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Proteomics/Protein Identification - Mass Spectrometry/Applications for mass spectrometry

This Section:

Protein Identification
The process of protein identification through mass spectrometry is done in two main ways:
 * Peptide Mass Fingerprinting
 * Tandem MS

Peptide mass fingerprinting typically uses the masses of peptides derived from a spectrum as to check against a database of predicted peptide masses. These predicted masses are recorded from digestion of a list of well documented proteins. If a protein sequence has a significant number of predicted masses that match the experimental values, there is a excellent chance that the given protein is present in the sample. Matrix Assisted Laser Desorption Ionization - Time of Flight(MALDI-TOF) mass spectrometers are the instrumentation that is commonly used for this type of peptide analysis.

The other technique that is typically used is Tandem MS. This technique utilizes collision-induced dissociation. This process breaks proteins within the peptide backbone and because of this fragmentation, comparisons between the observed fragment sizes and the database of predicted masses is possible. A very similar method to the Tandem MS approach is peptide fragmentation fingerprinting or 'PFF'. Instead of utilizing collision-induced dissociation, this method uses enzymatic digestion of a single peptide to generate a fragmentation pattern. These fragments are analyzed and compared to a database of observed fragments for the particular enzyme in a method similar to the Tandem MS approach. Tandem MS and peptide fragmentation fingerprinting can also be used for protein sequencing.

Only about four to five peptides are adequate to accurately identify a protein with a known amino acid sequence. Surprisingly, as the identification information contained within databases increases, the amount of information required to identify a specific protein increases. Over time, the number of peptides required to identify a protein has risen. In some cases, a specific peptide fragment or a few fragments in rare combination are unique to a particular type or class of protein. This fragment or group of fragments can be used as signature peptides to identify the presence of the particular protein. These signature fragments exist for both collision-induced dissociation and enzymatic digestion. The recognition of a fragment as an effective signature fragment requires a significant amount of background information.

Protein Sequencing
Tandem MS can be used in conjunction with fragmentation methods such as trypsin digestion. This allows a sample to generate overlapping peptides of varying length. This method works in a similar fashion to the approach taken by genome sequencers, when they generate overlapping DNA sequence fragments and assemble into the genome. On the mass spectra, the mass difference between two overlapping peaks can be used to determine the difference in sequence based on the well documented data on the weights of the amino acids. Sequences are usually determined using one or more search algorithms linked to an appropriate database, such as the International Protein Index (IPI) at the European Bioinformatics Institute or the nr (non-redundant) protein sequence database provided by NCBI. Commonly used search algorithms include SEQUEST, X! Tandem, and MASCOT.

Quantitative Proteomics
There are several methods available for the quantitation of proteins using mass spectrometry. These typically involve some labeling method to differentiate between two different cell types. In isotopic labeling, heavier isotopes are introduced to one sample while lighter isotopes are used to label the other. Before mass spectrometry analysis, these two differentially labeled samples are mixed. The peptide fragments associated with the samples can be differentiated based on their mass differences. The quantification is made by the comparison of the peak ratio of the intensities on the mass spectrum to derive the relative abundance. Multiple methods currently exist which are widely employed in quantitative proteomics, including stable isotope labeling with amino acids in cell culture, isotope-coded affinity tags and metal coded tags.

The objective of quantitative proteomics is to determine quantitative information about a particular protein sample. Although qualitative provide insights that are of value, quantitative datasets give a larger understanding. Quantitative proteomics is a cellular technique that is based on the use of mass spectrometry. Such quantitative technique enables the detection of quantitative changes in proteins and their post-translational modifications in biological systems.

Generally, major quantitative proteomics methods assist in carrying out the analysis of protein complexes, where the priority is to gain access to information on the bona fide interactions of affinity tagged protein of interest. This action is crucial in providing a certain understanding to the novel interactions with known proteins, which can unravel the mysteries of unknown proteins. The bona fide protein interactions can be examined with bait proteins and protein complexes that are usually carried out with or without stable isotopes. The study of the interactions of protein complexes have been made possible by affinity purification and specific quantitative proteomics techniques -such as the combination of cleavable ICAT, iTRAQ, and DNA-affinity purification.

Quantitative proteomic analyses of protein complexes also serve the purpose of determining the changes in protein-protein interactions within a protein complex under various cellular conditions. For example, an approach based on SILAC (Stable Isotope Labeling by Amino acid in Cell culture) is used to examine the mechanics of the TATA binding proteins along with their phosphorylation states during the cell cycle. Moreover, SILAC provides an analysis of the circadian rhythm mechanistic features and the extracellular signal regulation of kinases interactions under certain conditions.

Profoundly, quantitative proteomics has grown a useful application in the analysis of protein interaction networks. Despite the advanced technological approaches in discovering the protein complexes, these complexes mostly function with other protein complexes and most proteins inhabit in more than one distinct complex. Quantitative proteomic therefore has the ability to confirm the many functions of protein complexes when interacting with protein complexes, and to address questions in determining the probability of protein-protein interactions.

Proteomics research is usually categorized into discovery and targeted proteomics. Discovery proteomics optimizes the identification of the protein by dedicating more time per protein sample and limiting the number of samples. On the contrary, targeted proteomics limits the number of features that will be analyzed, and then efficiently perform chromatography to obtain the highest sensitivity through many samples.

Furthermore, the use of mass spectrometry does not inherently produce quantitative results due to the ionization efficiency of the peptide sample. Because of this, relative and absolute abundance quantitative proteomics developed as sub-categorized techniques. Relative quantitation can be obtained by examining samples individually by mass spectrometry and comparing the spectra in order to actuate the peptide abundance in one sample in comparison to another. Often times, relative proteomic quantitation requires a labeling process involving stable isotope; this allows the mass spectrometer to distinguish between identical proteins into separate samples. Absolute proteomic quantitation utilizes isotropic peptides that evoke known concentrations of synthetic, dense isotopopgoues of target peptides into a sample and then performing liquid- chromatography- mass spectrometry. Like relative quantitation, absolute proteomic quantitation uses isotropic labels. However, the experimental target peptide sample is compared to heavy peptide and reverse- calculated to the starting concentration of the standard with the help of a pre-determined standard curve to allow for the quantitation of the target peptide.

Quantitative proteomics is a constantly developing and highly renewable field of study. Because of quantitative proteomic analysis, a large variety of biological problems have been solved and resolved.

Bacterial Identification
Mass spectrometry has been applied as a tool for bacterial recognition by "fingerprinting" species based on their proteomic inventory, though until recently work using this method was hindered by the procedure's reliance on high resolution data and the consequently high cost of sufficiently accurate instruments. Recent work in making the method more accessible has seen success by using MALDI (Matrix-Assisted Laser Desorption/Ionization) mass spectrometry, which uses comparatively low energy nitrogen lasers for ionization and also stabilizes large, fragile protein species by supporting them on a matrix. These modifications help minimize fragmentation of protein species and ultimately reduces the complexity of the resulting mass spectrum, lessening the need for high-resolution data and thus making the process possible using less expensive mid-range spectrometers.

Mass spectrometry-based bacterial identification is preferable to current differentiation methods which generally involve multiple tests to identify species based on physiological, serological, chemotaxonomic, and overall biochemical characteristics, each of which requires time consuming culturing steps. In contrast, mass spectrometry methods generally take on the order of a few hours and require a single culturing step to increase cell counts and purity.

Identification is achieved using databases not unlike those used currently to identify unknown chemical compounds. Multiple spectra of a specific bacterial species are taken and averaged to remove noise, and the resulting averaged spectra is used as the species' proteomic fingerprint. This is then stored in a public database, and when an unknown is tested its spectra is compared via statistical algorithms specific to the database and identified based on closeness of fit to members of the database inventory.

Pharmaceuticals
Within pharmaceuticals, mass spectrometry is being used to due to the complicated mixtures of samples such as blood, urine, lymph, and is used with high sensitivity methods to measure low doses and long time point data. The most commonly used technique is LC/MS coupled to a triple quadrupole mass analyzer.

The sensitivity of mass spectrometry affords the capability to carry out measurements of microdosing experiments which minimize the necessity of animal experimentation, with approximately a 70% congruence between observed effects of the compound between microdosing and animal model experimentation.

Disease Biomarker Detection
Research has shown that tumors can introduce proteins into various bodily fluids which might not normally exist at those concentrations in those locations. This has led to the interest in the study of mass spectrometry in the use of clinical early detection of cancer through analysis of these fluids. These disease-associated molecules are known as biomarkers. The issues associated with clinical usage of such analytical methods is the problem of false positives and false negatives. There are investigations into whether a protein signature composed of multiple proteins can be capable of providing a diagnostic measure of sufficient quality for use in a clinical setting.

There are a number of hurdles that exist for this type of mass spectrometry analysis.
 * Variations in the protein content of samples between individuals and even between the same person make the construction of a disease signature very complex
 * High dynamic range of protein concentrations are present, possibly with biomarkers existing on the fringes of that range
 * Accuracy and reproducibility of the mass spectrometry method(most frequently SELDI TOF)

Edge Fractionation Technology provides a powerful density based separation and enrichment method for rapid screening of potential Biomarkers. This method was used in a study, A Density-Based Proteomics Sample Fractionation Technology: Folate Deficiency–Induced Oxidative Stress Response in Liver and Brain, on biomarkers for oxidative stress, glutathione peroxidase 1 (GPx1) and glucose-regulated protein 75 (GRP75), as a result of folate deficiency.

Classification and Identification of Bacteria by Mass Spectrometry and Computational Analysis
[http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2475672&tool=pmcentrez Sauer, S, et al. Classification and Identification of Bacteria by Mass Spectrometry and Computational Analysis. PLoS ONE 3(7): e2843.doi:10.1371/pournal.pone.0002843]

Main Focus

An alternative methodology coupled with database design in order to make bacterial identification with mass spectrometry more accessible.

