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Under the tendency of the marketisation of tertiary education, universities/colleges and governments across the world have increasingly regarded international education as an education services export sector. Vietnam has always been regarded as a country of importing rather than exporting international education. Nevertheless, more and more evidence shows that Vietnamese tertiary education institutions are increasingly successful in recruiting international students. To gain further insight into international students in Vietnam, we conducted a survey with 49 tertiary education institutions in Vietnam. Our findings reveal that while most full-time international students in Vietnam are intra-regional (i.e., coming from Asian countries), short-time international students are both intra-regional and extra-regional. Using a Bayesian analysis as a method, we found that, first, the main concerns of international students in Vietnam do not revolve around academic-related factors; and second, the success of Vietnamese tertiary education institutions in attracting international students was mainly associated with operation-related factors.

Many educators and researchers are trying to define statistical literacy for the 21st century. Kimura, a Japanese science educator, has suggested that a key task of statistical literacy is the ability to extract qualitative information from quantitative information, and/or to create new information from qualitative and quantitative information. This article presents research that offers a theoretical basis using the SOLO Taxonomy to capture students' ability to create new information from qualitative and quantitative information. This research shows that the "creation of dimensionally new information" is a complex construct requiring further research and a deeper analysis than Kimura appears to have used. (Contains 3 tables and 5 figures.)

Understanding the temporal variations that occur in river water pH is vital for the conservation of aquatic organisms in riverine ecosystems. Temporal variations in river water pH are caused by long-term inter-annual trends and short-term periodic or nonperiodic fluctuations. However, statistical methods that explicitly and quantitatively distinguish these components in time series pH data analyses have not been applied. Using a Bayesian structural time series model, we decomposed the 11-year (132-month) variations in pH in the five Tama River headwaters into trend and periodic (seasonal) variation components, nonperiodic temporary fluctuations caused by water temperature changes, and variations not explained by these factors. The model revealed that the trend and periodic components and the influence of water temperature were unclear in the time series changes in the pH of all five rivers. We concluded that the observed time series changes in the pH of the five rivers analyzed were mostly the result of observation errors and random walk caused by accumulated noise (process error). Bayesian structural time series models like this model have several advantages in the analysis of time series environmental data. For example, it does not require the assumption that the trend and periodic components are temporally constant. It is flexible and will facilitate future limnological studies particularly when the conditions surrounding freshwater environments are unlikely to remain constant under climate change. [ABSTRACT FROM AUTHOR]

Background: Neutralizing antibody cocktail therapy, REGN-COV2, is promising in preventing a severe form of coronavirus disease 2019 (COVID-19), but its effectiveness in Japan has not been fully investigated. Materials and Methods: To evaluate the effectiveness of REGN-COV2, clinical data of 20 patients with COVID-19 who received REGN-COV2 was compared with the control by matching age and sex. The primary outcome was the time from the onset to defervescence, the duration of hospitalization, and oxygen requirement. A sensitivity analysis using Bayesian analysis was also conducted. Results: The time to defervescence was significantly shorter in the treatment group (5.25 vs. 7.95 days, P = 0.02), and so was the duration of hospitalization (7.115 vs. 11.45, P = 0.0009). However, the oxygen therapy requirement did not differ between the two groups (15% vs. 35%, P = 0.27). For Bayesian analysis, the median posterior probability of the time to defervescence since the symptom onset on the REGN-COV2 group was 5.28 days [95% credible interval (CrI): 4.28 - 6.31 days], compared with the control of 7.99 days (95% CrI: 6.81 - 9.24 days). The posterior probability of the duration of the hospitalization on the REGN-COV2 group was 7.17 days (95% CrI: 5.99 - 8.24 days), compared with the control of 11.54 days (95% CrI: 10.28 - 13.14 days). The posterior probability of the oxygen requirement on the REGN-COV2 group was 18% (95% CrI: 3 - 33%), compared with the control of 36% (95% CrI: 16 - 54%). Conclusion: REGN-COV2 may be effective in early defervescence and shorter hospitalization. Its effectiveness for preventing a severe form of infection needs to be evaluated by further studies. [ABSTRACT FROM AUTHOR]

