Association of SNPs in LCP1 and CTIF with hearing in 11 year old children:findings from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort and the G-EAR consortium

BACKGROUND: The genetic basis of hearing loss in humans is relatively poorly understood. In recent years, experimental approaches including laboratory studies of early onset hearing loss in inbred mouse strains, or proteomic analyses of hair cells or hair bundles, have suggested new candidate molecules involved in hearing function. However, the relevance of these genes/gene products to hearing function in humans remains unknown. We investigated whether single nucleotide polymorphisms (SNPs) in the human orthologues of genes of interest arising from the above-mentioned studies correlate with hearing function in children. METHODS: 577 SNPs from 13 genes were each analysed by linear regression against averaged high (3, 4 and 8 kHz) or low frequency (0.5, 1 and 2 kHz) audiometry data from 4970 children in the Avon Longitudinal Study of Parents and Children (ALSPAC) birth-cohort at age eleven years. Genes found to contain SNPs with low p-values were then investigated in 3417 adults in the G-EAR study of hearing. RESULTS: Genotypic data were available in ALSPAC for a total of 577 SNPs from 13 genes of interest. Two SNPs approached sample-wide significance (pre-specified at p = 0.00014): rs12959910 in CBP80/20-dependent translation initiation factor (CTIF) for averaged high frequency hearing (p = 0.00079, β = 0.61 dB per minor allele); and rs10492452 in L-plastin (LCP1) for averaged low frequency hearing (p = 0.00056, β = 0.45 dB). For low frequencies, rs9567638 in LCP1 also enhanced hearing in females (p = 0.0011, β = -1.76 dB; males p = 0.23, β = 0.61 dB, likelihood-ratio test p = 0.006). SNPs in LCP1 and CTIF were then examined against low and high frequency hearing data for adults in G-EAR. Although the ALSPAC results were not replicated, a SNP in LCP1, rs17601960, is in strong LD with rs9967638, and was associated with enhanced low frequency hearing in adult females in G-EAR (p = 0.00084). CONCLUSIONS: There was evidence to suggest that multiple SNPs in CTIF may contribute a small detrimental effect to hearing, and that a sex-specific locus in LCP1 is protective of hearing. No individual SNPs reached sample-wide significance in both ALSPAC and G-EAR. This is the first report of a possible association between LCP1 and hearing function.

An in-depth characterisation of neonatal seizures by early continuous video-EEG analysis

Introduction Seizures are harmful to the neonatal brain; this compels many clinicians and researchers to persevere further in optimizing every aspects of managing neonatal seizures. Aims To delineate the seizure profile between non-cooled versus cooled neonates with hypoxic-ischaemic encephalopathy (HIE), in neonates with stroke, the response of seizure burden to phenobarbitone and to quantify the degree of electroclinical dissociation (ECD) of seizures. Methods The multichannel video-EEG was used in this research study as the gold standard to detect seizures, allowing accurate quantification of seizure burden to be ascertained in term neonates. The entire EEG recording for each neonate was independently reviewed by at least 1 experienced neurophysiologist. Data were expressed in medians and interquartile ranges. Linear mixed models results were presented as mean (95% confidence interval); p values <0.05 were deemed as significant. Results Seizure burden in cooled neonates was lower than in non-cooled neonates [60(39-224) vs 203(141-406) minutes; p=0.027]. Seizure burden was reduced in cooled neonates with moderate HIE [49(26-89) vs 162(97-262) minutes; p=0.020] when compared with severe HIE. In neonates with stroke, the background pattern showed suppression over the infarcted side and seizures demonstrated a characteristic pattern. Compared with 10 mg/kg, phenobarbitone doses at 20 mg/kg reduced seizure burden (p=0.004). Seizure burden was reduced within 1 hour of phenobarbitone administration [mean (95% confidence interval): -14(-20 to -8) minutes/hour; p<0.001], but seizures returned to pre-treatment levels within 4 hours (p=0.064). The ECD index in cooled, non-cooled neonates with HIE, stroke and in neonates with other diagnoses were 88%, 94%, 64% and 75% respectively. Conclusions Further research exploring the treatment effects on seizure burden in the neonatal brain is required. A change to our current treatment strategy is warranted as we continue to strive for more effective seizure control, anchored with use of the multichannel EEG as the surveillance tool.

Outpatient follow-up during the first year after heart transplantation, is it feasible?