Summary

The intention of Sauer et al. was to expand the use and improve the efficiency of mass spectometry-based microbiological identification procedures. Such procedures have been shown to be successful before, but the generally high cost of the equipment was exacerbated by the need for high-resolution data in identifying bacterial species based on their proteome. Consequently, the field of mass-spec microbe identification is not often used and is therefore both ill-developed and unproven in extensive field tests. However, the authors note that the potential of mass spec identification is appealing; current methods involve labor-intensive culturing and processing based on an assortment of tests used to elucidate the biochemical properties of the sample species, which takes considerable time and preparation. Genetic differentiation based on the 16S ribosome RNA sequence is possible and common, but requires pre-defined sequence data. In contrast, the authors’ described method can (ignoring culture times) be performed in 90 minutes or so, and requires only that a proteome “fingerprint” be established beforehand for comparison.

The proposed method centers on three key components which are intended to make the use of mass spec identification more accessible and accurate. First, the use of matrix-assisted laser desorption/ionization (MALDI) mass spectrometers allows for the identification of large, often fragile particles (specifically proteins or DNA fragments) by first fixing them on a supportive matrix and then ejecting them with comparatively low energy lasers (nitrogen based). This method allows for much larger fragments to be maintained than are generally possible with other mass spec methods, which in turn allows for much simpler mass spectra data and consequently a decreased reliance on resolution. Thus, a lower resolution (and therefore more affordable) mass spec system can be used for identification. The second implemented change was the use of single nucleotide polymorphism’s (SNPs) to differentiate between species with very similar proteomes (in the case of this study, to differentiate between European and North American strains of Erwinia amylovora). This was accomplished by sequencing the galE gene of the two strains and then using PCR to amplify this sequence using the necessary primers. This results in an amplified concentration of the galE gene, which could then be identified through MALDI mass spec as well. In this way it was now possible to differentiate between these species with approximately identical proteomes. The third and most crucial component to the broad-range use of mass spectrometric identification is the generation of a database of bacterial fingerprints. By generating mass spec data for a species twenty times, the data were averaged to give the most common spectrum for the species, which could then be compared to field results via statistical software to give a closeness of fit result not unlike the methods currently used for BLAST searches. The authors note that the database is available for free online, and that in addition to the 2800-plus strains already fingerprinted a global effort is anticipated which should allow the database to grow rapidly, particularly for species of interest in particular fields, such as plant infection.

With full implementation, the authors’ method for species identification is accomplished by first sampling a particular microbe species (such as a pathogen) and then culturing it to both increase the cell number and to allow for purification (minimizing the number of species being analyzed in a particular sample). Next, this culture can be fixed on the MALDI matrix and tested, possibly multiple times to allow for greater confidence levels. Assuming the species has already been fingerprinted, the resulting spectrum is statistically compared to those of the database and a closest fit is applied; with sufficiently high closeness values, a definitive identification can be made. In the event that the species is proteomically identical to other bacterial strains, database entries would also include SNP analysis results to allow this extra level of differentiation.

New Terms

 * MALDI: Matrix-assisted laser desorption/ionization mass spectroscopy; mass spec. method in which large, fragile molecules (such as proteins or DNA) are ionized by a “gentle” laser (such as nitrogen) and supported by a matrix surface, making them resistant to fragmentation. ( http://www.sigmaaldrich.com/analytical-chromatography/spectroscopy/maldi-mass.html )
 * SNP: Single Nucleotide Polymorphism; DNA sequence variation in which a genetic sequence is modified at one specific nucleotide, resulting in identical sequences with the exception of one pairing. ( http://en.wikipedia.org/wiki/Single_nucleotide_polymorphism )
 * Robustness: Act of attaching bacterial species to a slide or stage, often with heat; in the case of this article, the act of attaching them to the support matrix. (http://en.wikipedia.org/wiki/Robust)
 * House-keeping genes: Genes expressed over any conditions of treatment of an organism (non-conditional expression). (http://www.biology-online.org/dictionary/Housekeeping_genes)
 * Dendrogram: Tree diagram used to illustrate gene clustering. (http://en.wikipedia.org/wiki/Dendrogram)

Course Relevance

 * This work is of considerable value in the study of metabolism given that it functions by establishing “fingerprints” of microbiological species which contain data from their entire proteome. While this “big picture” approach appears effective for differentiation between species, it is conceivable that mass spec identification using MALDI could be used to differentiate between different conditions of the same species; for example, between a microbe in aerobic vs. anaerobic conditions. This procedure is currently undertaken using SDS-PAGE or similar approaches, but these methods are difficult to automate and very time consuming. Mass spectrometry has shown itself to be easily automated, and with established methodology can be done very quickly.

A Density-Based Proteomics Sample Fractionation Technology: Folate Deficiency–Induced Oxidative Stress Response in Liver and Brain
Lan W, Guhaniyogi J, Horn MJ, Xia JQ, Graham B. J Biomol Tech. 2007 September 18(4): 213–225.

Main Focus

Folate is important in both brain and liver and using it as a factor to force disease in rats it is possible to identify unique proteins and potential biomarkers for identifying the disease outside a controlled experiment.

Summary

Folate is a cofactor in biosynthetic pathways crucial for DNA synthesis, DNA repair and various methylation reactions. Most mammals including humans can not synthesize folate and must attain it from their diet. Folate deficiency is linked to stroke, Alzheimer’s disease, Parkinson’s disease, depression, cancer and cardiovascular disease. Folate deficiency disrupts one-carbon metabolism, increasing the level of homocysteine, a cytotoxic amino acid that can induce DNA strand breakage, apoptosis and induce oxidative stress, which will cascade DNA damage, alter one-carbon metabolism and trigger multiple organ damage and neurodegenerative disorders. Oxidative Stress and oxidative damage can alter protein functions in many ways, including directly modifying catalytic amino acids, changing critical amino acid residues in binding or regulatory sites, and altering proteolytic susceptibility and states of aggregation. In animals the liver is an important organ for folate storage and metabolism. Two groups of four rats each were given a folate-defined diet for two weeks, and then the experimental group was switched to a folate deficient diet. After four more weeks their organs were harvested. The proteins obtained from the organs were separated into 11 samples based on density using Edge 200 Fractionation. Fractionation is used to increase the visibility of less prevalent proteins. Each of these samples were measured with mass spectrometry to determine at which densities folate and biomarkers indicative of oxidative stress occur. In folate deficient rats, GPx1, a protein involved in the control of oxidative stress, was increased in abundance by 50 to 70% compared to rats with a diet containing folate. Oxidative stress induced by folate deficiency is less in brain tissue than in liver tissue. Several proteins related to oxidative stress were found in the experimental rats. Changes in many proteins of unknown function or unknown relation to folate deficiency were also observed in the experimental rats. Western blot results show changes in the relative percentage distribution of markers in each fraction, indicating their potential translocation in the cell.

New Terms

 * Edge 200 Fractionation: Non-denaturing front-end sample fractionation technology. It works by suspending the particles in an initial medium of known density. The suspension is pelleted and the supernatant is saved as the first sample. The remaining pellet is resuspended in a medium of increased density and the process is repeated for as many fractioned samples desired.
 * Oxidative Stress: An imbalance between the production of reactive oxygen and a biological system's ability to readily detoxify the reactive intermediates or repair the resulting damage.( 04-12-09)
 * Epidemiology: Study of factors affecting the health and illness of populations.( 04-12-09)
 * Ad libitum: Latin for “at one's pleasure”( 04-12-09)
 * Snap Frozen: Frozen by dipping in liquid nitrogen.( 04-12-09)
 * Brain PNS: Peripheral Nervous System, resides outside the central nervous system(brain and spinal cord).( 04-12-09)

Course Relevance

 * One of the biggest problems in Proteomics is separating specific proteins from a sample. This article shows the use of Edge 200 Fractionation to separate all the proteins in a sample to a user specified number of density specific samples.

Systems Biology
Ranish J. http://www.systemsbiology.org/technology/Data_Generation/Mass_Spectrometry_Analysis (3-20-09)

Main Focus

Use of Mass Spectrometry to find Post translational modification (PTM) of proteins which play a key role in the control of a wide range of biological functions and activities is enhanced by increasing the concentrations of the PTMs.

Summary

Analysis of Post Translational Modified (PTM) proteins is difficult because the modified proteins exist in far lower concentrations than the unmodified versions of the proteins and the modification itself is a hinderer to mass spectrometry. By increasing the concentration of modified form of the protein and using computational algorithms designed specifically for the analysis of the unique MS/MS fragmentation patterns generated by post-translationally modified peptides, the chances of identifying the modified peptide during mass spectrometric analysis are greatly improved. Enriching methods include purification to homogeneity and conjugation to a solid support using hydrazide chemistry and stable isotope labeling. Further advances in defining PTMs are being developed such as; Peptide mixtures to be methyl esterified to block carboxylates from further reaction in subsequent steps, and to introduce stable isotopes for quantitative analysis, elution and bonding to polyamine.

New Terms

 * Post Translational Modification (PTM): The chemical modification of a protein after its translation. It is one of the later steps in protein biosynthesis for many proteins. (http://en.wikipedia.org/wiki/Post_translational_modification 4-12-09)
 * Sumoylation: A PTM involving the attaching or detaching of Small Ubiquitin-like MOdifier proteins (http://en.wikipedia.org/wiki/SUMOylation 4-12-09)
 * MS/MS: Multiple Steps Mass Spectrometry with some form of fragmentation occurring in between the stages (http://en.wikipedia.org/wiki/MS/MS 4-12-09)
 * Sub-Stoichiometric Amounts: An amount to small to calculate the relationships between reactants and products. (http://www.answers.com/topic/stoichiometry 4-12-09)
 * Hydrazide Chemisty: Reactions involving binding to organic compounds sharing a common functional group characterized by a nitrogen to nitrogen covalent bond with 4 substituents with at least one of them being an acyl group. (http://en.wikipedia.org/wiki/Hydrazide 4-12-09)

Course Relevance

 * A large hurdle in proteomics is finding and isolating proteins of very low concentration. This website contains methods for surpassing this problem.

