LIPPRED:a web server for accurate prediction of lipoprotein signal sequences and cleavage sites

Bacterial lipoproteins have many important functions and represent a class of possible vaccine candidates. The prediction of lipoproteins from sequence is thus an important task for computational vaccinology. Naïve-Bayesian networks were trained to identify SpaseII cleavage sites and their preceding signal sequences using a set of 199 distinct lipoprotein sequences. A comprehensive range of sequence models was used to identify the best model for lipoprotein signal sequences. The best performing sequence model was found to be 10-residues in length, including the conserved cysteine lipid attachment site and the nine residues prior to it. The sensitivity of prediction for LipPred was 0.979, while the specificity was 0.742. Here, we describe LipPred, a web server for lipoprotein prediction; available at the URL: http://www.jenner.ac.uk/LipPred/. LipPred is the most accurate method available for the detection of SpaseIIcleaved lipoprotein signal sequences and the prediction of their cleavage sites.

Optical 2R regeneration at 40 Gbit/s using saturable absorber in long-haul dispersion-managed fiber links

The optical regeneration is an attractive method to improve the performance of long-distance data transmission, though its application in high-speed fiber systems requires careful design consideration/optimization. In this letter we investigate 40 Gbit/s dispersion-managed fiber transmission with optical 2R regeneration based on quantum well saturable absorber and highly non-linear fiber. We demonstrate through numerical modeling a feasibility of a single channel transmission over 10,000 km using optimized system design. © 2003 Elsevier B.V. All rights reserved.

Self-phase modulation dependent dispersion mitigation in high power SSB and DSB + dispersion compensated modulated radio-over-fiber links

The joint effect of fiber chromatic dispersion and fiber nonlinearity onto single-sideband and double-sideband modulated radio-over-fiber links is investigated. Experimental and simulated results show that modulation suppression caused by the chromatic dispersion in radio-over-fiber links can be successfully eliminated in both schemes only when the system is in the linear regime. Under nonlinear transmission the received microwave carrier power depends on the optical incident power.

Application of the Heterogeneous System Prediction Method to Pattern Recognition Problem

* This work was financially supported by RFBR-04-01-00858.

Application of the Multivariate Prediction Method to Time Series

* This work was financially supported by RFBR-04-01-00858.

Recognition on Finite Set of Events: Bayesian Analysis of Generalization Ability and Classification Tree Pruning

The problem of recognition on finite set of events is considered. The generalization ability of classifiers for this problem is studied within the Bayesian approach. The method for non-uniform prior distribution specification on recognition tasks is suggested. It takes into account the assumed degree of intersection between classes. The results of the analysis are applied for pruning of classification trees.

Mitigation of pattern sensitivity in full-field electronic dispersion compensation

We investigate the pattern-dependent decoding failures in full-field electronic dispersion compensation (EDC) by offline processing of experimental signals, and find that the performance of such an EDC receiver may be degraded by an isolated "1" bit surrounded by long strings of consecutive "0s". By reducing the probability of occurrence of this kind of isolated "1" and using a novel adaptive threshold decoding method, we greatly improve the compensation performance to achieve 10-Gb/s on-off keyed signal transmission over 496-km field-installed single-mode fiber without optical dispersion compensation.

A laser induced breakdown spectroscopy quantitative analysis method based on the robust least squares support vector machine regression model

Data fluctuation in multiple measurements of Laser Induced Breakdown Spectroscopy (LIBS) greatly affects the accuracy of quantitative analysis. A new LIBS quantitative analysis method based on the Robust Least Squares Support Vector Machine (RLS-SVM) regression model is proposed. The usual way to enhance the analysis accuracy is to improve the quality and consistency of the emission signal, such as by averaging the spectral signals or spectrum standardization over a number of laser shots. The proposed method focuses more on how to enhance the robustness of the quantitative analysis regression model. The proposed RLS-SVM regression model originates from the Weighted Least Squares Support Vector Machine (WLS-SVM) but has an improved segmented weighting function and residual error calculation according to the statistical distribution of measured spectral data. Through the improved segmented weighting function, the information on the spectral data in the normal distribution will be retained in the regression model while the information on the outliers will be restrained or removed. Copper elemental concentration analysis experiments of 16 certified standard brass samples were carried out. The average value of relative standard deviation obtained from the RLS-SVM model was 3.06% and the root mean square error was 1.537%. The experimental results showed that the proposed method achieved better prediction accuracy and better modeling robustness compared with the quantitative analysis methods based on Partial Least Squares (PLS) regression, standard Support Vector Machine (SVM) and WLS-SVM. It was also demonstrated that the improved weighting function had better comprehensive performance in model robustness and convergence speed, compared with the four known weighting functions.

