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Objective To compare the characteristics and outcomes of drug-induced liver injury (DILI) caused by paracetamol and non-paracetamol medications, particularly herbal and dietary supplements. Design Retrospective electronic medical record data analysis. Setting, participants Adults admitted with DILI to the Gastroenterology and Liver Centre at the Royal Prince Alfred Hospital, Sydney (a quaternary referral liver transplantation centre), 2009-2020. Main outcome measures 90-day transplant-free survival; drugs implicated as causal agents in DILI. Results A total of 115 patients with paracetamol-related DILI and 69 with non-paracetamol DILI were admitted to our centre. The most frequently implicated non-paracetamol medications were antibiotics (19, 28%), herbal and dietary supplements (15, 22%), anti-tuberculosis medications (six, 9%), and anti-cancer medications (five, 7%). The number of non-paracetamol DILI admissions was similar across the study period, but the proportion linked with herbal and dietary supplements increased from 2 of 13 (15%) during 2009-11 to 9 of 19 (47%) during 2018-20 (linear trend: P = 0.011). Despite higher median baseline model for end-stage liver disease (MELD) scores, 90-day transplant-free survival for patients with paracetamol-related DILI was higher than for patients with non-paracetamol DILI (86%; 95% CI, 79-93% v 71%; 95% CI, 60-82%) and herbal and dietary supplement-related cases (59%; 95% CI, 34-85%). MELD score was an independent predictor of poorer 90-day transplant-free survival in both paracetamol-related (per point increase: adjusted hazard ratio [aHR], 1.19; 95% CI, 1.09-3.74) and non-paracetamol DILI (aHR, 1.24; 95% CI, 1.14-1.36). Conclusion In our single centre study, the proportion of cases of people hospitalised with DILI linked with herbal and dietary supplements has increased since 2009. Ninety-day transplant-free survival for patients with non-paracetamol DILI, especially those with supplement-related DILI, is poorer than for those with paracetamol-related DILI.

In this paper, we consider Google trends (G‐trends) as a measure of investors' attention in the predictability of stock returns across eleven major US sectors. The theoretical motivation for our paper is clear. In seeking information to guide investment decisions, investors' sentiments are shaped by news such as G‐trends that could induce changes in the prices of stocks. Thus, we construct a predictive model that incorporates G‐trends series as a predictor of stock returns and thereafter we account for evident asymmetry in G‐trends to analyze the signficance of positive‐ and negative‐worded news in the predictability of stock returns. We also compare single‐ and multi‐factor predictive models augmented with distinctive statistical effects against the baseline time series model. We highlight three key findings: (1) G‐trends record consistent negative correlations with stock returns across sectors. (2) The proposed predictive model with G‐trends outperforms the baseline (random walk) model. (3) The inclusion of asymmetry and macroeconomic variables improves the outperformance of G‐trends over the baseline model.

We examine the determinants of high‐interest entrusted loans in China from the perspective of corporate risk‐taking. The results of a baseline model illustrate that the propensity to offer high‐interest entrusted loans increases with loose monetary policies, corporate cash holdings and firm age, and it decreases with firm size and growth opportunity. These findings support the claim that firms offer high‐interest entrusted loans mainly for short‐term profits. Other determining factors include CEO behavior traits, market imperfections and the intensity of corporate governance. Specifically, market imperfections create an opportunity for risk‐taking while CEO behavior and the intensity of corporate governance affect a firm's tendency to take risk and engage in high‐interest entrusted loans.

