Detecting sequence dependent transcriptional pauses from RNA and protein number time series

Background: Evidence suggests that in prokaryotes sequence-dependent transcriptional pauses a?ect the dynamics of transcription and translation, as well as of small genetic circuits. So far, a few pause-prone sequences have been identi?ed from in vitro measurements of transcription elongation kinetics.

Results: Using a stochastic model of gene expression at the nucleotide and codon levels with realistic parameter values, we investigate three di?erent but related questions and present statistical methods for their analysis. First, we show that information from in vivo RNA and protein temporal numbers is su?cient to discriminate between models with and without a pause site in their coding sequence. Second, we demonstrate that it is possible to separate a large variety of models from each other with pauses of various durations and locations in the template by means of a hierarchical clustering and a random forest classi?er. Third, we introduce an approximate likelihood function that allows to estimate the location of a pause site.

Conclusions: This method can aid in detecting unknown pause-prone sequences from temporal measurements of RNA and protein numbers at a genome-wide scale and thus elucidate possible roles that these sequences play in the dynamics of genetic networks and phenotype.

Screening tests for detecting open-angle glaucoma: systematic review and meta-analysis

PURPOSE: To assess the comparative accuracy of potential screening tests for open angle glaucoma (OAG).

METHODS: Medline, Embase, Biosis (to November 2005), Science Citation Index (to December 2005), and The Cochrane Library (Issue 4, 2005) were searched. Studies assessing candidate screening tests for detecting OAG in persons older than 40 years that reported true and false positives and negatives were included. Meta-analysis was undertaken using the hierarchical summary receiver operating characteristic model.

RESULTS: Forty studies enrolling over 48,000 people reported nine tests. Most tests were reported by only a few studies. Frequency-doubling technology (FDT; C-20-1) was significantly more sensitive than ophthalmoscopy (30, 95% credible interval [CrI] 0-62) and Goldmann applanation tonometry (GAT; 45, 95% CrI 17-68), whereas threshold standard automated perimetry (SAP) and Heidelberg Retinal Tomograph (HRT II) were both more sensitive than GAT (41, 95% CrI 14-64 and 39, 95% CrI 3-64, respectively). GAT was more specific than both FDT C-20-5 (19, 95% CrI 0-53) and threshold SAP (14, 95% CrI 1-37). Judging performance by diagnostic odds ratio, FDT, oculokinetic perimetry, and HRT II are promising tests. Ophthalmoscopy, SAP, retinal photography, and GAT had relatively poor performance as single tests. These findings are based on heterogeneous data of limited quality and as such are associated with considerable uncertainty.

CONCLUSIONS: No test or group of tests was clearly superior for glaucoma screening. Further research is needed to evaluate the comparative accuracy of the most promising tests.

Micro-arthropod communities under human disturbance: is taxonomic aggregation a valuable tool for detecting multivariate change? Evidence from Mediterranean soil oribatid coenoses

Animal communities are sensitive to environmental disturbance, and several multivariate methods have recently been developed to detect changes in community structure. The complex taxonomy of soil invertebrates constrains the use of the community level in monitoring environmental changes, since species identification requires expertise and time. However, recent literature data on marine communities indicate that little multivariate information is lost in the taxonomic aggregation of species data to high rank taxa. In the present paper, this hypothesis was tested on two oribatid mite (oribatida, Acari) assemblages under two different kinds of disturbance: metal pollution and fires. Results indicate that data sets built at the genus and family systematic rank can detect the effects of disturbance with little loss of information. This is an encouraging result in view of the use of the community level as a preliminary tool for describing patterns of human-disturbed soil ecosystems. (c) 2006 Elsevier SAS. All rights reserved.

METHOD OF DETECTING BIOPRODUCTS USING LOCALIZED SURFACE PLASMON RESONANCE SENSOR OF GOLD NANOPARTICLES

(EN)Disclosed is a method of detecting bioproducts using Localized Surface Plasmon Resonance (LSPR) of gold nanoparticles, which can diagnose bioproducts based on changes in the maximum wavelength occurred by an antigen-antibody reaction after immobilization of the gold nanoparticles onto a glass panel. A sensor using such method exhibits high sensitivity, is low in price, and makes quick diagnosis possible, thereby being applicable to various biological fields associated with environmental contaminants, pathogens and the like, as well as diagnosis of diseases. Further, it provides a technology for manufacturing a sensor having higher sensitivity, low price and quick performance, as compared to conventional methods using SPR.

Detecting the work statistics through Ramsey-like interferometry

Out-of-equilibrium statistical mechanics is attracting considerable interest due to the recent advances in the control and manipulations of systems at the quantum level. Recently, an interferometric scheme for the detection of the characteristic function of the work distribution following a time-dependent process has been proposed [L. Mazzola et al., Phys. Rev. Lett. 110 (2013) 230602]. There, it was demonstrated that the work statistics of a quantum system undergoing a process can be reconstructed by effectively mapping the characteristic function of work on the state of an ancillary qubit. Here, we expand that work in two important directions. We first apply the protocol to an interesting specific physical example consisting of a superconducting qubit dispersively coupled to the field of a microwave resonator, thus enlarging the class of situations for which our scheme would be key in the task highlighted above. We then account for the interaction of the system with an additional one (which might embody an environment), and generalize the protocol accordingly.

