Extended Regression Analysis for Debye-Einstein Models Describing Low Temperature Heat Capacity Data of Solids.

Heat capacity data of many crystalline solids can be described in a physically sound manner by Debye-Einstein integrals in the temperature range from 0K to 300K. The parameters of the Debye-Einstein approach are either obtained by a Markov chain Monte Carlo (MCMC) global optimization method or by a Levenberg-Marquardt (LM) local optimization routine. In the case of the MCMC approach the model parameters and the coefficients of a function describing the residuals of the measurement points are simultaneously optimized. Thereby, the Bayesian credible interval for the heat capacity function is obtained. Although both regression tools (LM and MCMC) are completely different approaches, not only the values of the Debye-Einstein parameters, but also their standard errors appear to be similar. The calculated model parameters and their associated standard errors are then used to derive the enthalpy, entropy and Gibbs energy as functions of temperature. By direct insertion of the MCMC parameters of all 4·105 computer runs the distributions of the integral quantities enthalpy, entropy and Gibbs energy are determined.

µ-STAR: A novel framework for spatio-temporal M/EEG source imaging optimized by microstates.

Source imaging of Electroencephalography (EEG) and Magnetoencephalography (MEG) provides a noninvasive way of monitoring brain activities with high spatial and temporal resolution. In order to address this highly ill-posed problem, conventional source imaging models adopted spatio-temporal constraints that assume spatial stability of the source activities, neglecting the transient characteristics of M/EEG. In this work, a novel source imaging method µ-STAR that includes a microstate analysis and a spatio-temporal Bayesian model was introduced to address this problem. Specifically, the microstate analysis was applied to achieve automatic determination of time window length with quasi-stable source activity pattern for optimal reconstruction of source dynamics. Then a user-specific spatial prior and data-driven temporal basis functions were utilized to characterize the spatio-temporal information of sources within each state. The solution of the source reconstruction was obtained through a computationally efficient algorithm based upon variational Bayesian and convex analysis. The performance of the µ-STAR was first assessed through numerical simulations, where we found that the determination and inclusion of optimal temporal length in the spatio-temporal prior significantly improved the performance of source reconstruction. More importantly, the µ-STAR model achieved robust performance under various settings (i.e., source numbers/areas, SNR levels, and source depth) with fast convergence speed compared with five widely-used benchmark models (including wMNE, STV, SBL, BESTIES, & SI-STBF). Additional validations on real data were then performed on two publicly-available datasets (including block-design face-processing ERP and continuous resting-state EEG). The reconstructed source activities exhibited spatial and temporal neurophysiologically plausible results consistent with previously-revealed neural substrates, thereby further proving the feasibility of the µ-STAR model for source imaging in various applications.

Adaptive changes in sexual behavior in the high-risk population in response to human monkeypox transmission in Canada can help control the outbreak: Insights from a two-group, two-route epidemic model.

Monkeypox, a zoonotic disease, is emerging as a potential sexually transmitted infection/disease, with underlying transmission mechanisms still unclear. We devised a risk-structured, compartmental model, incorporating sexual behavior dynamics. We compared different strategies targeting the high-risk population: a scenario of control policies geared toward the use of condoms and/or sexual abstinence (robust control strategy) with risk compensation behavior change, and a scenario of control strategies with behavior change in response to the doubling rate (adaptive control strategy). Monkeypox's basic reproduction number is 1.464, 0.0066, and 1.461 in the high-risk, low-risk, and total populations, respectively, with the high-risk group being the major driver of monkeypox spread. Policies imposing condom use or sexual abstinence need to achieve a 35% minimum compliance rate to stop further transmission, while a combination of both can curb the spread with 10% compliance to abstinence and 25% to condom use. With risk compensation, the only option is to impose sexual abstinence by at least 35%. Adaptive control is more effective than robust control where the daily sexual contact number is reduced proportionally and remains constant thereafter, shortening the time to epidemic peak, lowering its size, facilitating disease attenuation, and playing a key role in controlling the current outbreak.

