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Our vision a world where regular physical activity, good nutrition, and healthy weight are part of everyone''s life. Our mission to lead strategic public health efforts to prevent and control obesity, chronic disease, and other health conditions though regular physical activity and good nutrition. Our goals: * Increase health-related physical activity through population-based approaches. * Improve those aspects of dietary quality most related to the population burden of chronic disease and unhealthy child development. * Decrease prevalence of obesity through preventing excess weight gain and maintenance of healthy weight loss. Our Work With fiscal year (FY) 2008 funding of 38 million, CDC''s DNPAO is working to reduce obesity and obesity-related diseases. This is done through state programs, research, surveillance, training, intervention development and evaluation, leadership, policy and environmental change, communication and social marketing, and partnership development. See At A Glance 2009 for more. Supporting State Programs The Nutrition, Physical Activity and Obesity Program (NPAO) is a cooperative agreement between the Centers for Disease Control and Prevention''s Division of Nutrition, Physical Activity and Obesity (DNPAO) and 23 state health departments. The program goal is to prevent and control obesity and other chronic diseases through healthful eating and physical activity. The state program will develop strategies to leverage resources and coordinate statewide efforts with multiple partners to address all of the following DNPAO principal target areas: 1. Increase physical activity. 2. Increase the consumption of fruits and vegetables. 3. Decrease the consumption of sugar sweetened beverages. 4. Increase breastfeeding initiation, duration and exclusivity. 5. Reduce the consumption of high energy dense foods. 6. Decrease television viewing. Our Research DNPAO supports research to enhance the effectiveness of physical activity and nutrition programs. Topics of these research activities include: * the effectiveness of parent-focused strategies to reduce the time children spend watching television * the influences of the home environment on sugar-sweetened beverage consumption * the use of policy interventions to promote physical activity * the effectiveness of breastfeeding interventions in various settings. Publications: http://www.cdc.gov/nccdphp/DNPAO/aboutus/manuscripts/index.html

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• SciCrunch
• 17 years ago - by Anonymous

Recombineering (recombination-mediated genetic engineering) is a powerful method for fast and efficient construction of vectors for subsequent manipulation of the mouse genome or for use in cell culture experiments. It is also an efficient way of manipulating the bacterial genome directly. Recombineering is a method based on homologous recombination in E. Coli using recombination proteins provided from ? phage. Our bacterial strains contain a defective ? prophage inserted into the bacterial genome. The phage genes of interest, exo, bet, and gam, are transcribed from the ?PL promoter. This promoter is repressed by the temperature-sensitive repressor cI857 at 32C and derepressed (the repressor is inactive) at 42C. When bacteria containing this prophage are kept at 32C no recombination proteins are produced. However, after a brief (15 minutes) heat-shock at 42C a sufficient amount of recombination proteins are produced. exo is a 5''-3'' exonuclease that creates single-stranded overhangs on introduced linear DNA. bet protects these overhangs and assists in the subsequent recombination process. gam prevents degradation of linear DNA by inhibiting E. Coli RecBCD protein. Linear DNA (PCR product, oligo, etc.) with sufficient homology in the 5'' and 3'' ends to a target DNA molecule already present in the bacteria (plasmid, BAC, or the bacterial genome itself) can be introduced into heat-shocked and electrocompetent bacteria using electroporation. The introduced DNA will now be modified by exo and bet and undergo homologous recombination with the target molecule. The method is so efficient that co-electroporation of a supercoiled plasmid and a linear piece of DNA into heat-shocked, electrocompetent bacteria will work as well.

