Computer software (or simply software) is the programs and procedures that enable a computer to perform a specific task, as opposed to the physical components of the system (hardware). This includes application software such as a word processor, which enables a user to perform a task, and system software such as an operating system, which enables other software to run properly, by interfacing with hardware and with other software.

The term "software" was first used in this sense by John W. Tukey in 1957. In computer science and software engineering, computer software is all information processed by computer systems, programs and data. The concept of reading different sequences of instructions into the memory of a device to control computations was invented by Charles Babbage as part of his difference engine. The theory that is the basis for most modern software was first proposed by Alan Turing in his 1935 essay Computable numbers with an application to the Entscheidungsproblem. Hally, Mike (2005:79). Electronic brains/Stories from the dawn of the computer age. British Broadcasting Corporation and Granta Books, London. ISBN 1-86-207663-4.

Relationship to hardware

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Computer software is so called in contrast to computer hardware, which encompasses the physical interconnections and devices required to store and execute (or run) the software. In computers, software is loaded into RAM and executed in the central processing unit. At the lowest level, software consists of a machine language specific to an individual processor. A machine language consists of groups of binary values signifying processor instructions (object code), which change the state of the computer from its preceding state. Software is an ordered sequence of instructions for changing the state of the computer hardware in a particular sequence. It is generally written in high-level programming languages that are easier and more efficient for humans to use (closer to natural language) than machine language. High-level languages are compiled or interpreted into machine language object code. Software may also be written in an assembly language, essentially, a mnemonic representation of a machine language using a natural language alphabet. Assembly language must be assembled into object code via an assembler.

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Clinical Chemistry current issue

[Editorials] Cerebrospinal Fluid Biomarkers in the Evaluation of Alzheimer Disease
Verbeek, M. M., Olde Rikkert, M. G.M. Mon, 29 Sep 2008 00:00:00 -0000

[Editorials] Rapid and Effective Screening for Lysosomal Storage Disease: How Close Are We?
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McQueen, M. J., Don-Wauchope, A. C. Mon, 29 Sep 2008 00:00:00 -0000

[Editorials] Multiple Thiopurine S-Methyltransferase Variation Detection: A Step toward Personalized Medicine
Tsui, N. B. Y. Mon, 29 Sep 2008 00:00:00 -0000

[Mini-Review] Utility of Kallikrein-Related Peptidases (KLKs) as Cancer Biomarkers
Emami, N., Diamandis, E. P. Mon, 29 Sep 2008 00:00:00 -0000
Background: The human kallikrein-related peptidase (KLK) family consists of 15 highly conserved serine proteases, which are encoded by the largest uninterrupted cluster of protease genes in the human genome. To date, several members of the family have been reported as potential cancer biomarkers. Although primarily known for their biomarker value in prostate, ovarian, and breast cancers, more recent data suggest analogous roles of KLKs in several other cancers, including gastrointestinal, head and neck, lung, and brain malignancies. Among the proposed KLK cancer biomarkers, prostate-specific antigen (also known as KLK3) is the most widely recognized member in urologic oncology. Content: Despite substantial progress in the understanding of the biomarker utility of individual KLKs, the current challenge lies in devising biomarker panels to increase the accuracy of prognosis, prediction of therapy, and diagnosis. To date, multiparametric KLK panels have been proposed for prostate, ovarian, and lung cancers. In addition to their biomarker utility, emerging evidence has revealed a number of critical functional roles for KLKs in the pathogenesis of cancer and their potential use as therapeutic targets. Summary: KLKs have biomarker utility in many cancer types but individually lack sufficient specificity or sensitivity to be used in clinical practice; however, groups of KLKs and other candidate biomarkers may offer improved performance.
[Mini-Review] High-Abundance Polypeptides of the Human Plasma Proteome Comprising the Top 4 Logs of Polypeptide Abundance
Hortin, G. L., Sviridov, D., Anderson, N. L. Mon, 29 Sep 2008 00:00:00 -0000
Background: Plasma contains thousands of proteins, but a small number of these proteins comprise the majority of protein molecules and mass. Content: We surveyed proteomic studies to identify candidates for high-abundance polypeptide chains. We searched the literature for information on the plasma concentrations of the most abundant components in healthy adults and for the molecular mass of the mature polypeptide chains in plasma. Because proteomic studies usually dissociate proteins into polypeptide chains or detect short peptide segments of proteins, we summarized data on individual peptide chains for proteins containing multiple subunits or polypeptides. We collected data on about 150 of the most abundant polypeptides in plasma. The abundant polypeptides span approximately the top 4 logs of concentration in plasma, from 650 to 0.06 µmol/L on a molar basis or from about 50 000 to 1 mg/L mass abundance. Conclusions: Data on the concentrations of the high-abundance peptide chains in plasma assist in understanding the composition of plasma and potential approaches for clinical laboratory or proteomic analysis of plasma proteins. Development of more extensive databases regarding the plasma concentrations of proteins in health and diseases would promote diagnostic and proteomic advances.

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