Life span, growth, senescence and island syndrome: Accounting for imperfect detection and continuous growth.

Small vertebrates on islands are expected to attain a larger body size, and a greater survival than their mainland counterparts. Comparative studies have questioned whether lizards exhibit this set of adaptations, referred to as the 'island syndrome'. We collected data on 730 individuals the endemic Lilford's lizard Podarcis lilfordi throughout a 10-year period on a small island of the Balearic archipelago (Spain). We coupled a growth function with a capture-mark-recapture model to simultaneously estimate size- and sex-dependent growth rate and survival. To put our results into a wider context, we conducted a systematic review of growth, life span and age at maturity in different Podarcis species comparing insular and mainland populations. We found a low average growth coefficient (0.56 and 0.41 year-1 for males and females to reach an asymptotic size of 72.3 and 65.6 mm respectively), a high annual survival probability of 0.81 and 0.79 in males and females, and a large variability between individuals in growth parameters. Survival probability decreased with body size in both sexes, indicating a senescence pattern typical of long-lived species or in populations with a low extrinsic mortality. Assuming a constant survival after sexual maturity, at about 2 years old, the average life span was 6.18 years in males and 8.99 in females. The oldest animal was a male last captured at an estimated age of ≥13 years and still alive at the end of the study. Our results agree with the predictions of the 'island syndrome' for survival, life span and growth parameters. A comparative analysis of these values across 29 populations of 16 different species of Podarcis indicated that insular lizards grow slower and live longer than their mainland counterparts. However, our data differed from other island populations of the same species, suggesting that island-specific characteristics play an additional role to isolation. Within this study we developed an analytical approach to study the body size-dependent survival of small reptiles. We discuss its applicability to contrast hypotheses on senescence in different sexes of this species, and provide the code used to integrate the growth and capture-mark-recapture models.

Study on association factors of intestinal infectious diseases based-Bayesian spatio-temporal model.

BACKGROUND: Intestinal infectious diseases (IIDs) are a significant public health issue in China, and the incidence and distribution of IIDs vary greatly by region and are affected by various factors. This study aims to describe the spatio-temporal trends of IIDs in the Chinese mainland and investigate the association between socioeconomic and meteorological factors with IIDs. METHODS: In this study, IIDs in mainland China from 2006 to 2017 was analyzed using data obtained from the China Center for Disease Control and Prevention. Spatio-temporal mapping techniques was employed to visualize the spatial and temporal distribution of IIDs. Additionally, mean center and standard deviational ellipse analyses were utilized to examine the spatial trends of IIDs. To investigate the potential associations between IIDs and meteorological and socioeconomic variables, spatiotemporal zero-inflated Poisson and negative binomial models was employed within a Bayesian framework. RESULTS: During the study period, the occurrence of most IIDs has dramatically reduced, with uneven reductions in different diseases. Significant regional differences were found among IIDs and influential factors. Overall, the access rate to harmless sanitary toilets (ARHST) was positively associated with the risk of cholera (RR: 1.73, 95%CI: 1.08-2.83), bacillary dysentery (RR: 1.32, 95%CI: 1.06-1.63), and other intestinal infectious diseases (RR: 1.88, 95%CI: 1.52-2.36), and negatively associated with typhoid fever (RR: 0.66, 95%CI: 0.51-0.92), paratyphoid fever (RR: 0.71, 95%CI: 0.55-0.92). Urbanization is only associated with hepatitis E (RR: 2.48, 95%CI: 1.12-5.72). And GDP was negatively correlated with paratyphoid fever (RR: 0.82, 95%CI: 0.70-0.97), and bacillary dysentery (RR: 0.77, 95%CI: 0.68-0.88), and hepatitis A (RR: 0.84, 95%CI: 0.73-0.97). Humidity showed positive correlation with some IIDs except for amoebic dysentery (RR: 1.64, 95%CI: 1.23-2.17), while wind speed showed a negative correlation with most IIDs. High precipitation was associated with an increased risk of typhoid fever (RR: 1.52, 95%CI: 1.09-2.13), and high temperature was associated with an increased risk of typhoid fever (RR: 2.82, 95%CI: 2.06-3.89), paratyphoid fever (RR: 2.79, 95%CI: 2.02-3.90), and HMFD (RR: 1.34, 95%CI: 1.01-1.77). CONCLUSIONS: This research systematically and quantitatively studied the effect of socioeconomic and meteorological factors on IIDs, which provided causal clues for future studies and guided government planning.