National Center of Biotechnology Information Taxonomy Database
http://www.ncbi.nlm.nih.gov/Taxonomy

Main Focus

Establishing an expansive, freely accessible resource for the cataloging of current and future biological data.

Summary

The National Center of Biotechnology Information acts as a global center for accumulation of biotechnology-related information in the form of publicly accessible databases, and their Taxonomy database is maintained to be as up-to-date as possible in terms of the current classification of various organisms based on collaborative data. As data are generated by an assortment of independent research bodies, submission of this data to the NCBI website benefits authors in two ways in particular: by confirming their work through comparison to data in existence both before and after publication, and by making it available for citation.

For the general scientific community, the NCBI website is just one of many separate attempts to publicize and make both available and searchable the vast amounts of incoming scientific data published every year. All fields benefit from easy access to relevant data, but nowhere is this more apparent than the field of biology, in which new data is being constantly accrued over time as new methods are proven and then applied. The vast number of known organisms coupled with the continuous improvement of analytical methods ensures that a limitless amount of data will always remain to be gathered, for species and systems both newly and previously studied. Further, as the much of the global community continues to improve in terms of education and economic status, the number of individual outfits generating data is continuously increasing. This influx of data would be impossible to archive effectively in the methods of the past several centuries; individual genomes would fill entire volumes, and even these would need to be updated with such regularity that printing would need to occur more than annually. Most importantly, this data would be incredibly hard to access in terms of both cost and searching. Single procedures often necessitate the use of data gathered by anywhere between dozens and hundreds of sources, which would make locating this information incredibly difficult, and much data (such as protein or DNA sequence comparisons) requires the comparison of thousands of data points (base pairs), making computer application an absolute necessity.

For all of these reasons the NCBI Taxonomy website has been created and has quickly become an incredibly useful source for biological and related studies. The site allows for the searching of any species currently known and archived, and for each offers tabular summations of current works available, such as genome sequences or projects, known protein and/or other biochemical structures, and even the number of times a particular species has been cited in PubMed databases. Even more importantly, all of these references and data points are linked, allowing for incredible amounts of data to be accessed in moments. It is safe to assume that without NCBI and other public databases, many of our most recent biological and biochemical breakthroughs would have taken much longer, or may have been altogether impossible.

New Terms

 * GEO: Gene Expression Omnibus; a public database based at NCBI containing microarray and ChiP-chip data. (http://www.ncbi.nlm.nih.gov/geo/)

 * ChIP-chip: ChiP-on-chip; microarray method used to observe interactions between DNA and DNA-binding proteins. (http://www.chiponchip.org/)

 * PopSet: Collection of DNA which as been sequenced for statistical comparison of populations' evolutionary similarities. (http://www.ncbi.nlm.nih.gov/sites/entrez?db=popset)

 * SRA: Short Read Archive; archive for incomplete sequences of DNA from a particular organism generally containing several million base pairs each. (http://www.ncbi.nlm.nih.gov/sites/entrez?db=sra&cmd=Search&dopt=DocSum&term=txid562[Organism%3Aexp])

 * HomoloGene: Automated gene homologue detecting system; compares sequence data against completed genomes of eukaryotic organisms to detect shared gene characteristics. (http://www.ncbi.nlm.nih.gov/homologene)

Course Relevance

The value of websites like the NCBI Taxonomy website cannot be overstated in the current, ever-expanding state of biology, for both its usefulness in research ventures and its crucial archival functions.


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Proteomics/Protein Identification - Mass Spectrometry/Data Analysis/ Interpretation

This Section:

=Data Analysis=

Mass Spectrum
A mass spectrum is a plot of an intensity vs. mass-to-charge ratio of a separated chemical collection. The mass spectrum of a given sample is the distribution pattern of the components of that collection, whether atoms or molecules, based their mass-charge ratio.

The X-axis of the plot is the mass-charge ratio also seen as (m/z) which is the quantity obtained by dividing the mass number of an ion by its charge number. For mass analyzers such as Time of Flight, the direct X-axis measurement is the time series of the ions measured by the detector. For such cases, the spectra must be calibrated with known standards in order to transform the X-axis from a time series into a m/z ratio. The values for the standards are used to generate the parameters for the equations relating the time of flight to m/z ratio. After these parameters are determined, the m/z ratios for the unknown sample can be calculated from their time of flight. With the Fourier Transform Ion Cyclotron Resonance mass spectrometer, the frequency measurements gathered by the detector plates undergo Fast Fourier Transformation before they are mass calibrated. The Y-axis of a mass spectrum represents the signal intensity of the ions, and has arbitrary units. In most forms of mass spectrometry, the signal intensity of an ion current does not represent relative abundance accurately, but somewhat correlates loosely with it. Signal intensity is dependent on certain factors, the nature of the molecules being analyzed, how they ionize, the buffers' interaction, and the sample interaction.

Method of Interpretation
Samples can sometimes be highly difficult to analyze due to the amount of restrictions as well as variables that play roles in the output. Many factors can play roles in how a mass spectrum is interpreted, these factors may include: even electron vs. odd electron species, positive vs. negative ion mode, intact protein vs. fragmented peptide ions, etc.

Basis of resolution as well as peak height is reliant on the amount of sample being used and the amount of separation done prior to the mass spectroscopy. Based on certain heights and areas or peaks, structures can be determined.

Not all mass spectra can be interpreted similarly, due to the varying nature of the mass analyzers and ionization methods available. For example, some mass spectrometers break the analyte molecules into fragments; others observe the intact molecular masses with little fragmentation. A mass spectrum can represent many different types of information based on the type of mass spectrometer and the specific experimental conditions applied; however, all plots of intensity vs. mass-to-charge are referred to as mass spectra.

Normalization Techniques

The process of removing statistical error from data created from repeated measurements is called normalization. In MS normalization techniques are used to remove systematic biases from peptide samples. These biases can arise from various sources including protein degradation, measurement errors, and variation in loading samples. The common normalization techniques used on MS data require that the data is transformed from the linear to the log scale. Doing so allows the values to conform to the normal distribution and reduces the likelihood of masking more relevant proteins with less relevant ones.

Annotation
As described in the section above, there are a number of factors that are vital to meaningful interpretation of the data from a mass spectrometry experiment. Without this metadata about the variables of the experiment, it is difficult to use a mass spectrum to generate an assessment. Combine this with the variance among types of mass spectrometers, and the utility of a reporting standard emerges.

In the vein of Minimum Information About A Microarray Experiment(MIAME), the HUPO Proteomics Standards Initiative developed MIAPE MS, a Minimum Information About a Proteomics Experiment in Mass Spectrometry. The standard requires metadata to be recorded about general information such as the machine manufacturer and model, variables of the ion source for ESI and MALDI, mass analyzers and detectors involved, and post-processing involved in peak list generation and annotation. In addition to the mass spectrometer operation, post-processing methods that are often used in other fields of signal processing are frequently applied to mass spectra in order to generate a more useful spectrum.

Further work has been done to regulate the expression of Mass Spectroscopy data via the establishment of a Controlled Vocabulary (CV). Developed by HUPO under the Proteomics Standards Initiative, the CV provides an ontology, comprised of applicable terms and transitions, which will better control the representation of Mass Sepctrometry data by regulating the vocabulary used in their description.

Accurate Mass Time Tags

The accurate time tag approach, or AMT, takes advantage of the highly accurate readings from FTICR-MS and nanoLC. Data from these techniques can be used to form unique tags based on their molecular mass and retention times for a specific peptide. This technique assumes that there is enough separation power in two dimensions that it is very unlikely that two will occur with the same mass and separation time. Thus is it unlikely that a new species will be detected upon further analysis of the same sample with the same mass and retention time as any other species.

Decision Tree Ensembles

Decision tree is one of the machine learning methods widely used to analyze proteomics data. Generated from a given dataset, a single decision tree reports a classification result by each of its terminal leaves (classifiers). Even though there exist many algorithms such as C4.5 that can be used to generate a well-modeled single decision tree, it is still possible that its prediction is biased, thus adversely affecting its accuracy.

To overcome this problem, more than one decision trees are used to analyze the data. It is based on the concept of forming a panel of experts who will then vote to decide the final outcome. The panel of experts is analogous to the ensemble of decision trees, which provide a pool of classifiers. Similar to voting, the classifier that has a majority becomes the true classification result for the data. As reported by Ge et al., the decision tree ensemble is more accurate than a single decision tree.

The diagram shown summarizes the process of generating decision tree ensemble and classification of data using Bagging (bootstrap aggregating) algorithm. To explain briefly, Bagging algorithm begins with randomly sampling (with replacement) the data from the original dataset to form a training set. A multiple of training sets are usually generated. Note that, since the replacement is allowed, the data in the training set can be duplicated. Each of training sets then generates a decision tree. For a given test data, each decision tree predicts an outcome, represented by a classifier. The ensemble of decision trees forms a panel of experts whose votes determine the final classification result from this group of classifiers.