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• SciCrunch
• 17 years ago - by Anonymous

Software application for a fast exact Hardy-Weinberg Equilibrium test for SNPs (entry from Genetic Analysis Software)

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• SciCrunch
• 14 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. A business division of Sigma-Aldrich Corporation, focusing on providing custom manufactured products and specialized services used in the industrial development and manufacturing, including processes, that bring new drugs and new electronic products to market., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

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• SciCrunch
• 17 years ago - by Anonymous

Software automated tool for analysis and determination of Nuclear Localization Signals (NLS). Predicts that your protein is nuclear or finds out whether your potential NLS is found in our database. The program also compiles statistics on the number of nuclear/non-nuclear proteins in which your potential NLS is found. Finally, proteins with similar NLS motifs are reported, and the experimental paper describing the particular NLS are given.

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• SciCrunch
• 17 years ago - by Anonymous

LOCI is a biophotonics instrumentation laboratory stemming from the research activities of Kevin Eliceiri, Dave Beebe, Bill Bement, Paul Campagnola, Patti Keely, Brenda Ogle, Justin Williams and other LOCI investigators. Our mission is to develop advanced optical and computational techniques for imaging and experimentally manipulating living specimens. New and improved imaging instrumentation and optical-based experimental techniques are being developed. These projects are driven by demands arising from the scientific studies of external collaborators and the principal investigators and opportunities that arise with the emergence of new technology. Instrumentation development is undertaken in a form that is both accessible and beneficial to the scientific community. LOCI is directed by Kevin Eliceiri, and all inquiries about LOCI collaborations or general imaging questions may be directed to him.

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• SciCrunch
• 17 years ago - by Anonymous

ROCR is a package for evaluating and visualizing the performance of scoring classifiers in the statistical language R. It features over 25 performance measures that can be freely combined to create two-dimensional performance curves. Standard methods for investigating trade-offs between specific performance measures are available within a uniform framework, including receiver operating characteristic (ROC) graphs, precision/recall plots, lift charts and cost curves. ROCR integrates tightly with R''s powerful graphics capabilities, thus allowing for highly adjustable plots. Being equipped with only three commands and reasonable default values for optional parameters, ROCR combines flexibility with ease of usage. Performance measures that ROCR knows: Accuracy, error rate, true positive rate, false positive rate, true negative rate, false negative rate, sensitivity, specificity, recall, positive predictive value, negative predictive value, precision, fallout, miss, phi correlation coefficient, Matthews correlation coefficient, mutual information, chi square statistic, odds ratio, lift value, precision/recall F measure, ROC convex hull, area under the ROC curve, precision/recall break-even point, calibration error, mean cross-entropy, root mean squared error, SAR measure, expected cost, explicit cost. ROCR features: ROC curves, precision/recall plots, lift charts, cost curves, custom curves by freely selecting one performance measure for the x axis and one for the y axis, handling of data from cross-validation or bootstrapping, curve averaging (vertically, horizontally, or by threshold), standard error bars, box plots, curves that are color-coded by cutoff, printing threshold values on the curve, tight integration with Rs plotting facilities (making it easy to adjust plots or to combine multiple plots), fully customizable, easy to use (only 3 commands). ROCR can be used under the terms of the GNU General Public License. Running within R, it is platform-independent.

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• SciCrunch
• 17 years ago - by Anonymous

This server provides programs, web services, and databases, related to our work on RNA secondary structures. For general information and other offerings from our group see the main TBI web server. With the 1st of May 2009 we updated our servers to the Vienna RNA package version 1.8.2! The Vienna RNA Servers: * RNAfold server predicts minimum free energy structures and base pair probabilities from single RNA or DNA sequences. * RNAalifold server predicts consensus secondary structures from an alignment of several related RNA or DNA sequences. You need to upload an alignment. * RNAinverse server allows you to design RNA sequences for any desired target secondary structure. * RNAcofold server allows you to predict the secondary structure of a dimer. * RNAup server allows you to predict the accessibility of a target region. * LocARNA server generates structural alignments from a set of sequences. In collaboration with the Bioinformatics Group Freiburg. * barriers server allows you to get insights into RNA folding kinetics. * RNAz server will assist you in detecting thermodynamically stable and evolutionarily conserved RNA secondary structures in multiple sequence alignments. * Structure conservation analysis server will assist you in detecting evolutionarily conserved RNA secondary structures in multiple sequence alignments. * RNAstrand server allows you to predict the reading direction of evolutionarily conserved RNA secondary structures. * RNAxs server assists you in siRNA design. * Bcheck predicts rnpB genes Downloads Get the Source code for: * the Vienna RNA Package, our basic RNA secondary structure analysis software. * The ALIDOT package for finding conserved structure motifs (add-on) * The barriers program for analysis of RNA folding landscapes. Databases * Atlas of conserved Viral RNA Structures found by ALIDOT