Assessment of commercial sand barge radar for detecting overwintering diving ducks at Lough Neagh

Overwintering diving ducks at Lough Neagh have declined dramatically in recent years, but it has been suggested that on-to-offshore redistribution may have led to an underestimate of numbers. Most species feed nocturnally and their distribution at night is unknown. We used radar and visual observations from on board commercial sand barges to determine the diurnal distribution of diving duck flocks in an effort to assess the feasibility of using standard
boat-mounted radar to describe their nocturnal feeding distribution. Sand barge radar was poor in identifying flocks compared to independent visual observations as it was sensitive to interference by waves during windy conditions. However, visual observations were useful in describing diurnal distribution. Sand barges were on average 1.5km from shore when a flock of diving ducks was observed and the probability of detection declined with distance from shore. This supports the reliability of shore-based counts in monitoring and surveillance. Given the poor performance of commercially available boatmounted radar systems, we recommend the use of specialised terrestrial Bird Detecting Radar to determine the movements of diving ducks at Lough Neagh.

Development of a novel continuous statistical modelling technique for detecting the adulteration of extra virgin olive oil with hazelnut oil by using spectroscopic data

The adulteration of extra virgin olive oil with other vegetable oils is a certain problem with economic and health consequences. Current official methods have been proved insufficient to detect such adulterations. One of the most concerning and undetectable adulterations with other vegetable oils is the addition of hazelnut oil. The main objective of this work was to develop a novel dimensionality reduction technique able to model oil mixtures as a part of an integrated pattern recognition solution. This final solution attempts to identify hazelnut oil adulterants in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. The proposed Continuous Locality Preserving Projections (CLPP) technique allows the modelling of the continuous nature of the produced in house admixtures as data series instead of discrete points. This methodology has potential to be extended to other mixtures and adulterations of food products. The maintenance of the continuous structure of the data manifold lets the better visualization of this examined classification problem and facilitates a more accurate utilisation of the manifold for detecting the adulterants.

A Bayesian method for detecting stellar flares

We present a Bayesian-odds-ratio-based algorithm for detecting stellar flares in light-curve data. We assume flares are described by a model in which there is a rapid rise with a half-Gaussian profile, followed by an exponential decay. Our signal model also contains a polynomial background model required to fit underlying light-curve variations in the data, which could otherwise partially mimic a flare. We characterize the false alarm probability and efficiency of this method under the assumption that any unmodelled noise in the data is Gaussian, and compare it with a simpler thresholding method based on that used in Walkowicz et al. We find our method has a significant increase in detection efficiency for low signal-to-noise ratio (S/N) flares. For a conservative false alarm probability our method can detect 95 per cent of flares with S/N less than 20, as compared to S/N of 25 for the simpler method. We also test how well the assumption of Gaussian noise holds by applying the method to a selection of 'quiet' Kepler stars. As an example we have applied our method to a selection of stars in Kepler Quarter 1 data. The method finds 687 flaring stars with a total of 1873 flares after vetos have been applied. For these flares we have made preliminary characterizations of their durations and and S/N.

Detecting the progenitors of core collapse supernovae

The masses and the evolutionary states of the progenitors of core-collapse supernovae are not well constrained by direct observations. Stellar evolution theory generally predicts that massive stars with initial masses less than about 30M_sol should undergo core-collapse when they are cool M-type supergiants. However the only two detections of a SN progenitor before explosion are SN1987A and SN1993J, and neither of these was an M-type supergiant. Attempting to identify the progenitors of supernovae is a difficult task, as precisely predicting the time of explosion of a massive star is impossible for obvious reasons. There are several different types of supernovae which have different spectral and photometric evolution, and how exactly these are related to the evolutionary states of the progenitor stars is not currently known. I will describe a novel project which may allow the direct identification of core-collapse supernovae progenitors on pre-explosion images of resolved, nearby galaxies. This project is now possible with the excellent image archives maintained by several facilities and will be enhanced by the new initiatives to create Virtual Observatories, the earliest of which ASTROVIRTEL is already producing results.

Detecting Packed Executables using Steganalysis

This paper proposes a novel method of detecting packed executable files using steganalysis, primarily targeting the detection of obfuscated malware through packing. Considering that over 80% of malware in the wild is packed, detection accuracy and low false negative rates are important properties of malware detection methods. Experimental results outlined in this paper reveal that the proposed approach achieving an overall detection accuracy of greater than 99%, a false negative rate of 1% and a false positive rate of 0%.

Detecting localized homogeneous anomalies over spatio-temporal data

The last decade has witnessed an unprecedented growth in availability of data having spatio-temporal characteristics. Given the scale and richness of such data, finding spatio-temporal patterns that demonstrate significantly different behavior from their neighbors could be of interest for various application scenarios such as – weather modeling, analyzing spread of disease outbreaks, monitoring traffic congestions, and so on. In this paper, we propose an automated approach of exploring and discovering such anomalous patterns irrespective of the underlying domain from which the data is recovered. Our approach differs significantly from traditional methods of spatial outlier detection, and employs two phases – i) discovering homogeneous regions, and ii) evaluating these regions as anomalies based on their statistical difference from a generalized neighborhood. We evaluate the quality of our approach and distinguish it from existing techniques via an extensive experimental evaluation.

Detecting obfuscated malware using reduced opcode set and optimised runtime trace

The research presented, investigates the optimal set of operational codes (opcodes) that create a robust indicator of malicious software (malware) and also determines a program’s execution duration for accurate classification of benign and malicious software. The features extracted from the dataset are opcode density histograms, extracted during the program execution. The classifier used is a support vector machine and is configured to select those features to produce the optimal classification of malware over different program run lengths. The findings demonstrate that malware can be detected using dynamic analysis with relatively few opcodes.