First year follow-up after heart transplantation requires invasive tests. Although patients used to be hospitalized for this purpose, ambulatory invasive procedures now offer the possibility of outpatient follow-up. The feasibility and security of this strategy is unknown. From 2007 we transitioned to outpatient follow-up. We have retrospectively reviewed the clinical course of the outpatient group (2007 to 2014) and an inpatient group (2000–2006). Basal characteristics, hospital stay, infections, rejection episodes and vascular complications were evaluated. 87 patients had Inpatient Follow-up (IF) and 98 Outpatient Follow-up (OF). Basal characteristics were similar, with significant differences in immunosuppression (tacrolimus IF 44.8% vs. OF 90.8%, and mycophenolate IF 86.2% vs OF 100%, both p values < 0.001) and age (IF 52 ± 11.5 years vs. OF 56.1 ± 11 years, p = 0.016). In the OF group more clinical visits were performed (IF 10 vs. OF 13, p < 0.001) while hospital stay was lower (IF 23 days vs. OF 3 days, p < 0.001). The rate of infection, rejection, and vascular complications was similar. No difference was found in 1-year mortality (IF 2.3% vs. 1.0%, p = 0.60). First year post-cardiac transplantation outpatient follow-up seems to be feasible and safe in terms of infection, rejection, vascular complications and mortality.

Outpatient follow-up during the first year after heart transplantation, is it feasible?

First year follow-up after heart transplantation requires invasive tests. Although patients used to be hospitalized for this purpose, ambulatory invasive procedures now offer the possibility of outpatient follow-up. The feasibility and security of this strategy is unknown. From 2007 we transitioned to outpatient follow-up. We have retrospectively reviewed the clinical course of the outpatient group (2007 to 2014) and an inpatient group (2000–2006). Basal characteristics, hospital stay, infections, rejection episodes and vascular complications were evaluated. 87 patients had Inpatient Follow-up (IF) and 98 Outpatient Follow-up (OF). Basal characteristics were similar, with significant differences in immunosuppression (tacrolimus IF 44.8% vs. OF 90.8%, and mycophenolate IF 86.2% vs OF 100%, both p values < 0.001) and age (IF 52 ± 11.5 years vs. OF 56.1 ± 11 years, p = 0.016). In the OF group more clinical visits were performed (IF 10 vs. OF 13, p < 0.001) while hospital stay was lower (IF 23 days vs. OF 3 days, p < 0.001). The rate of infection, rejection, and vascular complications was similar. No difference was found in 1-year mortality (IF 2.3% vs. 1.0%, p = 0.60). First year post-cardiac transplantation outpatient follow-up seems to be feasible and safe in terms of infection, rejection, vascular complications and mortality.

Efecto del consumo concomitante de inhibidores de bomba protones y levotiroxina : revisión sistemática y metaanálisis

Estudios afirman que la acidez gástrica es importante en la absorción de levotiroxina (LT4) con resultados controversiales sobre la interacción entre inhibidores de bomba de protones (IBP) y LT4. El objetivo del estudio fue establecer el efecto del uso concomitante de LT4 e IBP en los niveles de TSH en pacientes adultos con hipotiroidismo primario. Se realizó una revisión sistemática mediante búsqueda en Medline, Embase, Lilacs, Bireme, Scielo, Cochrane y Universidad de York, Access Pharmacy, Google Scholar, Dialnet y Opengray. La búsqueda no se limito por lenguaje. Se evaluó el efecto en la diferencia de medias de TSH luego del consumo de LT4 y luego del consumo concomitante con IBP. Se hizo un metaanálisis, análisis de subgrupos y análisis de sensibilidad utilizando el programa Review Manager 5.3. Se eligieron 5 artículos para el análisis cualitativo y 3 para el metaanálisis. La calidad de los estudios fue buena y el riesgo de sesgos bajo. La diferencia de medias obtenida fue 0.21 mUI/L (IC95%: 0.02-0.40; p=0.03; I2:0%). En el análisis de subgrupos en pacientes mayores de 55 años la diferencia de medias fue 0.21 mUI/L (IC95%: 0.01-0.40; p=0.27; I2:19%). En el análisis de sensibilidad se excluyo el estudio con mayor muestra y la diferencia de medias fue 0.49 mUI/L (IC95%: -0.12 a 1.11; p=0.12; I2:0%). La diferencia de medias de TSH luego del consumo concomitante no se considera clínicamente significativa pues no representa riesgo para el paciente. Son necesarios estudios clínicos aleatorizados y evaluar el efecto en los niveles de T4 libre.

