In biology, homology is the existence of shared ancestry between a pair of structures, or genes, in different taxa. A common example of homologous structures is the forelimbs of vertebrates, where the wings of bats, the arms of primates, the front flippers of whales and the forelegs of dogs and horses are all derived from the same ancestral tetrapod structure. Evolutionary biology explains homologous structures adapted to different purposes as the result of descent with modification from a common ancestor; the term was first applied to biology in a non-evolutionary context by the anatomist Richard Owen in 1843. Homology was explained by Charles Darwin's theory of evolution in 1859, but had been observed before this, from Aristotle onwards, it was explicitly analysed by Pierre Belon in 1555. In developmental biology, organs that developed in the embryo in the same manner and from similar origins, such as from matching primordia in successive segments of the same animal, are serially homologous.
Examples include the legs of a centipede, the maxillary palp and labial palp of an insect, the spinous processes of successive vertebrae in a vertebral column. Male and female reproductive organs are homologous if they develop from the same embryonic tissue, as do the ovaries and testicles of mammals including humans. Sequence homology between protein or DNA sequences is defined in terms of shared ancestry. Two segments of DNA can have shared ancestry because of either a speciation event or a duplication event. Homology among proteins or DNA is inferred from their sequence similarity. Significant similarity is strong evidence that two sequences are related by divergent evolution from a common ancestor. Alignments of multiple sequences are used to discover the homologous regions. Homology remains controversial in animal behaviour, but there is suggestive evidence that, for example, dominance hierarchies are homologous across the primates. Homology was noticed by Aristotle, was explicitly analysed by Pierre Belon in his 1555 Book of Birds, where he systematically compared the skeletons of birds and humans.
The pattern of similarity was interpreted as part of the static great chain of being through the mediaeval and early modern periods: it was not seen as implying evolutionary change. In the German Naturphilosophie tradition, homology was of special interest as demonstrating unity in nature. In 1790, Goethe stated his foliar theory in his essay "Metamorphosis of Plants", showing that flower part are derived from leaves; the serial homology of limbs was described late in the 18th century. The French zoologist Etienne Geoffroy Saint-Hilaire showed in 1818 in his theorie d'analogue that structures were shared between fishes, reptiles and mammals; when Geoffroy went further and sought homologies between Georges Cuvier's embranchements, such as vertebrates and molluscs, his claims triggered the 1830 Cuvier-Geoffroy debate. Geoffroy stated the principle of connections, namely that what is important is the relative position of different structures and their connections to each other; the Estonian embryologist Karl Ernst von Baer stated what are now called von Baer's laws in 1828, noting that related animals begin their development as similar embryos and diverge: thus, animals in the same family are more related and diverge than animals which are only in the same order and have fewer homologies.
Von Baer's theory recognises that each taxon has distinctive shared features, that embryonic development parallels the taxonomic hierarchy: not the same as recapitulation theory. The term "homology" was first used in biology by the anatomist Richard Owen in 1843 when studying the similarities of vertebrate fins and limbs, defining it as the "same organ in different animals under every variety of form and function", contrasting it with the matching term "analogy" which he used to describe different structures with the same function. Owen codified 3 main criteria for determining if features were homologous: position and composition. In 1859, Charles Darwin explained homologous structures as meaning that the organisms concerned shared a body plan from a common ancestor, that taxa were branches of a single tree of life; the word homology, coined in about 1656, is derived from the Greek ὁμόλογος homologos from ὁμός homos "same" and λόγος logos "relation". Biological structures or sequences in different taxa are homologous if they are derived from a common ancestor.