Significance Analysis of Microarray Applied to MS data
While the Significance Analysis of Microarray (SAM) method was intended for the identification of differential gene expression, it has recently been utilized for identifying differential protein expression with data obtained from liquid chromatography mass spectrometry. Proteomic data retrieved from nanoLC/LTQ/FTMS and analyzed via SAM's algorithm has revealed reliable data not only relating to differential protein expression, but in determining false discovery rates with a level of accuracy previously unmatched by standard proteomic analysis tools. The superiority of this method over other methods of analysis such as identifying genes as significantly changed if an R-fold change is observed and significant changes in expression based on if an R-fold change was seen consistently between paired samples were discussed. Additionally, information regarding obtaining and using the SAM program is available (Section 1.11.4).

mzData
HUPO PSI developed this XML format meant to unify the existing formats into a standard format for reporting peak list information called mzData. Essentially mzData provided a standard meas to represent peak data from a Mass spectroscopy experiment, and establish the fundamental parameters necessary for proper interpretation of the spectra, such as negative or positive ion spectra, and other basal information.

mzXML
Developed by the Seattle Proteome Center at the Institute for Systems Biology this data format is similar in its minimal requirements for the presentation of MS data, but with several key differences which make it the preferred file format for MS data. Numerous converters and tanslaters exist which allow for the ubiquitous data transfer from a variety of the major Mass Spectrometers including Waters, Thermo, Bruker, MDS, Agilent, and ABI. Based from the same data structure as mzXML, prepXML (also produced by the Seattle Proteome Center), is a data format that has arisen to model protein sequencing data generated from MS-MS experimentation. Additional data formats for only protein and peptide data, such as protXML (Seattle Proteome Center), exist for purpose driven representation of protein MS experiments.

mzML
Resultant of the HUPO-Proteomics Standards Initiative's continuing progress towards standardization of proteomics data, specifically Mass Spectroscopy data, led to the development of a new unifying Mass Spectra data format called mzML. Simply put this data format attempts to combine the existing standards for both Mass Spectrometry experimental design as well as the resulting peak information.

Normalization Approaches for Removing Systematic Biases Associated with Mass Spectrometry and Label-Free Proteomics
Stephen J. Callister et al. J Proteome Res. Volume: 5(2) 227-286 (2006)

Main Focus

The focus is to attempt to find a technique for statistically normalizing MS data so that it can be meaningfully analyzed.

Summary

When looking at Mass Spectrometry (MS) data, the results can never be replicated, even when running replicate samples. Thus, there needs to be a way to make the results comparable. Since the difference in results can be related to bias and noise in addition to biological change, causing extraneous variability, normalization techniques are necessary. Four different normalization techniques commonly used in Microarray Analysis (central tendency, linear regression, locally weighted regression, and quantile techniques) were tested on three different sample sets (standard proteins, an early-log growth phase D. radiodurans sample and a stationary growth phase sample, and a striata brain sample of a control mouse and a striata brain sample of a methamphetamine-stressed mouse), which showed different levels of protein complexity.

Although all of the techniques did reduce systematic bias at least somewhat, because there was no definitive trend among the results obtained by the different techniques, none of these can yet be used for normalizing MS data; however, this study provides guidance for developing proper normalization techniques.

New Terms

 * Bias: variation arising from systematic errors in the experiment, sample preparation, or instrumentation (http://www.onesmartclick.com/exams/statistics-bias.html)
 * Noise: variation arising from random errors in the experiment, sample preparation, or instrumentation
 * Central tendency: this technique centers the abundance of a peptide around the mean or other constant to adjust for independent systematic bias (http://cnx.org/content/m10942/latest/)
 * Local Regression: technique that assumes that the systematic bias is not linearly dependent on the abundance of the peptides (http://www.biostat.jhsph.edu/~ririzarr/Teaching/754/section-03.pdf)
 * Quantile: nonparametric statistical approach which was originally designed for use with multiple high density arrays (http://mathworld.wolfram.com/Quantile.html)

Course Relevance

Mass spectrometry (MS) is one of the main tools of Proteomics, as it provides a way to obtain measurements of the abundance of individual proteins in the sample. Since the goal of Proteomics is to obtain measurements of the abundance of the different expressed proteins in different conditions, MS is a powerful tool.

Advances in Proteomics Data Analysis and Display Using an Accurate Mass Time Tag Approach
Zimmer JSD, Monroe ME, Qian WJ, Smith RD Mass Spectrom Rev. 25(3):450-482 (2006)

Main Focus

Recent advances in proteomic technologies have provided tools which provide both high efficiency and high-throughput proteomic analysis. These tools, specifically nanoLC-FTICR-MS and the necessary data processing and management tools, are the focus of this article.

Summary

While the field of proteomics is relatively new techniques have existed for 30 years which can now be applied to these new proteomic problems. These techniques, such as Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), provides both high sensitivity and high mass measurement accuracy (MMA) necessary for identification of species as well as a wide dynamic range. FTICR-MS is also well suited for both “top-down” and “bottom-up” proteomics in so much as it can determine protein/peptide identities from parent masses as well as based upon fragmented patterns, which can deal with very complex mixtures of peptides. In addition to the difficulties in the production of high throughput techniques, problems also exist in the data management of the collected data. Results from a single experiment using high performance FTICR-MS can typically produce a 10GB raw data file, much too large for storage of any significant volume. Using techniques which take advantage of low statistical probabilities existing that new species detected in a sample will be detected in additional analyses of the same system. Using clustering in n-dimensional space using the Euclidean distance sets of unique mass classes can be produced reducing redundancy. Clustering is necessary due to the unreliable reproduction of data from LC-MS. Typically shifts in elution time occur and differ between multiple runs of the same peptides, where one would expect the same result. These shifts are the result of flow rate, temperature, differences in the packing of the columns as well as contamination. These same problems can also exacerbate the process of normalization of the data sets. Sample preparation is a key step in the proteomic analysis and can be quite difficult. Protein concentrations can vary greatly with only slight environmental changes. Many such techniques have been developed, such as solid phase isotope-coded affinity tags (SPICAT) for quantitative analysis, post-digestion trypsin-catalyzed 16O/18O labeling which has its advantages in that all types of samples can be labeled in the manner, quantitative cysteinyl-peptide enrichment technology (QCET), for use in high throughput experiments involving mammalian cells. Each of these techniques has their own limitations and steps are being taken to overcome these limitations. Additionally methods have been developed to expand the dynamic range and minimize noise created by abundant species of a sample. Dynamic range enhancement applied to mass spectrometry (DREAMS) aids in the detection of species which are both biologically significant and have low relative abundance on the fly. Targeted, or data-directed, LC-MS/MS can focus on a subset of proteins that have significant changes in abundance between two samples, and multiplexed MS/MS provides a way to increase the rate and sensitivity of proteomic measurements. These techniques have a wide range of applications already being thoroughly researched as well as adapt current techniques to better understand the biological processes which require the presence of these proteins or changes in their relative abundance.

New Terms

 * Euclidean distance: The straight line distance between two points. In a plane with p1 at (x1, y1) and p2 at (x2, y2), it is √((x1 - x2)² + (y1 - y2)²). ( http://www.itl.nist.gov/div897/sqg/dads/HTML/euclidndstnc.html )
 * Normalization: The process of identifying and removing the systematic effects. ( http://www.absoluteastronomy.com/topics/Normalization_(statistics) )
 * Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS): A type of mass analyzer (or mass spectrometer) for determining the mass-to-charge ratio (m/z) of ions based on the cyclotron frequency of the ions in a fixed magnetic field. (http://www.ncbi.nlm.nih.gov/pubmed/9768511?dopt=Abstract)
 * Elution: the process of extracting one material from another by washing with a solvent to remove adsorbed material from an adsorbent. (http://wordnetweb.princeton.edu/perl/webwn?s=elution)
 * Clustering: The goal of data clustering, or unsupervised learning, is to discover "natural" groupings in a set of patterns, points, or objects, without prior knowledge of any class labels. (http://dataclustering.cse.msu.edu/)

Course Relevance

 * Techniques which are both highly efficient and high-throughput are necessary for analysis of large data sets, such as the proteome. Without these tools analysis of the proteome would be too slow.

Classification of Premalignant Pancreatic Cancer Mass-Spectrometry Data Using Decision Tree Ensembles
[http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2440392&tool=pmcentrez Ge G, Wong GW. BMC Bioinformatics 9:275 (2008)]

Main Focus

To compare the performance of several machine learning algorithms based on decision tree, Ge et al. conducted a series of statistical analyses on mass spectral data, obtained from a premalignant pancreatic cancer research. It was found that the classifier ensemble techniques outperformed their single algorithm counterparts in terms of the consistency in identifying the cancer biomarkers and the accuracy in the classification of the data.

Summary

In this article, Ge et al. reported that the performance in identifying biomarkers for premalignant pancreatic cancer could be enhanced by using the decision tree ensemble techniques instead of a single algorithm counterpart. These techniques had proven more likely to accurately distinguish disease class from normal class as indicated by a larger area under the Receiver Operating Characteristic curve. Moreover, they achieved comparatively lower root mean squared errors.

According to their method, the peptide mass-spectrometry data were processed first to improve data integrity and reduce variations among data due to the differences in sample loading conditions. The preprocessing steps involved baseline adjustment using group median, smoothing to remove noise using a Gaussian kernel, and normalization to make all the data comparable. After that, the data were randomly sampled such that 90% formed a training set and the remaining 10% formed a test set.

The training set was used in feature selection. In the study, the authors considered three different feature selection methods. The first method was a two-sample homoscedastic t test, which was used under the assumption that all the features from either normal or disease class had normal distribution. Unlike the first method, the second method based on Wilcoxon rank test considered that the features had no distribution. The last feature selection method was a genetic algorithm.

The test set was used to generate a single decision tree including the decision tree ensembles. The ensemble methods being studied were Random Forest, Random Tree, Bagging, Logitboost, Stacking, Adaboost, and Multiboost. Their performances were measured in terms of accuracy and error in the classification of the features, selected by each selection method. Then, they were compared against the performance of a single decision tree generated by C4.5 algorithm. The process repeated ten times to validate the resulting performance consistency.

According to the results reported, the decision tree ensembles achieved higher accuracy up to 70% regardless of the feature selection methods used. In terms of biomarker identification, both the t test and the Wilcoxon rank test had similarly impressive performance by consistently selecting the same biomarker-suspect features. Unlike the first two methods, the performance of the genetic algorithm was considerably poor. Ge et al. also noted that 70% accuracy was still lower than expected. This could be as a result from a naturally low concentration of the biomarkers at the premalignant stage of the cancer. In addition, it was also possible that one dataset might not be suitable for all algorithms, thus underestimating the accuracy.

