1.
Oxygen
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Oxygen is a chemical element with symbol O and atomic number 8. It is a member of the group on the periodic table and is a highly reactive nonmetal. By mass, oxygen is the third-most abundant element in the universe, after hydrogen, at standard temperature and pressure, two atoms of the element bind to form dioxygen, a colorless and odorless diatomic gas with the formula O2. This is an important part of the atmosphere and diatomic oxygen gas constitutes 20. 8% of the Earths atmosphere, additionally, as oxides the element makes up almost half of the Earths crust. Most of the mass of living organisms is oxygen as a component of water, conversely, oxygen is continuously replenished by photosynthesis, which uses the energy of sunlight to produce oxygen from water and carbon dioxide. Oxygen is too reactive to remain a free element in air without being continuously replenished by the photosynthetic action of living organisms. Another form of oxygen, ozone, strongly absorbs ultraviolet UVB radiation, but ozone is a pollutant near the surface where it is a by-product of smog. At low earth orbit altitudes, sufficient atomic oxygen is present to cause corrosion of spacecraft, the name oxygen was coined in 1777 by Antoine Lavoisier, whose experiments with oxygen helped to discredit the then-popular phlogiston theory of combustion and corrosion. One of the first known experiments on the relationship between combustion and air was conducted by the 2nd century BCE Greek writer on mechanics, Philo of Byzantium. In his work Pneumatica, Philo observed that inverting a vessel over a burning candle, Philo incorrectly surmised that parts of the air in the vessel were converted into the classical element fire and thus were able to escape through pores in the glass. Many centuries later Leonardo da Vinci built on Philos work by observing that a portion of air is consumed during combustion and respiration, Oxygen was discovered by the Polish alchemist Sendivogius, who considered it the philosophers stone. In the late 17th century, Robert Boyle proved that air is necessary for combustion, English chemist John Mayow refined this work by showing that fire requires only a part of air that he called spiritus nitroaereus. From this he surmised that nitroaereus is consumed in both respiration and combustion, Mayow observed that antimony increased in weight when heated, and inferred that the nitroaereus must have combined with it. Accounts of these and other experiments and ideas were published in 1668 in his work Tractatus duo in the tract De respiratione. Robert Hooke, Ole Borch, Mikhail Lomonosov, and Pierre Bayen all produced oxygen in experiments in the 17th and the 18th century but none of them recognized it as a chemical element. This may have been in part due to the prevalence of the philosophy of combustion and corrosion called the phlogiston theory, which was then the favored explanation of those processes. Established in 1667 by the German alchemist J. J. Becher, one part, called phlogiston, was given off when the substance containing it was burned, while the dephlogisticated part was thought to be its true form, or calx. The fact that a substance like wood gains overall weight in burning was hidden by the buoyancy of the combustion products
2.
Three-dimensional space
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Three-dimensional space is a geometric setting in which three values are required to determine the position of an element. This is the meaning of the term dimension. In physics and mathematics, a sequence of n numbers can be understood as a location in n-dimensional space, when n =3, the set of all such locations is called three-dimensional Euclidean space. It is commonly represented by the symbol ℝ3 and this serves as a three-parameter model of the physical universe in which all known matter exists. However, this space is one example of a large variety of spaces in three dimensions called 3-manifolds. Furthermore, in case, these three values can be labeled by any combination of three chosen from the terms width, height, depth, and breadth. In mathematics, analytic geometry describes every point in space by means of three coordinates. Three coordinate axes are given, each perpendicular to the two at the origin, the point at which they cross. They are usually labeled x, y, and z, below are images of the above-mentioned systems. Two distinct points determine a line. Three distinct points are either collinear or determine a unique plane, four distinct points can either be collinear, coplanar or determine the entire space. Two distinct lines can intersect, be parallel or be skew. Two parallel lines, or two intersecting lines, lie in a plane, so skew lines are lines that do not meet. Two distinct planes can either meet in a line or are parallel. Three distinct planes, no pair of which are parallel, can meet in a common line. In the last case, the three lines of intersection of each pair of planes are mutually parallel, a line can lie in a given plane, intersect that plane in a unique point or be parallel to the plane. In the last case, there will be lines in the plane that are parallel to the given line, a hyperplane is a subspace of one dimension less than the dimension of the full space. The hyperplanes of a space are the two-dimensional subspaces, that is
3.