Homology thus implies divergent evolution. For example, many insects possess two pairs of flying wings. In beetles, the first pair of wings has evolved into a pair of hard wing covers, while in Dipteran flies the second pair of wings has evolved into small halteres used for balance; the forelimbs of ancestral vertebrates have evolved into the front flippers of whales, the wings of birds, the running forelegs of dogs and horses, the short forelegs of frogs and lizards, the grasping hands of primates including humans. The same major forearm bones are found in fossils of lobe-finned fish such as Eusthenopteron; the opposite of homologous organs are analogous organs which do similar jobs in two taxa that were not present in their most recent common ancestor but rather evolved separately. For example, the wings of insects and birds evolved independently in separated groups, converged functionally to support powered flight, so they are analogous; the wings of a sycamore maple seed and the wings of a bird are analogous but not homologous, as they develop from quite different structures.
A structure can be only analogous at another. Pterosaur and bat wings are analogous as wings
A computer file is a computer resource for recording data discretely in a computer storage device. Just as words can be written to paper, so can information be written to a computer file. Files can be transferred through the internet. There are different types of computer files, designed for different purposes. A file may be designed to store a picture, a written message, a video, a computer program, or a wide variety of other kinds of data; some types of files can store several types of information at once. By using computer programs, a person can open, change and close a computer file. Computer files may be reopened and copied an arbitrary number of times. Files are organised in a file system, which keeps track of where the files are located on disk and enables user access; the word "file" derives from the Latin filum."File" was used in the context of computer storage as early as January 1940. In Punched Card Methods in Scientific Computation, W. J. Eckert stated, "The first extensive use of the early Hollerith Tabulator in astronomy was made by Comrie.
He used it for building a table from successive differences, for adding large numbers of harmonic terms". "Tables of functions are constructed from their differences with great efficiency, either as printed tables or as a file of punched cards." In February 1950: In a Radio Corporation of America advertisement in Popular Science Magazine describing a new "memory" vacuum tube it had developed, RCA stated: "the results of countless computations can be kept'on file' and taken out again. Such a'file' now exists in a'memory' tube developed at RCA Laboratories. Electronically it retains figures fed into calculating machines, holds them in storage while it memorizes new ones - speeds intelligent solutions through mazes of mathematics." In 1952, "file" denoted, information stored on punched cards. In early use, the underlying hardware, rather than the contents stored on it, was denominated a "file". For example, the IBM 350 disk drives were denominated "disk files"; the introduction, circa 1961, by the Burroughs MCP and the MIT Compatible Time-Sharing System of the concept of a "file system" that managed several virtual "files" on one storage device is the origin of the contemporary denotation of the word.
Although the contemporary "register file" demonstrates the early concept of files, its use has decreased. On most modern operating systems, files are organized into one-dimensional arrays of bytes; the format of a file is defined by its content since a file is a container for data, although, on some platforms the format is indicated by its filename extension, specifying the rules for how the bytes must be organized and interpreted meaningfully. For example, the bytes of a plain text file are associated with either ASCII or UTF-8 characters, while the bytes of image and audio files are interpreted otherwise. Most file types allocate a few bytes for metadata, which allows a file to carry some basic information about itself; some file systems can store arbitrary file-specific data outside of the file format, but linked to the file, for example extended attributes or forks. On other file systems this can be done via software-specific databases. All those methods, are more susceptible to loss of metadata than are container and archive file formats.
At any instant in time, a file might have a size expressed as number of bytes, that indicates how much storage is associated with the file. In most modern operating systems the size can be any non-negative whole number of bytes up to a system limit. Many older operating systems kept track only of the number of blocks or tracks occupied by a file on a physical storage device. In such systems, software employed other methods to track the exact byte count; the general definition of a file does not require that its size have any real meaning, unless the data within the file happens to correspond to data within a pool of persistent storage. A special case is a zero byte file. For example, the file to which the link /bin/ls points in a typical Unix-like system has a defined size that changes. Compare this with /dev/null, a file, but its size may be obscure. Information in a computer file can consist of smaller packets of information that are individually different but share some common traits. For example, a payroll file might contain information concerning all the employees in a company and their payroll details.
