, and was inducted into the LPGA Hall of Fame in 1975 and into the World Golf Hall of Fame in 1982. She was named Associated Press Female Athlete of the Year in 1965 and 1967. She was Named "Golfer of the Decade" by Golf Magazine for the years 1968 to 1977 during the 1988 Centennial of Golf in America celebration. She received the 1986 William Richardson Award from the Golf Writers Association of America for consistent outstanding contributions to golf. She is also a member of the New Mexico Hall of Fame, Texas Sports Hall of Fame, Texas Golf Hall of Fame, and the Women's Sports Foundation Hall of Fame.

Later life and death
Whitworth retired from competitive golf in 2005 after competing in the BJ's Charity Classic on the Women's Senior Golf Tour. In 2007, with Jay Golden, she published Kathy Whitworth's Little Book of Golf Wisdom. At the time of her death she lived in Flower Mound, Texas with her partner, Bettye Odle. She died in Flower Mound on December 24, 2022, at age 83, after collapsing while attending a neighbor's Christmas party.

LPGA Tour wins (88)
Note: Whitworth won the Colgate-Dinah Shore Winner's Circle (now known as the ANA Inspiration) before it became a major championship.

LPGA Tour playoff record (8–20)

Ladies European Tour wins

 * 1984 Smirnoff Ladies Irish Open

Other wins

 * 1962 Haig & Haig Scotch Foursome (with Mason Rudolph)
 * 1966 Lagunita Invitational
 * 1967 Ladies World Series of Golf
 * 1968 Ladies World Series of Golf
 * 1971 LPGA Four-Ball Championship (with Judy Kimball)
 * 1975 Colgate Triple Crown
 * 1978 Ping Classic Team Championship (with Donna Caponi)
 * 1980 Portland Ping Team Championship (with Donna Caponi)
 * 1981 Portland Ping Team Championship (with Donna Caponi)

title: Kathy Whitworth


text: Category:Security engineering

title: Category:Security engineering


text:
Talk:Loop invariant

I'm rewriting the description of loop invariants in Floyd-Hoare logic. Most of the existing text got deleted. I'm saving an example from the previous revision here, in case someone feels strongly that it should be preserved and wants to re-integrate it into the article. --MarkSweep 01:11, 17 Oct 2004 (UTC)

Example:

f(n) {  // {P: n >=0 } int k=n; int erg=1; while (k>0) {    erg=erg*k; k=k-1; }  // {Q: erg=k! } }

Suitable invariant:

{ I: (n!=erg*k!) ^ (k>=0) }

Proof of the 4 points:

1. (n>=0), erg=1; k=n; => n!=n! ^ (n>=0) => (n!=erg*k!) ^ (n>=0)

2. {(n!=erg*k!) ^ (k>=0)} (k>0) => {I}

3. (n!=erg*k!) ^ (k>=0) ^ (k>0)

Now evaluate the program:

(n!=erg*k*(k-1)!) ^ (k>=-1) ^ (k>0) => (n!=erg*k!) ^ (k>0) = I ^ b => I

4. (n!=erg*k!) ^ (k>=0) ^ (k<=0)

=> (n!=erg*k!) ^ (k=0)

=> (n!=erg)

q.e.d

No Floyd or Hoare references
It is strange that, in a discussion like this about loop invariants, there is no reference to Floyd or Hoare in the references.
 * These have just been added. It is indeed strange that they were not among the very first references added to the article. Deepmath (talk) 01:52, 25 August 2008 (UTC)

The link to [2], "Assigning Meanings to Programs" is broken. A copy exists online at https://classes.soe.ucsc.edu/cmps290g/Fall09/Papers/AssigningMeanings1967.pdf but I don't know how to update the reference properly. 72.68.150.92 (talk) 16:26, 11 November 2017 (UTC)

Update: I added the link but I'm not sure I did it 100% correctly, would appreciate someone checking my edit. Thanks! 72.68.150.92 (talk) 16:29, 11 November 2017 (UTC)

 * It seems that you deleted the Hoare.1969 reference by accident. I've restored it, and converted the Floyd reference to the format, keeping your new URL. - Jochen Burghardt (talk) 18:22, 11 November 2017 (UTC)

"In computer science, a loop invariant is an invariant used to prove properties of loops." When reading this definition, it is almost reads a loop invariant is an invariant loop. Maybe there is a better way to phrase this.

I wonder what this page says in english. 217.150.107.105 (talk) 12:32, 19 April 2010 (UTC)

Eiffel does not support loop invariants
Eiffel does not "support" loop invariants in any real sense; its loop invariants are simply syntactic sugar for assertions at the top of the loop. In particular, Eiffel makes no attempt to show that the invariant is inductive. Nor do the language semantics require a loop invariant to be inductive.

Many (essentially all) deductive verifiers for imperative programs support loop invariants. Erniecohen (talk) 12:45, 17 February 2013 (UTC)

 * I added your hint in the "Eiffel" section. I also changed the "until" condition there from "x=10" to "x>=10" (hope that's proper Eiffel syntax) to make the example more similar to the earlier C example.
 * Should some deductive verifiers be mentioned in the article?
 * Jochen Burghardt (talk) 12:59, 24 May 2013 (UTC)

Two definitions
Aren't there 2 definitions? The one provided, but also the one inferred by the page on Loop-invariant code motion where a loop invariant is code that doesn't depend on the actual loop and can be moved outside with no effect. — Preceding unsigned comment added by 78.22.121.243 (talk) 20:29, 13 August 2015 (UTC)
 * No, because "loop invariant" is a noun, and "loop-invariant" in "loop-invariant code" is an adverb. All the stuff in here about "loop-invariant code motion" is best left in Loop-invariant code motion. So I'm boldly removing it from here and leaving the short blurb linking to it at the top. 68.2.235.85 (talk) 21:34, 3 May 2016 (UTC)