Alpha helix
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This secondary structure is also sometimes called a classic Pauling–Corey–Branson α-helix. The name 3. 613-helix is also used for type of helix, denoting the average number of residues per helical turn. Among types of structure in proteins, the α-helix is the most regular. In the early 1930s, William Astbury showed that there were changes in the X-ray fiber diffraction of moist wool or hair fibers upon significant stretching. The data suggested that the unstretched fibers had a molecular structure with a characteristic repeat of ~5.1 ångströms. Astbury initially proposed a structure for the fibers. He later joined other researchers in proposing that, the protein molecules formed a helix the stretching caused the helix to uncoil. Hans Neurath was the first to show that Astburys models could not be correct in detail, neuraths paper and Astburys data inspired H. S. Taylor, Maurice Huggins and Bragg and collaborators to propose models of keratin that somewhat resemble the modern α-helix. The pivotal moment came in the spring of 1948, when Pauling caught a cold. Being bored, he drew a polypeptide chain of roughly correct dimensions on a strip of paper and folded it into a helix, after a few attempts, he produced a model with physically plausible hydrogen bonds. Pauling then worked with Corey and Branson to confirm his model before publication. The amino acids in an α-helix are arranged in a helical structure where each amino acid residue corresponds to a 100° turn in the helix. Dunitz describes how Paulings first article on the theme in fact shows a left-handed helix, short pieces of left-handed helix sometimes occur with a large content of achiral glycine amino acids, but are unfavorable for the other normal, biological L-amino acids. The pitch of the alpha-helix is 5.4 Å, which is the product of 1.5 and 3.6, the alpha-helices can be identified in protein structure using several computational methods, one of which being DSSP. Similar structures include the 310 helix and the π-helix, the subscripts refer to the number of atoms in the closed loop formed by the hydrogen bond. Residues in α-helices typically adopt backbone dihedral angles around, as shown in the image at right, in more general terms, they adopt dihedral angles such that the ψ dihedral angle of one residue and the φ dihedral angle of the next residue sum to roughly -105°. As a consequence, α-helical dihedral angles, in general, fall on a stripe on the Ramachandran diagram. For comparison, the sum of the angles for a 310 helix is roughly -75°
4.
Scientific visualization
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Scientific visualization is an interdisciplinary branch of science. It is also considered a subset of computer graphics, a branch of computer science, the purpose of scientific visualization is to graphically illustrate scientific data to enable scientists to understand, illustrate, and glean insight from their data. One of the earliest examples of scientific visualisation was Maxwells thermodynamic surface. This prefigured modern scientific techniques that use computer graphics. Scientific visualization using computer graphics gained in popularity as graphics matured, primary applications were scalar fields and vector fields from computer simulations and also measured data. The primary methods for visualizing two-dimensional scalar fields are color mapping and drawing contour lines, 2D vector fields are visualized using glyphs and streamlines or line integral convolution methods. For 3D scalar fields the primary methods are volume rendering and isosurfaces, methods for visualizing vector fields include glyphs such as arrows, streamlines and streaklines, particle tracing, line integral convolution and topological methods. Later, visualization techniques such as hyperstreamlines were developed to visualize 2D, computer animation is the art, technique, and science of creating moving images via the use of computers. It is becoming common to be created by means of 3D computer graphics, though 2D computer graphics are still widely used for stylistic, low bandwidth. Sometimes the target of the animation is the computer itself, but sometimes the target is another medium and it is also referred to as CGI, especially when used in films. Computer simulation is a program, or network of computers. The simultaneous visualization and simulation of a system is called visulation, computer simulations vary from computer programs that run a few minutes, to network-based groups of computers running for hours, to ongoing simulations that run for months. Information visualization focused on the creation of approaches for conveying information in intuitive ways. The key difference between scientific visualization and information visualization is that information visualization is often applied to data that is not generated by scientific inquiry, some examples are graphical representations of data for business, government, news and social media. Interface technology and perception shows how new interfaces and an understanding of underlying perceptual issues create new opportunities for the scientific visualization community. Rendering is the process of generating an image from a model, the model is a description of three-dimensional objects in a strictly defined language or data structure. It would contain geometry, viewpoint, texture, lighting, the image is a digital image or raster graphics image. The term may be by analogy with a rendering of a scene
5.