A text file may contain lines of corresponding to printed lines on a piece of paper. Alternatively, a file may contain an arbitrary binary image or it may contain an executable; the way information is grouped into a file is up to how it is designed. This has led to a plethora of more or less standardized file structures for all imaginable purposes, from the simplest to the most complex. Most computer files are used by computer programs which create, modify or delete the files for their own use on an as-needed basis; the programmers who create the programs decide what files are needed, how they are to be used and their names. In some cases, computer pr
Computer science is the study of processes that interact with data and that can be represented as data in the form of programs. It enables the use of algorithms to manipulate and communicate digital information. A computer scientist studies the theory of computation and the practice of designing software systems, its fields can be divided into practical disciplines. Computational complexity theory is abstract, while computer graphics emphasizes real-world applications. Programming language theory considers approaches to the description of computational processes, while computer programming itself involves the use of programming languages and complex systems. Human–computer interaction considers the challenges in making computers useful and accessible; the earliest foundations of what would become computer science predate the invention of the modern digital computer. Machines for calculating fixed numerical tasks such as the abacus have existed since antiquity, aiding in computations such as multiplication and division.
Algorithms for performing computations have existed since antiquity before the development of sophisticated computing equipment. Wilhelm Schickard designed and constructed the first working mechanical calculator in 1623. In 1673, Gottfried Leibniz demonstrated a digital mechanical calculator, called the Stepped Reckoner, he may be considered the first computer scientist and information theorist, among other reasons, documenting the binary number system. In 1820, Thomas de Colmar launched the mechanical calculator industry when he released his simplified arithmometer, the first calculating machine strong enough and reliable enough to be used daily in an office environment. Charles Babbage started the design of the first automatic mechanical calculator, his Difference Engine, in 1822, which gave him the idea of the first programmable mechanical calculator, his Analytical Engine, he started developing this machine in 1834, "in less than two years, he had sketched out many of the salient features of the modern computer".
"A crucial step was the adoption of a punched card system derived from the Jacquard loom" making it infinitely programmable. In 1843, during the translation of a French article on the Analytical Engine, Ada Lovelace wrote, in one of the many notes she included, an algorithm to compute the Bernoulli numbers, considered to be the first computer program. Around 1885, Herman Hollerith invented the tabulator, which used punched cards to process statistical information. In 1937, one hundred years after Babbage's impossible dream, Howard Aiken convinced IBM, making all kinds of punched card equipment and was in the calculator business to develop his giant programmable calculator, the ASCC/Harvard Mark I, based on Babbage's Analytical Engine, which itself used cards and a central computing unit; when the machine was finished, some hailed it as "Babbage's dream come true". During the 1940s, as new and more powerful computing machines were developed, the term computer came to refer to the machines rather than their human predecessors.
As it became clear that computers could be used for more than just mathematical calculations, the field of computer science broadened to study computation in general. In 1945, IBM founded the Watson Scientific Computing Laboratory at Columbia University in New York City; the renovated fraternity house on Manhattan's West Side was IBM's first laboratory devoted to pure science. The lab is the forerunner of IBM's Research Division, which today operates research facilities around the world; the close relationship between IBM and the university was instrumental in the emergence of a new scientific discipline, with Columbia offering one of the first academic-credit courses in computer science in 1946. Computer science began to be established as a distinct academic discipline in the 1950s and early 1960s; the world's first computer science degree program, the Cambridge Diploma in Computer Science, began at the University of Cambridge Computer Laboratory in 1953. The first computer science degree program in the United States was formed at Purdue University in 1962.
Since practical computers became available, many applications of computing have become distinct areas of study in their own rights. Although many believed it was impossible that computers themselves could be a scientific field of study, in the late fifties it became accepted among the greater academic population, it is the now well-known IBM brand that formed part of the computer science revolution during this time. IBM released the IBM 704 and the IBM 709 computers, which were used during the exploration period of such devices. "Still, working with the IBM was frustrating if you had misplaced as much as one letter in one instruction, the program would crash, you would have to start the whole process over again". During the late 1950s, the computer science discipline was much in its developmental stages, such issues were commonplace. Time has seen significant improvements in the effectiveness of computing technology. Modern society has seen a significant shift in the users of computer technology, from usage only by experts and professionals, to a near-ubiquitous user base.
