Gel technology has long found use in proteomics and much research has been done on it over the years. Characterisation of low abundance proteins and separating proteins at extremes of both isoelectric point & molecular weight have historically been a problem when it comes to 2D gels. There are signs that 2D gel image analysis can be used to map proteins with varying abundance but a fully automated system could give further insights on protein expression in different cell types, stages of development & various associated disease states. However, there have been recent advances. For instance, Tecan has come up with ProTeam FFE which is a device combining free flow electrophoresis and 2D gels to improve separation. Therefore better characterisation and visualisation of low abundant proteins can be achieved. Techniques like non-equilibrium pH gradient electrophoresis & strategic modifications to 2D electrophoresis (2DE) have been reported to give better resolution of basic proteins.

Bioinformatics is the use of mathematical, statistical and computer methods to analyze biological, biochemical & biophysical data. Because bioinformatics is a young, rapidly evolving field, however, it also has a number of other credible definitions. It can also be defined as the science and technology of learning, managing & processing biological information. Bioinformatics is often focussed on obtaining biologically oriented data, organizing this information into databases, developing methods to get useful information from such databases, and devising methods to integrate related data from disparate sources. The computer databases and algorithms are developed to speed up & enhance biological research. Bioinformatics can help answer such questions as whether a newly analyzed gene is similar to any previously known gene, whether a protein's sequence can suggest how the protein functions, and whether the genes turned on in a cancer cell are different from those turned on in a healthy cell

Bioinformatics is currently undergoing dramatic changes, as high-throughput laboratory methods lead to changes in key approaches, including sequence analysis, gene expression analysis, protein expression analysis, and protein structure prediction & modeling. High-throughput methodologies in computer biology bioinformatics are extremely complex, and many steps are involved in the conversion of information from low-level information structures (for example, microarray scan images) to statistical databases of expression measures coupled with design & covariate data. It is not possible to say how sensitive the ultimate analyses are to variations or errors in the many steps in the pipeline. Credible work in this domain requires exposure of the entire process.

Human genetics is the study of genes - or heredity - in humans. It also examines the effects of these genes on both individuals and societies. It has developed rapidly in the last decade as new technology has made it possible to study genes in much greater detail. Examples of remarkable advances in knowledge include the discovery of the molecular basis of many inherited disorders, the ability to trace the evolution of mankind and the application of DNA finger-printing to forensic science.

Genomics is the analysis of the entire genetic make-up of an organism. It includes a study of the functions of genes in cells, organs and organisms. This technology generates massive amounts of biological data through which it would be impossible to navigate without the use of computer systems. The data includes sequences of amino acids and nucleotides that underline genes & proteins. Genes carry information for making all the proteins required by all organisms. These proteins determine, among other things, how the organism looks, how well its body metabolizes food or fights infection, and sometimes even how it behaves. The completion of human genome sequencing in April 2003 marked the beginning of a new era for modern biology. Since that time, the impact of having the human sequence in hand has been nothing short of tremendous. The attainment of this goal, which many have compared to landing a man on the moon, will obviously have a profound effect on how biological and biomedical research will be conducted in the future. The intelligent use of sequence data from humans and other organisms, along with recent technological innovation fostered by the Human Genome Project, has already led to important advances in our understanding of diseases that have a genetic basis. More importantly, the advent of the genomic era will have a profound effect on how health care is delivered from this point forward.

The Instrument Maintenance & Calibration System is a laboratory equipment calibration tracking and management software program that allows you to schedule, track and report inspections on your instruments. This technique was specifically designed for automating equipment calibration and tracking instrument history. In this programme the term 'instrument history' refers to information regarding the type and identification of the instrument, where it is and who's using it, what problems are ongoing with the instrument, as well as what problems have occurred in the past and how they were corrected. This allows one to schedule periodic maintenance and inspection events, and to keep track of when they were done and what the outcome was. The program comes with an extensive array of reports, and allows you to create your own reports with the Report Wizard