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• SciCrunch
• 17 years ago - by Anonymous

RMAExpress is a standalone GUI program for Windows (and Linux) to compute gene expression summary values for Affymetrix Genechip data using the Robust Multichip Average expression summary and to carry out quality assessment using probe-level metrics. It does not require R nor is it dependent on any component of the BioConductor project. If focuses on processing 3'' IVT expression arrays, exon and WT gene arrays. What is RMA? RMA is the Robust Multichip Average. It consists of three steps: a background adjustment, quantile normalization (see the Bolstad et al reference) and finally summarization. Some references (currently published) for the RMA methodology are: Bolstad, B.M., Irizarry R. A., Astrand, M., and Speed, T.P. (2003), A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance. Bioinformatics 19(2):185-193 Supplemental information Rafael. A. Irizarry, Benjamin M. Bolstad, Francois Collin, Leslie M. Cope, Bridget Hobbs and Terence P. Speed (2003), Summaries of Affymetrix GeneChip probe level data Nucleic Acids Research 31(4):e15 Irizarry, RA, Hobbs, B, Collin, F, Beazer-Barclay, YD, Antonellis, KJ, Scherf, U, Speed, TP (2002) Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data. Accepted for publication in Biostatistics. [Abstract, PDF, PS, Complementary Color Figures-PDF, Software] What do I need? You will need the appropriate CDF and CEL files for your dataset. For Exon and WT Gene arrays, the PGF and CLF should be used instead of the CDF file to build a CDFRME file. The process for doing this is explained in the user manual. Some pre-built CDFRME files are also available. CDFRME files HuEx_CDFRME.zip (95.9MB) HuGene_CDFRME.zip (5.5MB) MoEx_CDFRME.zip (79.6MB) MoGene_CDFRME.zip (6.3MB) RaEx_CDFRME.zip (48.4MB) RaGene_CDFRME.zip (5.7MB) Can I use affy/BioConductor instead? Of course. Hypothetically you will get the same results from both places, provided you have consistent settings in affy/BioConductor and RMAExpress. Some people prefer the power and flexibility of R and others like the point and click simplicity of a GUI. RMAExpress caters to the second option. Since RMAExpress outputs the computed expression values to a text file, you may of course load the expression measures into R and use features of Bioconductor for the analysis of your gene expression values. You can of course open the results file in any other application that supports importing plain text files. Will I get the same results as I would using affy/Bioconductor? Yes. The results from RMAExpress should be consistent. What are the machine requirements? A good rule of thumb is the more RAM you have the better. I would recommend at least 1GB, though 512MB will work in most situations. At this point the program has been tested using Windows 2000, Windows XP, Windows Vista and Linux. Most recently I have had a report of over 10,000 arrays processed in a single session. Can I do any quality assessment? Yes, store the residuals when you compute the expression values. Then you may examine chip pseudo-images of the residuals. Note that high positive residuals are colored increasingly read and low negative residuals are colored increasingly blue. To better interpret these images and gain a better feel for what is typical you may visit the PLM Image Gallery where images for a number of different datasets are shown. Access to the NUSE and RLE quality assessment metrics is also provided. How do I download and install it? Click here for the current release Windows version. Use the installer to install the program. The current release version number is 1.0 (released June 29, 2008). A pre-built linux version is not currently available, but you may build it using the source code. You can download pre-release versions from the following table (the release versions will be more stable, the development versions may have features that are incomplete or will be removed or altered before the next release was supported by the PGA U01 HL66583.