Introduction: Key problems when transferring results of pharmacoeconomic studies between countries are: relative infrequency of observational design, utilization of unreliable estimates of input parameters in many of modelling studies, not reporting variability of the study outputs (e.g. ICER, net monetary benefit) in a publication, and large differences in costs of healthcare services and drugs from country to country. Aim: The aim of this study was to estimate Incremental Cost-effectiveness Ratio (ICER) of denosumab vs. alendronate for treatment of osteoporosis in postmenopausal women in Serbia, using results of published pharmacoeconomic studies in United States of America (USA) and Japan. Material and Methods: The estimate of the ICER was made through the following steps: (1) scaling the ICER of the published studies to Serbian healthcare milieu using unit costs in Serbia, USA and Japan; (2) defining prior distribution of the ICER using adjusted results of the earlier published study; (3) defining sampling distribution of the ICER using adjusted results of the next published study; (4) estimating posterior distribution of the ICER and calculating the most probable values the ICER with Bayesian statistics. Results: The ICER of the prior distribution was 572,787.00 RSD, with 99% confidence interval from 1,116.00 to 998,051.00 RSD, and that of the sampling distribution 706,057.00 RSD, with 99% confidence interval from 1,122.00 to 999,308.00 RSD. The most probable ICER value from the posterior distribution was 752,912.00 RSD, with 99% confidence interval from 667,631.00 to 771,552.07 RSD. The estimated ICER was below one Gross Domestic Product (GDP) of Serbia per capita (the 2019 value: 780,063.60 RSD). Conclusions: Denosumab should be considered cost-effective in Serbia if one to three GDPs/capita/Quality Adjusted Life Year (QALY) gained is taken as the upper limit of willingness to pay by Serbian Health Insurance Fund. [ABSTRACT FROM AUTHOR]

Background: Thyroid storm is a life-threatening disease with a mortality rate of over 10%. Although glucocorticoids have been recommended as a treatment option for thyroid storm, supportive evidence based on a large-scale clinical research is lacking. The objective of the current study was to evaluate the beneficial effects of glucocorticoids in the treatment of patients with severe thyroid storm.Methods: A retrospective nationwide cohort study was conducted using a Japanese national administrative claims database. Patients admitted to intensive care units due to severe thyroid storm between the financial years 2013 and 2017 were included in the study. The primary outcome was in-hospital mortality; secondary outcomes were mortality within 30 days and insulin administration during hospitalization. Generalized linear mixed model (GLMM) with maximum likelihood estimation (MLE) and Bayesian estimation using Markov chain Monte Carlo methods (MCMC), in addition to propensity score matching (PSM), were used for statistical analysis.Results: A total of 811 patients were included in the study, of which 600 patients were treated with glucocorticoids, and 211 patients were treated without glucocorticoids. The early administration of glucocorticoids was not associated with a significant improvement in the in-hospital mortality of patients with thyroid storm [adjusted odds ratio (95% confidence interval) = 1.77 (0.95-3.34), 1.44 (1.14-1.93), and 1.46 (0.72-3.00) in the GLMM (MLE), GLMM (MCMC), and PSM, respectively]. The results of mortality within 30 days were almost identical to the results of in-hospital mortality. However, insulin use was significantly higher in the glucocorticoid group.Conclusions: This analysis of a nationwide administrative database indicates that the administration of glucocorticoids does not improve the survival of patients with thyroid storm. [ABSTRACT FROM AUTHOR]