Classical matrix factorization techniques such as the LU, QR, EVD and SVD or their variants have been used with great success in various data analytic applications. These include information retrieval, text mining, bioinformatics, computer graphics, computer vision & product recommendations. In the past few years, internet applications have thrown some new challenges to the numerical linear algebraists - unprecedented features and problems inherent in internet data have rendered traditional matrix factorization techniques less effective. Some of the new issues that arise in internet data mining include, prohibitively large data size - internet data sets are often much too large to allow multiple random accesse, massively incomplete data - a significant proportion of the data may be missing, novel structures in data - most importantly, datasets whose underlying structure cannot be adequately unraveled by LU, QR, EVD, or SVD have become increasingly common, not just in internet applications but also in other scientific & engineering fields. In the last few years, several new matrix factorizations have been proposed to deal with these issues. Some notables ones include, Nonnegative Matrix Factorization, Maximum Margin Matrix Factorization, Matrix Subspace Factorization & Sparse Overcomplete Factorization (Compressed Sensing). The key difference between these & the classical matrix factorizations is that they are not rank-revealing in the traditional sense but instead they reveal other properties of the structure under consideration.

Laboratory Information Management System (LIMS) is a vital tool to any research lab. By providing much needed structure and organization to the lab, a LIMS will increase the efficiency & safety. It has provision to store database, update and share lab's information on plasmids, cell lines, bacteria, DNA, RNA, protein, reagents, research animals, viruse,& purchase orders online. It will also organize your lab's incident reports, mandatory safety reports for radioactive use, biohazard use & chemical use, and a database will be available to track your lab's equipment & schedule preventive maintenance. By this the data is safe, secure and can be accessed from anywhere that an internet connection exists.

Laboratory notebooks represent an important part of the research and development workflow. The role of the laboratory notebook is to record work that was done so that research can be repeated, or avoided if the outcome was not the desired outcome, and to allow subsequent research to move forward based on previous results. Traditionally, the laboratory notebook medium is paper and data entries are handwritten. This medium is portable, easy to use, well understood by users, and with a little care, a durable method of recording. The incorporation of high-throughput screening (HTS) and high-throughput synthesis into the research process has resulted in an increased volume of electronic data that needs to be transcribed resulting in the use of electronic notebooks. Electronic note books allow one to perform searches using combined criteria across text, metadata, chemical structures & reactions and thus mine your knowledge base in a very precise manner. Search results can be exported to create reports.

Systems biologists collect large quantities of data from wet lab experiments and high-throughput platforms & also from public database resources in the interest of probing their biological process of interest. Thus, technologies used for extracting useful information from the data are, Laboratory Information Management Systems (LIMS), Bioinformatics pipelines & Database frameworks. LIMS is used to manage laboratory workflow; track samples through a multi-step data collection protocol, perform quality control, monitor supply usage and capture costs. The goal of an LIMS is to ensure that the best possible data are collected, with minimal errors due to sample mix-up or insufficient maintenance of the equipment. Bioinformatics pipelines are used to convert raw data into data-types that researchers can use for analyses. Bearing in mind that data that is considered raw from one perspective might be rather cooked from another, and a wide variety of bioinformatics pipelines are used to collect, extract, store & interpret data at several different levels of analysis. Database frameworks serve to store data, allow data access by query and, in some cases, facilitate data curation.

Medical informatics is the scientific field that deals with the storage, retrieval, sharing and optimal use of biomedical information, data and knowledge for problem solving & decision making. The discipline shares a considerable degree of overlap with conventional bioinformatics, but, with its focus on computational methods in a medical environment, it also encompasses quantitative methods for studies in the fields of health and medicine, for example, the design of & statistical analysis of drug trials. Consequent to rise in patient data, hospitals, and clinical researchers have to focus on medical data maintenance. Electronic medical records (EMR) allows all the information about a patient from disparate clinics and labs to be kept in a single & central record that can move with the patient. Systems such as hospital information systems (HIS) and electronic data management systems (EDMS) also manage these records along with billing & administration. In search for quicker and more convenient access to critical patient information, researchers are now looking for a virtual clip-board, with which they can interact.