@68.2.235.85: As a reaction to 78.22.121.243's above question and 2 edits of on 4 Mar 2016, I'd written the section you deleted. It doesn't just duplicate Loop-invariant code motion; instead, it tries to explain the fundamental difference of, and the relation between, "loop invariant" and "loop invariant code". Therefore, I still consider it worth to be included in this article, and I reinsert it here. Imho, saying only "both concepts are different" is unsatisfactory, since (1) it leaves the reader with the question "Why then are both named almost similar", and (2) there is some relation between them (something doesn't change between successive loop iterations) which I tried to explain. I think that these explanations can still be improved (constructive edit are welcome), but they shouldn't be completely deleted. - Jochen Burghardt (talk) 11:53, 4 May 2016 (UTC)

 * To concur with Jochen (thanks for the explanation!): these are closely related but distinct notions, and thus should have their own articles and merit explanation of the connection and distinction, exactly as has been done. One can quibble about the level of detail, but some explanation is warranted somewhere!
 * —Nils von Barth (nbarth) (talk) 05:41, 9 May 2016 (UTC)

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title: Talk:Loop invariant


text: Category:Spokespersons of the Government of Iran


text:
Roto-Rooter

Roto-Rooter Plumbing & Water Cleanup (formerly called Roto-Rooter Plumbing & Drain Service) is a Cincinnati, Ohio based plumbing company. The company, founded in 1935, originally specialized in clearing tree roots and other obstructions from sewer lines.

, it employs thousands of plumbers, and service technicians throughout the US and Canada who provide plumbing and sewer and water damage cleanup services. Roto-Rooter also employs manufacturing technicians and assembly workers at its manufacturing plant in West Des Moines, Iowa. The plant manufactures the company's patented, proprietary Roto-Rooter sewer and drain cleaning machines as well as sewer and drain cleaning cables and blades. Roto-Rooter is one of the two subsidiary divisions of the publicly traded Chemed Corporation, of which the other one is a hospice care provider VITAS Healthcare.

Early years
In the late 1920s, Samuel Oscar Blanc (1883–1964) was cleaning a blocked sewer at his son's (Milton L. Blanc) Des Moines, Iowa, apartment to seek a better solution than digging up the sewer line to remove tree roots that had invaded the underground sewer pipe.

By 1933, Samuel Blanc had fashioned a sewer-cleaning machine from a washing machine motor, wheels from a child's little red wagon, and a 3/8" steel cable. The cable rotated with a sharp C-shaped blade attached to the tip. The rotating motion cut tree roots out of sewer lines, eliminating the need to dig up pipes and clear obstructions by hand, thus inventing the world's first heavy-duty plumber's Snake. Blanc's wife, Lettie (née Lettie Jensen), called his invention the "Roto-Rooter".

By the mid-1930s, Blanc was selling his patented "Roto-Rooter" machines for $250 and incorporated a business around it called Roto-Rooter Corporation. Many in need of during the Great Depression started their own Roto-Rooter businesses throughout the upper Midwest, the Great Plains, and the Northeast. Roto-Rooter's sewer cleaning service allowed homeowners to avoid digging up lawns and landscaping to reach underground sewer pipes. This breakthrough was a revolutionary concept. In the 1930s and 40s, Roto-Rooter featured an illustration of a mound of dirt over a recently excavated sewer pipe with the caption, "Why put a grave in your yard?"

In 1939, after his Roto-Rooter sewer cleaning machine proved successful, Sam Blanc designed and built a smaller model that operated on the same principle but could clean and clear bathroom and kitchen sink drains as well as laundry drains and other household plumbing drains. Blanc also built a much larger version of the Roto-Rooter machine called the Roto-Rooter Royal. It had to be towed by a truck and featured a 10-horsepower gasoline engine and 1000 feet of steel cable. It was capable of cleaning large industrial and municipal sewer pipes. An improved version called "The Mainliner" was introduced in 1958 that could also thread telephone cable. It was discontinued in 1973.

Selling of the company
In 1980, the Blanc family sold Roto-Rooter Corporation to Cincinnati-based Chemed Corporation. Chemed began purchasing independent Roto-Rooter franchises and operating them as company-owned service locations under the newly formed Roto-Rooter Services Company, whose corporate headquarters is in downtown Cincinnati. Chemed sold off some of its holdings in Roto-Rooter in both 1984 and 1985, bringing its ownership stake to just below 60%, but launched a bid in 1996 to reacquire the 42% of shares that it had sold off.

In the 1980s, some Roto-Rooter franchises and company-owned locations began to offer around-the-clock service and general full-service plumbing repair for both residential and commercial customers. Roto-Rooter's plumbing services represent a major part of the company's business. The company has more than 600 service locations operating throughout North America that serve more than 90% of the US population in all fifty states, and more than 40% of the Canadian population. Roto-Rooter is the largest provider of plumbing repair and sewer and drain cleaning services in both countries.

International franchise operations have been established in Canada, Japan, the Philippines, Hong Kong, Indonesia, Singapore, Brazil, South Africa, and Australia.

Original logo
For the first five years of Roto-Rooter's existence (1935-1940), the company had no official logo and allowed its franchisees to paint their trucks however they saw fit. Due to copycat imitators, Roto-Rooter unveiled the first official Roto-Rooter logo in November 1940. The logo featured the patented Roto-Rooter electric sewer cleaning machine inside of an outline of the United States proclaiming, "A Specialized Service From Coast-to-Coast". The new emblem was used on service vehicles, on guarantees, and in all advertising to help homeowners select the original Roto-Rooter.

1953-1979
In 1953, Roto-Rooter updated the logo to a much simpler design which incorporated a new black and white font with “SEWER-DRAIN SERVICE” inside a drainpipe. This logo was used from 1953 through the 1970s. The only update made to this design was switching out the black outline to a blue outline.