Aim: Gingyogedokusan (GGGS) is a herbal medicine approved for upper respiratory infections in Japan, and could be utilized for the treatment of influenza. We conducted a multi‐center, prospective trial, comparing GGGS against oseltamivir in patients with influenza, but failed to include enough participants for conventional analysis. We further conducted a Bayesian analysis, however, using the Markov chain Monte Carlo (MCMC) method, by utilizing our original data. Methods: We used our data from 2010 and 2011, which compared GGGS and oseltamivir. A total of 10 patients were diagnosed with influenza and enrolled in the study (six for GGGS and four for oseltamivir). We conducted MCMC to elucidate posterior distributions for outcomes. Outcomes were time to alleviation of symptoms, time to recovery of activity level, time to recovery of health status, and time to resolution of fever. Results: Calculated mean time to alleviate symptoms was 3.99 days for GGGS, while it was 5.66 days in the oseltamivir group (difference of 1.66 days, 95% credible interval: −3.64 to 6.46). The posterior probability of the mean time for the oseltamivir group being longer than that for the GGGS group was 69%. Likewise, that probability was 81% for recovery of activity level, 86% for health status, and 24%, for fever resolution. Conclusion: According to Bayesian analysis, GGGS appears to have superiority over oseltamivir in resolving symptoms, although fever may resolve earlier with oseltamivir use. This is a proof of concept study, to encourage further research into the efficacy of GGGS for the treatment of influenza. [ABSTRACT FROM AUTHOR]

A critical step in age-at-death estimation is to identify the age-at-death distribution to which the unknown skeletal remains most likely belong. Age estimation based on a frequentist approach assumes that the age distribution of the target population is same as that of the reference sample. In a Bayesian framework, researchers have greater flexibility, with the freedom to specify mortality information via a prior distribution and integrate it with osteological data to produce more accurate age estimates. The selection of an optimal prior remains challenging, as forensic anthropologists analyze unidentified individuals often originating from unknown populations. Understanding the effects of the prior choice on the final age produced for various populations is essential to the interpretation of these estimates. In this article we investigate the effects of three different priors on age estimates using scores of three traditional methods collected on Asian skeletal samples representing peoples from Japan and Thailand. We test a uniform prior, which assumes the equal chance of death regardless of age, as well as various informative priors, derived from a Japanese mortality database and skeletal collections. We combine each of the priors with the parameters of the cumulative probit regression model to obtain age estimates. The results of our analyses show that the informative prior outperforms when it is carefully chosen to reflect the geographic and temporal origins of the target population. While the uniform prior produces the least-biased age estimates, one should be cautious, as it can generate unrealistically old and inaccurate ages. [ABSTRACT FROM AUTHOR]

Tide gauge observations along the coastline of Japan have recorded the land sinking due to the continuous subduction of the oceanic plates, indicating that stress and strain energies have been accumulating at the plate boundary, which would eventually cause large oceanic trench earthquakes like the 2011 Great East Japan Earthquake. The proposed method extracts such long-term activities of the Earth's crust together with rapid displacements related to earthquakes, even before the establishment of the global positioning system, from monthly mean data of the sea levels. A state space model decomposes the tide gauge time series into trend, seasonal, autoregressive and observation noise components, each of which are estimated using the particle filter algorithm. The spatial and temporal distributions of the extracted trend component clearly indicate high-risk regions, near which giant earthquakes have occurred or are predicted to occur. A multivariate analysis of the observatories located at the northeast coast of Japan successfully determines the past crustal displacement in the case of the 1978 Off-Miyagi Earthquake. The proposed method has the potential application for monitoring crustal activities related to the accumulation of earthquake energy. [ABSTRACT FROM PUBLISHER]

Biotic ligand models (BLMs) have been broadly accepted and used in ecological risk assessment of heavy metals for toxicity normalization with respect to water chemistry. However, the importance of assessing bioavailability by using BLMs has not been widely recognized among Japanese stakeholders. Failing to consider bioavailability may result in less effective risk management than would be possible if currently available state-of-the-art methods were used to relate bioavailable concentrations to toxic effects. In this study, an ecological risk assessment was conducted using BLMs for 6 rivers in Tokyo to stimulate discussion about bioavailability of heavy metals and the use of BLMs in ecological risk management in Japan. In the risk analysis, a Bayesian approach was used to take advantage of information from previous analyses and to calculate uncertainties in the estimation of risk. Risks were judged to be a concern if the predicted environmental concentration exceeded the 5th percentile concentration (HC5) of the species sensitivity distribution. Based on this criterion, risks to stream biota from exposure to Cu were judged not to be very severe, but it would be desirable to conduct further monitoring and field surveys to determine whether temporary exposure to concentrations exceeding the HC5 causes any irreversible effects on the river ecosystem. The risk of exposure to Ni was a concern at only 1 of the 6 sites. BLM corrections affected these conclusions in the case of Cu but were moot in the case of Ni. The use of BLMs in risk assessment calculations for Japanese rivers requires water quality information that is, unfortunately, not always available. Integr Environ Assess Manag 2013; 9: 63-69. © 2012 SETAC [ABSTRACT FROM AUTHOR]