Molecular biology is the study of biology at a molecular level. This field overlaps with other areas of biology, particularly with genetics and biochemistry. Molecular biology chiefly concerns itself with understanding the interactions between the various systems of a cell, including the interrelationship of DNA, RNA & protein synthesis and learning how these interactions are regulated. Molecular biology is the study of molecular underpinnings of the process of replication, transcription and translation of the genetic material. The central dogma of molecular biology where genetic material is transcribed into RNA and then translated into protein, despite being an oversimplified picture of molecular biology, still provides a good starting point for understanding the field. This picture, however, is undergoing revision in light of emerging novel roles for RNA. Much of the work in molecular biology is quantitative, and recently much work has been done at the interface of molecular biology & computer science in bioinformatics & computational biology. As of the early 2000s, the study of gene structure & function, molecular genetics, has been amongst the most prominent sub-field of molecular biology.

Molecular Design provides an easy-to-read introduction to the principles and concepts of computer-assisted drug discovery. Computer-Aided Drug Design (CADD) is a specialized discipline that uses computational methods to simulate drug-receptor interactions. CADD methods are heavily dependent on bioinformatics tools, applications and databases. As such, there is considerable overlap in CADD research and bioinformatics. Molecular models of drug compounds can reveal intricate, atomic scale binding properties that are difficult to envision in any other way. When we show researchers new molecular models of their putative drug compounds, their protein targets and how the two bind together, they often come up with new ideas on how to modify the drug compounds for an improved fit. This is an intangible benefit that can help design research programs.

Molecular modeling is a technique for the investigation of molecular structures and properties using computational chemistry & graphical visualization techniques in order to provide a plausible three-dimensional representation under a given set of circumstances. Molecular modeling applications use falls into two broad categories, interactive visualization & computational analyses. Three of the most prominent uses of modern molecular modeling applications are structure analysis, homology modeling & docking. Objective modeling revolves around three different approaches (each based on different underlying physical & chemical theories), molecular dynamics, molecular mechanics & quantum mechanics. All of these are concerned with developing a unique solution to what is referred to as the protein folding problem - designing & testing algorithms and applications that will reliably predict 3-D structured from a primary sequence.

The pharmaceutical industry has embraced genomics as a source of drug targets. It also recognises that the field of bioinformatics is crucial for validating these potential drug targets and for determining which ones are the most suitable for entering the drug development pipeline. In the past, new synthetic organic molecules were tested in animals or in whole organ preparations. This has been replaced with a molecular target approach in which in-vitro screening of compounds against purified, recombinant proteins or genetically modified cell lines is carried out with a high throughput. This change has come about as a consequence of better and ever improving knowledge of the molecular basis of disease. All marketed drugs today target only about 500 gene products. The elucidation of the human genome which has an estimated 30,000 to 40,000 genes, presents immense new opportunities for drug discovery and simultaneously creates a potential bottleneck regarding the choice of targets to support the drug discovery pipeline. The major advances in genomics and sequencing means that finding an attractive target is no longer a problem but finding the targets that are most likely to succeed has become the challenge. The focus of bioinformatics in the drug discovery process has therefore shifted from target identification to target validation.

Numerical analysis is the study of algorithms for the problems of continuous mathematics. Numerical analysis naturally finds applications in all fields of engineering and the physical sciences, but in the 21st century, the life sciences & even the arts have adopted elements of scientific computations. Numerical linear algebra is essential to quantitative psychology, stochastic differential equations & Markov chains are essential in simulating living cells for medicine & biology. In recent years, a number of sophisticated methods for dealing with ordinary and partial differential equations, inverse & ill-posed problems, learning machines, have been developed in the applied mathematics community.The availability of these novel methods provides the possibility to address biological problems without precedent and with unmatched complexity, like genome-wide system analysis, structural & dynamic characterization of complex biological networks, high-throughput feature analysis. Due to the huge amount of data often involved in these applications, we make a significant effort to merge state-of-the-art techniques of numerical analysis with high performance computing, to produce tools able to manage large-scale problems within reasonable computer time.