1980-1995
By 1980, Roto-Rooter was in need of an updated logo for its service vans and equipment. The new logo for the 1980s was a refreshed version of the previous logo. The new logo was all blue and eliminated the rightward slant of the previous logo, standing up straight the letters of the Roto-Rooter name. The drainpipe portion was also updated to include more waves.

1996-present
In 1996, the Roto-Rooter Visual Identity Program introduced a new logo to replace all previous logos. Variations of this design are still used to this day by Roto-Rooter locations in the United States, Canada, and around the world. The only variation since its introduction was the addition of a single-line logo to complement the over-and-under version. Since the single-line logo uses the same font, it is considered an extension of the other logo.

Razor-Kleen!
In the 1930s and 1940s, Roto-Rooter service vehicles bore the slogan "Roto-Rooter's patented cutting blades slice through roots and cut them away ... Razor-Kleen!"

Away go troubles down the drain
The Roto-Rooter advertising jingle used today on TV and radio was created in 1954 and has been one of the longest-running and best-remembered musical jingles in history:

"Call Roto-Rooter, that's the name, and away go troubles down the drain."

The memorable bass voice in the commercial was that of Tom Fouts, more widely known as Captain Stubby of Captain Stubby and the Buccaneers.

Many memorable Roto-Rooter TV commercials were introduced over the years that featured the iconic Roto-Rooter advertising jingle, helping to make it a familiar piece of Americana.

This jingle is well-known as inspiration for the episode of The Simpsons, Mr. Plow. In the episode, Homer builds a snow-plowing business down to filming an advertisement where the jingle has a similar melody.

We do both!
In December 2017, Roto-Rooter released a TV commercial that introduced a new slogan, "We Do Both", to explain to the public that Roto-Rooter does both plumbing repair and water damage cleanup. The commercial, which began airing on television and in targeted internet ads, features a nosy neighbor asking a Roto-Rooter plumber standing at the curb "what are you doing with the Johnsons' sofa?" The Roto-Rooter plumber explains that the Johnsons had a pipe break that flooded their house and that he had fixed the broken pipe and now Roto-Rooter is "cleaning up the damage".

Yeah, We Do That Too!
As a follow-up to “We Do Both”, in August 2019 Roto-Rooter premiered a sequel slogan campaign titled “We Do That Too” to showcase service options that go beyond the core services of plumbing repair, drain cleaning, and water damage clean-up. The “We Do That Too” campaign highlights the secondary services that Roto-Rooter offers such as pipe inspection, sewer repair, water heater repair, and sump pump replacement.

title: Roto-Rooter


text:
Transcriptome

The transcriptome is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells. The term can also sometimes be used to refer to all RNAs, or just mRNA, depending on the particular experiment. The term transcriptome is a portmanteau of the words transcript and genome; it is associated with the process of transcript production during the biological process of transcription.

The early stages of transcriptome annotations began with cDNA libraries published in the 1980s. Subsequently, the advent of high-throughput technology led to faster and more efficient ways of obtaining data about the transcriptome. Two biological techniques are used to study the transcriptome, namely DNA microarray, a hybridization-based technique and RNA-seq, a sequence-based approach. RNA-seq is the preferred method and has been the dominant transcriptomics technique since the 2010s. Single-cell transcriptomics allows tracking of transcript changes over time within individual cells.

Data obtained from the transcriptome is used in research to gain insight into processes such as cellular differentiation, carcinogenesis, transcription regulation and biomarker discovery among others. Transcriptome-obtained data also finds applications in establishing phylogenetic relationships during the process of evolution and in in vitro fertilization. The transcriptome is closely related to other -ome based biological fields of study; it is complementary to the proteome and the metabolome and encompasses the translatome, exome, meiome and thanatotranscriptome which can be seen as ome fields studying specific types of RNA transcripts. There are quantifiable and conserved relationships between the Transcriptome and other -omes, and Transcriptomics data can be used effectively to predict other molecular species, such as metabolites. There are numerous publicly available transcriptome databases.

Etymology and history
The word transcriptome is a portmanteau of the words transcript and genome. It appeared along with other neologisms formed using the suffixes -ome and -omics to denote all studies conducted on a genome-wide scale in the fields of life sciences and technology. As such, transcriptome and transcriptomics were one of the first words to emerge along with genome and proteome. The first study to present a case of a collection of a cDNA library for silk moth mRNA was published in 1979. The first seminal study to mention and investigate the transcriptome of an organism was published in 1997 and it described 60,633 transcripts expressed in S. cerevisiae using serial analysis of gene expression (SAGE). With the rise of high-throughput technologies and bioinformatics and the subsequent increased computational power, it became increasingly efficient and easy to characterize and analyze enormous amount of data. Attempts to characterize the transcriptome became more prominent with the advent of automated DNA sequencing during the 1980s. During the 1990s, expressed sequence tag sequencing was used to identify genes and their fragments. This was followed by techniques such as serial analysis of gene expression (SAGE), cap analysis of gene expression (CAGE), and massively parallel signature sequencing (MPSS).

Transcription
The transcriptome encompasses all the ribonucleic acid (RNA) transcripts present in a given organism or experimental sample. RNA is the main carrier of genetic information that is responsible for the process of converting DNA into an organism's phenotype. A gene can give rise to a single-stranded messenger RNA (mRNA) through a molecular process known as transcription; this mRNA is complementary to the strand of DNA it originated from. The enzyme RNA polymerase II attaches to the template DNA strand and catalyzes the addition of ribonucleotides to the 3' end of the growing sequence of the mRNA transcript.

In order to initiate its function, RNA polymerase II needs to recognize a promoter sequence, located upstream (5') of the gene. In eukaryotes, this process is mediated by transcription factors, most notably Transcription factor II D (TFIID) which recognizes the TATA box and aids in the positioning of RNA polymerase at the appropriate start site. To finish the production of the RNA transcript, termination takes place usually several hundred nuclecotides away from the termination sequence and cleavage takes place. This process occurs in the nucleus of a cell along with RNA processing by which mRNA molecules are capped, spliced and polyadenylated to increase their stability before being subsequently taken to the cytoplasm. The mRNA gives rise to proteins through the process of translation that takes place in ribosomes.