Objective To compare the recommended three-view fetal heart screening method to detect major congenital heart disease (CHD) with more elaborate screening strategies to determine the cost-effective strategy in unselected (low-risk) pregnancies. Methods A decision-analytic model was designed to compare four screening strategies to identify fetuses with major CHD in a theoretical cohort of 4 000 000 births in the USA. The four strategies were: (1) three views: four-chamber view (4CV) and views of the left (LVOT) and right (RVOT) ventricular outflow tracts; (2) five views: 4CV, LVOT, RVOT and longitudinal views of the ductal arch and aortic arch; (3) five axial views: 4CV, LVOT, RVOT, three-vessel (3V) view and three-vessels-and-trachea view; and (4) six views: 4CV, LVOT, RVOT and 3V views and longitudinal views of the ductal arch and aortic arch. Outcomes related to neonatal mortality and neurodevelopmental disability were evaluated. The analysis was performed from a healthcare-system perspective, with a cost-effectiveness willingness-to-pay threshold set at $100 000 per quality-adjusted life year (QALY). Baseline analysis, one-way sensitivity analysis and Monte-Carlo simulation were performed. Results In our baseline model, screening with five axial views was the optimal strategy, detecting 3520 more CHDs, and resulting in 259 fewer children with neurodevelopmental disability, 40 fewer neonatal deaths and only slightly higher costs, compared with screening with the currently recommended three views. Screening with six views was more effective, but also cost considerably more, compared with screening with five axial views, and had an incremental cost of $490 023/QALY, which was over the willingness-to-pay threshold. The five-view strategy was dominated by the other three strategies, i.e. it was more costly and less effective in comparison. The data were robust when tested with Monte-Carlo and one-way sensitivity analysis. Conclusion Although current guidelines recommend a minimum of three views for detecting CHD during the mid-trimester anatomy scan, screening with five axial views is a cost-effective strategy that may lead to improved outcome compared with three-view screening. Copyright © 2020 ISUOG. Published by John Wiley & Sons Ltd.

We examine the efficiency and distributional effects of regressive and progressive public R&D policies that target high-tech and low-tech sectors using a heterogenous-agent growth model with in-house R&D and incomplete capital markets. We find that such policies have important implications for efficiency, inequality and social mobility. A regressive public R&D investment financed by income tax could boost growth and welfare via a positive effect on individual savings and effort. However, it could also lower growth and welfare via its effect on the efficiency--inequality trade off. Thus, the relationship between public R&D spending and welfare is hump shaped admitting an optimal degree of regressivity in public R&D spending. Using our baseline model and the US state level GDP data, we back out the degree of regressiveness of public R&D investment in US states. We find that US states are more regressive in their R&D investment than the optimal regressiveness implied by our growth model.

We describe the baseline model configuration and simulation characteristics of the Geophysical Fluid Dynamics Laboratory (GFDL)'s Atmosphere Model version 4.1 (AM4.1), which builds on developments at GFDL over 2013–2018 for coupled carbon‐chemistry‐climate simulation as part of the sixth phase of the Coupled Model Intercomparison Project. In contrast with GFDL's AM4.0 development effort, which focused on physical and aerosol interactions and which is used as the atmospheric component of CM4.0, AM4.1 focuses on comprehensiveness of Earth system interactions. Key features of this model include doubled horizontal resolution of the atmosphere (~200 to ~100 km) with revised dynamics and physics from GFDL's previous‐generation AM3 atmospheric chemistry‐climate model. AM4.1 features improved representation of atmospheric chemical composition, including aerosol and aerosol precursor emissions, key land‐atmosphere interactions, comprehensive land‐atmosphere‐ocean cycling of dust and iron, and interactive ocean‐atmosphere cycling of reactive nitrogen. AM4.1 provides vast improvements in fidelity over AM3, captures most of AM4.0's baseline simulations characteristics, and notably improves on AM4.0 in the representation of aerosols over the Southern Ocean, India, and China—even with its interactive chemistry representation—and in its manifestation of sudden stratospheric warmings in the coldest months. Distributions of reactive nitrogen and sulfur species, carbon monoxide, and ozone are all substantially improved over AM3. Fidelity concerns include degradation of upper atmosphere equatorial winds and of aerosols in some regions.

This paper documents state dependence in labor market \ud uctuations. Using a Threshold\ud Vector Autoregression model (TVAR), we establish that the unemployment rate, the job\ud separation rate, and the job finding rate exhibit a larger response to productivity shocks\ud during periods with low aggregate productivity. A Diamond-Mortensen-Pissarides model\ud with endogenous job separation and on-the-job search replicates these empirical regularities\ud well. We calibrate the model to match the standard deviation of the job-transition rates\ud explained by productivity shocks in the TVAR, and show that the model explains 88 percent\ud of the state dependence in the unemployment rate, 76 percent for the separation rate and\ud 36 percent for the job finding rate. The key channel underpinning state dependence in both\ud job separation and job finding rates is the interaction of the firm's reservation productivity\ud level and the distribution of match-specific idiosyncratic productivity. Results are robust\ud across several variations to the baseline model.

