36 monocyte subpopulations counts and associations with global longitudinal strain in st-elevation myocardial infarction patients with normal ejection fraction

Introduction - Monocytes, with 3 different subsets, are implicated in the initiation and progression of the atherosclerotic plaque contributing to plaque instability and rupture. Mon1 are the “classical” monocytes with inflammatory action, whilst Mon3 are considered reparative with fibroblast deposition ability. The function of the newly described Mon2 subset is yet to be fully described. In PCI era, fewer patients have globally reduced left ventricular ejection fraction post infarction, hence the importance of studying regional wall motion abnormalities and deformation at segmental levels using longitudinal strain. Little is known of the role for the 3 monocyte subpopulations in determining global strain in ST elevation myocardial infarction patients (STEMI). Conclusion In patients with normal or mildly impaired EF post infarction, higher counts of Mon1 and Mon2 are correlated with GLS within 7 days and at 6 months of remodelling post infarction. Adverse clinical outcomes in patients with reduced convalescent GLS were predicted with Mon1 and Mon2 suggestive of an inflammatory role for the newly identified Mon2 subpopulation. These results imply an important role for monocytes in myocardial healing when assessed by subclinical ventricular function indices. Methodology - STEMI patients (n = 101, mean age 64 ± 13 years; 69% male) treated with percutaneous revascularisation were recruited within 24 h post-infarction. Peripheral blood monocyte subpopulations were enumerated and characterised using flow cytometry after staining for CD14, CD16 and CCR2. Phenotypically, monocyte subpopulations are defined as: CD14++CD16-CCR2+ (Mon1), CD14++CD16+CCR2+ (Mon2) and CD14+CD16++CCR2- (Mon3). Phagocytic activity of monocytes was measured using flow cytometry and Ecoli commercial kit. Transthoracic 2D echocardiography was performed within 7 days and at 6 months post infarct to assess global longitudinal strain (GLS) via speckle tracking. MACE was defined as recurrent acute coronary syndrome and death. Results - STEMI patients with EF ≥50% by Simpson’s biplane (n = 52) had GLS assessed. Using multivariate regression analysis higher counts of Mon1 and Mon 2 and phagocytic activity of Mon2 were significantly associated with GLS (after adjusting for age, time to hospital presentation, and peak troponin levels) (Table 1). At 6 months, the convalescent GLS remained associated with higher counts of Mon1, Mon 2. At one year follow up, using multivariate Cox regression analysis, Mon1 and Mon2 counts were an independent predictor of MACE in patients with a reduced GLS (n = 21)

Potential causes of ethnic group disparities in infertility:a hospital database study

Introduction - Lower success rates of in vitro fertilisation (IVF) in South East Asian countries compared to Western countries in informal studies and surveys was considered a reflection of variations in methodology and expertise. However, recent studies on the effects of ethnicity on success rates of infertility procedures in western countries have suggested other inherent contributing factors to the ethnic disparity but the evidence evaluating these is lacking. In our study we aim to investigate some of the comorbidities that might cause ethnic disparity to infertility and related procedures from hospital admissions data. Methods - Anonymous hospital admissions data on patients of various ethnic groups with infertility, comorbidities and infertility procedures from multiple hospitals in Birmingham andManchester, UK between 2000 and 2013 were obtained from the local health authority computerised hospital activity analysis register using ICD-10 and OPCS coding systems. Statistical analysis was performed using SPSS version 20.Results Of 522 223 female patients aged 18 and over, there were44 758 (8.4%) patients from South Asian (SA) community. 1156(13.4%) of the 8653 patients coded for infertility were SA, whichis a considerably higher proportion of the background SA population. For IVF procedures, the percentage of SA increased to15.4% (233 of the total 1479 patients). The mean age of SA codedfor infertility (30.6 ± 4.7 SD years versus 32.8 ± 4.9 SD years)and IVF (30.4 ± 4.3 SD years versus 32.7 ± 4.4 SD years) was significantly lower than caucasian patien ts (P < 0.001). A multivariate logistic regression model looking at patients with infertility, accounting for variations in age, showed that SA have significantly higher prevalence of hypothyroidism, obesity andiron-deficiency anaemia compared to caucasians but lower prevalence of endometriosis. Interestingly, psychiatric and psychological conditions diagnoses were seldom registered in infertility patients. Conclusion - Other studies suggest that various cultural, lifestyles, psychosocial and socio-economic factors may explain the disparities in IVF success rates between South Asians and caucasians. The fact that SA infertility and IVF patients, in ou rstudy, were significantly younger than caucasians and that their proportion is considerably higher than the background South Asian population suggests the influence of these factors. A significant psychiatric disease burden in other conditions and low numbers in our data suggest under diagnosis in this group.Despite the limitations of the coding data, from our study, we propose that hypothyroidism, obesity and/or iron-deficiency anaemia should be considered for the ethnic disparity. Further research in this topic is essential to fully investigate the reasons for such ethnic disparities.