Types of RNA transcripts
Almost all functional transcripts are derived from known genes. The only exceptions are a small number of transcripts that might play a direct role in regulating gene expression near the prompters of known genes. (See Enhancer RNA.)

Gene occupy most of prokaryotic genomes so most of their genomes are transcribed. Many eukaryotic genomes are very large and known genes may take up only a fraction of the genome. In mammals, for example, known genes only account for 40-50% of the genome. Nevertheless, identified transcripts often map to a much larger fraction of the genome suggesting that the transcriptome contains spurious transcripts that do not come from genes. Some of these transcripipts are known to be non-functional because they map to transcribed pseudogenes or degenerative transposons and viruses. Others map to unidentified regions of the genome that may be junk DNA.

Spurious transcription is very common in eukaryotes, especially those with large genomes that might contain a lot of junk DNA. Some scientists claim that if a transcript has not been assigned to a known gene then the default assumption must be that it is junk RNA until it has been shown to be functional. This would mean that much of the transcriptome in species with large genomes is probably junk RNA. (See Non-coding RNA)

The transcriptome includes the transcripts of protein-coding genes (mRNA plus introns) as well as the transcripts of non-coding genes (functional RNAs plus introns).

 * Ribosomal RNA/rRNA: Usually the most abundant RNA in the transcriptome.
 * Long non-coding RNA/lncRNA: Non-coding RNA transcripts that are more than 200 nucleotides long. Members of this group comprise the largest fraction of the non-coding transcriptome other than introns. It is not known how many of these transcripts are functional and how many are junk RNA.
 * transfer RNA/tRNA
 * micro RNA/miRNA: 19-24 nucleotides (nt) long. Micro RNAs up- or downregulate expression levels of mRNAs by the process of RNA interference at the post-transcriptional level.
 * small interfering RNA/siRNA: 20-24 nt
 * small nucleolar RNA/snoRNA
 * Piwi-interacting RNA/piRNA: 24-31 nt. They interact with Piwi proteins of the Argonaute family and have a function in targeting and cleaving transposons.
 * enhancer RNA/eRNA:

Scope of study
In the human genome, all genes get transcribed into RNA because that's how the molecular gene is defined. (See Gene.) The transcriptome consists of coding regions of mRNA plus non-coding UTRs, introns, non-coding RNAs, and spurious non-functional transcripts.

Several factors render the content of the transcriptome difficult to establish. These include alternative splicing, RNA editing and alternative transcription among others. Additionally, transcriptome techniques are capable of capturing transcription occurring in a sample at a specific time point, although the content of the transcriptome can change during differentiation. The main aims of transcriptomics are the following: "catalogue all species of transcript, including mRNAs, non-coding RNAs and small RNAs; to determine the transcriptional structure of genes, in terms of their start sites, 5′ and 3′ ends, splicing patterns and other post-transcriptional modifications; and to quantify the changing expression levels of each transcript during development and under different conditions".

The term can be applied to the total set of transcripts in a given organism, or to the specific subset of transcripts present in a particular cell type. Unlike the genome, which is roughly fixed for a given cell line (excluding mutations), the transcriptome can vary with external environmental conditions. Because it includes all mRNA transcripts in the cell, the transcriptome reflects the genes that are being actively expressed at any given time, with the exception of mRNA degradation phenomena such as transcriptional attenuation. The study of transcriptomics, (which includes expression profiling, splice variant analysis etc.), examines the expression level of RNAs in a given cell population, often focusing on mRNA, but sometimes including others such as tRNAs and sRNAs.

Methods of construction
Transcriptomics is the quantitative science that encompasses the assignment of a list of strings ("reads") to the object ("transcripts" in the genome). To calculate the expression strength, the density of reads corresponding to each object is counted. Initially, transcriptomes were analyzed and studied using expressed sequence tags libraries and serial and cap analysis of gene expression (SAGE).

Currently, the two main transcriptomics techniques include DNA microarrays and RNA-Seq. Both techniques require RNA isolation through RNA extraction techniques, followed by its separation from other cellular components and enrichment of mRNA.

There are two general methods of inferring transcriptome sequences. One approach maps sequence reads onto a reference genome, either of the organism itself (whose transcriptome is being studied) or of a closely related species. The other approach, de novo transcriptome assembly, uses software to infer transcripts directly from short sequence reads and is used in organisms with genomes that are not sequenced.

DNA microarrays

The first transcriptome studies were based on microarray techniques (also known as DNA chips). Microarrays consist of thin glass layers with spots on which oligonucleotides, known as "probes" are arrayed; each spot contains a known DNA sequence.

When performing microarray analyses, mRNA is collected from a control and an experimental sample, the latter usually representative of a disease. The RNA of interest is converted to cDNA to increase its stability and marked with fluorophores of two colors, usually green and red, for the two groups. The cDNA is spread onto the surface of the microarray where it hybridizes with oligonucleotides on the chip and a laser is used to scan. The fluorescence intensity on each spot of the microarray corresponds to the level of gene expression and based on the color of the fluorophores selected, it can be determined which of the samples exhibits higher levels of the mRNA of interest.

One microarray usually contains enough oligonucleotides to represent all known genes; however, data obtained using microarrays does not provide information about unknown genes. During the 2010s, microarrays were almost completely replaced by next-generation techniques that are based on DNA sequencing.

RNA sequencing
RNA sequencing is a next-generation sequencing technology; as such it requires only a small amount of RNA and no previous knowledge of the genome. It allows for both qualitative and quantitative analysis of RNA transcripts, the former allowing discovery of new transcripts and the latter a measure of relative quantities for transcripts in a sample.