The IAS-MEEG Package: A Flexible Inverse Source Reconstruction Platform for Reconstruction and Visualization of Brain Activity from M/EEG Data.

We present a standalone Matlab software platform complete with visualization for the reconstruction of the neural activity in the brain from MEG or EEG data. The underlying inversion combines hierarchical Bayesian models and Krylov subspace iterative least squares solvers. The Bayesian framework of the underlying inversion algorithm allows to account for anatomical information and possible a priori belief about the focality of the reconstruction. The computational efficiency makes the software suitable for the reconstruction of lengthy time series on standard computing equipment. The algorithm requires minimal user provided input parameters, although the user can express the desired focality and accuracy of the solution. The code has been designed so as to favor the parallelization performed automatically by Matlab, according to the resources of the host computer. We demonstrate the flexibility of the platform by reconstructing activity patterns with supports of different sizes from MEG and EEG data. Moreover, we show that the software reconstructs well activity patches located either in the subcortical brain structures or on the cortex. The inverse solver and visualization modules can be used either individually or in combination. We also provide a version of the inverse solver that can be used within Brainstorm toolbox. All the software is available online by Github, including the Brainstorm plugin, with accompanying documentation and test data.

Prioritization of risk genes in multiple sclerosis by a refined Bayesian framework followed by tissue-specificity and cell type feature assessment.

BACKGROUND: Multiple sclerosis (MS) is a debilitating immune-mediated disease of the central nervous system that affects over 2 million people worldwide, resulting in a heavy burden to families and entire communities. Understanding the genetic basis underlying MS could help decipher the pathogenesis and shed light on MS treatment. We refined a recently developed Bayesian framework, Integrative Risk Gene Selector (iRIGS), to prioritize risk genes associated with MS by integrating the summary statistics from the largest GWAS to date (n = 115,803), various genomic features, and gene-gene closeness. RESULTS: We identified 163 MS-associated prioritized risk genes (MS-PRGenes) through the Bayesian framework. We replicated 35 MS-PRGenes through two-sample Mendelian randomization (2SMR) approach by integrating data from GWAS and Genotype-Tissue Expression (GTEx) expression quantitative trait loci (eQTL) of 19 tissues. We demonstrated that MS-PRGenes had more substantial deleterious effects and disease risk. Moreover, single-cell enrichment analysis indicated MS-PRGenes were more enriched in activated macrophages and microglia macrophages than non-activated ones in control samples. Biological and drug enrichment analyses highlighted inflammatory signaling pathways. CONCLUSIONS: In summary, we predicted and validated a high-confidence MS risk gene set from diverse genomic, epigenomic, eQTL, single-cell, and drug data. The MS-PRGenes could further serve as a benchmark of MS GWAS risk genes for future validation or genetic studies.

Zero-augmented beta-prime model for multilevel semi-continuous data: a Bayesian inference.

Semi-continuous data characterized by an excessive proportion of zeros and right-skewed continuous positive values appear frequently in medical research. One example would be the pharmaceutical expenditure (PE) data for which a substantial proportion of subjects investigated may report zero. Two-part mixed-effects models have been developed to analyse clustered measures of semi-continuous data from multilevel studies. In this study, we propose a new flexible two-part mixed-effects model with skew distributions for nested semi-continuous cost data under the framework of a Bayesian approach. The proposed model specification consists of two mixed-effects models linked by the correlated random effects: Part I) a model on the occurrence of positive values using a generalized logistic mixed model; and Part II) a model on the magnitude of positive values using a linear mixed model where the model errors follow skew distributions including beta-prime (BP). The proposed method is illustrated with pharmaceutical expenditure data from a multilevel observational study and the analytic results are reported by comparing potential models under different skew distributions. Simulation studies are conducted to assess the performance of the proposed model. The DIC3, LPML, WAIC, and LOO as the Bayesian model selection criteria and measures of divergence used to compare the models.