Electron microscope
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An electron microscope is a microscope that uses a beam of accelerated electrons as a source of illumination. Transmission electron microscopes use electrostatic and electromagnetic lenses to control the electron beam and these electron optical lenses are analogous to the glass lenses of an optical light microscope. Industrially, electron microscopes are used for quality control and failure analysis. Modern electron microscopes produce electron micrographs using specialized digital cameras and frame grabbers to capture the image, the first electromagnetic lens was developed in 1926 by Hans Busch. According to Dennis Gabor, the physicist Leó Szilárd tried in 1928 to convince Busch to build an electron microscope, two years later, in 1933, Ruska built an electron microscope that exceeded the resolution attainable with an optical microscope. Moreover, Reinhold Rudenberg, the director of Siemens-Schuckertwerke, obtained the patent for the electron microscope in May 1931. In 1932, Ernst Lubcke of Siemens & Halske built and obtained images from an electron microscope. Five years later, the firm financed the work of Ernst Ruska and Bodo von Borries, also in 1937, Manfred von Ardenne pioneered the scanning electron microscope. The first commercial electron microscope was produced in 1938 by Siemens, although contemporary transmission electron microscopes are capable of two million-power magnification, as scientific instruments, they remain based upon Ruska’s prototype. The original form of electron microscope, the electron microscope uses a high voltage electron beam to illuminate the specimen. The electron beam is produced by a gun, commonly fitted with a tungsten filament cathode as the electron source. When it emerges from the specimen, the electron beam carries information about the structure of the specimen that is magnified by the lens system of the microscope. The image detected by the camera may be displayed on a monitor or computer. The resolution of TEMs is limited primarily by spherical aberration, but a new generation of aberration correctors have been able to partially overcome spherical aberration to increase resolution. The ability to determine the positions of atoms within materials has made the HRTEM an important tool for nano-technologies research, transmission electron microscopes are often used in electron diffraction mode. The major disadvantage of the electron microscope is the need for extremely thin sections of the specimens. Biological specimens are required to be chemically fixed, dehydrated and embedded in a polymer resin to stabilize them sufficiently to allow ultrathin sectioning. Sections of biological specimens, organic polymers and similar materials may require treatment with heavy atom labels in order to achieve the required image contrast
6.
Beta sheet
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The β-sheet is a common motif of regular secondary structure in proteins. Beta sheets consist of beta strands connected laterally by at least two or three hydrogen bonds, forming a generally twisted, pleated sheet. A β-strand is a stretch of polypeptide chain typically 3 to 10 amino acids long with backbone in an extended conformation, the supramolecular association of β-sheets has been implicated in formation of the protein aggregates and fibrils observed in many human diseases, notably the amyloidoses such as Alzheimers disease. The first β-sheet structure was proposed by William Astbury in the 1930s and he proposed the idea of hydrogen bonding between the peptide bonds of parallel or antiparallel extended β-strands. However, Astbury did not have the data on the bond geometry of the amino acids in order to build accurate models. A refined version was proposed by Linus Pauling and Robert Corey in 1951 and their model incorporated the planarity of the peptide bond which they previously explained as resulting from keto-enol tautomerization. In the fully extended β-strand, successive side chains point straight up, then straight down, then straight up, adjacent β-strands in a β-sheet are aligned so that their Cα atoms are adjacent and their side chains point in the same direction. 5°. The pleating causes the distance between C α i and C i +2 α to be approximately 6 Å, rather than the 7.6 Å expected from two fully extended trans peptides, the sideways distance between adjacent Cα atoms in hydrogen-bonded β-strands is roughly 5 Å. However, β-strands are rarely extended, rather, they exhibit a twist due to the chirality of their component amino acids. The energetically preferred dihedral angles near = diverge significantly from the extended conformation =. The twist is often associated with alternating fluctuations in the angles to prevent the individual β-strands in a larger sheet from splaying apart. A good example of a strongly twisted β-hairpin can be seen in the protein BPTI, the side chains point outwards from the folds of the pleats, roughly perpendicularly to the plane of the sheet, successive residues point outwards on alternating faces of the sheet. Because peptide chains have a directionality conferred by their N-terminus and C-terminus and they are usually represented in protein topology diagrams by an arrow pointing toward the C-terminus. Adjacent β-strands can form hydrogen bonds in antiparallel, parallel, or mixed arrangements, in an antiparallel arrangement, the successive β-strands alternate directions so that the N-terminus of one strand is adjacent to the C-terminus of the next. The peptide backbone dihedral angles are about in antiparallel sheets, the dihedral angles are about in parallel sheets. For example, residue i may form bonds to residues j −1 and j +1. By contrast, residue j may hydrogen-bond to different residues altogether, finally, an individual strand may exhibit a mixed bonding pattern, with a parallel strand on one side and an antiparallel strand on the other. Also proteins are inherently constrained by folding kinetics as well as folding thermodynamics, the hydrogen bonding of β-strands need not be perfect, but can exhibit localized disruptions known as β-bulges
7.