The three main steps of sequencing transcriptomes of any biological samples include RNA purification, the synthesis of an RNA or cDNA library and sequencing the library. The RNA purification process is different for short and long RNAs. This step is usually followed by an assessment of RNA quality, with the purpose of avoiding contaminants such as DNA or technical contaminants related to sample processing. RNA quality is measured using UV spectrometry with an absorbance peak of 260 nm. RNA integrity can also be analyzed quantitatively comparing the ratio and intensity of 28S RNA to 18S RNA reported in the RNA Integrity Number (RIN) score. Since mRNA is the species of interest and it represents only 3% of its total content, the RNA sample should be treated to remove rRNA and tRNA and tissue-specific RNA transcripts.

The step of library preparation with the aim of producing short cDNA fragments, begins with RNA fragmentation to transcripts in length between 50 and 300 base pairs. Fragmentation can be enzymatic (RNA endonucleases), chemical (trismagnesium salt buffer, chemical hydrolysis) or mechanical (sonication, nebulisation). Reverse transcription is used to convert the RNA templates into cDNA and three priming methods can be used to achieve it, including oligo-DT, using random primers or ligating special adaptor oligos.

Single-cell transcriptomics
Transcription can also be studied at the level of individual cells by single-cell transcriptomics. Single-cell RNA sequencing (scRNA-seq) is a recently developed technique that allows the analysis of the transcriptome of single cells, including bacteria. With single-cell transcriptomics, subpopulations of cell types that constitute the tissue of interest are also taken into consideration. This approach allows to identify whether changes in experimental samples are due to phenotypic cellular changes as opposed to proliferation, with which a specific cell type might be overexpressed in the sample. Additionally, when assessing cellular progression through differentiation, average expression profiles are only able to order cells by time rather than their stage of development and are consequently unable to show trends in gene expression levels specific to certain stages. Single-cell trarnscriptomic techniques have been used to characterize rare cell populations such as circulating tumor cells, cancer stem cells in solid tumors, and embryonic stem cells (ESCs) in mammalian blastocysts.

Although there are no standardized techniques for single-cell transcriptomics, several steps need to be undertaken. The first step includes cell isolation, which can be performed using low- and high-throughput techniques. This is followed by a qPCR step and then single-cell RNAseq where the RNA of interest is converted into cDNA. Newer developments in single-cell transcriptomics allow for tissue and sub-cellular localization preservation through cryo-sectioning thin slices of tissues and sequencing the transcriptome in each slice. Another technique allows the visualization of single transcripts under a microscope while preserving the spatial information of each individual cell where they are expressed.

Analysis
A number of organism-specific transcriptome databases have been constructed and annotated to aid in the identification of genes that are differentially expressed in distinct cell populations.

RNA-seq is emerging (2013) as the method of choice for measuring transcriptomes of organisms, though the older technique of DNA microarrays is still used. RNA-seq measures the transcription of a specific gene by converting long RNAs into a library of cDNA fragments. The cDNA fragments are then sequenced using high-throughput sequencing technology and aligned to a reference genome or transcriptome which is then used to create an expression profile of the genes.

Mammals
The transcriptomes of stem cells and cancer cells are of particular interest to researchers who seek to understand the processes of cellular differentiation and carcinogenesis. A pipeline using RNA-seq or gene array data can be used to track genetic changes occurring in stem and precursor cells and requires at least three independent gene expression data from the former cell type and mature cells.

Analysis of the transcriptomes of human oocytes and embryos is used to understand the molecular mechanisms and signaling pathways controlling early embryonic development, and could theoretically be a powerful tool in making proper embryo selection in in vitro fertilisation. Analyses of the transcriptome content of the placenta in the first-trimester of pregnancy in in vitro fertilization and embryo transfer (IVT-ET) revealed differences in genetic expression which are associated with higher frequency of adverse perinatal outcomes. Such insight can be used to optimize the practice. Transcriptome analyses can also be used to optimize cryopreservation of oocytes, by lowering injuries associated with the process.

Transcriptomics is an emerging and continually growing field in biomarker discovery for use in assessing the safety of drugs or chemical risk assessment.

Transcriptomes may also be used to infer phylogenetic relationships among individuals or to detect evolutionary patterns of transcriptome conservation.

Transcriptome analyses were used to discover the incidence of antisense transcription, their role in gene expression through interaction with surrounding genes and their abundance in different chromosomes. RNA-seq was also used to show how RNA isoforms, transcripts stemming from the same gene but with different structures, can produce complex phenotypes from limited genomes.

Plants
Transcriptome analysis have been used to study the evolution and diversification process of plant species. In 2014, the 1000 Plant Genomes Project was completed in which the transcriptomes of 1,124 plant species from the families viridiplantae, glaucophyta and rhodophyta were sequenced. The protein coding sequences were subsequently compared to infer phylogenetic relationships between plants and to characterize the time of their diversification in the process of evolution. Transcriptome studies have been used to characterize and quantify gene expression in mature pollen. Genes involved in cell wall metabolism and cytoskeleton were found to be overexpressed. Transcriptome approaches also allowed to track changes in gene expression through different developmental stages of pollen, ranging from microspore to mature pollen grains; additionally such stages could be compared across species of different plants including Arabidopsis, rice and tobacco.

Relation to other ome fields
Similar to other -ome based technologies, analysis of the transcriptome allows for an unbiased approach when validating hypotheses experimentally. This approach also allows for the discovery of novel mediators in signaling pathways. As with other -omics based technologies, the transcriptome can be analyzed within the scope of a multiomics approach. It is complementary to metabolomics but contrary to proteomics, a direct association between a transcript and metabolite cannot be established.

There are several -ome fields that can be seen as subcategories of the transcriptome. The exome differs from the transcriptome in that it includes only those RNA molecules found in a specified cell population, and usually includes the amount or concentration of each RNA molecule in addition to the molecular identities. Additionally, the transcritpome also differs from the translatome, which is the set of RNAs undergoing translation.