Thermal face recognition under different conditions.

BACKGROUND: A thermal face recognition under different conditions is proposed in this article. The novelty of the proposed method is applying temperature information in the recognition of thermal face. The physiological information is obtained from the face using a thermal camera, and a machine learning classifier is utilized for thermal face recognition. The steps of preprocessing, feature extraction and classification are incorporated in training phase. First of all, by using Bayesian framework, the human face can be extracted from thermal face image. Several thermal points are selected as a feature vector. These points are utilized to train Random Forest (RF). Random Forest is a supervised learning algorithm. It is an ensemble of decision trees. Namely, RF merges multiple decision trees together to obtain a more accurate classification. Feature vectors from the testing image are fed into the classifier for face recognition. RESULTS: Experiments were conducted under different conditions, including normal, adding noise, wearing glasses, face mask, and glasses with mask. To compare the performance with the convolutional neural network-based technique, experimental results of the proposed method demonstrate its robustness against different challenges. CONCLUSIONS: Comparisons with other techniques demonstrate that the proposed method is robust under less feature points, which is around one twenty-eighth to one sixtieth of those by other classic methods.

Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines.

Recently, we demonstrated that the qualitative American College of Medical Genetics and Genomics/Association for Medical Pathology (ACMG/AMP) guidelines for evaluation of Mendelian disease gene variants are fundamentally compatible with a quantitative Bayesian formulation. Here, we show that the underlying ACMG/AMP "strength of evidence categories" can be abstracted into a point system. These points are proportional to Log(odds), are additive, and produce a system that recapitulates the Bayesian formulation of the ACMG/AMP guidelines. The strengths of this system are its simplicity and that the connection between point values and odds of pathogenicity allows empirical calibration of the strength of evidence for individual data types. Weaknesses include that a narrow range of prior probabilities is locked in and that the Bayesian nature of the system is inapparent. We conclude that a points-based system has the practical attribute of user-friendliness and can be useful so long as the underlying Bayesian principles are acknowledged.

Recapitulating the Bayesian framework for neurosurgical outpatient care and a cost-benefit analysis of telemedicine for socioeconomically disadvantaged patients in the Philippines during the pandemic.

OBJECTIVE: In the Philippines during recent months, a neurosurgical center that caters primarily to socioeconomically disadvantaged patients has encountered unprecedented changes in practice patterns brought about by the COVID-19 pandemic. In particular, the usual task of outpatient care has shifted to the telemedicine format, bringing along all of its attendant advantages and gargantuan challenges. The authors sought to determine the responsiveness of this telemedicine setup to the needs of their disadvantaged patients and explored the application of Bayesian inference to enhance the use of teleconsultation in daily clinical decision-making. METHODS: The authors used the following methods to assess the telemedicine setup used in a low-resource setting during the pandemic: 1) a cross-sectional survey of patients who participated in a medical consultation via telemedicine during the 16-week period from March 16, 2020, to July 15, 2020; 2) a cost-benefit analysis of the use of telemedicine by patients; and 3) a case illustration of a Bayesian approach application unique to the teleconsultation scenario. RESULTS: Of the 272 patient beneficiaries of telemedicine in a 16-week period, 57 responded to the survey. The survey responses regarding neurosurgical outpatient care through telemedicine yielded high ratings of utility for the patients and their caregivers. According to 64% of respondents, the affordability of the telemedicine setup also prevented them from borrowing money from others, among other adverse life events prevented. There were realized financial gains on the part of the patients in terms of cost savings and protection from further impoverishment. The benefit-cost ratio was 3.51 for the patients, signifying that the benefits outweighed the costs. An actual teleconsultation case vignette was reported that is meant to be instructive and contributory to the preparedness of the neurosurgeon on the provider end of the service delivery. CONCLUSIONS: Telemedicine holds promise as a viable and safe method for health service delivery during the pandemic. In the setting of a health system that is continually challenged by shortages of resources, this study shows that an effective telemedicine setup can come with high benefit-cost ratios and quality of care, along with the assurance of patient satisfaction. The potential for high-quality care can be enhanced by the inclusion of the Bayesian framework to the basic toolkit of remote clinical assessment. When confronted with choices in terms of differential diagnosis and tests, the rigor of a simple application of the Bayesian framework can minimize costs arising from uncertainties.

