Enhancing the humidity response time of polymer optical fiber Bragg grating by using laser micromachining

The humidity sensors constructed from polymer optical fiber Bragg gratings (POFBG) respond to the water content change in the fiber induced by varying environmental condition. The water content change is a diffusion process. Therefore the response time of the POFBG sensor strongly depends on the geometry and size of the fiber. In this work we investigate the use of laser micromachining of D-shaped and slotted structures to improve the response time of polymer fiber grating based humidity sensors. A significant improvement in the response time has been achieved in laser micromachined D-shaped POFBG humidity sensors. The slotted geometry allows water rapid access to the core region but this does not of itself improve response time due to the slow expansion of the bulk of the cladding. We show that by straining the slotted sensor, the expansion component can be removed resulting in the response time being determined only by the more rapid, water induced change in core refractive index. In this way the response time is reduced by a factor of 2.5.

Simultaneous measurement of temperature and external refractive index by use of a hybrid grating in D fiber with enhanced sensitivity by HF etching

We propose a dual-parameter optical sensor device achieved by UV inscription of a hybrid long-period grating-fiber Bragg grating structure in D fiber. The hybrid configuration permits the detection of the temperature from the latter's response and measurement of the external refractive index from the former's response. In addition, the host D fiber permits effective modification of the device's sensitivity by cladding etching. The grating sensor has been used to measure the concentrations of aqueous sugar solutions, demonstrating its potential capability to detect concentration changes as small as 0.01%.

SVRMHC prediction server for MHC-binding peptides

The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers.

Automated prediction of deterioration of infants in paediatric intensive care using SpO2

Acute life-threatening events are mostly predictable in adults and children. Despite real-time monitoring these events still occur at a rate of 4%. This paper describes an automated prediction system based on the feature space embedding and time series forecasting methods of the SpO2 signal; a pulsatile signal synchronised with heart beat. We develop an age-independent index of abnormality that distinguishes patient-specific normal to abnormal physiology transitions. Two different methods were used to distinguish between normal and abnormal physiological trends based on SpO2 behaviour. The abnormality index derived by each method is compared against the current gold standard of clinical prediction of critical deterioration. Copyright © 2013 Inderscience Enterprises Ltd.

Immunoinformatics and the in silico prediction of immunogenicity:an introduction

Immunoinformatics is the application of informatics techniques to molecules of the immune system. One of its principal goals is the effective prediction of immunogenicity, be that at the level of epitope, subunit vaccine, or attenuated pathogen. Immunogenicity is the ability of a pathogen or component thereof to induce a specific immune response when first exposed to surveillance by the immune system, whereas antigenicity is the capacity for recognition by the extant machinery of the adaptive immune response in a recall response. In thisbook, we introduce these subjects and explore the current state of play in immunoinformatics and the in silico prediction of immunogenicity.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity:in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

In silico prediction of peptide-MHC binding affinity using SVRMHC

The binding between peptide epitopes and major histocompatibility complex (MHC) proteins is a major event in the cellular immune response. Accurate prediction of the binding between short peptides and class I or class II MHC molecules is an important task in immunoinformatics. SVRMHC which is a novel method to model peptide-MHC binding affinities based on support rector machine regression (SVR) is described in this chapter. SVRMHC is among a small handful of quantitative modeling methods that make predictions about precise binding affinities between a peptide and an MHC molecule. As a kernel-based learning method, SVRMHC has rendered models with demonstrated appealing performance in the practice of modeling peptide-MHC binding.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Fibre laser using a microchannel based loss tuning element for refractive index sensing

We have proposed and demonstrated a fibre laser system using a microchannel as a cavity loss tuning element for surrounding medium refractive index (SRI) sensing. A ~6µm width microchannel was created by femtosecond (fs) laser inscription assisted chemical etching in the cavity fibre, which offers a direct access to the external liquids. When the SRI changes, the microchannel behaves as a loss tuning element, hence modulating the laser cavity loss and output power. The results indicate that the presented laser sensing system has a linear response to the SRI with a sensitivity in the order of 10-5. Using higher pump power and more sensitive photodetector, the SRI sensitivity could be further enhanced.

Regrouping metric-space search index for search engine size adaptation

This work contributes to the development of search engines that self-adapt their size in response to fluctuations in workload. Deploying a search engine in an Infrastructure as a Service (IaaS) cloud facilitates allocating or deallocating computational resources to or from the engine. In this paper, we focus on the problem of regrouping the metric-space search index when the number of virtual machines used to run the search engine is modified to reflect changes in workload. We propose an algorithm for incrementally adjusting the index to fit the varying number of virtual machines. We tested its performance using a custom-build prototype search engine deployed in the Amazon EC2 cloud, while calibrating the results to compensate for the performance fluctuations of the platform. Our experiments show that, when compared with computing the index from scratch, the incremental algorithm speeds up the index computation 2–10 times while maintaining a similar search performance.

AllerTOP - a server for in silico prediction of allergens

Background: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.Results: A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity.Conclusions: AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. © 2013 Dimitrov et al.; licensee BioMed Central Ltd.

