Analysis of hepatitis B and C virus infections amongst members of the Dental National Health Insurance Society in the Oita Prefecture.

In Japan, ~3 million individuals are estimated to be infected with hepatitis B virus (HBV) or hepatitis C virus (HCV). The rates of hepatitis virus infection amongst dentists is higher than that amongst other healthcare workers due to increased exposure to both saliva and blood. However, an efficient method for the testing of hepatitis virus infections amongst dentists remains to be established. The aim of the present study was to examine the rate of hepatitis virus infection amongst dental healthcare workers (DHWs) by introducing a health checkup that included screening for HBV and HCV infections. A total of 1,834 members of the Dental National Health Insurance Society in the Oita Prefecture, consisting of dentists and other employees, were tested for hepatitis B surface antigen (HBsAg), antibodies to HBsAg (anti-HBs) and antibodies to HCV (anti-HCV) during routine medical checkups. Anonymized data, including the age, sex, occupation (dentist or employee), and presence of a hepatitis virus marker, was collected and analyzed. The positive rates of HBsAg, anti-HBs and anti-HCV in the study sample were 0.6, 44.1 and 0.5%, respectively; the positive rates were higher amongst dentists than the employees. Furthermore, the positive rates of HBsAg and anti-HCV increased with age and were higher in subjects aged 50-79 (1.7-2.2%). The positive rate of presence of anti-HBs was significantly higher in the dentists compared with employees (56.4 vs. 39.6%; respectively; P<0.0001). The three factors associated with anti-HB positivity were HBsAg negativity, occupation (dentist) and age (20-29 years) with adjusted odds ratios of 8.29, 2.27 and 1.59, respectively (P<0.05). These results suggest that introducing a hepatitis virus examination during routine health checkups of DHWs may prove useful in identifying infected individuals.

Grain growth prediction based on data assimilation by implementing 4DVar on multi-phase-field model.

We propose a method to predict grain growth based on data assimilation by using a four-dimensional variational method (4DVar). When implemented on a multi-phase-field model, the proposed method allows us to calculate the predicted grain structures and uncertainties in them that depend on the quality and quantity of the observational data. We confirm through numerical tests involving synthetic data that the proposed method correctly reproduces the true phase-field assumed in advance. Furthermore, it successfully quantifies uncertainties in the predicted grain structures, where such uncertainty quantifications provide valuable information to optimize the experimental design.

Data assimilation for massive autonomous systems based on a second-order adjoint method.

Data assimilation (DA) is a fundamental computational technique that integrates numerical simulation models and observation data on the basis of Bayesian statistics. Originally developed for meteorology, especially weather forecasting, DA is now an accepted technique in various scientific fields. One key issue that remains controversial is the implementation of DA in massive simulation models under the constraints of limited computation time and resources. In this paper, we propose an adjoint-based DA method for massive autonomous models that produces optimum estimates and their uncertainties within reasonable computation time and resource constraints. The uncertainties are given as several diagonal elements of an inverse Hessian matrix, which is the covariance matrix of a normal distribution that approximates the target posterior probability density function in the neighborhood of the optimum. Conventional algorithms for deriving the inverse Hessian matrix require O(CN^{2}+N^{3}) computations and O(N^{2}) memory, where N is the number of degrees of freedom of a given autonomous system and C is the number of computations needed to simulate time series of suitable length. The proposed method using a second-order adjoint method allows us to directly evaluate the diagonal elements of the inverse Hessian matrix without computing all of its elements. This drastically reduces the number of computations to O(C) and the amount of memory to O(N) for each diagonal element. The proposed method is validated through numerical tests using a massive two-dimensional Kobayashi phase-field model. We confirm that the proposed method correctly reproduces the parameter and initial state assumed in advance, and successfully evaluates the uncertainty of the parameter. Such information regarding uncertainty is valuable, as it can be used to optimize the design of experiments.

Bayesian Inference of Forces Causing Cytoplasmic Streaming in Caenorhabditis elegans Embryos and Mouse Oocytes.

Cellular structures are hydrodynamically interconnected, such that force generation in one location can move distal structures. One example of this phenomenon is cytoplasmic streaming, whereby active forces at the cell cortex induce streaming of the entire cytoplasm. However, it is not known how the spatial distribution and magnitude of these forces move distant objects within the cell. To address this issue, we developed a computational method that used cytoplasm hydrodynamics to infer the spatial distribution of shear stress at the cell cortex induced by active force generators from experimentally obtained flow field of cytoplasmic streaming. By applying this method, we determined the shear-stress distribution that quantitatively reproduces in vivo flow fields in Caenorhabditis elegans embryos and mouse oocytes during meiosis II. Shear stress in mouse oocytes were predicted to localize to a narrower cortical region than that with a high cortical flow velocity and corresponded with the localization of the cortical actin cap. The predicted patterns of pressure gradient in both species were consistent with species-specific cytoplasmic streaming functions. The shear-stress distribution inferred by our method can contribute to the characterization of active force generation driving biological streaming.

Estimating cellular parameters through optimization procedures: elementary principles and applications.

Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. The aim of optimization is to minimize the sum of squared errors (SSE) in a prediction or to maximize likelihood. A (local) maximum of likelihood or (local) minimum of the SSE can efficiently be identified using gradient approaches. Addition of a stochastic process enables us to identify the global maximum/minimum without becoming trapped in local maxima/minima. Sampling approaches take advantage of increasing computational power to test numerous sets of parameters in order to determine the optimum set. By combining Bayesian inference with gradient or sampling approaches, we can estimate both the optimum parameters and the form of the likelihood function related to the parameters. Finally, we introduce four examples of research that utilize parameter optimization to obtain biological insights from quantified data: transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation. With practical knowledge of parameter optimization, cell and developmental biologists can develop realistic models that reproduce their observations and thus, obtain mechanistic insights into phenomena of interest.

Bayesian experts in exploring reaction kinetics of transcription circuits.

MOTIVATION: Biochemical reactions in cells are made of several types of biological circuits. In current systems biology, making differential equation (DE) models simulatable in silico has been an appealing, general approach to uncover a complex world of biochemical reaction dynamics. Despite of a need for simulation-aided studies, our research field has yet provided no clear answers: how to specify kinetic values in models that are difficult to measure from experimental/theoretical analyses on biochemical kinetics. RESULTS: We present a novel non-parametric Bayesian approach to this problem. The key idea lies in the development of a Dirichlet process (DP) prior distribution, called Bayesian experts, which reflects substantive knowledge on reaction mechanisms inherent in given models and experimentally observable kinetic evidences to the subsequent parameter search. The DP prior identifies significant local regions of unknown parameter space before proceeding to the posterior analyses. This article reports that a Bayesian expert-inducing stochastic search can effectively explore unknown parameters of in silico transcription circuits such that solutions of DEs reproduce transcriptomic time course profiles. AVAILABILITY: A sample source code is available at the URL http://daweb.ism.ac.jp/~yoshidar/lisdas/.