The inability of a simple real business cycle model to predict a rise in consumption in response to increased government expenditures observed in many empirical studies, has stimulated the development of alternative theories of government spending shocks. Our objective is to evaluate these models in terms of how they fit the data.We use Bayesian approach to estimate and compare macroeconomic models of government spending.We find that neither of the considered transmission mechanisms for government spending helps improve the fit of the baseline model. Moreover, all estimated models fail to predict a rise in consumption in response to increased government spending.

Drawing on Harrod, Kalecki and Kaldor, this paper seeks to revive the view that ceteris paribus firms reduce investment if they have already built up high capacities relative to their assessment of the normal potential of their markets. This reaction introduces a fundamental stabilizing mechanism into the economy. The paper adapts the idea to a growth context and applies it to the neo-Kaleckian baseline model with its Harrodian instability. It demonstrates that, in principle, a sufficiently strong feedback could stabilize the steady state.

Previously, Google Trends indices have been found to be useful in improving the tourism demand forecast accuracy relative to a purely autoregressive baseline model. The purpose of this study is to extend previous research in terms of comparing the forecasting accuracy of cities and countries using Google Trends Web and image indices. The study compares forecasting models with Web and/or image search indices regarding 2 cities (Vienna and Barcelona) and 2 countries (Austria and Belgium). Overall, the forecast accuracy of Vienna with the Web and/or image indices was the best among the 4 destinations, followed by Belgium, Barcelona, and Austria.

Survival models are developed to predict response and time-to-response for mortality in rabbits following exposures to single or multiple aerosol doses of Bacillus anthracis spores. Hazard function models were developed for a multiple-dose data set to predict the probability of death through specifying functions of dose response and the time between exposure and the time-to-death (TTD). Among the models developed, the best-fitting survival model (baseline model) is an exponential dose-response model with a Weibull TTD distribution. Alternative models assessed use different underlying dose-response functions and use the assumption that, in a multiple-dose scenario, earlier doses affect the hazard functions of each subsequent dose. In addition, published mechanistic models are analyzed and compared with models developed in this article. None of the alternative models that were assessed provided a statistically significant improvement in fit over the baseline model. The general approach utilizes simple empirical data analysis to develop parsimonious models with limited reliance on mechanistic assumptions. The baseline model predicts TTDs consistent with reported results from three independent high-dose rabbit data sets. More accurate survival models depend upon future development of dose-response data sets specifically designed to assess potential multiple-dose effects on response and time-to-response. The process used in this article to develop the best-fitting survival model for exposure of rabbits to multiple aerosol doses of B. anthracis spores should have broad applicability to other host-pathogen systems and dosing schedules because the empirical modeling approach is based upon pathogen-specific empirically-derived parameters.

Within the framework of the Kaleckian baseline model where firms finance investment by equities and debt, this note is concerned with a recent proposal by E. Hein to replace the common concept of a given retention rate with a given dividend rate. Considering in more detail the implications of his additional assumption of constant stock prices, it is revealed that the retained earnings of the firms will then be non-positive in a long-run financial equilibrium. It is furthermore shown that another and empirically more plausible assumption on the firms’ equity issuance policy overcomes this deficiency and can also imply numerically acceptable retention rates.

Summary This paper presents a rehabilitation technique developed under a design and construction scheme, termed minimal-disturbance seismic rehabilitation. This scheme pursues enhancing the seismic performance of buildings with the intention of improving the continuity of business while minimizing obstruction of the visual and physical space of building users and the use of heavy construction equipment and hot work (welding/cutting). The developed rehabilitation technique consists of light-weight steel elements and aims to decrease demands to beam-ends of steel moment-resisting frames. The behavior of the baseline model was verified through numerical analysis and proof-of-concept testing. Furthermore, the effectiveness of rehabilitation is studied through retrofitting a four-story steel moment-resisting frame originally designed with Japanese design guidelines. Copyright © 2015 John Wiley & Sons, Ltd.