Kinemage
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A kinemage is an interactive graphic scientific illustration. It often is used to visualize molecules, especially proteins although it can represent other types of 3-dimensional data. The kinemage system is designed to ease of use, interactive performance. The kinemage information is stored in a file, human- and machine-readable, that describes the hierarchy of display objects and their properties. The kinemage format is a defined chemical MIME type of chemical/x-kinemage with the file extension. kin, kinemages were first developed by David Richardson at Duke University School of Medicine, for the Protein Societys journal Protein Science that premiered in January 1992. Mage and RasMol were the first widely used macromolecular graphics programs to support interactive display on personal computers, kinemages are used for teaching, and for textbook supplements, individual exploration, and analysis of macromolecular structures. All-atom contact analysis adds and optimizes explicit hydrogen atoms, and then uses patches of dot surface to display the hydrogen bond, van der Waals, the interactive nature of kinemages is their primary purpose and attribute. To appreciate their nature, the demonstration KiNG in browser has two examples that can be moved around in 3D, plus instructions for how to embed a kinemage on a web page, the figure below shows KiNG being used to remodel a lysine sidechain in a high-resolution crystal structure. KiNG is one of the viewers provided on each page at the Protein Data Bank site. Kinemages can also be shown in immersive virtual reality systems, with the open-source KinImmerse software, all of the kinemage display and all-atom contact software is available free and open-source on the kinemage web site
8.
Protein structure
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Protein structure is the three-dimensional arrangement of atoms in a protein molecule. Proteins are polymers — specifically polypeptides — formed from sequences of amino acids, a single amino acid monomer may also be called a residue indicating a repeating unit of a polymer. Proteins form by amino acids undergoing condensation reactions, in which the amino acids lose one water molecule per reaction in order to attach to one another with a peptide bond. By convention, a chain under 30 amino acids is often identified as a peptide, to understand the functions of proteins at a molecular level, it is often necessary to determine their three-dimensional structure. Protein structures range in size from tens to several amino acids. By physical size, proteins are classified as nanoparticles, between 1–100 nm, very large aggregates can be formed from protein subunits. For example, many thousands of actin molecules assemble into a microfilament, a protein may undergo reversible structural changes in performing its biological function. The alternative structures of the protein are referred to as different conformational isomers, or simply, conformations. There are four levels of protein structure. Each α-amino acid consists of a backbone that is present in all the amino acid types, an exception from this rule is proline. Because the carbon atom is bound to four different groups it is chiral, glycine however, is not chiral since its side chain is a hydrogen atom. A simple mnemonic for correct L-form is CORN, when the Cα atom is viewed with the H in front, the primary structure of a protein refers to the linear sequence of amino acids in the polypeptide chain. The primary structure is held together by covalent bonds such as peptide bonds, the two ends of the polypeptide chain are referred to as the carboxyl terminus and the amino terminus based on the nature of the free group on each extremity. Counting of residues always starts at the N-terminal end, which is the end where the group is not involved in a peptide bond. The primary structure of a protein is determined by the corresponding to the protein. A specific sequence of nucleotides in DNA is transcribed into mRNA, the sequence of amino acids in insulin was discovered by Frederick Sanger, establishing that proteins have defining amino acid sequences. The sequence of a protein is unique to that protein, and defines the structure, the sequence of a protein can be determined by methods such as Edman degradation or tandem mass spectrometry. Often, however, it is directly from the sequence of the gene using the genetic code
9.