The term meiome is used in functional genomics to describe the meiotic transcriptome or the set of RNA transcripts produced during the process of meiosis. Meiosis is a key feature of sexually reproducing eukaryotes, and involves the pairing of homologous chromosome, synapse and recombination. Since meiosis in most organisms occurs in a short time period, meiotic transcript profiling is difficult due to the challenge of isolation (or enrichment) of meiotic cells (meiocytes). As with transcriptome analyses, the meiome can be studied at a whole-genome level using large-scale transcriptomic techniques. The meiome has been well-characterized in mammal and yeast systems and somewhat less extensively characterized in plants.

The thanatotranscriptome consists of all RNA transcripts that continue to be expressed or that start getting re-expressed in internal organs of a dead body 24–48 hours following death. Some genes include those that are inhibited after fetal development. If the thanatotranscriptome is related to the process of programmed cell death (apoptosis), it can be referred to as the apoptotic thanatotranscriptome. Analyses of the thanatotranscriptome are used in forensic medicine.

eQTL mapping can be used to complement genomics with transcriptomics; genetic variants at DNA level and gene expression measures at RNA level.

Relation to proteome
The transcriptome can be seen as a subset of the proteome, that is, the entire set of proteins expressed by a genome.

However, the analysis of relative mRNA expression levels can be complicated by the fact that relatively small changes in mRNA expression can produce large changes in the total amount of the corresponding protein present in the cell. One analysis method, known as gene set enrichment analysis, identifies coregulated gene networks rather than individual genes that are up- or down-regulated in different cell populations.

Although microarray studies can reveal the relative amounts of different mRNAs in the cell, levels of mRNA are not directly proportional to the expression level of the proteins they code for. The number of protein molecules synthesized using a given mRNA molecule as a template is highly dependent on translation-initiation features of the mRNA sequence; in particular, the ability of the translation initiation sequence is a key determinant in the recruiting of ribosomes for protein translation.

Transcriptome databases

 * Ensembl:
 * OmicTools:
 * Transcriptome Browser:
 * ArrayExpress:

title: Transcriptome


text: Category:Iranian governors

title: Category:Iranian governors


text:
Talk:Narcoterrorism

Narcoterrorist definition
Added in the three objectives of the narco-terrorist: Lukewilson 01:14, 17 Oct 2004 (UTC)
 * Nobbling the police
 * Corrupting judges
 * Influencing politicians

POV
Although I personally agree completely with what is being said here, I must admit that the wording is a little too POV for Wikipedia:

nsandwich 19:03, 5 March 2006 (UTC)

Pretentious pseudo-intellectualism, poetry and POV. While I agree to some extent with the POV, the language and tone is unacceptable for an encyclopedia. --217.153.176.154 13:01, 10 April 2006 (UTC)

There's no proof that the ELN, Hezbollah, etc etc traffic drugs. If there is, source it.

STREETasmyCanvas 21:10, 17 September 2006 (UTC)

Needs Organization, Focus and Objectivity
This article seems to be a collection of random thoughts from contributors who are looking to vent their emotions on the subject. Encyclopedia articles are not supposed to be about the personal opinions and emotions of the authors. Encyclopedia articles are supposed to explain, illustrate and provide relevant facts. Furthermore, the information incorporated into the article needs to conform to a logical outline.--Motorsportsmark 23:00, 5 August 2006 (UTC)

Corrections
The encyclopedic information provided by wikipedia gives insight into a variety if not countless number of topics. We all know that. Despite the logic, the supreme logic, the readers hardly get the clear understanding that they initially sought. And, in some instances, especially when one has experienced, felt and analyzed, the person approaching this intellectual source leaves with the sense that once again people continue to make an artless parody of history. If not a farce, then they feel the pove

Regionalism and Propaganda
This should be an interesting article, although it is not finished. It goes into some detail regarding the Western hemisphere while not including much on the Eastern.

Part of the interesting nature of the term "narcoterrorism" is barely hinted at in the article: its politicization. For example if groups were using cocaine profits to fund repressive regimes that employed execution-style murderers of political opponents outside of any functioning legal system, then for example the USA might be defined as "narcoterrorist" but not often in the mainstream U.S. business press. Similar agreements in the Peoples Republic of China or former Soviet states, between narcotics traffickers and and authoritarians might conceivably exist, and also be proscribed from accurate mention. Similarly, differing and politicized views of Afghani opium production might be significant in the meaning of the term "narcoterrorism."

Unsourced sections and possible original research
There are a lot of unsourced sections, which makes me think that this article is filled with original research. I'll put a tag on the article so this is brought to greater attention. I'm open to work with any editors to help improve this, but I probably don't have to time to tackle the problems on my own right now. MX ( ✉  •  ✎  ) 00:14, 21 July 2017 (UTC)

title: Talk:Narcoterrorism


text:
WxPython

wxPython is a wrapper for the cross-platform GUI API (often referred to as a "toolkit") wxWidgets (which is written in C++) for the Python programming language. It is one of the alternatives to Tkinter. It is implemented as a Python extension module (native code).

History
In 1995, Robin Dunn needed a GUI application to be deployed on HP-UX systems but also run Windows 3.1 within short time frame. He needed a cross-platform solution. While evaluating free and commercial solutions, he ran across Python bindings on the wxWidgets toolkit webpage (known as wxWindows at the time). This was Dunn's introduction to Python. Together with Harri Pasanen and Edward Zimmerman he developed those initial bindings into wxPython 0.2.

In August 1998, version 0.3 of wxPython was released. It was built for wxWidgets 2.0 and ran on Win32, with a wxGTK version in the works.

The first versions of the wrapper were created by hand. However, the code became difficult to maintain and keep synchronized with wxWidgets releases. By 1997, versions were created with SWIG, greatly decreasing the amount of work to update the wrapper.