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• SciCrunch
• 17 years ago - by Anonymous

A software library for Apache Pig for the distributed analysis of large sequencing datasets on Hadoop clusters.

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• SciCrunch
• 13 years ago - by Anonymous

Founded on the principle that strong science would lead to important new medicines, Regeneron has become an integrated biopharmaceutical company that discovers, develops, and commercializes medicines for the treatment of serious medical conditions. Regeneron currently markets ARCALYST (rilonacept) Injection for Subcutaneous Use for the treatment of a rare, inherited, inflammatory condition. Regeneron has therapeutic candidates in Phase 3 clinical trials for the potential treatment of gout, age-related macular degeneration, central retinal vein occlusion, and certain cancers. Additional therapeutic candidates are in earlier stage development programs in rheumatoid arthritis and other inflammatory conditions, pain, cholesterol reduction, allergic conditions, and cancer. The Company''s ability to develop product candidates is enhanced by the application of several proprietary technologies that Regeneron has incorporated into a comprehensive drug discovery and development process. This process is designed to thoroughly understand the biology of specific diseases, discover potential therapeutic candidates, and evaluate these candidates in clinical trials. One specific area of Regeneron expertise is the rapid development of fully-human monoclonal antibodies. In November 2007, Regeneron and sanofi-aventis entered into a global, strategic collaboration to discover, develop, and commercialize fully-human therapeutic antibodies utilizing Regeneron''s proprietary VelociSuite of technologies, and we expanded the collaboration in November 2009. Five human antibodies developed under the sanofi-aventis collaboration are in clinical development today, with a goal of advancing an average of four to five new antibodies into clinical development each year through 2017. In addition to the Company''s corporate headquarters and research laboratories in Tarrytown, New York, Regeneron has a large-scale biologics manufacturing facility in Rensselaer, New York, where it produces commercial and investigational products for its clinical trials, and a satellite office in Bridgewater, New Jersey.

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• SciCrunch
• 17 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. A database containing all genomic human and mouse binding sites of the Repressor Element 1 Silencing Transcription factor (REST), identified by PSSM. The RE1 silencing transcription factor (REST; also known as the neuron-restrictive silencer factor), is a nine zinc-finger transcription factor, related to the Gli-Kruppel family. REST binds to a conserved 21-nucleotide element, known as repressor element 1 (RE1; also known as the neuron-restrictive silencer element). REST was proposed to be a ''master'' silencer of neuron specific gene expression in non-neuronal tissues and undifferentiated neuroepithelium (precursor of neuronal cells), preventing the default expression of the neuronal phenotype during embryogenesis. It has been shown to function independently of orientation and distance from a gene promoter. REST has an important role during embryonic development, as homozygous gene knockout mice (Rest-/-) die by embryonic day 11.5. The constitutive expression of REST has also been shown to disrupt neuronal gene expression and cause axon path finding errors in chicken embryos (Paquette et al. 2000). RE1 sequences that are known to bind REST have also been found near to non-neuronal genes, including keratin and cytochrome P450 genes.

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• SciCrunch
• 17 years ago - by Anonymous