Background, aim, and scope: Quantitative risk comparison of toxic substances is necessary to decide which substances should be prioritized to achieve effective risk management. This study compared the ecological risk among nine major toxic substances (ammonia, bisphenol-A, chloroform, copper, hexavalent chromium, lead, manganese, nickel, and zinc) in Tokyo surface waters by adopting an integrated risk analysis procedure using Bayesian statistics. Methods: Species sensitivity distributions of these substances were derived by using four Bayesian models. Environmental concentration distributions were derived by a hierarchical Bayesian model that explicitly considered the differences between within-site and between-site variations in environmental concentrations. Medians and confidence intervals of the expected potentially affected fraction (EPAF) of species were then computed by the Monte Carlo method. Results: The estimated EPAF values suggested that risk from nickel was highest and risk from zinc and ammonia were also high relative to other substances. The risk from copper was highest if bioavailability was not considered, although toxicity correction by a biotic ligand model greatly reduced the estimated risk. The risk from manganese was highest if a conservative risk index estimate (90% upper EPAF confidence limit) was selected. Conclusion: It is suggested that zinc is not a predominant risk factor in Tokyo surface waters and strategic efforts are required to reduce the total ecological risk from multiple substances. The presented risk analysis procedure using EPAF and Bayesian statistics is expected to advance methodologies and practices in quantitative ecological risk comparison. [ABSTRACT FROM AUTHOR]

Abstract: To develop effective strategies for managing biological invasions, it is important to understand and be able to predict patterns of invasion and range expansion, and particularly the rate of spread and factors controlling this rate. To predict the spatial dynamics of invasion by an alien bumblebee (Bombus terrestris) in Hokkaido, Japan, we explicitly constructed a stochastic spatio-temporal model that incorporates immigration and establishment processes. Using a Bayesian approach, we parameterized the model based on spatio-temporal presence/absence data collected by citizen volunteers and used the model to predict control of the near-future spread of the bumblebee under several management strategies. The range expansion dynamics of B. terrestris were significantly negatively affected by two aspects of environmental heterogeneity: the land-use pattern (the proportion of woodland) and climate (the snow depth). Of the several spatial management strategies, suppressing the outlying (edge) colonies would be the most efficient strategy to reduce the bumblebee’s spread, irrespective of the level of effort, and would significantly slow the bumblebee’s range expansion during the next 30years. The modeling approach employed in the present study will be broadly useful for studying real-world biological invasion problems, for which prediction of the progress of an invasion, even in the very near future, is urgently needed to support effective spatial management options and countermeasures. In addition, the model demonstrates that incorporating the dynamics of environmental heterogeneity is a fundamental requirement for prediction and risk assessment during biological invasions, especially in the context of recent rapid changes in the environment at regional and global scales. [Copyright &y& Elsevier]