Jmol
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Jmol is computer software for molecular modelling chemical structures in 3-dimensions. Jmol returns a 3D representation of a molecule that may be used as a teaching tool and it is written in the programming language Java, so it can run on the operating systems Windows, macOS, Linux, and Unix, if Java is installed. It is free and open-source software released under a GNU Lesser General Public License version 2.0, a standalone application and a software development kit exist that can be integrated into other Java applications, such as Bioclipse and Taverna. A popular feature is an applet that can be integrated into web pages to display molecules in a variety of ways, for example, molecules can be displayed as ball-and-stick models, space-filling models, ribbon diagrams, etc. Jmol supports a range of chemical file formats, including Protein Data Bank, Crystallographic Information File, MDL Molfile. There is also a JavaScript-only version, JSmol, that can be used on computers with no Java, the Jmol applet, among other abilities, offers an alternative to the Chime plug-in, which is no longer under active development. While Jmol has many features that Chime lacks, it does not claim to reproduce all Chime functions, most notably, Chime requires plug-in installation and Internet Explorer 6.0 or Firefox 2.0 on Microsoft Windows, or Netscape Communicator 4.8 on Mac OS9. Jmol requires Java installation and operates on a variety of platforms. For example, Jmol is fully functional in Mozilla Firefox, Internet Explorer, Opera, Google Chrome, fast and Scriptable Molecular Graphics in Web Browsers without Java3D
10.
Jane S. Richardson
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Jane Shelby Richardson is an American biophysicist who developed the Richardson diagram, or ribbon diagram, method of representing the 3D structure of proteins. She is a professor in biochemistry at Duke University, Richardson was born on January 25,1941 and grew up in Teaneck, New Jersey. Her father was an engineer and her mother was an English teacher. Her parents encouraged an interest in science and she was a member of local clubs as early as elementary school. She continued her education in science at Swarthmore College, enrolling with the intention of studying mathematics, astronomy, after finding herself unsuited to teaching high school, she joined her husband David C. Richardson, then completing his PhD work at MIT, in studying the 3-dimensional structure of the Staphylococcal nuclease protein by X-ray crystallography and it was one of the first dozen protein structures solved. She later began drawing her eponymous diagrams as a method of interpreting the structures of protein molecules and she has been promoted to many prestigious positions in academia. In July 1985 she was awarded a MacArthur Fellowship for her work in biochemistry and she was elected to the National Academy of Sciences and the American Academy of Arts and Sciences in 1991 and to the Institute of Medicine in 2006. She was elected president of the Biophysical Society for the 2012-2013 year, Richardson is currently a James B. Duke Professor of Biochemistry at Duke University, as part of her role in the National Academy of Sciences, Richardson serves on panels that advise the White House and Pentagon regarding nationally important scientific matters. The Richardsons jointly head a group at Duke University. Richardson is a contributor to Wikipedia, where she is a prominent member of WikiProject Biophysics, Richardsons first forays into science were in the field of astronomy. By observing the position of Sputnik – at the time, the only artificial satellite – on two nights, she managed to calculate its predicted orbit. She submitted her results to the Westinghouse Science Talent Search, winning third place in 1958, after earning her masters degree in philosophy, Richardson rejoined the scientific world in the mid 1960s, working as a technician in the same laboratory as her husband. Classes in botany and evolution that she had taken while pursuing her degree shaped her thinking about the work she was doing in the chemistry laboratory. During her crystallographic studies, Jane Richardson had come to realize that a classification scheme can be developed from the recurring structural motifs of the proteins. In 1977 she published her results in Nature, in a paper entitled β-sheet topology, in the meantime, Jane and David Richardson had moved to Duke University in 1970, where they solved the first crystal structure of superoxide dismutase. Richardson had developed the ribbon diagram over the course of her taxonomic research and her iconic images first appeared in the review journal Advances in Protein Chemistry in 1981, an early hallmark publication in structural bioinformatics
11.