Background and Aim Chronic hepatitis C virus (HCV) infection is an important cause of advanced liver disease and liver-related deaths in Australia. Our aim was to describe the burden of HCV infection and consider treatment strategies to reduce HCV-related morbidity and mortality. Methods Baseline model parameters were based upon literature review and expert consensus with a focus on Australian data. Three treatment scenarios based on anticipated introduction of improved direct-acting antiviral regimens were considered to reduce HCV disease burden. Scenario 1 evaluated the impact of increased treatment efficacy alone (to 80–90% by 2016). Scenario 2 evaluated increased efficacy and increased treatment uptake (2550 to 13 500 by 2018) without treatment restriction, while Scenario 3 considered the same increases with treatment limited to ≥ F3 during 2015–2017. Results In 2013, there were an estimated 233 490 people with chronic HCV infection: 13 850 with cirrhosis, 590 with hepatocellular carcinoma (HCC) and 530 liver-related deaths. If the current HCV treatment setting is unchanged, threefold increases in the number of people with cirrhosis, HCC, and liver disease deaths will be seen by 2030. Scenario 1 resulted in modest impacts on disease burden (4% decrease in HCC, decompensated cirrhosis, and liver deaths) and costs. Scenario 3 had the greatest impact on disease burden (approximately 50% decrease in HCC, decompensated cirrhosis, and liver deaths) and costs, while Scenario 2 had slightly lesser impact. Conclusions Considerable increases in the burden of HCV-related advanced liver disease and its complications will be seen in Australia under current treatment levels and outcomes. Introduction of improved direct-acting antiviral regimens with enhanced efficacy at current treatment levels will lead to limited impacts on this disease burden. A combination of increased treatment efficacy and greater uptake is required to achieve major reductions in advanced liver disease and related costs.

Using high-quality Norwegian register data on 49,879 children from 23,655 families, the authors estimated sibling fixed-effects models to explore whether children who are younger at the time of a parental union dissolution perform less well academically, as measured by their grades at age 16, than their older siblings who have spent more time living with both biological parents. Results from a baseline model suggest a positive age gradient that is consistent with findings in some of the extant family structure literature. Once birth order is taken into account, the gradient reverses. When analyses also control for grade inflation by adding year of birth to the model, only those children who experience a dissolution just prior to receiving their grades appear relatively disadvantaged. The results illustrate the need to specify and interpret sibling fixed-effects model with great care.

Short-TE MRS has been proposed recently as a method for the in vivo detection and quantification of gamma-aminobutyric acid (GABA) in the human brain at 3 T. In this study, we investigated the accuracy and reproducibility of short-TE MRS measurements of GABA at 3 T using both simulations and experiments. LCModel analysis was performed on a large number of simulated spectra with known metabolite input concentrations. Simulated spectra were generated using a range of spectral linewidths and signal-to-noise ratios to investigate the effect of varying experimental conditions, and analyses were performed using two different baseline models to investigate the effect of an inaccurate baseline model on GABA quantification. The results of these analyses indicated that, under experimental conditions corresponding to those typically observed in the occipital cortex, GABA concentration estimates are reproducible (mean reproducibility error

This paper studies effects of price floors in a simple model of vertical product differentiation. We find that even non-binding price floor (i.e., minimum price set below the lowest Nash equilibrium price in the baseline model) can increase quality on the market, if the cost of quality is sufficiently low. Where a binding price floor does not increase the equilibrium quality, it makes consumers worse off. There is also a possibility of over-investment into quality as a result of the binding minimum price.

Broiler meat is regarded as the most common source of Campylobacter infection and risk management measures are required to reduce broiler meat contamination. Among the quantitative risk assessments for Campylobacter in broiler meat, evaluation of the poultry processing stage is particularly important for predicting the contamination level of broiler meat and the effects of control measures. In this study, we built a simulation model for cross-contamination during poultry processing focusing on Campylobacter contamination at the individual carcass level. Using this model, we examined changes in the prevalence of contaminated carcasses and the number of Campylobacter per carcass after processing. As a result, it was found that the prevalence and number of Campylobacter after processing were largely influenced by the number of Campylobacter on the contaminated carcasses before processing. In the baseline model, where it was assumed that the mean number of Campylobacter on contaminated carcasses before processing was 4.7 log₁₀ cfu per carcass, the prevalence after processing was less than that before processing. Although the median value of Campylobacter on contaminated carcasses was reduced after processing, the distributions after processing became wider and the upper limit of the 95% credible interval of Campylobacter on contaminated carcasses remained elevated. The individual-based simulation model can trace individual level changes considering discrete interactions, while models tracing mean values cannot handle these interactions in detail. The individual-based approach is considered useful for modelling cross-contamination among individual carcasses during poultry processing.