Computers were quite costly, some degree of humanitarian aid was needed for efficient use—in part from professional computer operators. As computer adoption became more widespread and affordable, less human assistance was needed for common usage. Despite its short history as a formal academic discipline, computer science has made a number of fundamental contributions to science and society—in fact, along with electronics, it is
Data Management comprises all disciplines related to managing data as a valuable resource. The concept of data management arose in the 1980s as technology moved from sequential processing to random access storage. Since it was now possible to store a discreet fact and access it using random access disk technology, those suggesting that data management was more important than business process management used arguments such as "a customer's home address is stored in 75 places in our computer systems." However, during this period, random access processing was not competitively fast, so those suggesting "process management" was more important than "data management" used batch processing time as their primary argument. As software applications evolved into real-time, interactive usage, it became obvious that both management processes were important. If the data was not well defined, the data would be mis-used in applications. If the process wasn't well defined, it was impossible to meet user needs.
Topics in data management include: In modern management usage, the term data is replaced by information or knowledge in a non-technical context. Thus data management has become knowledge management; this trend obscures the raw data processing and renders interpretation implicit. The distinction between data and derived value is illustrated by the information ladder. However, data has staged a comeback with the popularisation of the term Big data, which refers to the collection and analyses of massive sets of data. Several organisations have established data management centers for their operations. Integrated data management is a tools approach to facilitate data management and improve performance. IDM consists of an integrated, modular environment to manage enterprise application data, optimize data-driven applications over its lifetime. IDM's purpose is to: Produce enterprise-ready applications faster Improve data access, speed iterative testing Empower collaboration between architects, developers and DBAs Consistently achieve service level targets Automate and simplify operations Provide contextual intelligence across the solution stack Support business growth Accommodate new initiatives without expanding infrastructure Simplify application upgrades and retirement Facilitate alignment and governance Define business policies and standards up front.
There are a number of DMFs available. William Richard Evans, of South Africa, has developed three Fully Integrated Data Management Frameworks: The Data Atom Data Management Framework version 1.0 was developed between 2010 and 2014. Version 2.0 was developed between 2014 and 2017. With the advent of artificial intelligence, the Internet of Things and data lakes, version 2.0 was replaced with the more comprehensive Multi Dimensional Data Management Framework V3.0. It covers 7 data environments. On 20 October 2018 He released The Multi Dimensional Data Management Framework V4.0, which includes 8 Data Management Considerations at the core. Four relate to the impact time has on Data and another four provide insight on the current trajectory towards Managed Technological Singularity using Artificial intelligence; the definition provided by DAMA International, the professional organization for the data management profession, is: "Data Management is the development and execution of architectures, policies and procedures that properly manage the full data life-cycle needs of an enterprise."
This broad definition encompasses professions which may not have direct technical contact with lower-level aspects of Data Management, such as relational database management. Alternatively, the definition in the DAMA International Data Management Body of Knowledge is: "Data management is the development and supervision of plans, policies and practices that control, protect and enhance the value of data and information assets." Corporate data quality management is, according to the European Foundation for Quality Management and the Competence Center Corporate Data Quality, the whole set of activities intended to improve corporate data quality. Main premise of CDQM is the business relevance of high-quality corporate data. CDQM comprises with following activity areas:. Strategy for Corporate Data Quality: As CDQM is affected by various business drivers and requires involvement of multiple divisions in an organization. Corporate Data Quality Controlling: Effective CDQM requires compliance with standards and procedures.