High density oligonucleotide expression arrays are a widely used tool for the measurement of gene expression on a large scale. Affymetrix GeneChip arrays appear to dominate this market. These arrays use short oligonucleotides to probe for genes in an RNA sample. Due to optical noise, non-specific hybridization, probe-specific effects and measurement error, ad-hoc measures of expression, that summarize probe intensities, can lead to imprecise & inaccurate results. Various researchers have demonstrated that expression measures based on simple statistical models can provide great improvements over the ad-hoc procedure offered by Affymetrix. Recently, physical models based on molecular hybridization theory, have been proposed as useful tools for prediction of, for example, non-specific hybridization. These physical models show great potential in terms of improving existing expression measures.

Carbohydrate microarrays have been used in glycomics research to examine the interactions of carbohydrates with other molecules. The chip-based format of microarrays offers important advantages over common screening techniques, such as enzyme-linked immunosorbent assays (ELISAs), because several thousand binding events can be screened on a single glass slide & only minuscule amounts of analyte and ligand are required. Assay miniaturization is particularly suitable for glycomics, because access to pure oligosaccharides is the limiting factor. The first carbohydrate microarrays relied on isolated saccharides that were non-covalently attached to membranes. Current screening efforts rely on carbohydrate arrays in which chemically or enzymatically synthesized & isolated oligosaccharides with a linker on the reducing terminus are covalently attached to glass slides. Standard DNA printing and scanning equipment is used to produce & analyse the carbohydrate microarrays Carbohydrate microarrays can be used to address the interactions of sugars with other types of molecules, as well as entire cells. Carbohydrate-RNA interactions were screened by incubating labelled RNA with aminoglycoside microarrrays. Mechanisms responsible for antibiotic resistance were studied using these arrays together with resistance-causing enzymes.

To protect large investments of time and money in bioinformatics, it is imperative for researchers, management & investors in industry to be aware of the scope of protection afforded to bioinformatics tools by the legal system. The U.S. patent statute provides that whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new & useful improvement thereof, may obtain a patent therefor. The quid pro quo for obtaining patent protection is public disclosure of the invention in sufficient detail to enable a person skilled in the claimed subject matter (or art) to make and use the invention without undue experimentation. Thus, the U.S. legal system provides a framework of intellectual property protection that justifies continued investment in private research and development in bioinformatics, which is expected to revolutionize biology. In short, patent law now provides layers of protection for novel, useful and nonobvious bioinformatics-related inventions. Well-crafted patent claims, which provide the metes & bounds of the right to exclude, create valuable intellectual capital. Such intellectual capital includes the generation of royalties from licenses to make, use, market, or sell the patented invention, protection of technology-access fees & business cooperation of technology transfer. Because disciplines such as e-commerce, finance, banking, insurance and bioinformatics use related tools, the scope of such intellectual capital for bioinformatics-related inventions is vast.

Regular performance testing and calibration verification of liquid handling devices is essential to ensure their precision & accuracy. Potential problems must be identified and corrected before they impact the integrity of laboratory data. Implementing an automated system for scheduling, conducting and documenting performance testing & calibration ensures that these devices are performing according to their specifications. A gravimetric pipette calibration verification solution for working in a regulated environment where system security, electronic signatures and audit trails are required.

Plasmids are autonomously replicating extra-chromosomal circular DNA molecules, distinct from the normal bacterial genome and nonessential for cell survival under nonselective conditions. Plasmid processor is a simple tool for plasmid presentation for scientific & educational purposes. It features both circular and linear DNA, user defined restriction sites, genes & multiple cloning sites. In addition, you can manipulate plasmids by inserting and deleting fragments. Created drawings can be copied to clipboard or saved to disk for later use. Printing from within the program is also supported. To a molecular biologist, the plasmid map is as valuable as a road map is to a world traveler. Plasmid maps are key for identifying all the restriction sites, gene orientations, selective markers, promoters and other genetic miscellany that make it possible to sequence, clone genes, design new vectors or express novel proteins.

