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Target C : Simple Hands-On Programming With Vis...


Figure 1. Common problems that arise with primers and 3-step PCR amplification of target DNA. (a) Self-annealing of primers resulting in formation of secondary hairpin loop structure. Note that primers do not always anneal at the extreme ends and may form smaller loop structures. (b) Primer annealing to each other, rather than the DNA template, creating primer dimers. Once the primers anneal to each other they will elongate to the primer ends. (c) PCR cycles generating a specific amplicon. Standard 3-step PCR cycling include denaturation of the template DNA, annealing of primers, and extension of the target DNA (amplicon) by DNA polymerase.




Target C : Simple Hands-On Programming with Vis...


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IPM is an ecosystem-based strategy that focuses on long-term prevention of pests or their damage through a combination of techniques such as biological control, habitat manipulation, modification of cultural practices, and use of resistant varieties. Pesticides are used only after monitoring indicates they are needed according to established guidelines, and treatments are made with the goal of removing only the target organism. Pest control materials are selected and applied in a manner that minimizes risks to human health, beneficial and nontarget organisms, and the environment.


Optical imaging offers great opportunities to monitor and analyze disease-related metabolites in live cells with a high spatiotemporal resolution1,2,3,4,5. Recent advances achieved in the development of fluorescence imaging techniques and biosensing systems allow for the precise and rapid detection of intracellular species for biomedical applications6,7,8,9. Ideally, fluorescence probes are designed to selectively recognize a target species in complicated biological systems. However, determination of a given analyte in a realistic biological milieu (such as in cells and in vivo) with fluorescence is easily interfered by the complexity of microenvironment (e.g., change of pH and salt strength, and the existence of structurally complicated biomacromolecules) and the inevitable background signals10,11,12,13. In particular, fluorescence probes that rely on the emission intensity change (fluorimetric) are prone to being suffered from these issues. To circumvent this problem, several elegant approaches have been proposed including the ratiometric sensing rationale (emission shift upon selectively recognizing an analyte), which is currently a method of choice for biosensing and bioimaging14,15,16.


The SP-Gal (Fig. 1a and Supplementary Fig. 18) and a control compound SP-PEG (Supplementary Fig. 19) with a polyethylene glycol (PEG), instead of Gal, were synthesized. SP is a popular photochromic molecule, which can be light-converted to the charge-separated zwitterionic MR structure after photoisomerization19, 25, 29. A variety of photochromic probes have been constructed based on SP since the zwitterionic isomer (MR) offers a coordination site for various analytes, such as ions and biomolecules30,31,32. In addition, photoisomerization of SP produces the MR isomer with an extended conjugated system, which can serve as a FRET acceptor to tune the fluorescence of closely coupled fluorophores33, 34. The double bond of the hemicyanine-like motif of MR is also prone to undergoing Michael addition reactions with nucleophiles, making possible the development of reaction-based probes35,36,37. Bearing these points in mind, the glycoprobes were synthesized through a simple coupling of naphthalimide with SP, producing SP-Gal in good yields. The fluorophore (naphthalimide) was coupled with D-galactose to target a specific transmembrane glycoprotein receptor, by a click reaction. The control compound (SP-PEG) was synthesized in a similar way.


The description of a programming language is usually split into the two components of syntax (form) and semantics (meaning), which are usually defined by a formal language. Some languages are defined by a specification document (for example, the C programming language is specified by an ISO Standard) while other languages (such as Perl) have a dominant implementation that is treated as a reference. Some languages have both, with the basic language defined by a standard and extensions taken from the dominant implementation being common.


The term computer language is sometimes used interchangeably with programming language.[2] However, the usage of both terms varies among authors, including the exact scope of each. One usage describes programming languages as a subset of computer languages.[3] Similarly, languages used in computing that have a different goal than expressing computer programs are generically designated computer languages. For instance, markup languages are sometimes referred to as computer languages to emphasize that they are not meant to be used for programming.[4]One way of classifying computer languages is by the computations they are capable of expressing, as described by the theory of computation. The majority of practical programming languages are Turing complete,[5] and all Turing complete languages can implement the same set of algorithms. ANSI/ISO SQL-92 and Charity are examples of languages that are not Turing complete, yet are often called programming languages.[6][7] However, some authors restrict the term "programming language" to Turing complete languages.[1][8]


The domain of the language is also worth consideration. Markup languages like XML, HTML, or troff, which define structured data, are not usually considered programming languages.[12][13][14] Programming languages may, however, share the syntax with markup languages if a computational semantics is defined. XSLT, for example, is a Turing complete language entirely using XML syntax.[15][16][17] Moreover, LaTeX, which is mostly used for structuring documents, also contains a Turing complete subset.[18][19]


Another early programming language was devised by Grace Hopper in the US, called FLOW-MATIC. It was developed for the UNIVAC I at Remington Rand during the period from 1955 until 1959. Hopper found that business data processing customers were uncomfortable with mathematical notation, and in early 1955, she and her team wrote a specification for an English programming language and implemented a prototype.[30] The FLOW-MATIC compiler became publicly available in early 1958 and was substantially complete in 1959.[31] FLOW-MATIC was a major influence in the design of COBOL, since only it and its direct descendant AIMACO were in actual use at the time.[32]


The rapid growth of the Internet in the mid-1990s created opportunities for new languages. Perl, originally a Unix scripting tool first released in 1987, became common in dynamic websites. Java came to be used for server-side programming, and bytecode virtual machines became popular again in commercial settings with their promise of "Write once, run anywhere" (UCSD Pascal had been popular for a time in the early 1980s). These developments were not fundamentally novel; rather, they were refinements of many existing languages and paradigms (although their syntax was often based on the C family of programming languages).


A language is typed if the specification of every operation defines types of data to which the operation is applicable.[43] For example, the data represented by "this text between the quotes" is a string, and in many programming languages dividing a number by a string has no meaning and will not be executed. The invalid operation may be detected when the program is compiled ("static" type checking) and will be rejected by the compiler with a compilation error message, or it may be detected while the program is running ("dynamic" type checking), resulting in a run-time exception. Many languages allow a function called an exception handler to handle this exception and, for example, always return "-1" as the result.


Statically typed languages can be either manifestly typed or type-inferred. In the first case, the programmer must explicitly write types at certain textual positions (for example, at variable declarations). In the second case, the compiler infers the types of expressions and declarations based on context. Most mainstream statically typed languages, such as C++, C# and Java, are manifestly typed. Complete type inference has traditionally been associated with functional languages such as Haskell and ML.[44] However, many manifestly typed languages support partial type inference; for example, C++, Java, and C# all infer types in certain limited cases.[45] Additionally, some programming languages allow for some types to be automatically converted to other types; for example, an int can be used where the program expects a float.


Programming languages share properties with natural languages related to their purpose as vehicles for communication, having a syntactic form separate from its semantics, and showing language families of related languages branching one from another.[48][49] But as artificial constructs, they also differ in fundamental ways from languages that have evolved through usage. A significant difference is that a programming language can be fully described and studied in its entirety since it has a precise and finite definition.[50] By contrast, natural languages have changing meanings given by their users in different communities. While constructed languages are also artificial languages designed from the ground up with a specific purpose, they lack the precise and complete semantic definition that a programming language has.


Many programming languages have been designed from scratch, altered to meet new needs, and combined with other languages. Many have eventually fallen into disuse. Although there have been attempts to design one "universal" programming language that serves all purposes, all of them have failed to be generally accepted as filling this role.[51] The need for diverse programming languages arises from the diversity of contexts in which languages are used: 041b061a72


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