Compliance is monitored according to defined metrics and performance indicators and reported to stakeholders. Corporate Data Quality Organization: CDQM requires clear roles and responsibilities for the use of corporate data; the CDQM organization defines tasks and privileges for decision making for CDQM. Corporate Data Quality Processes and Methods: In order to handle corporate data properly and in a standardized way across the entire organization and to ensure corporate data quality, standard procedures and guidelines must be embedded in company’s daily processes. Data Architecture for Corporate Data Quality: The data architecture consists of the data object model - which comprises the unambiguous definition and the conceptual model of
Deoxyribonucleic acid is a molecule composed of two chains that coil around each other to form a double helix carrying the genetic instructions used in the growth, development and reproduction of all known organisms and many viruses. DNA and ribonucleic acid are nucleic acids; the two DNA strands are known as polynucleotides as they are composed of simpler monomeric units called nucleotides. Each nucleotide is composed of one of four nitrogen-containing nucleobases, a sugar called deoxyribose, a phosphate group; the nucleotides are joined to one another in a chain by covalent bonds between the sugar of one nucleotide and the phosphate of the next, resulting in an alternating sugar-phosphate backbone. The nitrogenous bases of the two separate polynucleotide strands are bound together, according to base pairing rules, with hydrogen bonds to make double-stranded DNA; the complementary nitrogenous bases are divided into two groups and purines. In DNA, the pyrimidines are cytosine. Both strands of double-stranded DNA store the same biological information.
This information is replicated as and when the two strands separate. A large part of DNA is non-coding, meaning that these sections do not serve as patterns for protein sequences; the two strands of DNA are thus antiparallel. Attached to each sugar is one of four types of nucleobases, it is the sequence of these four nucleobases along the backbone. RNA strands are created using DNA strands as a template in a process called transcription. Under the genetic code, these RNA strands specify the sequence of amino acids within proteins in a process called translation. Within eukaryotic cells, DNA is organized into long structures called chromosomes. Before typical cell division, these chromosomes are duplicated in the process of DNA replication, providing a complete set of chromosomes for each daughter cell. Eukaryotic organisms store most of their DNA inside the cell nucleus as nuclear DNA, some in the mitochondria as mitochondrial DNA, or in chloroplasts as chloroplast DNA. In contrast, prokaryotes store their DNA only in circular chromosomes.
Within eukaryotic chromosomes, chromatin proteins, such as histones and organize DNA. These compacting structures guide the interactions between DNA and other proteins, helping control which parts of the DNA are transcribed. DNA was first isolated by Friedrich Miescher in 1869, its molecular structure was first identified by Francis Crick and James Watson at the Cavendish Laboratory within the University of Cambridge in 1953, whose model-building efforts were guided by X-ray diffraction data acquired by Raymond Gosling, a post-graduate student of Rosalind Franklin. DNA is used by researchers as a molecular tool to explore physical laws and theories, such as the ergodic theorem and the theory of elasticity; the unique material properties of DNA have made it an attractive molecule for material scientists and engineers interested in micro- and nano-fabrication. Among notable advances in this field are DNA origami and DNA-based hybrid materials. DNA is a long polymer made from repeating units called nucleotides.
The structure of DNA is dynamic along its length, being capable of coiling into tight loops and other shapes. In all species it is composed of two helical chains, bound to each other by hydrogen bonds. Both chains are coiled around the same axis, have the same pitch of 34 angstroms; the pair of chains has a radius of 10 angstroms. According to another study, when measured in a different solution, the DNA chain measured 22 to 26 angstroms wide, one nucleotide unit measured 3.3 Å long. Although each individual nucleotide is small, a DNA polymer can be large and contain hundreds of millions, such as in chromosome 1. Chromosome 1 is the largest human chromosome with 220 million base pairs, would be 85 mm long if straightened. DNA does not exist as a single strand, but instead as a pair of strands that are held together; these two long strands coil in the shape of a double helix. The nucleotide contains both a segment of the backbone of a nucleobase. A nucleobase linked to a sugar is called a nucleoside, a base linked to a sugar and to one or more phosphate groups is called a nucleotide.