The retinoblastoma tumor suppressor gene RB plays a major role in cell cycle entry: RB sequesters a family of transcription factors, E2Fs, responsible for the transcription of many genes involved in the cell cycle regulation, DNA replication and in the activation of the apoptotic pathway. RB functions as a brake in the cell cycle which is released when external signals (growth factors, ) inform the cell that it can proceed to S phase. The target of the external signals is the G1 cyclin/Cdk complex. Once active, the complex phosphorylates pRB which can then free E2F. E2F can then participate in the synthesis of many genes, among them, cyclin E, which immediately binds to the Cdc2 kinase Cdc2. The complex then fires DNA replication. The understanding of the pathway regulating the tumor suppressor RB and the transcription factors E2F might give insight in the regulation of many cancers. Constructing the pathway Based on the literature, a quasi-exhaustive pathway has been built around RB to relate the regulation around the phosphorylation of the tumor suppressor protein. Once validated, the pathway can be exploited by computers to analyze deregulations observed in some cancers and used by biologists as a reference of the signalling pathways leading to RB phosphorylation. The diagram allowed to integrate an important amount of information. However, the goal would also be to exploit these data in order to understand the pathway and anticipate the behaviour of any kind of mutations in the cell. For that purpose, different tools are available to manipulate this kind of diagrams (Cytoscape, BiNoM, ...). Click here to find the page you are interested in Modeling the pathway The diagram was divided in different modules, each module representing a subpart of the diagram that describes the mechanism of modification (acetylation, phosphorylation) of a major protein in the cell cycle. The simplified diagram can be analyzed qualitatively (Boolean or discrete approaches) or quantitatively (ODEs) to provide a first understanding of the network. What for? The final purpose of the construction of this diagram is to provide a map of the RB pathway that can become a reference when studying different cancers and mutations. This project was partly funded by the EC contract ESBIC-D (LSHG-CT-2005-518192), the PIC Retinoblastome from Institut Curie, the PIC Bioinformatique et Biostatistiques from Institut Curie and the Research Networks Program in Bioinformatics from the High Council for Scientific and Technological Cooperation between France and Israel (Ministere des Affaires Etrangeres, Ministere de l''Education Nationale, de l''Enseignement Superieur et de la Recherche).

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• SciCrunch
• 17 years ago - by Anonymous

RANDOM.ORG is a true random number service that generates randomness via atmospheric noise. This page explains why it''s hard (and interesting) to get a computer to generate proper random numbers. Random numbers are useful for a variety of purposes, such as generating data encryption keys, simulating and modeling complex phenomena and for selecting random samples from larger data sets. They have also been used aesthetically, for example in literature and music, and are of course ever popular for games and gambling. When discussing single numbers, a random number is one that is drawn from a set of possible values, each of which is equally probable, i.e., a uniform distribution. When discussing a sequence of random numbers, each number drawn must be statistically independent of the others. With the advent of computers, programmers recognized the need for a means of introducing randomness into a computer program. However, surprising as it may seem, it is difficult to get a computer to do something by chance. A computer follows its instructions blindly and is therefore completely predictable. (A computer that doesn''t follow its instructions in this manner is broken.) There are two main approaches to generating random numbers using a computer: Pseudo-Random Number Generators (PRNGs) and True Random Number Generators (TRNGs). The approaches have quite different characteristics and each has its pros and cons.

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• SciCrunch
• 17 years ago - by Anonymous

The abbreviated name, mfold web server, describes a number of closely related software applications available on the World Wide Web (WWW) for the prediction of the secondary structure of single stranded nucleic acids. The objective of this web server is to provide easy access to RNA and DNA folding and hybridization software to the scientific community at large. By making use of universally available web GUIs (Graphical User Interfaces), the server circumvents the problem of portability of this software. Detailed output, in the form of structure plots with or without reliability information, single strand frequency plots and energy dot plots, are available for the folding of single sequences. A variety of bulk servers give less information, but in a shorter time and for up to hundreds of sequences at once. The portal for the mfold web server is http://www.bioinfo.rpi.edu/applications/mfold. This URL will be referred to as MFOLDROOT.

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• SciCrunch
• 17 years ago - by Anonymous