A Bayesian model updating framework for robust seismic fragility analysis of non-isolated historic masonry towers.

Seismic assessment of existing masonry structures requires a numerical model able to both reproduce their nonlinear behaviour and account for the different sources of uncertainties; the latter have to be dealt with since the unavoidable lack of knowledge on the input parameters (material properties, geometry, boundary conditions, etc.) has a relevant effect on the reliability of the seismic response provided by the numerical approaches. The steadily increasing necessity of combining different sources of information/knowledge makes the Bayesian approach an appealing technique, not yet fully investigated for historic masonry constructions. In fact, while the Bayesian paradigm is currently employed to solve inverse problems in several sectors of the structural engineering domain, only a few studies pay attention to its effectiveness for parameter identification on historic masonry structures. This study combines a Bayesian framework with probabilistic structural analyses: starting from the Bayesian finite element model updating by using experimental data it provides the definition of robust seismic fragility curves for non-isolated masonry towers. A comparison between this method and the standard deterministic approach illustrates its benefits. This article is part of the theme issue 'Environmental loading of heritage structures'.

Active control as evidence in favor of sense of ownership in the moving Virtual Hand Illusion.

The sense of ownership, the feeling that our body belongs to ourselves, relies on multiple sources of sensory information. Among these sources, the contribution of visuomotor information is still debated. We tested the effect of active control in the sense of ownership in the moving Virtual Hand Illusion. Participants reported sense of ownership and sense of agency over a virtual arm in which we manipulated the morphological congruence of the hand and the visuomotor information. We found that congruent active control enhanced and maintained the reported sense of ownership over a hand that appeared detached from the body, but not in a morphological congruent limb. Also, incongruent active control, achieved by adding noise to the trajectory of the movement, decreased both reported sense of agency and ownership. Overall, our results are consistent with a framework in which active control acts as evidence for eliciting a sense of ownership.

Effect of grass sublingual tablet immunotherapy is similar in children and adults: A Bayesian approach to design pediatric sublingual immunotherapy trials.

BACKGROUND: Large sample sizes are needed for sublingual immunotherapy (SLIT) trials because of inherent data variability secondary to inconsistent allergen exposure. Obtaining large sample sizes for pediatric SLIT trials is challenging, but a Bayesian approach using prior adult data can reduce the necessary sample size. OBJECTIVE: We sought to describe how a Bayesian framework using prior information from adult trials can be used to improve pediatric SLIT clinical development. METHODS: Data were compiled by using a frequentist approach (conventional clinical trial approach independent of prior data) from trials conducted during the clinical development of timothy grass SLIT-tablets. RESULTS: The treatment effect of timothy grass SLIT-tablets was considered similar between pediatric (n = 795) and adult (n = 2299) data pools, with relative total combined symptom plus medication score improvement versus placebo of 21% (95% CI, 11.0% to 30.4%) and 20% (95% CI, 14.6% to 24.4%), respectively. Phleum pratense-specific IgG4 and IgE-blocking factor increased from baseline in both children and adults treated with timothy grass SLIT-tablets. Given the reasonable assumption in similarity of treatment response between adults and children, a Bayesian approach is described to demonstrate rigorous efficacy criteria for pediatric trials incorporating information from prior adult trials and thereby reduce the sample size. CONCLUSIONS: Data support the similarity of efficacy and immunologic changes between children and adults treated with SLIT for allergic rhinoconjunctivitis. Therefore it is appropriate to use data from adult trials to design feasible trials in children, which might reduce unsafe off-label use by promoting more quickly proper labeling of approved products.

Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework.

PURPOSE: We evaluated the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) variant pathogenicity guidelines for internal consistency and compatibility with Bayesian statistical reasoning. METHODS: The ACMG/AMP criteria were translated into a naive Bayesian classifier, assuming four levels of evidence and exponentially scaled odds of pathogenicity. We tested this framework with a range of prior probabilities and odds of pathogenicity. RESULTS: We modeled the ACMG/AMP guidelines using biologically plausible assumptions. Most ACMG/AMP combining criteria were compatible. One ACMG/AMP likely pathogenic combination was mathematically equivalent to pathogenic and one ACMG/AMP pathogenic combination was actually likely pathogenic. We modeled combinations that include evidence for and against pathogenicity, showing that our approach scored some combinations as pathogenic or likely pathogenic that ACMG/AMP would designate as variant of uncertain significance (VUS). CONCLUSION: By transforming the ACMG/AMP guidelines into a Bayesian framework, we provide a mathematical foundation for what was a qualitative heuristic. Only 2 of the 18 existing ACMG/AMP evidence combinations were mathematically inconsistent with the overall framework. Mixed combinations of pathogenic and benign evidence could yield a likely pathogenic, likely benign, or VUS result. This quantitative framework validates the approach adopted by the ACMG/AMP, provides opportunities to further refine evidence categories and combining rules, and supports efforts to automate components of variant pathogenicity assessments.

A dynamic Bayesian Markov model for health economic evaluations of interventions in infectious disease.

BACKGROUND: Health economic evaluations of interventions in infectious disease are commonly based on the predictions of ordinary differential equation (ODE) systems or Markov models (MMs). Standard MMs are static, whereas ODE systems are usually dynamic and account for herd immunity which is crucial to prevent overestimation of infection prevalence. Complex ODE systems including distributions on model parameters are computationally intensive. Thus, mainly ODE-based models including fixed parameter values are presented in the literature. These do not account for parameter uncertainty. As a consequence, probabilistic sensitivity analysis (PSA), a crucial component of health economic evaluations, cannot be conducted straightforwardly. METHODS: We present a dynamic MM under a Bayesian framework. We extend a static MM by incorporating the force of infection into the state allocation algorithm. The corresponding output is based on dynamic changes in prevalence and thus accounts for herd immunity. In contrast to deterministic ODE-based models, PSA can be conducted straightforwardly. We introduce a case study of a fictional sexually transmitted infection and compare our dynamic Bayesian MM to a deterministic and a Bayesian ODE system. The models are calibrated to simulated time series data. RESULTS: By means of the case study, we show that our methodology produces outcome which is comparable to the "gold standard" of the Bayesian ODE system. CONCLUSIONS: In contrast to ODE systems in the literature, the dynamic MM includes distributions on all model parameters at manageable computational effort (including calibration). The run time of the Bayesian ODE system is 15 times longer.

Bayesian assessment of two competitive enzyme-linked immunosorbent assays for the detection of bovine viral diarrhoea virus antibodies in bovine sera.