A Note on Bayesian Estimation for the Negative-Binomial Model

2000 Mathematics Subject Classification: 62F15.

Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions

In this paper we develop set of novel Markov Chain Monte Carlo algorithms for Bayesian smoothing of partially observed non-linear diffusion processes. The sampling algorithms developed herein use a deterministic approximation to the posterior distribution over paths as the proposal distribution for a mixture of an independence and a random walk sampler. The approximating distribution is sampled by simulating an optimized time-dependent linear diffusion process derived from the recently developed variational Gaussian process approximation method. The novel diffusion bridge proposal derived from the variational approximation allows the use of a flexible blocking strategy that further improves mixing, and thus the efficiency, of the sampling algorithms. The algorithms are tested on two diffusion processes: one with double-well potential drift and another with SINE drift. The new algorithm's accuracy and efficiency is compared with state-of-the-art hybrid Monte Carlo based path sampling. It is shown that in practical, finite sample applications the algorithm is accurate except in the presence of large observation errors and low to a multi-modal structure in the posterior distribution over paths. More importantly, the variational approximation assisted sampling algorithm outperforms hybrid Monte Carlo in terms of computational efficiency, except when the diffusion process is densely observed with small errors in which case both algorithms are equally efficient. © 2011 Springer-Verlag.

Furnace chemical vapor deposition (FCVD) method for special optical fibers fabrication

The features of the Furnace Chemical Vapor Deposition (FCVD) method of manufacturing preforms for special optical fibers are considered. It is shown that misalignment of substrate silica tube and furnace hole axes has a negative effect on the quality of fabricated preforms, leading to angular and radial asymmetry of the refractive index profile. Ways of getting rid of this and other disadvantages of the FCVD method are described. Some advantages of the FCVD method over the MCVD method are shown. It was demonstrated that the FCVD method, despite some drawbacks, allows to manufacture high-quality fiber preforms with good symmetry of the refractive index profile, and thus it is promising for fabrication of dispersion, dispersion varying and active fibers. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Filter-based dispersion-managed versatile ultrafast fibre laser

We present the operation of an ultrafast passively mode-locked fibre laser, in which flexible control of the pulse formation mechanism is readily realised by an in-cavity programmable filter the dispersion and bandwidth of which can be software configured. We show that conventional soliton, dispersion- managed (DM) soliton (stretched-pulse) and dissipative soliton mode-locking regimes can be reliably targeted by changing the filter’s dispersion and bandwidth only, while no changes are made to the physical layout of the laser cavity. Numerical simulations are presented which confirm the different nonlinear pulse evolutions inside the laser cavity. The proposed technique holds great potential for achieving a high degree of control over the dynamics and output of ultrafast fibre lasers, in contrast to the traditional method to control the pulse formation mechanism in a DM fibre laser, which involves manual optimisation of the relative length of fibres with opposite-sign dispersion in the cavity. Our versatile ultrafast fibre laser will be attractive for applications requiring different pulse profiles such as in optical signal processing and optical communications.

Efficacy and safety of infliximab-biosimilar compared to other biological drugs in rheumatoid arthritis: a mixed treatment comparison.

OBJECTIVE: The aim of this meta-analysis was to compare the efficacy and safety of infliximab-biosimilar and other available biologicals for the treatment of rheumatoid arthritis (RA), namely abatacept, adalimumab, certolizumab pegol, etanercept, golimumab, infliximab, rituximab and tocilizumab. METHODS: A systematic literature review of MEDLINE database until August 2013 was carried out to identify relevant randomized controlled trials (RCTs). Bayesian mixed treatment comparison method was applied for the pairwise comparison of treatments. Improvement rates by the American College of Rheumatology criteria (ACR20 and ACR50) at week 24 were used as efficacy endpoints, and the occurrence of serious adverse events was considered to assess the safety of the biologicals. RESULTS: Thirty-six RCTs were included in the meta-analysis. All the biological agents proved to be superior to placebo. For ACR20 response, certolizumab pegol showed the highest odds ratio (OR) compared to placebo, OR 7.69 [95 % CI 3.69-14.26], followed by abatacept OR 3.7 [95 % CI 2.17-6.06], tocilizumab OR 3.69 [95 % CI 1.87-6.62] and infliximab-biosimilar OR 3.47 [95 % CI 0.85-9.7]. For ACR50 response, certolizumab pegol showed the highest OR compared to placebo OR 8.46 [3.74-16.82], followed by tocilizumab OR 5.57 [95 % CI 2.77-10.09], and infliximab-biosimilar OR 4.06 [95 % CI 1.01-11.54]. Regarding the occurrence of serious adverse events, the results show no statistically significant difference between infliximab-biosimilar and placebo, OR 1.87 [95 % CI 0.74-3.84]. No significant difference regarding efficacy and safety was found between infliximab-biosimilar and the other biological treatments. CONCLUSION: This is the first indirect meta-analysis in RA that compares the efficacy and safety of biosimilar-infliximab to the other biologicals indicated in RA. We found no significant difference between infliximab-biosimilar and other biological agents in terms of clinical efficacy and safety.