New Terms

 * Biomarker: Bio-molecules that can be used to distinguish an abnormal from normal process, or a disease from condition. They can also be used as an indicator for a particular process such as drug interaction.  These bio-molecules are usually found in blood, other body fluids, or tissues ( http://www.cancer.gov/dictionary/?searchTxt=biomarker )
 * Receiver Operating Characteristic curve: A graphical plot of the sensitivity vs. 1-specificity for a binary classifier system for different thresholds ( http://en.wikipedia.org/wiki/Receiver_operating_characteristic )
 * Gaussian kernel smoothing: A process of averaging the data points by applying a Gaussian function. Basically, the Gaussian function is used to generate a set of normalized weighting coefficients for the data points whose weighted sum generates a new value.  This new value replaces the old one at the center of Gaussian curve. ( http://imaging.mrc-cbu.cam.ac.uk/imaging/PrinciplesSmoothing )
 * Homoscedastic: A sequence or a vector of random variables that have the same finite variance for all random variables ( http://en.wikipedia.org/wiki/Homoscedasticity )
 * C4.5 algorithm: An algorithm used to generate a decision tree from a set of training data (a set of classified samples) ( http://en.wikipedia.org/wiki/C4.5_algorithm )

Course Relevance

 * The feature selection methods and decision tree ensembles introduced in this article contribute an interesting methodology for data analysis to the mass spectrometry topic in proteomics class.

Significance Analysis of Microarray for Relative Quantitation of LC/MS Data in Proteomics
[http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2335280 Li Q, Roxas BAP. "Significance Analysis of Microarray for Relative Quantitation of LC/MS Data in Proteomics" BMC Bioinformatics 9:187 (2008)]6

Main Focus

The Significance Analysis of Microarray (SAM) analysis method which is normally used in the analysis of DNA microarrays to identify differential gene expression may also be used to identify differential protein expression. This analysis method can identify false positive results more accuretly than the conventional test generally used for this purpose.

Summary

The Significance Analysis of Microarray (SAM) analysis method was developed by researchers at the Stanford University to analyze genes in microarrays in order to identify genes that are differentially expressed (statistically) and obtain accurate false discovery rate statistics. In "Significance Analysis of Microarray for Relative Quantitation of LC/MS Data in Proteomics" (Li et al.) the SAM method is applied to proteomics data retrieved from nanoLC/LTQ/FTMS in order to determine differential expression for the proteins in a biological sample. SAM was also used to determine and estimate of rates of false discovery as well as miss rates. SAM results were them compared to results obtained from more tradition proteomics analysis tools such as the conventions t-test and by fold change. The biological system that was used to test the analysis techniques involved growing mycobacterium smegmatis cultures at pH5 and pH7, and looking for differences in protein expression under these two conditions. They compared protein abundance and focused on changes in protein expression and rates for false positives. The analysis appears to reveal that the SAM method can "zero in" on false positives more accurately than the t-test, making it the more accurate test, and allowing protein changes to be identified with a 5% false positive rate. The chart below contains data showing that a larger number of differentially expressed proteins were found using SAM, while maintaining a low false positive rate.

New Terms

 * nanoLC/LTQ-FTMS: This stands for nanoliquid chromatography/linear ion trap-fourier transform mass spectrometry. This type of Mass spectrometry analysis combines the high quality and reproducible data obtain from nanoLC with the power of a linear ion trap quadrapole, and accuracy of Fourier transform analysis.  This is a very powerful proteomics tool. (https://products.appliedbiosystems.com/ab/en/US/adirect/ab?cmd=catNavigate2&catID=601452&tab=DetailInfo)
 * t- test: A statistical test involving means of normal populations with unknown standard deviations; small samples are used, based on a variable t equal to the difference between the mean of the sample and the mean of the population divided by a result obtained by dividing the standard deviation of the sample by the square root of the number of individuals in the sample. ( http://www.answers.com/topic/t-test)
 * DNA microarray: A multiple assay procedure used in molecular biology. Thousands of short pieces of DNA call oglioneucleotides are placed on microscopic spots where they can then be exposed to probes that may or may not bind to them depending on sequence. They can be used to measure changes in DNA expression.  (http://en.wikipedia.org/wiki/DNA_microarray)
 * Protein expression: A measurement of which proteins in a biological system have been translated and are thus present in a cell. This includes post-translationally modified proteins. ( http://www1.qiagen.com/about/Press/Glossary.aspx)
 * Biomarkers: Proteins found to have a biological significance for relevant biological conditions such as diseases. Biomarkers are highly important in drug discovery. (http://www1.qiagen.com/about/Press/Glossary.aspx)

Course Relevance

 * The Utilization of the Significance Analysis of Microarray (SAM) analysis method in proteomic interpretation of data is a novel approach to solving the accuracy problems of LC data analysis. If false positive results can be identified accurately,  then researchers could focus on the proteins that have been correctly characterized as changing in expression between two sets of conditions.  This would allow for biomarkers to be found more readily.

Significance Analysis of Microarrays Applied to the Ionizing Radiation Response
Tusher VG., Tibshirani, R., Chu G., Significance Analysis of Microarrays Applied to the Ionizing Radiation Response, Proceedings of the National Academy of Science 98:5116-5121 (2001)3

Main Focus

The SAM analytical method is discussed as well as it validity for a given data set. The superiority of the analysis method over other methods is also discussed.

Summary

DNA Microarrays are capable of measuring the expression of thousands of genes within a single experiment. They are often used to identify any changes in expression of genes under a number of different conditions. Given that these experiments generate massive amounts of data, systems must be developed to analyze the data that is generated for any experimental significance. The authors of "Significance Analysis of Microarrays applied to the Ionizing Radiation Response" described a method for doing this known as SAM. This method provides a score for each gene based on its change in gene expression as compared to the standard deviation of repeat measurements. The system also provides an estimate for the false discovery rate. In the paper, the authors explain the algorithm behind how the SAM method arrives at these scores as well as the math behind determining the false discovery rate. SAM was tested on a data set that was collected by the authors and its validity was checked by Northern blots. The authors discuss SAM and its superiority over other methods of identifying experimental significance. These methods include identifying genes as having been significantly changed, if an R-fold change is observed, and declaring a significant change in expression based on whether or not an R-fold change was seen consistently between paired samples. SAM was shown to be superior to the other methods discussed3.

The algorithm behind SAM calculates the relative difference in gene expression based on permutation analysis of expression data and provides a score for these changes. It also calculates a false discovery rate. The algorithm used to calculate the score for each gene based on its change (if any) in gene expression is shown below:

New Terms

 * SAM: Significance Analysis of Microarray; A statistical analysis for determining whether or not changes in gene expression are experimentally significant. ( http://en.wikipedia.org/wiki/Significance_analysis_of_microarrays )
 * False Discovery Rate (FDR): The percentage of genes incorrectly identified by chance. ( http://www-stat.stanford.edu/~tibs/SAM/pnassam.pdf )
 * DNA Microarray: DNA Microarrays are used in both molecular biology and medicine. They are made of a series of thousands of spots of DNA oligonucleotides arranged in a highly ordered fashion and can be used to measure changes in expression levels, to detect single nucleotide polymorphisms, and/or to resequence mutant genes. ( http://en.wikipedia.org/wiki/DNA_microarray )
 * Northern Blot: The northern blot is a technique typically used in microbiology that allows for the studying of gene expression by looking at RNA or isolated mRNA in the sample of interest. The technique allows for the observation of cellular control over structure and function by determining the particular gene expression levels during differentiation, morphogenesis, as well as abnormal or diseased conditions. ( http://en.wikipedia.org/wiki/Northern_blot )
 * Ionizing: The process of converting an atom or molecule into an ion by adding or removing charged particles. ( http://en.wikipedia.org/wiki/Ionization )
 * Ion: An atom or molecule that has either a positive or negative charge as a result of the addition or removal of an electron. ( http://en.wikipedia.org/wiki/Ion )
 * R-fold Change: This is a numerical values that evaluates the difference of group means. ( http://strimmerlab.org/software/st/html/diffmean.stat.html )

Course Relevance

 * This method, which has previously been used to analyze DNA microarrays, is now being applied to proteomics data.

Mass Spectrometry Data Mining for Early Diagnosis and Prognosis of Stroke
Kalousis A. http://cui.unige.ch/AI-group/research/massspectrometry/massspectrometry.htm (28 March 2009)

Main Focus

The author describes the necessary signal conditioning steps in mass spectrometry spectral analysis before proteomics data are used in the classification of biomarkers.

Summary

From this website, the author describes the signal conditioning steps used to improve the integrity of mass spectrometry data and to reduce their high dimensionality. These steps include the uses of statistical techniques to exclude the influence of processing matrix, remove noises, minimize a data variation due to experiment conditions, and reduce the redundancy in the dataset.

Baseline removal was applied first to remove the data offset caused by the processing matrix usually used in the crystallization of protein sample for mass spectrometry. According to the website, the weighted quadratic fitting was used in this step. However, not all the effects of the matrix could be removed. Some remained and contributed in a form of noise in addition to the electrical noise from the machine itself. To alleviate the influence of noise, the denoising and smoothing techniques were used. The author reported that the wavelet decomposition together with a median filter was applied for this purpose. The spectral data was then normalized to dissociate the data from the experiment conditions. This process helped reduce a variation among data.

In the next step, a peak detection technique was used to remove the spatial redundancy of the data. Conceptually, it selected a mass-to-charge ratio value at the peak intensity in a specified range between two adjacent minima to represent the data in that range. Then, the selected spectral data were calibrated such that the data possessed the same spectral characteristic were clustered together. These clusters were referred to as the selected features, which were used by a machine learning algorithm in the classification of biomarkers.