Project Phoenix
In 2010, the Project Phoenix began; an effort to clean up the wxPython implementation and in the process make it compatible with Python 3. The project is a new implementation of wxPython, focused on improving speed, maintainability and extensibility. Like the previous version of wxPython, it wraps the wxWidgets C++ toolkit and provides access to the user interface portions of the wxWidgets API.

With the release of 4.0.0a1 wxPython in 2017, the Project Phoenix version became the official version. wxPython 4.x is the current version being developed as of June 2022.

Use
wxPython enables Python to be used for cross-platform GUI applications requiring very little, if any, platform-specific code.

Example
This is a simple "Hello world" module, depicting the creation of the two main objects in wxPython (the main window object and the application object), followed by passing the control to the event-driven system (by calling ) which manages the user-interactive part of the program.

This is another example of the wxPython Close Button with wxPython GUI display show in Windows 10 operating system.

License
Being a wrapper, wxPython uses the same free software license used by wxWidgets (wxWindows License) —which is approved by Free Software Foundation and Open Source Initiative.

Applications developed with wxPython

 * Chandler, a personal information manager
 * Dropbox, desktop client for the Dropbox cloud-based storage
 * Editra, a multi-platform text editor
 * Google Drive, desktop client for the Google cloud-based storage system
 * GRASS GIS, a free, open source geographical information system
 * Métamorphose, a batch renamer
 * Phatch, a photo batch processor
 * PlayOnLinux and PlayOnMac, Wine front-ends
 * PsychoPy, experiment creation tool for neuroscience and psychology research

title: WxPython


text:
Talk:Haliburton County

General
The official name of the municipality is Dysart et al, according to its own home page, and Haliburton County's. Radagast 01:15, Oct 17, 2004 (UTC)
 * I noticed that however MapArt and Statistics Canada both have different names. I think et al is good, but redirects needed of course when the page is made. Earl Andrew 04:48, 17 Oct 2004 (UTC)

Hi there. I've made a couple of edits to this page re: the Arts and Culture, and Education sectors of the County (with which I am involved on an employee and volunteer basis). If you have any experience with the Ecotourism areas, Business, or even have more to add about Arts and Culture, please do! I would like to see this article expand :) Erin 09:46, 25 Apr 2008 (EST)

Economy
I would like to see the Haliburton County as the poorest jurisdiction in Ontario expanded upon. If I recall, the article by Jen Watt went into some detail about the numbers possibly being somewhat distorted by the high seniors population. Eekernohan (talk) 20:09, 28 May 2009 (UTC)

 * Enter reply here.

I disagree with the statement that Haliburton County has no resource, industrial, or agricultural basis for its economy. Forestry is still a major industry in the area. I haven't changed any information, but I'll research this further and contribute to the section. Eekernohan (talk) 20:13, 28 May 2009 (UTC)

 * Enter reply here.

title: Talk:Haliburton County


text: File:The B-52's - Cosmic Thing.jpg

title: File:The B-52's - Cosmic Thing.jpg


text:
Douglas Ivester

Douglas Ivester (born 1947) is an American businessman. He served as the chairman and chief executive officer of The Coca-Cola Company from 1997 to 2000.

Early life
Melvin Douglas Ivester was born in 1947 in New Holland, Georgia. He attended New Holland Elementary School, where he met Kay Grindle in the third grade. He grew up to marry her. He attended North Hall High School and went on to the University of Georgia, where he earned a degree in accounting, graduating with honors in 1969 from the university’s Terry College of Business.

Career
Ivester began his career with the accounting firm of Ernst and Ernst.

In 1979, Ivester joined Coca-Cola as assistant controller and director of corporate auditing, and in 1981 he became the youngest vice president in the company's history. Two years later he was elected senior vice president of finance, and in 1985 he was elected CFO at the age of 37. Ivester was elected chairman of the board and chief executive officer of The Coca-Cola Company on October 23, 1997. Ivester received a retirement package estimated to be worth $166 million. Ivester received the FIFA Order of Merit in 1996.

Ivester serves on the board of directors of SunTrust Bank, now Truist Financial.

In 1996 Ivester was honored with an Edison Achievement Award for his commitment to innovation throughout his career.

Philanthropy
Ivester contributes to the University of Georgia and its Terry College of Business as Executive-at-Large through the "Deer Run Fellows" program.

title: Douglas Ivester


text:
Kenneth Slessor

Kenneth Adolphe Slessor (27 March 1901 – 30 June 1971) was an Australian poet, journalist and official war correspondent in World War II. He was one of Australia's leading poets, notable particularly for the absorption of modernist influences into Australian poetry. The Kenneth Slessor Prize for Poetry is named after him.

Early life
Slessor was born Kenneth Adolphe Schloesser in Orange, New South Wales. As a boy, he lived in England for a time with his parents and in Australia visited the mines of rural New South Wales with his father, a Jewish mining engineer whose father and grandfather had been distinguished musicians in Germany.

His family moved to Sydney in 1903. Slessor attended Mowbray House School (1910–1914) and the Sydney Church of England Grammar School (1915–1918), where he began to write poetry. His first published poem, "Goin'", about a wounded digger in Europe, remembering Sydney and its icons, appeared in The Bulletin in 1917. Slessor passed the 1918 NSW Leaving Certificate with first-class honours in English and joined the Sydney Sun as a journalist. In 1919, seven of his poems were published. He married for the first time in 1922.

Career
Slessor made his living as a newspaper journalist, mostly for The Sun, and was a war correspondent during World War II (1939–1945). In that capacity, he reported not only from Australia but from Greece, Syria, Libya, Egypt, and New Guinea.

Slessor also wrote on rugby league football for the popular publication Smith's Weekly.