Bayesian imputation of non-chosen attribute values in revealed preference surveys

Obtaining attribute values of non-chosen alternatives in a revealed preference context is challenging because non-chosen alternative attributes are unobserved by choosers, chooser perceptions of attribute values may not reflect reality, existing methods for imputing these values suffer from shortcomings, and obtaining non-chosen attribute values is resource intensive. This paper presents a unique Bayesian (multiple) Imputation Multinomial Logit model that imputes unobserved travel times and distances of non-chosen travel modes based on random draws from the conditional posterior distribution of missing values. The calibrated Bayesian (multiple) Imputation Multinomial Logit model imputes non-chosen time and distance values that convincingly replicate observed choice behavior. Although network skims were used for calibration, more realistic data such as supplemental geographically referenced surveys or stated preference data may be preferred. The model is ideally suited for imputing variation in intrazonal non-chosen mode attributes and for assessing the marginal impacts of travel policies, programs, or prices within traffic analysis zones.

Bayesian Analysis of a Superimposed Renewal Process

Stochastic behavior of an aero-engine failure/repair process has been analyzed from a Bayesian perspective. Number of failures/repairs in the component-sockets of this multi-component system are assumed to follow independent renewal processes with Weibull inter-arrival times. Based on the field failure/repair data of a large number of such engines and independent Gamma priors on the scale parameters and log-concave priors on the shape parameters, an exact method of sampling from the resulting posterior distributions of the parameters has been proposed. These generated parameter values are next utilised in obtaining the posteriors of the expected number of system repairs, system failure rate, and the conditional intensity function, which are computed using a recursive formula.

A bayesian probabilistic approach to structural health monitoring

A Bayesian probabilistic methodology for on-line structural health monitoring which addresses the issue of parameter uncertainty inherent in problem is presented. The method uses modal parameters for a limited number of modes identified from measurements taken at a restricted number of degrees of freedom of a structure as the measured structural data. The application presented uses a linear structural model whose stiffness matrix is parameterized to develop a class of possible models. Within the Bayesian framework, a joint probability density function (PDF) for the model stiffness parameters given the measured modal data is determined. Using this PDF, the marginal PDF of the stiffness parameter for each substructure given the data can be calculated.

Monitoring the health of a structure using these marginal PDFs involves two steps. First, the marginal PDF for each model parameter given modal data from the undamaged structure is found. The structure is then periodically monitored and updated marginal PDFs are determined. A measure of the difference between the calibrated and current marginal PDFs is used as a means to characterize the health of the structure. A procedure for interpreting the measure for use by an expert system in on-line monitoring is also introduced.

The probabilistic framework is developed in order to address the model parameter uncertainty issue inherent in the health monitoring problem. To illustrate this issue, consider a very simplified deterministic structural health monitoring method. In such an approach, the model parameters which minimize an error measure between the measured and model modal values would be used as the "best" model of the structure. Changes between the model parameters identified using modal data from the undamaged structure and subsequent modal data would be used to find the existence, location and degree of damage. Due to measurement noise, limited modal information, and model error, the "best" model parameters might vary from one modal dataset to the next without any damage present in the structure. Thus, difficulties would arise in separating normal variations in the identified model parameters based on limitations of the identification method and variations due to true change in the structure. The Bayesian framework described in this work provides a means to handle this parametric uncertainty.

The probabilistic health monitoring method is applied to simulated data and laboratory data. The results of these tests are presented.

Linear gaussian computations for near-exact Bayesian Monte Carlo inference in skewed alpha-stable time series models

In this paper we study parameter estimation for time series with asymmetric α-stable innovations. The proposed methods use a Poisson sum series representation (PSSR) for the asymmetric α-stable noise to express the process in a conditionally Gaussian framework. That allows us to implement Bayesian parameter estimation using Markov chain Monte Carlo (MCMC) methods. We further enhance the series representation by introducing a novel approximation of the series residual terms in which we are able to characterise the mean and variance of the approximation. Simulations illustrate the proposed framework applied to linear time series, estimating the model parameter values and model order P for an autoregressive (AR(P)) model driven by asymmetric α-stable innovations. © 2012 IEEE.

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.