Complex systems are defined as systems with many interdependent parts which give rise to non-linear and emergent properties determining the high-level functioning and behavior of such systems. Due to the interdependence of their constituent elements and other characteristics of complex systems, it is difficult to predict system behavior based on the sum of their parts alone. Examples of complex systems include bee hives, bees themselves, human economies and societies, nervous systems, molecular interactions, cells and living things, ecosystems, as well as modern energy or telecommunication infrastructures. Arguably one of the most striking properties of complex systems is that conventional experimental and engineering approaches are inadequate to capture and predict the behavior of such systems. To complement the conventional experimental and engineering approaches, computer-based simulations of complex natural phenomena and complex man-made artifacts are increasingly employed across a wide range of sectors. Typically, such simulations require computing environments which meet very high specifications in terms of processing units, primary and secondary storage, and communication. Supercomputers constitute the de facto technology to deliver the required specifications. Acquiring, operating and maintaining supercomputers involve considerable costs, which many organizations cannot afford. The working assumption of the QosCosGrid project is that a grid could be enhanced by suitable middleware to provide features and performance characteristics that resemble those of a supercomputer., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

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• SciCrunch
• 17 years ago - by Anonymous

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The brain is made of billions of neurons, which together form the world''s most powerful information-processing machine. Despite decades of research, the fundamental principle by which these cells work together is still unknown. Many theories for brain function have been proposed over the last century. But only in the last few years has it become possible to record simultaneously from large enough numbers of neurons to put these theories to the test experimentally. This is an unprecedented opportunity, but it opens up a new question: how do we go from the gigabytes of experimental data that we now have, to concise conclusions about the function of the brain? The data processing methods traditionally used in neuroscience are not sophisticated enough to exploit this new flood of information. Fortunately, modern statistics and machine learning theory is making great strides in precisely the type of techniques needed to process these large multivariate databases. By applying these methods to neuronal data, we can now test long-standing hypotheses about brain function. The Cell Assembly The main focus of our research is an experimental search for cell assemblies. Before describing what a cell assembly is, it will be useful to describe what it is not. The brain is often thought of as a feed-forward system. In this scheme, sensory information is processed by successive levels of cortical analyzers, each of which transforms the results of previous levels, until sensory information is in a suitable form to guide the animals behavior. In support of this idea, the pattern of connections in the cortex does appear to respect a hierarchical organization, with the output of low-level areas corresponding to a single sensory modality being integrated into high-level multi-modal areas. Responses in higher-level sensory areas appear to have more complex responses to sensory stimuli, in agreement with increased abstraction as the hierarchy is traversed. However, there are several levels at which this feed-forward picture is incomplete. At the circuit diagram level, there more connections projecting across and down the hierarchy, than there are feed-forward projections. What''s more, if information were processed in a strictly feed-forward manner, one would expect a neuron to respond identically to repeated presentations of the same sensory stimulus. Although this is a fairly good approximation in primary sensory areas of cortex, in high-level structures responses are often more variable than expected from strict sensory control. Finally, although feed-forward processing can describe how an animal could perform simple stimulus-response behaviors, it cannot explain more complex top-down behaviors such as memory or thought. An alternative point of view, put forward over 50 years ago by Canadian psychologist Donald Hebb, holds that recurrent and feedback connections play an essential role in brain function. The principal actor in this view is the cell assembly, an anatomically distributed subset of neurons, amongst which mutually excitatory connections have been strengthened by repeated co-activation, allowing the assembly to later maintain its activity through reverberation without direct sensory stimulation. This theory allows for sensory-response behavior, and also behavior resulting purely from internally generated cognitive activity, by the sequential activation of a series of assemblies, leading in turn to the production of motion. In our research, we search for signatures of assembly activity in simultaneous recordings from multiple neurons, and aim to characterize the properties of assembly activity in ways not possible from theory alone. Software for Automatic Clustering KlustaKwik is a program developed in the lab for automatic cluster analysis, specifically designed to run fast on large data sets. In order facilitate open-source development, it is now located at klustakwik.sourceforge.net. This study was supported by NIH grants MH073245 and DC009947; NSF grant SBE-0542013 to the Temporal Dynamics of Learning Center, an NSF Science of Learning Center; a National Institute on Deafness and Other Communication Disorders, NIH, grant DC-005787-01A1; and a Spanish grant FIS 2006-09294. K.D.H. is an Alfred P. Sloan fellow. We would like to dedicate this work to the memory of D. J. Amit.