The bulk of Slessor's poetic work was produced before the end of World War II. His poem "Five Bells"—relating to Sydney Harbour, time, the past, memory, and the death of the artist, friend and colleague of Slessor at Smith's Weekly, Joe Lynch—remains probably his best known poem, followed by "Beach Burial", a tribute to Australian troops who fought in World War II.

In 1965, Australian writer Hal Porter wrote of having met and stayed with Slessor in the 1930s. He described Slessor as: "...a city lover, fastidious and excessively courteous, in those qualities resembles Baudelaire, as he does in being incapable of sentimentalizing over vegetation, in finding in nature something cruel, something bordering on effrontery. He prefers chiselled stone to the disorganization of grass."

Ronald McCuaig was the first to produce an in-depth review of Kenneth Slessor (in The Bulletin in August 1939 and republished in "Tales out of bed" (1944)). The review was favourable, ranking Slessor above C.J. Brennan and W.B. Yeats. It was written a year before "Five Bells", which marked Slessor's move to modernism, a move inspired, according to Rundle and others, by McCuaig. The review therefore covers the pre-modernist parts of Slessor's poetry.

According to poet Douglas Stewart, Kenneth Slessor's poem "Five Visions of Captain Cook" is equally as important as "Five Bells" and was the 'most dramatic break-through' in Australian poetry of the twentieth century.

In 1944 he published his definitive volume of poetry, One Hundred Poems, and from that point on Slessor published only three short poems. Instead of writing poetry, after 1944, and for the rest of his life, Slessor chose to concentrate on journalism and supporting literary projects whose aim was to help develop Australian poetry.

Slessor was a member of The Journalists' Club Sydney and served as its Vice-President 1940–1957, then as its President 1957–1965. A portrait of Slessor was painted by fellow Journalists' Club member William Pidgeon, who painted the portraits of practically every club president up to 1976.

Awards
In the 1959 New Year Honours, Slessor was appointed an Officer of the Order of the British Empire (OBE) for services to literature.

Personal life
Slessor counted Norman Lindsay, Hugh McCrae and Jack Lindsay among his friends.

At the age of 21, Slessor married 28-year-old Noëla Beatrice Myer Ewart Glasson (born 25 December 1893) in Ashfield, Sydney, on 18 August 1922. Noëla was the daughter of Australian soprano and music composer Annie May Colette Summerbelle (1867–1949) and Herbert Edward Glasson (1867–1893). She never knew her father who was executed at the Bathurst Gaol on 29 November 1893, a month before her birth, after he was found guilty of a double—almost quadruple—murder of 24 September 1893. Noëla died of cancer on 22 October 1945.

He married Pauline Wallace in 1951; and a year later celebrated the birth of his only child, Paul Slessor, before the marriage dissolved in 1961.

He died suddenly of a heart attack on 30 June 1971 at the Mater Misericordiae Hospital, North Sydney. His ashes are interred in Rookwood Cemetery.

Journals
Vision: A Literary Quarterly, edited by Frank C. Johnson, Jack Lindsay & Kenneth Slessor:

 * Vision: A Literary Quarterly, No. 1 (May 1923), Sydney: The Vision Press.
 * Vision: A Literary Quarterly, No. 2 (August 1923), Sydney: The Vision Press.
 * Vision: A Literary Quarterly, No. 3 (November 1923), Sydney: The Vision Press.
 * Vision: A Literary Quarterly, No. 4 (February 1924), Sydney: The Vision Press.

Poetry collections

 * Thief of the Moon, Sydney: Hand press of J. T. Kirtley (1924); limited edition of 150 copies.
 * Earth-visitors: Poems, London: Fanfrolico Press (1926); limited edition of 425 numbered copies.
 * Trio: A Book of Poems, with Harley Matthews and Colin Simpson, Sydney: Sunnybrook Press (1931); limited edition of 75 copies.
 * Darlinghurst Nights and Morning Glories: Being 47 Strange Sights Observed from Eleventh Storeys, in a Land of Cream Puffs and Crime, by a Flat-Roof Professor: and here set forth in Sketch and Rhyme, with illustrations by Virgil Reilly, Sydney: Frank C. Johnson (1931).
 * Cuckooz Contrey, Sydney: Frank C. Johnson (1932); limited edition of 500 copies.
 * Funny Farmyard: Nursery Rhymes and Painting Book, with drawings by Sydney Miller, Sydney: Frank Johnson (1933).
 * Five Bells: XX Poems, Sydney: Frank C. Johnson (1939).
 * One Hundred Poems, 1919–1939, Sydney: Angus & Robertson (1944); revised edition published as Poems, 1957; new edition published as Selected Poems, 1978.

Essays/prose

 * Bread and Wine, Sydney, Angus & Robertson (1970).

Edited

 * Australian Poetry (1945)

Individual works

 * "Five Visions of Captain Cook" (1931)
 * "Five Bells" (1939)

Recognition

 * Slessor has a street in the Canberra suburb of McKellar named after him.
 * Kenneth Slessor Park in Chatswood in named in his honour; the park features architecture with his poem, "Five Bells".
 * The 1988 musical Darlinghurst Nights is based on his poetry and he is featured as a character.
 * The bells motif in "Five Bells" is referenced at the end of the 1999 song "You Gotta Love This City" by The Whitlams, which also involves a drowning death in Sydney Harbour.
 * Slessor's poetry was chosen to be placed on the Higher School Certificate English reading lists, and was also examined in the final English exam.
 * Kenneth Slessor has a plaque dedicated to him on the Sydney Writers Walk at Circular Quay.
 * The poem "For Kenneth Slessor" was written by Slessor's close friend Douglas Stewart soon after his death in 1971
 * The poem "To Kenneth Slessor" by Peter Skrzynecki is about his relationship with the poet. This poem was featured in the poetry anthology Old/New World.
 * The poem "Time" by David McIlwain explores Slessor's poetry in terms of his life around Sydney Harbour

title: Kenneth Slessor