Estimation and prediction of average vehicle occupancies using traffic accident records

As congestion management strategies begin to put more emphasis on person trips than vehicle trips, the need for vehicle occupancy data has become more critical. The traditional methods of collecting these data include the roadside windshield method and the carousel method. These methods are labor-intensive and expensive. An alternative to these traditional methods is to make use of the vehicle occupancy information in traffic accident records. This method is cost effective and may provide better spatial and temporal coverage than the traditional methods. However, this method is subject to potential biases resulting from under- and over-involvement of certain population sectors and certain types of accidents in traffic accident records. In this dissertation, three such potential biases, i.e., accident severity, driver’s age, and driver’s gender, were investigated and the corresponding bias factors were developed as needed. The results show that although multi-occupant vehicles are involved in higher percentages of severe accidents than are single-occupant vehicles, multi-occupant vehicles in the whole accident vehicle population were not overrepresented in the accident database. On the other hand, a significant difference was found between the distributions of the ages and genders of drivers involved in accidents and those of the general driving population. An information system that incorporates adjustments for the potential biases was developed to estimate the average vehicle occupancies (AVOs) for different types of roadways on the Florida state roadway system. A reasonableness check of the results from the system shows AVO estimates that are highly consistent with expectations. In addition, comparisons of AVOs from accident data with the field estimates show that the two data sources produce relatively consistent results. While accident records can be used to obtain the historical AVO trends and field data can be used to estimate the current AVOs, no known methods have been developed to project future AVOs. Four regression models for the purpose of predicting weekday AVOs on different levels of geographic areas and roadway types were developed as part of this dissertation. The models show that such socioeconomic factors as income, vehicle ownership, and employment have a significant impact on AVOs.

Estimation and Prediction of Average Vehicle Occupancies using Traffic Accident Records

As congestion management strategies begin to put more emphasis on person trips than vehicle trips, the need for vehicle occupancy data has become more critical. The traditional methods of collecting these data include the roadside windshield method and the carousel method. These methods are labor-intensive and expensive. An alternative to these traditional methods is to make use of the vehicle occupancy information in traffic accident records. This method is cost effective and may provide better spatial and temporal coverage than the traditional methods. However, this method is subject to potential biases resulting from under- and over-involvement of certain population sectors and certain types of accidents in traffic accident records. In this dissertation, three such potential biases, i.e., accident severity, driver¡¯s age, and driver¡¯s gender, were investigated and the corresponding bias factors were developed as needed. The results show that although multi-occupant vehicles are involved in higher percentages of severe accidents than are single-occupant vehicles, multi-occupant vehicles in the whole accident vehicle population were not overrepresented in the accident database. On the other hand, a significant difference was found between the distributions of the ages and genders of drivers involved in accidents and those of the general driving population. An information system that incorporates adjustments for the potential biases was developed to estimate the average vehicle occupancies (AVOs) for different types of roadways on the Florida state roadway system. A reasonableness check of the results from the system shows AVO estimates that are highly consistent with expectations. In addition, comparisons of AVOs from accident data with the field estimates show that the two data sources produce relatively consistent results. While accident records can be used to obtain the historical AVO trends and field data can be used to estimate the current AVOs, no known methods have been developed to project future AVOs. Four regression models for the purpose of predicting weekday AVOs on different levels of geographic areas and roadway types were developed as part of this dissertation. The models show that such socioeconomic factors as income, vehicle ownership, and employment have a significant impact on AVOs.