• We estimate parameter and predictive uncertainty of a soil respiration model in a cool-temperate forest ecosystem based on Bayesian inversion approach. • We use a Gaussian innovative residual error model with a generalised likelihood function that accounts for correlated, heteroscedastic, and non-normally distributed residual error. • We demonstrate that the Bayesian inversion approach is useful as a means by which to evaluate effectively parameter and predictive uncertainty of a soil respiration model with precise representation of residual errors. Careful modelling of soil carbon sequestration is essential to evaluate future terrestrial feedback to the earth climate system through atmosphere–surface carbon exchange. Few studies have evaluated, in bio- and geo-applications, parameter and predictive uncertainty of soil respiration models by considering the difference between observations and model predictions; i.e. residual error, which is assumed neither to be independent nor to be described by a normal (i.e. Gaussian) probability distribution with a mean of zero and constant variance. In this paper, we use 2-year observations of soil carbon flux from 2017 to 2018 (hereafter referred to as 'long-term simulation') obtained with two open-top chambers to estimate parameter and predictive uncertainty of a simple soil respiration model based on Bayesian statistics in a cool-temperate forest in western Japan. We also use a Gaussian innovative residual error model in which a generalised likelihood uncertainty estimation that accounts for correlated, heteroscedastic, non-normally distributed (i.e. non-Gaussian) residual error flexibly handles statistics varying in skewness and kurtosis. Results show that the effects of correlation and heteroscedasticity were eliminated adequately. Additionally, the posterior distribution of the residuals had a pattern intermediate to those of Gaussian and Laplacian (or double-exponential) distributions. Consequently, the predicted soil respiration rate, and range of uncertainty therein, well-matched the observational data. Furthermore, we compare results of parameter and predictive inference of the soil respiration model from the long-term simulation with those constrained of short-term simulations (i.e. 4-month subsets of the 2-year dataset) to determine the extent to which the approach used affects the estimation of parameter and predictive uncertainty. No significant difference in parameter estimates was found between the long-term simulation versus any of the short-term simulations, whereas short-term simulation analysis of the uncertainty at 50 %—i.e. between the lower (25 %) and upper (75 %) quartiles of the probability range—indicated distinctive variations in model parameters in summer when more vigorous activity of trees and organisms promotes carbon cycling between the atmosphere and ecosystem. Overall we demonstrate that the Bayesian inversion approach is useful as a means by which to evaluate effectively parameter and predictive uncertainty of a soil respiration model with precise representation of residual errors. [ABSTRACT FROM AUTHOR]

Objective Treating latent tuberculosis infection (LTBI) is essential for eliminating the serious endemicity of tuberculosis. A shorter regimen is preferred to longer regimens because the former has better adherence with a better safety profile. However, lengthy treatment with isoniazid is still recommended in Japan. Based on the latest evidence, we switched from a conventional nine-month isoniazid regimen to a shorter four-month rifampin regimen for the treatment of LTBI. Methods To evaluate the safety and efficacy of the shorter regimen, we conducted Bayesian analyses using a stochastic mathematical model to calculate the posterior probabilities of several parameters. Patients Clinical data of 13 patients in the isoniazid group and 5 in the rifampin group were used for the Bayesian analyses. The outcomes measured were completion of the treatment, adverse effects, number of clinic visits, and medical costs. Results The medial posterior probability of the isoniazid group completing the treatment was 66% [95% credible interval (CrI) 43-89%], whereas that of the rifampin group was 86% (95% CrI 60-100%). The probability that the completion rate in the rifampin group was better than that in the isoniazid group was as high as 88% (95% CrI 0-100%). Other parameters, such as the number of clinical visits and duration of treatment, were better with rifampin therapy than with isoniazid therapy, with comparable medical costs. Conclusion Four months of rifampin therapy might be preferred to isoniazid for treating LTBI in Japan.

Treatment decisions in patients with metastatic bone disease rely on accurate survival estimation. We developed the original PATHFx models using expensive, proprietary software and now seek to provide a more cost-effective solution. Using open-source machine learning software to create PATHFx version 2.0, we asked whether PATHFx 2.0 could be created using open-source methods and externally validated in two unique patient populations. The training set of a well-characterized, database records of 189 patients and the bnlearn package within R Version 3.5.1 (R Foundation for Statistical Computing), was used to establish a series of Bayesian belief network models designed to predict survival at 1, 3, 6, 12, 18, and 24 months. Each was externally validated in both a Scandinavian (n = 815 patients) and a Japanese (n = 261 patients) data set. Brier scores and receiver operating characteristic curves to assessed discriminatory ability. Decision curve analysis (DCA) evaluated whether models should be used clinically. DCA showed that the model should be used clinically at all time points in the Scandinavian data set. For the 1-month time point, DCA of the Japanese data set suggested to expect better outcomes assuming all patients will survive greater than 1 month. Brier scores for each curve demonstrate that the models are accurate at each time point. Statement of Clinical Significance: we successfully transitioned to PATHFx 2.0 using open-source software and externally validated it in two unique patient populations, which can be used as a cost-effective option to guide surgical decisions in patients with metastatic bone disease., (© 2020 Orthopaedic Research Society. Published by Wiley Periodicals LLC.)