A biopolymer comprising multiple linked nucleotides is called a polynucleotide. The backbone of the DNA strand is made from alternating sugar residues; the sugar in DNA is 2-deoxyribose, a pentose sugar. The sugars are joined together by phosphate groups that form phosphodiester bonds between the third and fifth carbon atoms of adjacent sugar rings; these are known as the 3′-end, 5′-end carbons, the prime symbol being used to distinguish these carbon atoms from those of the base to which the deoxyribose forms a glycosidic bond. When imagining DNA, each phosphoryl is considered to "belong" to the nucleotide whose 5′ carbon forms a bond therewith. Any DNA strand therefore has one end at which there is a phosphoryl attached to the 5′ carbon of a ribose and another end a
In mathematics, the pigeonhole principle states that if n items are put into m containers, with n > m at least one container must contain more than one item. This theorem is exemplified in real life by truisms like "in any group of three gloves there must be at least two left gloves or at least two right gloves", it is an example of a counting argument. This obvious statement can be used to demonstrate unexpected results; the first formalization of the idea is believed to have been made by Peter Gustav Lejeune Dirichlet in 1834 under the name Schubfachprinzip. For this reason it is commonly called Dirichlet's box principle or Dirichlet's drawer principle; the principle can be stated in various ways. In a more quantified version: for natural numbers k and m, if n = k m + 1 objects are distributed among m sets the pigeonhole principle asserts that at least one of the sets will contain at least k + 1 objects. For arbitrary n and m this generalizes to k + 1 = ⌊ / m ⌋ + 1 = ⌈ n / m ⌉, where ⌊ ⋯ ⌋ and ⌈ ⋯ ⌉ denote the floor and ceiling functions, respectively.
Though the most straightforward application is to finite sets, it is used with infinite sets that cannot be put into one-to-one correspondence. To do so requires the formal statement of the pigeonhole principle, "there does not exist an injective function whose codomain is smaller than its domain". Advanced mathematical proofs like Siegel's lemma build upon this more general concept. Dirichlet published his works in both French and German, using either the German Schubfach, or the French tiroir; the strict original meaning of both corresponds to the English drawer, an open-topped box that can be slid in and out of the cabinet that contains it. These terms were morphed to the word pigeonhole, standing for a small open space in a desk, cabinet, or wall for keeping letters or papers, metaphorically rooted in the structures that house pigeons. Since Dirichlet's father was a postmaster, furniture with pigeonholes is used for storing or sorting things into many categories, the translation pigeonhole may be a perfect rendering of Dirichlet's metaphor.
That understanding of the word, referring to some furniture features, is fading —especially among those who do not speak English natively but as a lingua franca in the scientific world— in favour of the more pictorial interpretation involving pigeons and holes. The suggestive, though not misleading interpretation of "pigeonhole" as "dovecote" has found its way back to a German back-translation of the "pigeonhole"-principle as the "Taubenschlag"-principle. Besides the original terms "Schubfach-Prinzip" in German and "Principe des tiroirs" in French, other literal translations are still in use in Polish, Turkish, Italian, Danish, Persian and Japanese. Assume a drawer contains a mixture of black socks and blue socks, each of which can be worn on either foot, that you are pulling a number of socks from the drawer without looking. What is the minimum number of pulled socks required to guarantee a pair of the same color? Using the pigeonhole principle, to have at least one pair of the same color using one pigeonhole per color, you need to pull only three socks from the drawer.
Either you have three of one color. If there are n people who can shake hands with one another, the pigeonhole principle shows that there is always a pair of people who will shake hands with the same number of people. In this application of the principle, the'hole' to which a person is assigned is the number of hands shaken by that person. Since each person shakes hands with some number of people from 0 to n − 1, there are n possible holes. On the other hand, either the'0' hole or the'n − 1' hole or both must be empty, for it is impossible for some person to shake hands with everybody else while some person shakes hands with nobody; this leaves n people to be placed into at most n − 1 non-empty holes. We can demonstrate there must be at least two people in London with the same number of hairs on their heads as follows. Since a typical human head has an average of around 150,000 hairs, it is reasonable to assume that no one has more than 1,000,000 hairs on their head. There are more than 1,000,000 people in London.
Assigning a pigeonhole to each number of hairs on a person's hea