Plasmid map generation is one of the oldest and most frequently performed operations in bioinformatics. Indeed, probably almost every practicing molecular biologist has worked with or generated a plasmid map to guide them through the cloning or plasmid manipulation process. Because of the size and complexity of plasmid molecules, computer-generated maps are absolutely essential to identify, locate & analyze key regions in a vector sequence. As early as the 1980s standalone computer programs were being described that supported the presentation and manipulation of plasmid maps on specific platforms & computer operating systems. Many of these early freeware packages have since been replaced by more sophisticated and far more user-friendly commercial packages, such as SimVector (Premier BioSoft), GeneTool (BioTools), VectorNTI (Informax, Invitrogen), MacVector (Accelrys), DNA Strider & LaserGene (DNAStar). Currently there are remarkably few freeware plasmid mapping programs still available, although pDRAW32 (AcaClone) is one example of an installable standalone package that supports plasmid mapping.

A primer is a short synthetic oligonucleotide which is used in many molecular techniques from PCR to DNA sequencing. These primers are designed to have a sequence which is the reverse complement of a region of template or target DNA to which we wish the primer to anneal. Plasmid map drawing system primer design has two essential & distinct phases, physical design and selectivity design. Physical design of primers involves the consideration of factors, such as GC-content, primer length, annealing & melting temperatures, starting nucleotides & higher-order oligonucleotide structure. These factors are essential to ensure that a primer is able to bind to a template and initiate extension by the polymerase in an efficient, consistent manner. Primer selectivity refers to the ability of a primer to bind to a single location within the initial pool of DNA. For microarray validation studies, there are two broad factors determining selectivity. First, a primer should be specific to a given mRNA transcript and should not bind in a region conserved across a gene or protein family (transcriptomicselectivity). Second, the primer should be robust against contamination by genomic DNA (genomic selectivity). If either type of selectivity is poor, a primer will amplify multiple products and thereby decrease PCR efficiency & introduce a confounding effect that will reduce the sensitivity & accuracy of quantitative PCR methods.

The number of proteins that constitute the human proteome is quite large. Many efforts are underway to examine the human proteome, and the protein complements in other species, as well. To assist in this effort there is a wide variety of bioinformatics tools and databases available for the analysis of proteins. Protein Microarrays, consist of antibodies, proteins, protein fragments, peptides, aptamers or carbohydrate elements that are immobilized in a grid-like pattern on a glass surface. The arrayed molecules are then used to screen and assess protein interaction patterns with samples containing distinct proteins or classes of proteins. Protein Amino Acid Sequences, the analysis of amino acid sequences, or primary structure, of proteins provides the foundation for many other types of protein studies. The primary structure ultimately determines how proteins fold into functional 3D structures. Primary structure is used in multiple sequence alignment studies to determine the evolutionary relationships between proteins, and to determine relationships between structure & function in related proteins.

Most or all data are structured. These files are the hardest to set up & maintain, and require specific knowledge by a searcher, but they are the easiest to use when doing analysis or integration. Data is categorized by specific fields, and so, by knowing the fields one should be able to capture all the relevant data, quite easily. The searchability of a relational database is totally dependent on how well the database has been structured. For example, in a relational database management system (RDBMS), indices may be created, and the user doesn't have to query against the index. The user still queries against logical relations, and the system automatically determines if it is faster to use the indices to answer a query. The user is thus insulated from worrying about various details, such as physical organization of data on disk, the exact location of the data, tuning the representation for better performance and choosing the best plan for evaluating a query. This declarative querying paradigm has been a huge success for relational DBMSs, and today commercial RDBMSs manage terabytes of data & allow very complex querying on these databases. Database management systems can provide similar benefits to the life sciences community, just as it did three decades ago to the business data management community. Many of the data sets that are used in life sciences are growing at an astonishing rate, such as sequence data.

