Attitude diagnostique et therapeutique devant un souffle carotidien asymptomatique decouvert en pre-operatoire

no abstrac

A Fully Abstract Symbolic Semantics for Psi-Calculi

We present a symbolic transition system and bisimulation equivalence for psi-calculi, and show that it is fully abstract with respect to bisimulation congruence in the non-symbolic semantics. A psi-calculus is an extension of the pi-calculus with nominal data types for data structures and for logical assertions representing facts about data. These can be transmitted between processes and their names can be statically scoped using the standard pi-calculus mechanism to allow for scope migrations. Psi-calculi can be more general than other proposed extensions of the pi-calculus such as the applied pi-calculus, the spi-calculus, the fusion calculus, or the concurrent constraint pi-calculus. Symbolic semantics are necessary for an efficient implementation of the calculus in automated tools exploring state spaces, and the full abstraction property means the semantics of a process does not change from the original

Top-gate microcrystalline silicon TFTs processed at low temperature (<200ºC)

N-type as well P-type top-gate microcrystalline silicon thin film transistors (TFTs) are fabricated on glass substrates at a maximum temperature of 200 °C. The active layer is an undoped μc-Si film, 200 nm thick, deposited by Hot-Wire Chemical Vapor. The drain and source regions are highly phosphorus (N-type TFTs) or boron (P-type TFTs)-doped μc-films deposited by HW-CVD. The gate insulator is a silicon dioxide film deposited by RF sputtering. Al-SiO 2-N type c-Si structures using this insulator present low flat-band voltage,-0.2 V, and low density of states at the interface D it=6.4×10 10 eV -1 cm -2. High field effect mobility, 25 cm 2/V s for electrons and 1.1 cm 2/V s for holes, is obtained. These values are very high, particularly the hole mobility that was never reached previously

STING Report: convenient web-based application for graphic and tabular presentations of protein sequence, structure and function descriptors from the STING database

The Sting Report is a versatile web-based application for extraction and presentation of detailed information about any individual amino acid of a protein structure stored in the STING Database. The extracted information is presented as a series of GIF images and tables, containing the values of up to 125 sequence/structure/function descriptors/parameters. The GIF images are generated by the Gold STING modules. The HTML page resulting from the STING Report query can be printed and, most importantly, it can be composed and visualized on a computer platform with an elementary configuration. Using the STING Report, a user can generate a collection of customized reports for amino acids of specific interest. Such a collection comes as an ideal match for a demand for the rapid and detailed consultation and documentation of data about structure/function. The inclusion of information generated with STING Report in a research report or even a textbook, allows for the increased density of its contents. STING Report is freely accessible within the Gold STING Suite at http://www.cbi.cnptia.embrapa.br, http://www.es.embnet.org/SMS/, http://gibk26.bse.kyutech.ac.jp/SMS/ and http://trantor.bioc.columbia.edu/SMS (option: STING Report)

Electrochemical activation and inhibition of neuromuscular systems through modulation of ion concentrations with ion-selective membranes

Conventional functional electrical stimulation aims to restore functional motor activity of patients with disabilities resulting from spinal cord injury or neurological disorders. However, intervention with functional electrical stimulation in neurological diseases lacks an effective implantable method that suppresses unwanted nerve signals. We have developed an electrochemical method to activate and inhibit a nerve by electrically modulating ion concentrations in situ along the nerve. Using ion-selective membranes to achieve different excitability states of the nerve, we observe either a reduction of the electrical threshold for stimulation by up to approximately 40%, or voluntary, reversible inhibition of nerve signal propagation. This low-threshold electrochemical stimulation method is applicable in current implantable neuroprosthetic devices, whereas the on-demand nerve-blocking mechanism could offer effective clinical intervention in disease states caused by uncontrolled nerve activation, such as epilepsy and chronic pain syndromes.Massachusetts Institute of Technology. Faculty Discretionary Research FundNational Institutes of Health (U.S.) (Award UL1 RR 025758)Harvard Catalyst (Grant

Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies

[Image: see text] Proteogenomics has the potential to advance genome annotation through high quality peptide identifications derived from mass spectrometry experiments, which demonstrate a given gene or isoform is expressed and translated at the protein level. This can advance our understanding of genome function, discovering novel genes and gene structure that have not yet been identified or validated. Because of the high-throughput shotgun nature of most proteomics experiments, it is essential to carefully control for false positives and prevent any potential misannotation. A number of statistical procedures to deal with this are in wide use in proteomics, calculating false discovery rate (FDR) and posterior error probability (PEP) values for groups and individual peptide spectrum matches (PSMs). These methods control for multiple testing and exploit decoy databases to estimate statistical significance. Here, we show that database choice has a major effect on these confidence estimates leading to significant differences in the number of PSMs reported. We note that standard target:decoy approaches using six-frame translations of nucleotide sequences, such as assembled transcriptome data, apparently underestimate the confidence assigned to the PSMs. The source of this error stems from the inflated and unusual nature of the six-frame database, where for every target sequence there exists five “incorrect” targets that are unlikely to code for protein. The attendant FDR and PEP estimates lead to fewer accepted PSMs at fixed thresholds, and we show that this effect is a product of the database and statistical modeling and not the search engine. A variety of approaches to limit database size and remove noncoding target sequences are examined and discussed in terms of the altered statistical estimates generated and PSMs reported. These results are of importance to groups carrying out proteogenomics, aiming to maximize the validation and discovery of gene structure in sequenced genomes, while still controlling for false positives

A proteogenomic update to Yersinia: enhancing genome annotation

<p>Abstract</p> <p>Background</p> <p>Modern biomedical research depends on a complete and accurate proteome. With the widespread adoption of new sequencing technologies, genome sequences are generated at a near exponential rate, diminishing the time and effort that can be invested in genome annotation. The resulting gene set contains numerous errors in even the most basic form of annotation: the primary structure of the proteins.</p> <p>Results</p> <p>The application of experimental proteomics data to genome annotation, called proteogenomics, can quickly and efficiently discover misannotations, yielding a more accurate and complete genome annotation. We present a comprehensive proteogenomic analysis of the plague bacterium, <it>Yersinia pestis KIM</it>. We discover non-annotated genes, correct protein boundaries, remove spuriously annotated ORFs, and make major advances towards accurate identification of signal peptides. Finally, we apply our data to 21 other <it>Yersinia </it>genomes, correcting and enhancing their annotations.</p> <p>Conclusions</p> <p>In total, 141 gene models were altered and have been updated in RefSeq and Genbank, which can be accessed seamlessly through any NCBI tool (e.g. blast) or downloaded directly. Along with the improved gene models we discover new, more accurate means of identifying signal peptides in proteomics data.</p