New Terms

 * Baseline removal (or baseline fitting): A method of removing an offset segment of spectral data so that the data can be further analyzed on the same basis. In general, the technique is to fit a curve to that offset segment and then subtract the values along the curve from the original data.  As a result, the new spectral data will have a flat base. ( http://www.gb.nrao.edu/~rmaddale/140ft/unipops/unipops_7.html )
 * Weighted quadratic fitting: A curve fitting technique that uses a quadratic function to fit the weighted version of the original data. Usually, the weighting factor is derived from the error incurred from the regular quadratic fit.  By accounting for this error, a better fit can be obtained. ( http://class.phys.psu.edu/p559/experiments/html/error.html )
 * (Orthogonal) wavelet decomposition: A signal analysis technique that uses a particular basis function, which is finite, to fit the signal and generates a series of time-varying coefficients. The decomposition yields a set of orthogonal signals.  Each reflects a local change in the original signal at a given time. ( http://www.tideman.co.nz/Salalah/OrthWaveDecomp.html )
 * Median filter: A method of removing noise or data that has a distinctively large standard deviation for a given set of data. Consider a window of 2n + 1 data points.  The median filter replaces the (n + 1)st data point with a median from this window.  If the (n + 1)st data appears to be an out-of-range noise, it will be removed by this process. ( http://fourier.eng.hmc.edu/e161/lectures/smooth_sharpen/node3.html )
 * Machine learning algorithm: A sequence of instructions used by a computer such that the computer can adaptively improve its computational performance and efficiency in predicting the outcome based on the database it is collecting. ( http://en.wikipedia.org/wiki/Machine_learning )

Course Relevance

 * This website addresses the problems associated with the data obtained from mass spectrometry and how to overcome those problems. It points out why the mass spectral data needs to be processed and how to do it before the data can be used in the analysis.  This introduces another important aspect to the study of mass spectrometry in proteomics class.

Stanford School of Medicine Proteomics Center: Chu: Tibshirani Groups
Property of: Stanford University (3/29/09)

Main Focus

Gilbert Chu, M.D., Ph.D. and Robert Tibshirani, Ph.D. are attempting to create protein analysis tools/programs that will incorporated variable such as protein levels and post-translational modifications, and protein interactions in order to obtain more usable information from proteomic data.

Summary

This site offers information about the proteomic analysis research conducted by Gilbert Chu, M.D., Ph.D. and Robert Tibshirani, Ph.D. The site includes publications by Chu and Tibshirani (books and journal articles), as well as laboratory profiles for both professors. The overview provides concise information about the challenges and benefits of separating and analyzing proteomic data, and then merging it with gene expression data in order to obtain new information about biological systems. The software section on the website provides access to four different types of proteomic analysis software including Significance Analysis of Microarray(SAM), Prediction Analysis for Microarrays (PAM), laboratory statistical analysis for microarrays, and Peak Probability Contrast (PPC). The site also provides biographical background on the two professors as well as current research interests they are pursuing. These interest include elucidating a way to diagnose autoimmune diseases (such as systemic lupus erythematosus)from the analysis of proteins expressed in serum blood samples.

New Terms

 * PPC: Peak Probability Contrast - This is a class prediction software for protein MS data. It does this by obtaining a list of significant peaks where each class has an intensity level assigned to it. It can compare and contrast the intensity levels of the data to predict class and generate a false positive rate.  Either raw spectra or extracted peaks can be input into this program.( http://proteomics.stanford.edu/chu/software.html )
 * PAM: Prediction Analysis for Microarrays - This is a program that performs sample classification from antibody reactivity data to provide a list of significant genes whose expression characterizes each sample group. The software can work with either cDNA or oligo microarrays. ( http://proteomics.stanford.edu/chu/software.html )
 * cDNA: complementary DNA - This is a single-stranded DNA that is reverse transcribed via the enzyme reverse transcriptase using a messenger RNA template. It is complimentary to the mRNA. (http://en.wikipedia.org/wiki/Complementary_DNA)
 * Autoimmune disease: A medical condition that occurs when the body's immune system mistakes its own normal tissue for foreign tissue and therefore launches an immune response attacking the tissue. There are over 80 known autoimmune disorders. (http://www.medterms.com/script/main/art.asp?articlekey=2402)
 * SLE: Systemic Lupus Erythematosus - This is an autoimmune disease more generally known as Lupus. The autoimmune response creates inflammation (both acute and chronic) in various tissues including heart, joints, skin, lungs, blood vessels, liver, kidneys, and nervous system.  Most fatalities are due to renal failure and the 10yr survival rate is 80%. (http://www.medicinenet.com/systemic_lupus/article.htm)

Course Relevance

 * The site provides information and access for multiple analysis software programs that may be used to analyze proteomic data

Introduction to FTICR/MS Technology
Wang B INTRODUCTION TO FTICR/MS TECHNOLOGY 2001

Main Focus

A brief introduction to the parts of a FTICR/MS machine and how it differs from other forms of MS.

Summary

Fourier transform mass spectrometry is a technique that has gained attention due to its ability to provide mass and resolution information from bio-molecules at levels that are higher than many forms of mass spectrometry. As an ion trapping form of mass spectroscapy it is unique in its technique on collecting these ions, it is for this reason that it is able to measure with far greater accuracy than other forms of mass spec. While the primary ideas for FTICR were first developed in the 1930s it was not until the late 1970s when Fourier transform techniques were adapted for ICR techniques. Since that time this technique has quickly increased in popularity and availability. While there are many types of FTMS machines they all contain four basic parts, which are necessary for their function. Primarily there is a magnet, which has a profound effect on the performance of the machine with stronger magnets yielding better results. It is for this reason that there is a constant movement toward stronger and stronger magnets. Secondly there is a cell that is used to store ions and is where they are detected and analyzed. Two types of cells can be utilized, cubic cells, composed of six magnetic plates arranged in a cube shape, hence its name, and open-ended cylindrical cells that perform in a manner similar to the cubic cells but utilizes six electrodes instead. The third feature is a vacuum system, a requirement to all mass spectrometers, but which is particularly important to the proper and accurate function to FTMS instruments, however this vacuum is only necessary when ions are detected in the trap. The final part of the system is the data system. The several components of this system, including a computer to monitor and analyze all of the other components and data have all improved in quality over the past ten years while their computer performance has also increased.

New Terms

 * ion trap: The ability to trap single to millions of atomic ions in good isolation from the outside world for a long period of time (http://jilawww.colorado.edu/pubs/thesis/king/ch2.pdf)

Course Relevance

 * FTICR/MS is a popular tool in the feild of proteomics due to its ability to accurately identify peptide and protein products.

SAM: Significance Analysis of Microarrays
Property of: Stanford University (3/29/09)

Main Focus

This website provides information specifically about the SAM program developed at Standford University. It includes links to downloading and using the program as well as information regarding its background and algorithm.

Summary

This website is specifically dedicated to the SAM program. It includes information about how the program works (the algorithms behind it) as well as how it differs from other analysis programs. It provides a link to the instruction manual. They give a list of features for the program as well as links to other extras that may be used alongside it (such as an Excel add-in). A FAQ section is also available to answer the most popular questions. There are links to download the SAM program in addition to information about how to get licensing (if it is necessary). Most users can download the program simply by registering at the appropriate site (http://www-stat-class.stanford.edu/~tibs/clickwrap/sam.html), however, if SAM is going to be for commercial use, more formal licensing is required. Additionally, the site keeps everyone up to date about any changes they make to SAM as well as when new versions come out.

New Terms

 * PAM: Prediction Analysis of Microarrays; Another system available through the SAM website that is used for class prediction and survival analysis for gene expression as well as data mining. The system performs sample classification from gene expression data. ( http://www-stat.stanford.edu/~tibs/PAM/index.html )
 * Algorithm: A finite sequence of instructions usually in the form of an explicit, step-by-step procedure for solving a problem. It is often used for calculation and data processing. ( http://en.wikipedia.org/wiki/Algorithm )
 * Two Class (Unpaired) Groups: Two sets of measurements, in which the experiment units are all different in the two groups. ( http://www-stat.stanford.edu/~tibs/SAM/sam.pdf )
 * Multiclass: There are more than two groups, each containing different experimental units. ( http://www-stat.stanford.edu/~tibs/SAM/sam.pdf )
 * Normalization: This is a way of systematically making sure that a database is able to carry out general-purpose queries and that these queries don't result in undesirable results by either insertion, updating, or deletion of any anomalies that can results in loss of the quality of the data. ( http://en.wikipedia.org/wiki/Database_normalization )

Course Relevance

The site offers further information about the SAM program, which is now being utilized in proteomics studies. It also provides access to downloading SAM.

An (Opinionated) Guide to Microarray Data Analysis
http://www.bea.ki.se/staff/reimers/Web.Pages/Microarray.Home.htm (3/29/09)

Main Focus

Overall, this website provides information about common problems in the analysis of microarray data and presents suggestions on how to resolve these problems. Some of these solutions take the form of normalization techniques.

Summary

The website is organized into eleven total sections—Experimental design, Distributions and transforms, Approaches to Normalization, Quality Control of spotted arrays, Normalization of spotted arrays, Quality control, Normalization, Estimates of Abundance (methods for combining data from multiple probes to get single estimates), Graphics, Clustering, and Statistical Significance—that explain some of the different techniques people use to solve some of the problems that arise when analyzing microarray data. The first three sections talk about problems that arise in all types of microarray analysis. The website gives a brief explanation of the concepts being covered and then proceeds to talk about their strong and weak points. The next five sections then talk about these concepts specifically for two-color cDNA spotted microarrays and for the Affymetrix arrays respectively. In the Quality Control sections the website goes over some basic controls that should be kept in mind before running the experiment just to cut down on systematic bias. While normalization of the data will account for some of the bias, it won't necessarily catch everything, so it is best to avoid introducing bias if possible. The last section discusses different statistical methods that are appropriate for microarray data and the other two sections discuss different ways that the data is then visualized for human analysis.