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics:comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.

Laser-induced breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.

Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.

A probabilistic indirect adaptive control for systems with input-dependent noise

A probabilistic indirect adaptive controller is proposed for the general nonlinear multivariate class of discrete time system. The proposed probabilistic framework incorporates input–dependent noise prediction parameters in the derivation of the optimal control law. Moreover, because noise can be nonstationary in practice, the proposed adaptive control algorithm provides an elegant method for estimating and tracking the noise. For illustration purposes, the developed method is applied to the affine class of nonlinear multivariate discrete time systems and the desired result is obtained: the optimal control law is determined by solving a cubic equation and the distribution of the tracking error is shown to be Gaussian with zero mean. The efficiency of the proposed scheme is demonstrated numerically through the simulation of an affine nonlinear system.

Fully probabilistic control design in an adaptive critic framework

Optimal stochastic controller pushes the closed-loop behavior as close as possible to the desired one. The fully probabilistic design (FPD) uses probabilistic description of the desired closed loop and minimizes Kullback-Leibler divergence of the closed-loop description to the desired one. Practical exploitation of the fully probabilistic design control theory continues to be hindered by the computational complexities involved in numerically solving the associated stochastic dynamic programming problem. In particular very hard multivariate integration and an approximate interpolation of the involved multivariate functions. This paper proposes a new fully probabilistic contro algorithm that uses the adaptive critic methods to circumvent the need for explicitly evaluating the optimal value function, thereby dramatically reducing computational requirements. This is a main contribution of this short paper.

Toward prediction of class II mouse major histocompatibility complex peptide binding affinity:in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide−MHC binding affinity. The ISC−PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide−MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms - q2, SEP, and NC - ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. 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 freely available online (http://www.jenner.ac.uk/MHCPred).

Correlating liposomal adjuvant characteristics to in-vivo cell-mediated immunity using a novel Mycobacterium tuberculosis fusion protein:a multivariate analysis study

Objective In this study, we have used a chemometrics-based method to correlate key liposomal adjuvant attributes with in-vivo immune responses based on multivariate analysis. Methods The liposomal adjuvant composed of the cationic lipid dimethyldioctadecylammonium bromide (DDA) and trehalose 6,6-dibehenate (TDB) was modified with 1,2-distearoyl-sn-glycero-3-phosphocholine at a range of mol% ratios, and the main liposomal characteristics (liposome size and zeta potential) was measured along with their immunological performance as an adjuvant for the novel, postexposure fusion tuberculosis vaccine, Ag85B-ESAT-6-Rv2660c (H56 vaccine). Partial least square regression analysis was applied to correlate and cluster liposomal adjuvants particle characteristics with in-vivo derived immunological performances (IgG, IgG1, IgG2b, spleen proliferation, IL-2, IL-5, IL-6, IL-10, IFN-γ). Key findings While a range of factors varied in the formulations, decreasing the 1,2-distearoyl-sn-glycero-3-phosphocholine content (and subsequent zeta potential) together built the strongest variables in the model. Enhanced DDA and TDB content (and subsequent zeta potential) stimulated a response skewed towards a cell mediated immunity, with the model identifying correlations with IFN-γ, IL-2 and IL-6. Conclusion This study demonstrates the application of chemometrics-based correlations and clustering, which can inform liposomal adjuvant design.