With 5 figures and 6 tables Abstract Early generation plant breeding trials are often laid out in unreplicated designs with replicated checks. This study compares the systematic arrangement of check plots to an allocation, which has two or more different arrangements of check plots within blocks. Four uniformity trials were overlaid with augmented designs, which differed in block size, number and type of checks. Another aim was to compare different spatial models and a baseline model, which did not consider spatial trend. Model fit was assessed using the average empirical variance (EMP) for each arrangement. For three of the four uniformity trials the lowest EMP was found for the arrangement of check plots with different defined distributions of checks within blocks. The systematic arrangement was superior only in one uniformity trial. We propose therefore to allocate checks with different arrangements within blocks. The comparison of the spatial models showed that the linear variance model, the AR(1) model, and the spherical model, all assuming a nugget, were the most promising candidates. A further gain in precision could be detected by a two-dimensional analysis.

Measuring the size of China's trade surplus with the U.S. has always been controversial among academics and policy makers. The issue becomes more complicated with unprecedented impact of foreign direct investment on the bilateral trade between China and the U.S. Any proposal about China-US trade policies has to be able to explain the implications of this impact. This paper examines the trade imbalance between China and the U.S. under the ownership-based framework. It extends the baseline model developed, respectively, by NAS, Julius and BEA into a three-country framework, consisting of the domestic economy, the foreign economy and the rest of the world. The paper also experiments with the simplified form of this new framework by estimating the size of the China-US trade imbalance. The results of the study show that non-US foreign direct investment in China is mostly responsible for China's trade surplus with the U.S. In 2003, 71.5 percent of China's trade surplus with the U.S. could be attributed to non-US foreign direct investment in China. As a result, China's ownership-based trade surplus was surprisingly smaller than that measured with conventional cross-border method. In 2003, for instance, China's ownership-based trade surplus with the U.S. was $11.9 billion, only 20 percent of the conventional cross-border measure. It is believed that the approach adopted in this study is capable of revealing more information than both NAS-Julius-BEA approach and the conventional trade accounting method.

The results of this study include the incorporation of water quality parameters and a growth function into a bioeconomic model of shrimp culture. Since the level of various parameters is unpredictable for future years (i.e., weather, ponds experiencing low oxygen), randomization of these parameters was introduced into the model. The results consist of a mean and standard deviation of profit determined from 25 replications of the model. The baseline model indicates that a mean profit of $679 per hectare would be achieved with only a 5% chance of a loss. Sensitivity tests of profit in the model, consisting primarily of changes in biological and environmental parameters, illustrate the usefulness of the model in directing future research.

This paper extends the work on challenger spending in congressional elections to allow for the possibility that such spending has varying effects, depending upon circumstances. Using data from the 1988 and 1990 U.S. House elections, it begins with a baseline model for the incumbent's margin. Pairs of dummy variables and interaction terms were then added to test several hypotheses. The results provide some evidence to suggest that challenger spending is less effective if the challenger faced a toughprimary. Contrary to previous work, an advantage was found for repeat challengers. Also contrary to previous work, the results do not suggest that high-quality challengers spend their money more efficiently than do other challengers.