Algorithm
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In mathematics and computer science, an algorithm is a self-contained sequence of actions to be performed. Algorithms can perform calculation, data processing and automated reasoning tasks, an algorithm is an effective method that can be expressed within a finite amount of space and time and in a well-defined formal language for calculating a function. The transition from one state to the next is not necessarily deterministic, some algorithms, known as randomized algorithms, giving a formal definition of algorithms, corresponding to the intuitive notion, remains a challenging problem. In English, it was first used in about 1230 and then by Chaucer in 1391, English adopted the French term, but it wasnt until the late 19th century that algorithm took on the meaning that it has in modern English. Another early use of the word is from 1240, in a manual titled Carmen de Algorismo composed by Alexandre de Villedieu and it begins thus, Haec algorismus ars praesens dicitur, in qua / Talibus Indorum fruimur bis quinque figuris. Which translates as, Algorism is the art by which at present we use those Indian figures, the poem is a few hundred lines long and summarizes the art of calculating with the new style of Indian dice, or Talibus Indorum, or Hindu numerals. An informal definition could be a set of rules that precisely defines a sequence of operations, which would include all computer programs, including programs that do not perform numeric calculations. Generally, a program is only an algorithm if it stops eventually, but humans can do something equally useful, in the case of certain enumerably infinite sets, They can give explicit instructions for determining the nth member of the set, for arbitrary finite n. An enumerably infinite set is one whose elements can be put into one-to-one correspondence with the integers, the concept of algorithm is also used to define the notion of decidability. That notion is central for explaining how formal systems come into being starting from a set of axioms. In logic, the time that an algorithm requires to complete cannot be measured, from such uncertainties, that characterize ongoing work, stems the unavailability of a definition of algorithm that suits both concrete and abstract usage of the term. Algorithms are essential to the way computers process data, thus, an algorithm can be considered to be any sequence of operations that can be simulated by a Turing-complete system. Although this may seem extreme, the arguments, in its favor are hard to refute. Gurevich. Turings informal argument in favor of his thesis justifies a stronger thesis, according to Savage, an algorithm is a computational process defined by a Turing machine. Typically, when an algorithm is associated with processing information, data can be read from a source, written to an output device. Stored data are regarded as part of the state of the entity performing the algorithm. In practice, the state is stored in one or more data structures, for some such computational process, the algorithm must be rigorously defined, specified in the way it applies in all possible circumstances that could arise. That is, any conditional steps must be dealt with, case-by-case
12.
Cubic function
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In algebra, a cubic function is a function of the form f = a x 3 + b x 2 + c x + d, where a is nonzero. Setting f =0 produces an equation of the form. The solutions of this equation are called roots of the polynomial f, If all of the coefficients a, b, c, and d of the cubic equation are real numbers then there will be at least one real root. All of the roots of the equation can be found algebraically. The roots can also be found trigonometrically, alternatively, numerical approximations of the roots can be found using root-finding algorithms like Newtons method. The coefficients do not need to be complex numbers, much of what is covered below is valid for coefficients of any field with characteristic 0 or greater than 3. The solutions of the cubic equation do not necessarily belong to the field as the coefficients. For example, some cubic equations with rational coefficients have roots that are complex numbers. Cubic equations were known to the ancient Babylonians, Greeks, Chinese, Indians, Babylonian cuneiform tablets have been found with tables for calculating cubes and cube roots. The Babylonians could have used the tables to solve cubic equations, the problem of doubling the cube involves the simplest and oldest studied cubic equation, and one for which the ancient Egyptians did not believe a solution existed. Methods for solving cubic equations appear in The Nine Chapters on the Mathematical Art, in the 3rd century, the Greek mathematician Diophantus found integer or rational solutions for some bivariate cubic equations. In the 11th century, the Persian poet-mathematician, Omar Khayyám, in an early paper, he discovered that a cubic equation can have more than one solution and stated that it cannot be solved using compass and straightedge constructions. He also found a geometric solution, in the 12th century, the Indian mathematician Bhaskara II attempted the solution of cubic equations without general success. However, he gave one example of an equation, x3 + 12x = 6x2 +35. He used what would later be known as the Ruffini-Horner method to approximate the root of a cubic equation. He also developed the concepts of a function and the maxima and minima of curves in order to solve cubic equations which may not have positive solutions. He understood the importance of the discriminant of the equation to find algebraic solutions to certain types of cubic equations. Leonardo de Pisa, also known as Fibonacci, was able to approximate the positive solution to the cubic equation x3 + 2x2 + 10x =20