Infections due to bovine viral diarrhoea virus (BVDV) are endemic in most cattleproducing countries throughout the world and bovine viral diarrhoea is considered a transboundary disease. The key elements of a BVDV control programme are vaccination, biosecurity, elimination of persistently infected (PI) animals and surveillance. The aim of this study was to assess the sensitivity (Se) and the specificity (Sp) of two commercial competitive enzyme-linked immunosorbent assays (ELISAs) based on selected immune-dominant BVDV proteins: the non-structural protein NS3 (p80) and the recombinant envelope glycoprotein E0(Erns). Both tests were used on individual serum samples from randomly sampled young bovines in southern Belgium in order to detect specific BVDV antibodies. The Se and Sp were assessed using a Bayesian approach and were estimated, respectively, at 97.2% (with 95% credibility interval [Cr I]: 95.1-99.8) and 98.7%(95% Cr I: 96.6-99.9) for the first test and 95.8% (95% Cr I: 91.1-99.7) and 96.1%(95% Cr I: 95.1-97.7) for the second test. The results obtained with the two tests were not significantly different. In addition, using both ELISAs, the current BVDV exposure among young bovines in southern Belgium was estimated at 23.3% (95%Cr I: 20.6-26.2). Combining virological testing of all newborns to detect PI animals with regular serological testing of young stock using ELISAs is recommended in the surveillance of BVDV.

Les infections par le virus de la diarrhée virale bovine (VDVB) sont endémiques dans la plupart des pays d'élevage du monde et la diarrhée virale bovine estune maladie transfrontalière. Les éléments d'un programme de contrôle de la VDVB sont la vaccination, la biosécurité, l'élimination d'animaux porteurs d'une infection persistante et la surveillance. L'objectif de cette étude était d'évaluer la sensibilité (Se) et la spécificité (Sp) de deux kits commerciaux ELISA (épreuve immuno-enzymatique) par compétition basés sur une sélection de protéines immunodominantes du VDVB, la protéine non structurale NS3(p80) et la glycoprotéine d'enveloppe recombinante E0 (Erns). Les deux kits étaient testés sur des échantillons individuels de sérum collectés demanière aléatoire chez de jeunes bovins dans le sud de la Belgique afin de détecter les anticorps VDVB spécifiques. L'analyse bayésienne montrait une Se de 97,2 % (intervalle de crédibilité de 95 % [ICr] de 95,1 à 99,8) et une Sp de 98,7 (ICr 95 % de 96,6 à 99,9) pour le premier kit et une Se de 95,8 (ICr 95 % de91,1 à 99,7) et une Sp de 96,1 (ICr 95 % de 95,1 à 97,7) pour le deuxième. Les différences n'étaient pas significatives. De même, l'application des deux ELISA montrait que l'exposition actuelle des jeunes bovins du sud de la Belgique au VDVB s'élevait à 23,3 % (ICr 95 % de 20,6 à 26,2). L'association de tests virologiques effectués chez tous les nouveau-nés afin de détecter des animaux à infection persistante et de tests sérologiques de routine par ELISA chez les jeunes animaux est recommandée pour la surveillance de VDVB.

Las infecciones por el virus de la diarrea viral bovina (BVDV), considerada enfermedad transfronteriza, son endémicas en la mayoría de los países del mundo que albergan producción bovina. Los principales elementos de todo programa de lucha contra este virus son la vacunación, la seguridad biológica, la eliminación de los animales con infección persistente y la vigilancia. Los autores describen un estudio encaminado a evaluar la sensibilidad y especificidad de dos ensayos inmunoenzimáticos (ELISA) comerciales basados en sendas proteínas inmunodominantes del virus: la proteína no estructural NS3 (p80) y la glucoproteína recombinante de envoltura E0 (Erns). Ambas pruebas fueron aplicadas a sueros procedentes de una muestra aleatoria de bovinos jóvenes del sur de Bélgica con el fin de detectar anticuerpos específicos contra el virus. Empleando un método de estadística bayesiana se calcularon la sensibilidad y la especificidad, que resultaron, respectivamente, de un 97,2% (intervalo de credibilidad [I Cr] al 95%: 95,1­99,8) y un 98,7% (I Cr 95%: 96,6­99,9) en el caso dela primera prueba y de un 95,8% (I Cr 95%: 91,1­99,7) y un 96,1% (I Cr 95%: 95,1­97,7) en el caso de la segunda. Los resultados obtenidos con una y otra prueba no diferían significativamente entre sí. Además, utilizando ambas técnicas ELISA se calculó que la exposición actual al virus de los bovinos jóvenes del sur de Bélgica se cifraba en un 23,3% (I Cr 95%: 20,6­26,2). Para las tareas de vigilancia del BVDV se recomienda combinar el análisis virológico de todos los recién nacidos ­ para detectar animales con infección persistente ­ con la realización periódica de pruebas serológicas en el ganado joven con la técnica ELISA.