Refractive index and temperature sensitivity characteristics of a micro-slot fiber Bragg grating

Fabrication and characterization of a UVinscribed fiber Bragg grating (FBG) with a micro-slot liquid core is presented. Femtosecond (fs) laser patterning/chemical etching technique was employed to engrave a micro-slot with dimensions of 5.74 μm(h) × 125 μm(w) × 1388.72 μm(l) across the whole grating. The device has been evaluated for refractive index (RI) and temperature sensitivities and exhibited distinctive thermal response and RI sensitivity beyond the detection limit of reported fiber gratings. This structure has not just been RI sensitive, but also maintained the robustness comparing with the bare core FBGs and long-period gratings with the partial cladding etched off. © 2012 Optical Society of America.

Optical fibre gratings with response to 2μm and their sensing capabilities

Recently, we have extended fibre grating devices in to mid-IR range. Fibre Bragg gratings (FBGs) and long-period gratings (LPGs) with spectral responses from near-IR (800nm) to mid-IR ( ∼ 2μm) have been demonstrated with transmission loss as strong as 10-20dB. 2μm FBG and LPG showed temperature and refractive index (RI) sensitivities of ∼ 91pm/°C and 357nm/RIU respectively. Finally, we have performed a bio sensing experiment by monitoring the degradation of foetal bovine serum at room temperature. The results encouragingly show that the mid-IR LPGs can be an ideal biosensor platform as they have high RI sensitivity and can be used to detect concentration change of bio-samples. © 2012 SPIE.

Relationship between changes in postprandial glucagon, patient characteristics, and response to lixisenatide as add-on to oral antidiabetics

Background and aims: Lixisenatide, a once-daily prandial glucagon-like peptide-1 receptor agonist, reduces postprandial (PP) glycaemic excursions and HbA 1c . We report an exploratory analysis of the GetGoal-M and S trials in patients with type 2 diabetes mellitus (T2DM) with different changes in PP glucagon levels in response to lixisenatide treatment. Materials and methods: Patients (n=423) were stratified by their change in 2 hour PP glucagon level between baseline evaluation and Week 24 of treat - ment with lixisenatide as add-on to oral antidiabetics (OADs) into groups of Greater Change (GC; n=213) or Smaller Change (SC; n=210) in plasma glucagon levels (median change -23.57 ng/L). ANOVA and Chi-squared tests were used for the comparison of continuous and categorical variables, respec - tively. Baseline and endpoint continuous measurements in each group were compared using paired t -tests. Results: Mean change from baseline in 2 hour PP glucagon levels for the GC vs SC groups was -47.19 vs -0.59 ng/L (p<0.0001), respectively. Patients in the GC group had a shorter mean duration of diabetes (7.3 vs 9.0 years; p=0.0036) and lesser OAD use (4.5 vs 5.7 years; p=0.0092) than those in the SC group. Patients in the GC group had a greater mean reduction in HbA 1c (-1.10 vs -0.67%; p<0.0001), fasting plasma glucose (FPG; -25.20 vs -9.30 mg/dL [p<0.0001]), PP plasma glucose (PPG; -129.40 vs -78.22 mg/dL [p<0.0001]), and a greater drop in weight (-2.27 vs -1.17 kg; p=0.0002) and body mass index (-0.84 vs -0.44 kg/m 2 ; p=0.0002) than those in the SC group. More patients in the GC group also achieved composite endpoints, including HbA 1c <7% with no symptomatic hypoglycaemia and no weight gain (40.38 vs 19.52%; p<0.0001), than in the SC group. Conclusion: Greater reductions in PP glucagon associated with lixisenatide as add-on to OADs in patients with T2DM are also associated with greater reductions in HbA1c, FPG, PPG, and greater weight loss, highlighting the importance of glucagon suppression on therapeutic response. Clinical Trial Registration Number: NCT00712673; NCT00713830 Supported by: Sanof

Handling high level of censoring for endovascular aortic repair risk prediction

Feature selection is important in medical field for many reasons. However, selecting important variables is a difficult task with the presence of censoring that is a unique feature in survival data analysis. This paper proposed an approach to deal with the censoring problem in endovascular aortic repair survival data through Bayesian networks. It was merged and embedded with a hybrid feature selection process that combines cox's univariate analysis with machine learning approaches such as ensemble artificial neural networks to select the most relevant predictive variables. The proposed algorithm was compared with common survival variable selection approaches such as; least absolute shrinkage and selection operator LASSO, and Akaike information criterion AIC methods. The results showed that it was capable of dealing with high censoring in the datasets. Moreover, ensemble classifiers increased the area under the roc curves of the two datasets collected from two centers located in United Kingdom separately. Furthermore, ensembles constructed with center 1 enhanced the concordance index of center 2 prediction compared to the model built with a single network. Although the size of the final reduced model using the neural networks and its ensembles is greater than other methods, the model outperformed the others in both concordance index and sensitivity for center 2 prediction. This indicates the reduced model is more powerful for cross center prediction.