New Terms

 * p-value: probability that given the null hypothesis is true, the results obtained from the experiment make sense (http://www.childrensmercy.org/stats/definitions/pvalue.htm)
 * Bonferroni correction: a method that is used to keep results arising from multiple comparison tests from being incorrectly deemed statistically significant (http://www.utdallas.edu/~herve/Abdi-Bonferroni2007-pretty.pdf)
 * Sidak correction: another correction method that is less stringent than the Bonferroni correction allowing a greater chance for something to be considered statistically significant (http://www.utdallas.edu/~herve/Abdi-Bonferroni2007-pretty.pdf)
 * Lowess curve (locally weighted linear regression curve): a smooth curve that's plotted through the points and is calculated by using locally weighted linear regression over the values (http://www.itl.nist.gov/div898/software/dataplot/refman1/ch3/lowess_s.pdf)
 * t-test: a statistical hypothesis test that follows the Student's t distribution if the null hypothesis is true (http://www.socialresearchmethods.net/kb/stat_t.php)

Course Relevance

Most of the normalization techniques used for Proteomics were first found useful for microarray data, so learning about problems that arise in microarray data analysis can be useful when doing experimental design.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
Proteomics/Protein Identification - Mass Spectrometry/References

 * 1) Instrumentation
 * 2) Types of Mass Spectrometry
 * 3) Data Analysis/ Interpretation
 * 4) Applications for mass spectrometry
 * 5) Databases
 * 6) References

=References= -F. Hillenkamp, M. Karas, R. C. Beavis, B. T. Chait, Anal. Chem., 1991, 63, 1193

-P. Roepstorrf, J. Fohlmann, Biomed. Mass Spectrom., 1984, 11, 601; R. S. Johnson, K. Biemann, Biomed. Environ. Mass Spectrom., 1989, 18, 945

-Sysoev A, Adamov A, Viidanoja J, Ketola RA, Kostiainen R, Kotiaho T. Development of an ion mobility spectrometer for use in an atmospheric pressure ionization ion mobility spectrometer/mass spectrometer instrument for fast screening analysis. 2004 Rapid Commun Mass Spectrom;18(24):3131-9.

-McLafferty, F. W. and Turecek, F., Interpretation of Mass Spectra, University Science Books; 4th edition (May, 1993) ISBN 0935702253

-Merenbloom, S.I., Koeniger, S.L., Valentine, S.J., Plasencia, M.D., and Clemmer, D.E.; IMS-IMS and IMS-IMS-IMS/MS for Separating Peptide and Protein Fragment Ions; Anal. Chem., 78, 8, 2802 - 2809, 2006

-Owens, K. G.; How well do we REALLY understand MALDI?; 4th Annual NIST Polymer MS Workshop, November 9, 2005, Retrieved from http://polymers.msel.nist.gov/maldirecipes/PDF/Owens.pdf


text:
User:Clbyard

Welcome to my Wikibooks site. Hopefully, you will see updates on here from time to time. Keep checking back for more information! --Clbyard 06:25, 16 April 2006 (UTC)

title: User:Clbyard


text:
User:Aquafire~enwikibooks

I'm a Chinese student who would also like to help contribute to others' learning of the language.

title: User:Aquafire~enwikibooks


text:
Windows Vista

This book is dedicated to those who loved Windows Vista despite its reputation.

Front matter

 * /Introduction/
 * New features
 * Edition comparison
 * Activation

Interface

 * Installation
 * Aero
 * The Start menu
 * /Windows Explorer/
 * Special Folders
 * Filesystem structure and conventions

Programs

 * /Programs/
 * Windows Mail

Authors & Contributors

 * /Authors & Contributors/

title: Windows Vista


text:
User talk:69.163.240.190

Your edit at Votes for Deletion
I took liberty of moving and modifying your apparent vote at Votes for Deletion, see. I result was to change your comment on a non-existent book named "Keep" to a vote to keep the book How to Become a Christian. I did this because that seemed to be your intent. If I misunderstood your intent, please feel free to make changes as necessary.

I also added the template which results in:

—The preceding unsigned comment was added by 69.163.240.190 (talk • contribs).

For that change see. Please sign your votes with --~. --JMRyan T E C 12:47, 16 April 2006 (UTC)

title: User talk:69.163.240.190


text:
Book of Magic/Coin Magic

Palming
Palming is a technique used in the performance of magic, wherein the performer secretly conceals one or more objects in their hand. Its use is prevalent in the field of coin magic.

The graphic below shows a human hand. The point at which the hand/fingers bend, are potential palming locations. In addition to the fingers and their respective joints, items are also palmed using the muscles in the palm of the hand.

A technique often referred to as "edge palming" can be achieved by holding a coin on its edge. There are also two areas between the bends that can hold coins in standard palm position. Palming is an art of hiding necessary items without the knowledge of others it can be used in most sleight of hand. Want to try? First, place a coin in the center of your palm and try to hold in by shrinking your fingers. Avoid folding your fingers at first, as doing so would make it obvious that you are holding something in your hand.

But... after a little practice you will be able to keep a neutral positioned palm and still can keep coins small balls inside the palm even without your knowledge,, I tried to keep coins in my hand for six to seven hours a day with out anybody knows even typing this I have  a One dhs coin in my hand.

title: Book of Magic/Coin Magic


text:
Puzzles/Logic puzzles/False Execution/Hint 1

Puzzles | Logic puzzles | False Execution | Hint 1

It's important that the judge said "you will get to know" instead of "you will be told".


text:
User:Joecool94~enwikibooks

Hi

title: User:Joecool94~enwikibooks


text:
Java Programming/Classes, Objects and Types

An object is composed of fields and methods. The fields, also called data members, characteristics, attributes, or properties, describe the state of the object. The methods generally describe the actions associated with a particular object. Think of an object as a noun, its fields as adjectives describing that noun, and its methods as the verbs that can be performed by or on that noun.

For example, a sports car is an object. Some of its fields might be its height, weight, acceleration, and speed. An object's fields just hold data about that object. Some of the methods of the sports car could be "drive", "park", "race", etc. The methods really don't mean much unless associated with the sports car, and the same goes for the fields.

The blueprint that lets us build our sports car object is called a class. A class doesn't tell us how fast our sports car goes, or what color it is, but it does tell us that our sports car will have a field representing speed and color, and that they will be say, a number and a word (or hex color code), respectively. The class also lays out the methods for us, telling the car how to park and drive, but these methods can't take any action with just the blueprint — they need an object to have an effect.

In Java, a class is located in a file similar to its own name. If you want to have a class called, its source file needs to be. The class is created by placing the following in the source file:

The class doesn't do anything yet, as you will need to add methods and field variables first.

The objects are different from the primitive types because:
 * 1) The primitive types are not instantiated.
 * 2) In the memory, for a primitive type only its value is stored. For an object, also a reference to an instance can be stored.
 * 3) In the memory, the allocated space of a primitive type is fixed, whatever their value. The allocated space of an object can vary, for instance either the object is instantiated or not.
 * 4) The primitive types don't have methods callable on them.
 * 5) A primitive type can't be inherited.

Instantiation and constructors
In order to get from class to object, we "build" our object by instantiation. Instantiation simply means to create an instance of a class. Instance and object are very similar terms and are sometimes interchangeable, but remember that an instance refers to a specific object, which was created from a class.

This instantiation is brought about by one of the class's methods, called a constructor. As its name implies, a constructor builds the object based on the blueprint. Behind the scenes, this means that computer memory is being allocated for the instance, and values are being assigned to the data members.

In general there are four constructor types: default, non-default, copy, and cloning.

A default constructor will build the most basic instance. Generally, this means assigning all the fields values like null, zero, or an empty string. Nothing would stop you, however, from setting the color of your default sports car color to red, but this is generally bad programming style. Another programmer would be confused if your basic car came out red instead of say, colorless.

A non-default constructor is designed to create an object instance with prescribed values for most, if not all, of the object's fields. The car is red, goes from 0-60 in 12 seconds, tops out at 190mph, etc.

A copy constructor is not included in the Java language, however one can easily create a constructor that does the same as a copy constructor. It's important to understand what it is. As the name implies, a copy constructor creates a new instance to be a duplicate of an already existing one. In Java, this can be also accomplished by creating the instance with the default constructor, and then using the assignment operator to equivocate them. This is not possible in all languages though, so just keep the terminology under your belt.

Java has the concept of cloning an object, and the end results are similar to the copy constructor. Cloning an object is faster than creation with the keyword, because all the object memory is copied at once to the destination cloned object. This is possible by implementing the  interface, which allows the method   to perform a field-by-field copy.

Type
When an object is created, a reference to the object is also created. The object can not be accessed directly in Java, only through this object reference. This object reference has a type assigned to it. We need this type when passing the object reference to a method as a parameter. Java does strong type checking.

Type is basically a list of features/operations, that can be performed through that object reference. The object reference type is basically a contract that guarantees that those operations will be there at run time.

When a car is created, it comes with a list of features/operations listed in the user manual that guarantees that those will be there when the car is used.

When you create an object from a class by default its type is the same as its class. It means that all the features/operations the class defined are there and available, and can be used. See below:

You can assign this to a variable having the same type as the class:

You can assign the created object reference to the class, super class, or to an interface the class implements:

In the car analogy, the created car may have different Types of drivers. We create separate user manuals for them, an Average user manual, a Power user manual, a Child user manual, or a Handicapped user manual. Each type of user manual describes only those features/operations appropriate for the type of driver. For instance, the Power driver may have additional gears to switch to higher speeds, that are not available to other type of users...

When the car key is passed from an adult to a child we are replacing the user manuals, that is called Type Casting.