A 3D Relative-Motion Context Constraint-Based MAP Solution for Multiple-Object Tracking Problems.

Multi-object tracking (MOT), especially by using a moving monocular camera, is a very challenging task in the field of visual object tracking. To tackle this problem, the traditional tracking-by-detection-based method is heavily dependent on detection results. Occlusion and mis-detections will often lead to tracklets or drifting. In this paper, the tasks of MOT and camera motion estimation are formulated as finding a maximum a posteriori (MAP) solution of joint probability and synchronously solved in a unified framework. To improve performance, we incorporate the three-dimensional (3D) relative-motion model into a sequential Bayesian framework to track multiple objects and the camera's ego-motion estimation. A 3D relative-motion model that describes spatial relations among objects is exploited for predicting object states robustly and recovering objects when occlusion and mis-detections occur. Reversible jump Markov chain Monte Carlo (RJMCMC) particle filtering is applied to solve the posteriori estimation problem. Both quantitative and qualitative experiments with benchmark datasets and video collected on campus were conducted, which confirms that the proposed method is outperformed in many evaluation metrics.

Bayesian methods for setting sample sizes and choosing allocation ratios in phase II clinical trials with time-to-event endpoints.

Conventional phase II trials using binary endpoints as early indicators of a time-to-event outcome are not always feasible. Uveal melanoma has no reliable intermediate marker of efficacy. In pancreatic cancer and viral clearance, the time to the event of interest is short, making an early indicator unnecessary. In the latter application, Weibull models have been used to analyse corresponding time-to-event data. Bayesian sample size calculations are presented for single-arm and randomised phase II trials assuming proportional hazards models for time-to-event endpoints. Special consideration is given to the case where survival times follow the Weibull distribution. The proposed methods are demonstrated through an illustrative trial based on uveal melanoma patient data. A procedure for prior specification based on knowledge or predictions of survival patterns is described. This enables investigation into the choice of allocation ratio in the randomised setting to assess whether a control arm is indeed required. The Bayesian framework enables sample sizes consistent with those used in practice to be obtained. When a confirmatory phase III trial will follow if suitable evidence of efficacy is identified, Bayesian approaches are less controversial than for definitive trials. In the randomised setting, a compromise for obtaining feasible sample sizes is a loss in certainty in the specified hypotheses: the Bayesian counterpart of power. However, this approach may still be preferable to running a single-arm trial where no data is collected on the control treatment. This dilemma is present in most phase II trials, where resources are not sufficient to conduct a definitive trial.

Bayesian interpretation of the prefrontal P2 ERP component based on stimulus/response mapping uncertainty.

The brain can be seen as a predictive system continuously computing prior information to guess posterior probabilities minimizing sources of uncertainty. To test this Bayesian view of the brain, event-related potentials (ERP) methods have been used focusing on the well-known P3 component, traditionally associated with decision-making processes and sources of uncertainty regarding target probability. Another ERP component linked with decision-making is the prefrontal P2 (pP2) component, which has never been considered within the Bayesian framework. To test which source of uncertainty could be associated with the pP2, uncertainty induced by target probability and stimulus-response (S/R) mapping were modulated in three visuomotor tasks. Results showed that the pP2 had the largest amplitude in the task with the largest uncertainty regarding the S/R mapping and degraded as the S/R mapping became more predictable. The P3 was maximal in the tasks with larger uncertainty regarding the target probability. While we confirmed the P3 association with target probability, we extended our knowledge on the pP2 associating it with S/R mapping uncertainty. This component, which has been previously localized within the anterior insular cortex, may minimize S/R mapping uncertainty allowing response-related evidence accumulation and comparing current events with internal representations to extract action-related probabilities.