PROSTATIC INTRAEPITHELIAL NEOPLASIA (PIN) - PERFORMANCE OF BAYESIAN BELIEF NETWORK FOR DIAGNOSIS AND GRADING

Prostatic intraepithelial neoplasia (PIN) diagnosis and grading are affected by uncertainties which arise from the fact that almost all knowledge of PIN histopathology is expressed in concepts, descriptive linguistic terms, and words. A Bayesian belief network (BBN) was therefore used to reduce the problem of uncertainty in diagnostic clue assessment, while still considering the dependences between elements in the reasoning sequence. A shallow network was used with an open-tree topology, with eight first-level descendant nodes for the diagnostic clues (evidence nodes), each independently linked by a conditional probability matrix to a root node containing the diagnostic alternatives (decision node). One of the evidence nodes was based on the tissue architecture and the others were based on cell features. The system was designed to be interactive, in that the histopathologist entered evidence into the network in the form of likelihood ratios for outcomes at each evidence node. The efficiency of the network was tested on a series of 110 prostate specimens, subdivided as follows: 22 cases of non-neoplastic prostate or benign prostatic tissue (NP), 22 PINs of low grade (PINlow), 22 PINs of high grade (PINhigh), 22 prostatic adenocarcinomas with cribriform pattern (PACcri), and 22 prostatic adenocarcinomas with large acinar pattern (PAClgac). The results obtained in the benign and malignant categories showed that the belief for the diagnostic alternatives is very high, the values being in general more than 0.8 and often close to 1.0. When considering the PIN lesions, the network classified and graded most of the cases with high certainty. However, there were some cases which showed values less than 0.8 (13 cases out of 44), thus indicating that there are situations in which the feature changes are intermediate between contiguous categories or grades. Discrepancy between morphological grading and the BBN results was observed in four out of 44 PIN cases: one PINlow was classified as PINhigh and three PINhigh were classified as PINlow. In conclusion, the network can grade PlN lesions and differentiate them from other prostate lesions with certainty. In particular, it offers a descriptive classifier which is readily implemented and which allows the use of linguistic, fuzzy variables.

Elements of a methodology to assess the alignment of core-values in collaborative networks

Collaborative networks are typically formed by heterogeneous and autonomous entities, and thus it is natural that each member has its own set of core-values. Since these values somehow drive the behaviour of the involved entities, the ability to quickly identify partners with compatible or common core-values represents an important element for the success of collaborative networks. However, tools to assess or measure the level of alignment of core-values are lacking. Since the concept of 'alignment' in this context is still ill-defined and shows a multifaceted nature, three perspectives are discussed. The first one uses a causal maps approach in order to capture, structure, and represent the influence relationships among core-values. This representation provides the basis to measure the alignment in terms of the structural similarity and influence among value systems. The second perspective considers the compatibility and incompatibility among core-values in order to define the alignment level. Under this perspective we propose a fuzzy inference system to estimate the alignment level, since this approach allows dealing with variables that are vaguely defined, and whose inter-relationships are difficult to define. Another advantage provided by this method is the possibility to incorporate expert human judgment in the definition of the alignment level. The last perspective uses a belief Bayesian network method, and was selected in order to assess the alignment level based on members' past behaviour. An example of application is presented where the details of each method are discussed.

Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation

Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and population size changes. DIY ABC can be used to compare competing scenarios, estimate parameters for one or more scenarios and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated and one real dataset, both with complex evolutionary scenarios, illustrates the main possibilities of DIY ABC.

Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation

Many modern statistical applications involve inference for complex stochastic models, where it is easy to simulate from the models, but impossible to calculate likelihoods. Approximate Bayesian computation (ABC) is a method of inference for such models. It replaces calculation of the likelihood by a step which involves simulating artificial data for different parameter values, and comparing summary statistics of the simulated data with summary statistics of the observed data. Here we show how to construct appropriate summary statistics for ABC in a semi-automatic manner. We aim for summary statistics which will enable inference about certain parameters of interest to be as accurate as possible. Theoretical results show that optimal summary statistics are the posterior means of the parameters. Although these cannot be calculated analytically, we use an extra stage of simulation to estimate how the posterior means vary as a function of the data; and we then use these estimates of our summary statistics within ABC. Empirical results show that our approach is a robust method for choosing summary statistics that can result in substantially more accurate ABC analyses than the ad hoc choices of summary statistics that have been proposed in the literature. We also demonstrate advantages over two alternative methods of simulation-based inference.