In Java, casts can occur in three ways:
 * up casting going up in the inheritance tree, until we reach the
 * up casting to an interface the class implements
 * down casting until we reach the class the object was created from

Autoboxing/unboxing
Autoboxing and unboxing, language features since Java 1.5, make the programmer's life much easier when it comes to working with the primitive wrapper types. Consider this code fragment:

Primitive wrapper objects were Java's way of allowing one to treat primitive data types as though they were objects. Consequently, one was expected to wrap one's primitive data type with the corresponding primitive wrapper object, as shown above.

Since Java 1.5, one may write as below and the compiler will automatically create the wrap object. The extra step of wrapping the primitive is no longer required. It has been automatically boxed up on your behalf:

Each primitive type has a class wrapper:

Unboxing uses the same process in reverse. Study the following code for a moment. The statement requires a  primitive value, yet it was given a Boolean wrapper object. No problem! Java 1.5 will automatically unbox this.

Question 3.11: Consider the following code:

How many autoboxings and unboxings are there in this code?

3
 * 1 autoboxing at line 1 to assign.
 * 1 unboxing at line 2 to do the addition.
 * 1 autoboxing at line 2 to assign.
 * No autoboxing nor unboxing at line 3 as  supports the   class as parameter.

Methods in the class
Methods in the  class are inherited, and thus shared in common by all classes.

The method
The  method returns a new object that is a copy of the current object. Classes must implement the marker interface  to indicate that they can be cloned.

The method
The  method compares the object to another object and returns a   result indicating if the two objects are equal. Semantically, this method compares the contents of the objects whereas the equality comparison operator " " compares the object references. The  method is used by many of the data structure classes in the   package. Some of these data structure classes also rely on the  method—see the   method for details on the contract between   and. Implementing equals isn't always as easy as it seems, see 'Secrets of equals' for more information.

The method
The  method is called exactly once before the garbage collector frees the memory for object. A class overrides  to perform any clean up that must be performed before an object is reclaimed. Most objects do not need to override.

There is no guarantee when the  method will be called, or the order in which the   method will be called for multiple objects. If the JVM exits without performing garbage collection, the OS may free the objects, in which case the  method doesn't get called.

The  method should always be declared   to prevent other classes from calling the   method.

protected void finalize throws Throwable { ... }

The method
The  method returns the   object for the class that was used to instantiate the object. The class object is the base class of reflection in Java. Additional reflection support is provided in the  package.

The method
The  method returns an integer. This integer can be used to distinguish objects although not completely. It quickly separates most of the objects and those with the same hash code are separated later in another way. It is used by the classes that provide associative arrays, for instance, those that implement the  interface . They use the hash code to store the object in the associative array. A good  implementation will return a hash code:
 * Stable: does not change
 * Evenly distributed: the hash codes of unequal objects tend to be unequal and the hash codes are evenly distributed across integer values.

The second point means that two different objects can have the same hash code so two objects with the same hash code are not necessarily the same !

Since associative arrays depend on both the  and   methods, there is an important contract between these two methods that must be maintained if the objects are to be inserted into a  :

 * For two objects a and b
 * if  then
 * but if  then
 * but if  then

In order to maintain this contract, a class that overrides the  method must also override the   method, and vice versa, so that   is based on the same properties (or a subset of the properties) as.

A further contract that the map has with the object is that the results of the  and   methods will not change once the object has been inserted into the map. For this reason, it is generally a good practice to base the hash function on immutable properties of the object.

The method
The  method returns a   that contains a text representation of the object. The  method is implicitly called by the compiler when an object operand is used with the string concatenation operators (  and  ).

The wait and notify thread signaling methods
Every object has two wait lists for threads associated with it. One wait list is used by the  keyword to acquire the mutex lock associated with the object. If the mutex lock is currently held by another thread, the current thread is added to the list of blocked threads waiting on the mutex lock. The other wait list is used for signaling between threads accomplished through the  and   and   methods.

Use of wait/notify allows efficient coordination of tasks between threads. When one thread needs to wait for another thread to complete an operation, or needs to wait until an event occurs, the thread can suspend its execution and wait to be notified when the event occurs. This is in contrast to polling, where the thread repeatedly sleeps for a short period of time and then checks a flag or other condition indicator. Polling is both more computationally expensive, as the thread has to continue checking, and less responsive since the thread won't notice the condition has changed until the next time to check.

The methods
There are three overloaded versions of the  method to support different ways to specify the timeout value: ,   and. The first method uses a timeout value of zero (0), which means that the wait does not timeout; the second method takes the number of milliseconds as a timeout; the third method takes the number of nanoseconds as a timeout, calculated as.

The thread calling  is blocked (removed from the set of executable threads) and added to the object's wait list. The thread remains in the object's wait list until one of three events occurs:
 * 1) another thread calls the object's   or   method;
 * 2) another thread calls the thread's   method; or
 * 3) a non-zero timeout that was specified in the call to   expires.

The  method must be called inside of a block or method synchronized on the object. This insures that there are no race conditions between  and. When the thread is placed in the wait list, the thread releases the object's mutex lock. After the thread is removed from the wait list and added to the set of executable threads, it must acquire the object's mutex lock before continuing execution.

The and   methods
The  and   methods remove one or more threads from an object's wait list and add them to the set of executable threads. removes a single thread from the wait list, while  removes all threads from the wait list. Which thread is removed by  is unspecified and dependent on the JVM implementation.

The notify methods must be called inside of a block or method synchronized on the object. This insures that there are no race conditions between  and.

de:Java Standard: Class Programación en Java/Clases Programmation Java/Les classes en Java Java/Classi e oggetti Programmeren in Java/Klassen Java/Introdução às classes


text:
Nintendo Wars

This Wikibook is about the Nintendo Wars series of video games.

Contents
Famicom Wars games
 * /Famicom Wars/
 * /Super Famicom Wars/
 * /Game Boy Wars/ (1/2/Turbo)
 * /Game Boy Wars 3/

Advance Wars games Battalion Wars games
 * /Advance Wars/
 * /Advance Wars 2: Black Hole Rising/
 * /Advance Wars: Dual Strike/
 * Advance Wars: Dual Strike Units
 * /Advance Wars 1+2: Re-Boot Camp/
 * /Battalion Wars/
 * /Battalion Wars 2/

title: Nintendo Wars


text:
User talk:Ronyclau

Direct Translations
Hi Ronclau,

Welcome to Wikibooks. Please don't take offense, but I reverted your direct translation edits to the Chinese Wikibook lessons. Thanks for your contributions to the 'Nations of the World' page, but I think word-for-word translations are a bit redundant, considering a vocab list will be made available for all lessons. Not having direct translations there will also help test vocab recall. --Everlong 20:25, 16 April 2006 (UTC)

C++ Programming
Hi, just saw your edits to the C++ memory management text -- better than my original text, so thanks. Please feel very free (indeed, asked if you would be willing) to do likewise elsewhere in the C++ Programming wikibook. I think you'll find your contributions welcome by the others who have worked on the book so far (I'm a fairly minor contributor myself). -- James Dennett 21:48, 2 February 2007 (UTC)

Hi again... noted your comment on http://en.wikibooks.org/wiki/Talk:C%2B%2B_Programming/Functions and responded to it with examples of passing arrays by reference. Thanks for the feedback, it's always useful to see what's not clear to readers. -- James Dennett 07:57, 1 March 2007 (UTC)

title: User talk:Ronyclau


text:
Educational Technology Innovation and Impact/Why use Technology in Education/Linguistic Intelligence

Linguistic Intelligence

“Linguistic intelligence is the ability to use languages to excite, please, convince, stimulate or convey information, http://www.cookps.act.edu.au/mi_ling.htm. Linguistic intelligence involves not only ease in producing language, but also sensitivity to the nuances, order and rhythm of words.

From a very early age we have all been encouraged to develop the art of communication. Even if we do not consider ourselves to be particularly talented, we have at least learned sufficient so as to interact fairly successfully with others. There are however, individuals who have developed their linguistic skills to the point where it becomes an art. They have the ability to write and talk fluently, utilising a broad vocabulary to express the precise meaning of what they wish to convey and they can speak almost melodically with changing intonations and rhythms of sound to express feelings and promote memory.

“ Language, the chief product of linguistic intelligence, is surprisingly flexible. The deaf can learn language, and people can learn to read language through totally different symbol-processing systems—through a sound-oriented system of syllables (like our alphabet) or through a visually oriented system of ideograms (like Chinese symbols). Studies indicate that syllables and ideograms are processed in entirely different areas of the brain, yet the linguistic intelligence can successfully make use of either method of encoding. It is characteristic of intelligence, in Gardner's view, to appropriate whatever faculties or senses it needs, without being entirely dependent upon any of them. Gardner's linguistic intelligence includes qualities of both left and right hemispheric processing of language—both language in the linear sense and language in the enfolded, holistic sense. The linguistic intelligence appears to be a combination of several differently evolved systems—expressive gesture, intonation, the cognitive abilities of naming and classifying, and syntactical parsing”

Characteristic of linguistic intelligence:

“A deep understanding of words and sensitivity to the literal and figurative meaning of words, high developed oral and written communication skills, knowledge of grammar rules and when it is appropriate to disregard those rules, knowledge of the many different uses for languages, such as persuasion, information, or pleasure” http://www.arches.uga.edu/~hmt/webwrite/linguistic.htm

Possible career interests:

Journalist, writer, teacher, lawyer, politician, translator, poet, actor, comedian, orator novelist, and speechwriter.

Exercises to strengthen linguistic intelligence

“The advantages of developing ones Linguistic Intelligence further are many. We use language to explain, persuade, sell, argue, speak publicly, describe, write and so on”  Activities that can aid to improve Linguistic Intelligence are: learn a new word every day, write poems and stories, do puzzles and crosswords, learn about neuro-Linguistic Programming and use computer to write letters and emails.