As defined by the Council of Logistics Management in the U.S.A., logistics is that part of the supply chain process that plans, implements & controls the efficient, effective flow and storage of goods, services & related information from the point-of-origin to the point-of-consumption in order to meet customers' requirements (http://www.clm1.org). Research in logistics aims at complementing the research in the supply chain management. The topics of interest are primarily location facilities, managing warehouses, transportation management, and fleet management. Optimization models have been developed for the planning, organizing and controlling of logistics systems & provide exact or approximation solutions to the problems that may arise in logistics' systems. Attention has been paid in vehicle routing problems, location problems, and the interaction of elements in the whole logistics network.

Because of practical computational limitations and prior knowledge, data mining isn't simply about searching for every possible relationship in a database. In a large database or data warehouse, there may be hundreds or thousands of valueless relationships. Because there may be millions of records involved and thousands of variables, initial data mining is typically restricted to computationally tenable samples of the holding in an entire data warehouse. The evaluation of the relationships that are revealed in these samples can be used to determine which relationships in the data should be mined further using the complete data warehouse. With large complex databases, even with sampling, the computational resource requirements associated with non-directed data mining may be excessive. In this situation, researchers generally rely on their knowledge of biology to identify potentially valuable relationships, and they limit sampling based on these heuristics. In the transformation and reduction phase of the knowledge discovery process, data sets are reduced to the minimum size possible through sampling or summary statistics. For example, tables of data may be replaced by descriptive statistics, such as mean & standard deviation.

The overall objective of the Bioinformatics is to provide support to scientific data analysis for researchers and to promote the application of modern computational approaches to solving basic biological problems. In general, the facilities required for scientific data processing are, routine laboratory data processing and analysis using established software systems & service protocols (biological sequence cleaning, assembly, annotation & categorization), implementation & application of statistical & heuristic algorithms to explorative analysis of experimental data for particular problem solving & knowledge discovery, development of scientific software for data processing, data management & data publication as integral components of research projects in the foundation, training in bioinformatics software utilization & data analysis & assisting manuscript writing & grant applications on related bioinformatics topics.

Database management systems are designed to support large volumes of data storage, data processing, data querying & most recently, data mining and knowledge discovery activities. Rapid increase in computing power and advances in data management techniques in recent decades have led many researchers to pursue knowledge discovery with databases & database management systems as their primary computing platform. A recent trend of general database research in this direction has been the incorporation of domain semantics into the representation and management of data. Biological data are often characterized as having large volumes, complex structures, high dimensionality, evolving biological concepts and insufficient data modeling practices. These characteristics require database researchers and developers to make many special considerations while developing biological databases & database systems. If the central task of bioinformatics is the computational analysis of biological sequences, structures, and relationships, it is crucial that biological sequences & all associated data be accurately captured, annotated & maintained, even in the face of rapid growth & frequent updates. It is also critical to be able to retrieve data of interest from multiple distributed heterogeneous data sources in a timely manner, and precisely enough to be able effectively to separate them from the distracting noise of irrelevant, unreliable or insignificant data.

Sequences are the simplest way to represent a macromolecule. The structure of genes that code for the sequence of amino acids in proteins is produced in this form by genome sequencing projects. A simple database might be a single file containing many records, each of which includes the same set of information. Many laboratories generate large volumes of such data as DNA sequences, gene expression information, three-dimensional molecular structure and high-throughput screening. Consequently, they must develop effective databases for storing and quickly accessing data. For each type of data, it is likely that a different database organization must be used. A database must be designed to allow efficient storage, search and analysis of the data it contains. Designing a high-quality database is complicated by the fact that there are several formats for many types of data and a wide variety of ways in which scientists may want to use the data. Many of these databases are best built using a relational database architecture, often based on Oracle. A strong background in relational databases is a fundamental requirement for working in database development. Having some background in the molecular biology techniques used to generate the data is also important. Most critical for the bioinformatics specialist is to have a strong working relationship with the researchers who will be using the database and the ability to understand & interpret their needs into functional database capabilities.

