1,203,401 research outputs found
A Systematic Evaluation of Chip-Based Nanoelectrospray Parameters for Rapid Identification of Proteins from a Complex Mixture
HPLC-MS/MS is widely used for protein identification from gel spots and shotgun fractions. Although HPLC has well recognized benefits, this type of sample infusion also has some undesirable attributes: relatively low sample throughput, potential sample-to-sample carryover, time-varying sample composition, and no option for longer sample infusion for longer MS analyses. An automated chip-based ESI device (CB-ESI) has the potential to overcome these limitations. This report describes a systematic evaluation of the information-dependant acquisition (IDA) and sample preparation protocols for rapid protein identification from a complex mixture using a CB-ESI source compared with HPLC-ESI (gradient and isocratic elutions). Cytochrome c and a six-protein mixture (11â117 kDa) were used to develop an IDA protocol for rapid protein identification and to evaluate the effects of sample preparation protocols. MS (1â10 s) and MS/MS (1â60 s) scan times, sample concentration (50â500 fmol/ÎŒL), and ZipTipC18 cleanup were evaluated. Based on MOWSE scores, protein coverage, experimental run time, number of identified proteins, and reproducibility, a 12.5 min experiment (22 cycles, each with one 3 s MS and eight 10 s MS/MS scans) was determined to be the optimal IDA protocol for CB-ESI. This work flow yielded up to 220% greater peptide coverage compared with gradient HPLC-ESI and provided protein identifications with up to a 2-fold higher throughput rate than either HPLC-ESI approach, whilst employing half the amount of sample over the same time frame. The results from this study support the use of CB-ESI as a rapid alternative to the identification of protein mixtures
Rapid Sequence Identification of Potential Pathogens Using Techniques from Sparse Linear Algebra
The decreasing costs and increasing speed and accuracy of DNA sample
collection, preparation, and sequencing has rapidly produced an enormous volume
of genetic data. However, fast and accurate analysis of the samples remains a
bottleneck. Here we present DRAGenS, a genetic sequence identification
algorithm that exhibits the Big Data handling and computational power of the
Dynamic Distributed Dimensional Data Model (D4M). The method leverages linear
algebra and statistical properties to increase computational performance while
retaining accuracy by subsampling the data. Two run modes, Fast and Wise, yield
speed and precision tradeoffs, with applications in biodefense and medical
diagnostics. The DRAGenS analysis algorithm is tested over several
datasets, including three utilized for the Defense Threat Reduction Agency
(DTRA) metagenomic algorithm contest
Adaptive Detection of Instabilities: An Experimental Feasibility Study
We present an example of the practical implementation of a protocol for
experimental bifurcation detection based on on-line identification and feedback
control ideas. The idea is to couple the experiment with an on-line
computer-assisted identification/feedback protocol so that the closed-loop
system will converge to the open-loop bifurcation points. We demonstrate the
applicability of this instability detection method by real-time,
computer-assisted detection of period doubling bifurcations of an electronic
circuit; the circuit implements an analog realization of the Roessler system.
The method succeeds in locating the bifurcation points even in the presence of
modest experimental uncertainties, noise and limited resolution. The results
presented here include bifurcation detection experiments that rely on
measurements of a single state variable and delay-based phase space
reconstruction, as well as an example of tracing entire segments of a
codimension-1 bifurcation boundary in two parameter space.Comment: 29 pages, Latex 2.09, 10 figures in encapsulated postscript format
(eps), need psfig macro to include them. Submitted to Physica
LCC-DCU C-C question answering task at NTCIR-5
This paper describes the work for our participation in the NTCIR-5 Chinese to Chinese Question Answering task. Our strategy is based on the âRetrieval plus Extractionâ approach. We first retrieve relevant documents, then retrieve short passages from the above documents, and finally extract named entity answers from the most relevant passages. For question type identification, we use simple heuristic rules which can cover most questions. The Lemur toolkit with the OKAPI model is used for document retrieval. Results of our task submission are given and some preliminary conclusions drawn
Verifying and Monitoring IoTs Network Behavior using MUD Profiles
IoT devices are increasingly being implicated in cyber-attacks, raising
community concern about the risks they pose to critical infrastructure,
corporations, and citizens. In order to reduce this risk, the IETF is pushing
IoT vendors to develop formal specifications of the intended purpose of their
IoT devices, in the form of a Manufacturer Usage Description (MUD), so that
their network behavior in any operating environment can be locked down and
verified rigorously. This paper aims to assist IoT manufacturers in developing
and verifying MUD profiles, while also helping adopters of these devices to
ensure they are compatible with their organizational policies and track devices
network behavior based on their MUD profile. Our first contribution is to
develop a tool that takes the traffic trace of an arbitrary IoT device as input
and automatically generates the MUD profile for it. We contribute our tool as
open source, apply it to 28 consumer IoT devices, and highlight insights and
challenges encountered in the process. Our second contribution is to apply a
formal semantic framework that not only validates a given MUD profile for
consistency, but also checks its compatibility with a given organizational
policy. We apply our framework to representative organizations and selected
devices, to demonstrate how MUD can reduce the effort needed for IoT acceptance
testing. Finally, we show how operators can dynamically identify IoT devices
using known MUD profiles and monitor their behavioral changes on their network.Comment: 17 pages, 17 figures. arXiv admin note: text overlap with
arXiv:1804.0435
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