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 (11–117 kDa) were used to develop an IDA protocol for rapid protein identification and to evaluate the effects of sample preparation protocols. MS (1–10 s) and MS/MS (1–60 s) scan times, sample concentration (50–500 fmol/μL), 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 D$^{4}$RAGenS, 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 D$^{4}$RAGenS 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 “Retrieval plus Extraction” 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