With DNA sequencing and gene mapping growing at exponential rates, scientists have come to rely more & more on sophisticated software to collect, analyze, catalogue & interpret the data. But today an array of software tools are available for scientists to manage sequencing projects, no matter how large or small. Some of these tools have been around for years & continue to meet the needs of many conventional labs, others have been released only in the past several months & make use of the latest graphical interfaces & search and assembly algorithms, as well as on-line functionality, to keep pace with the ever-increasing demand for faster & more intuitive computer analysis tools. The software packages are available on a variety of platforms, they provide an extensive array of base calling, nucleic acid & protein analysis features & they handle multiple sequence alignment, contiain assembly, primer & probe design and database searching.

A powerful storage or analysis server is a great way to perform in-house analysis and securely store important data. Workstation computers are a must-have in todays molecular biology laboratory. Biomatica will custom tailor a certified and scalable HP server or HP workstation running Windows XP Professional, Windows XP Professional 64bit, Red Hat Fedora Core Linux, or any flavour of UNIX you require. Additionally, we sell notebook computers for laboratories.

Spectrum analyzer is a device used to examine the spectral composition of some electrical, acoustic, or optical waveform. Often, it measures the power spectrum. Spectrum analysis is concerned primarily with characterizing signal components, (such as, its spurious and harmonic components, modulation, noise). Spectrum analyzer is used to locate frequencies where microwave energy exists. There are analogue and digital spectrum analyzers. An analogue spectrum analyzer uses either a variable bandpass filter whose mid-frequency is automatically tuned (shifted, swept) through the range of frequencies of which the spectrum is to be measured or a superheterodyne receiver where the local oscillator is swept through a range of frequencies. A digital spectrum analyzer uses the Fast Fourier Transform (FFT), a mathematical process that transforms a waveform into its frequency components in the spectrum. As a result, computer programs can compute such transforms, and makes audio processing easier. FFTs have applications in much wider fields. Spectrum analyzer is used in the measurement analysis of Distortion measurements, Modulation measurements and Pulsed RF measurements.

Spectroscopy was originally the study of the interaction between radiation and matter as a function of wavelength. Spectrometry is the spectroscopic technique used to assess the concentration or amount of a given species. In those cases, the instrument that performs such measurements is a spectrometer or spectrograph. Spectroscopy/spectrometry is often used in physical and analytical chemistry for the identification of substances through the spectrum emitted from or absorbed by them. Spectrometry is the spectroscopic technique used to assess the concentration or amount of a given species. In those cases, the instrument that performs such measurements is a spectrometer or spectrograph.

Scientists at the Blueprint Initiative have created a new visual language for biology called OntoGlyphs. This new language helps scientists quickly identify biological attributes of the molecules they are studying. The OntoGlyphs are a collection of pictographs representing different biological attributes and containing Gene Ontology (GO) terms. The term OntoGlyph is derived from the concepts of gene ontology (Onto) and symbols or pictorial representations (Glyphs). In total, there are 83 OntoGlyph characters, which represent three types of molecule attributes, function, binding & cellular localization. Ontoglyphs are derived from a combination of the U.S. National Centre for Biological Information's Cluster of Orthologous Groups functional categories & GO terms, and are based on grouping the nearly 17,000 GO terms in the categories used most frequently by biologists in describing genes & protein function. Using this geometrical language, researchers can create, store & combine symbols efficiently and on the fly. This mechanism helps researchers to make better sense of complex interaction networks by allowing them to focus on specific subsets of the data, without the distraction of secondary or tertiary partners. Similarly, through pattern recognition, researchers are more likely to see linkages through common interacting partners between different pathways that have not yet been identified in the literature. This has the potential to open new doors of scientific inquiry.

Temperature Dependent Crystallinity Software is a powerful package used to determine the % crystallinity of a semi-crystalline polymer versus temperature. This new software provides an easy to use procedure which leads to an analyst-independent determination of temperature dependent crystallinity by utilizing transition enthalpies with data available mainly from the Advanced Thermal Analysis Laboratory (ATHAS) data bank for 16 different polymers. The method assumes that the heat capacity contribution of the amorphous content can be extrapolated from the melting region to substantially lower temperatures. This information, together with the ATHAS enthalpy data and the experimental curve is sufficient to calculate the percent crystallinity versus temperature.