BACKGROUND: β-Glucan is a bioactive component of cereal grains that has many potential uses and health-promoting benefits. Recent research has focused on improving the nutritional value of food by increasing human exposure to β-glucan. This study looks at the development of a farm-level baseline model (including scenario analysis) to evaluate the impact of pre- and postharvest stages (including genotypic factors, environmental conditions, agronomic factors and storage) on β-glucan levels in barley. Monte Carlo simulation techniques were employed to model various stages in pre- and postharvest processes and to simulate the factors influencing the level of β-glucan content in both hulled barley (HB) and hull-less barley (HLB) genotypes. RESULTS: The baseline model found that the mean simulated level of β-glucan was 40.99 and 56.77 g kg−1 for HB and HLB genotypes respectively. A sensitivity analysis highlighted that genotype was the most important parameter in determining the final β-glucan content (correlation coefficients of 0.66 and 0.78 for HB and HLB respectively), more so than any of the agronomic factors analysed. The scenario analysis highlighted the importance of harvest date (scenario 2) and storage conditions (scenario 3), with a potential 32.6 and 32.7% decrease in β-glucan (compared with the baseline model) if harvesting is carried out early during physiological maturity (i.e. at growth stage 92) and a potential 20.1 and 19.5% increase in β-glucan for HB and HLB respectively if storage time is minimised. CONCLUSION: This study predicted the influence of genotypic, pre- and postharvest operations on β-glucan content and thus allows strategies to be identified to influence β-glucan content in barley products. Copyright © 2008 Society of Chemical Industry

This paper surveys models of voting on redistribution. Under reason- able assumptions, the baseline model produces an equilibrium with the extent of redistributive taxation chosen by the median income earner. If the median is poorer than average, redistribution is from rich to poor, and increasing inequality increases redistribution. However, under different assumptions about the economic environment, redistribution may not be simply rich to poor, and inequality need not increase redistribution. Several lines of argument are presented, in particular, political participation, public provision of private goods, public pensions, and tax avoidance or evasion.

Purpose: To investigate the impact of outliers on knowledge modeling in radiation therapy, and develop a systematic workflow for identifying and analyzing geometric and dosimetric outliers using pelvic cases. Methods: Four groups (G1-G4) of pelvic plans were included: G1 (37 prostate cases), G2 (37 prostate plus lymph node cases), and G3 (37 prostate bed cases) are all clinical IMRT cases. G4 are 10 plans outside G1 re-planned with dynamic-arc to simulate dosimetric outliers. The workflow involves 2 steps: 1. identify geometric outliers, assess impact and clean up; 2. identify dosimetric outliers, assess impact and clean up.1. A baseline model was trained with all G1 cases. G2/G3 cases were then individually added to the baseline model as geometric outliers. The impact on the model was assessed by comparing leverage statistic of inliers (G1) and outliers (G2/G3). Receiver-operating-characteristics (ROC) analysis was performed to determine optimal threshold. 2. A separate baseline model was trained with 32 G1 cases. Each G4 case (dosimetric outliers) was then progressively added to perturb this model. DVH predictions were performed using these perturbed models for remaining 5 G1 cases. Normal tissue complication probability (NTCP) calculated from predicted DVH were used to evaluate dosimetric outliers’ impact. Results: The leverage of inliers and outliers was significantly different. The Area-Under-Curve (AUC) for differentiating G2 from G1 was 0.94 (threshold: 0.22) for bladder; and 0.80 (threshold: 0.10) for rectum. For differentiating G3 from G1, the AUC (threshold) was 0.68 (0.09) for bladder, 0.76 (0.08) for rectum. Significant increase in NTCP started from models with 4 dosimetric outliers for bladder (p

A system dynamics model of HIV/AIDS epidemiology is employed to simulate the effects of triple combination antiretroviral therapy in the treatment of HIV/AIDS. The epidemic data on homosexual men in the UK (1981--1998) have been used to fit the baseline model. The new combination therapies, which supplanted antiviral mono and dual therapy in 1996, are proving to be the most effective prophylaxis yet for halting viral replication in vivo, but many uncertainties still surround their use. A range of model-generated scenarios are created as a means of considering these uncertainties. Possible futures for the HIV/AIDS epidemic are constructed.

Influence functions are considered as diagnostics for model departures in parametric Bayesian inference. A baseline model density is expressed as a mixture; then the mixing distribution is perturbed. This is designed to engender perturbations which are plausible a priori. The influence of perturbations is measured for both Bayes estimates and their associated posterior expected losses. To assess the plausibility of perturbations a posteriori, an additional influence function is constructed for the Bayes factor comparing the perturbed and baseline models. The effect of perturbation on various estimands is incorporated in the analysis. Les fonctions d'influence sont considerees en tant que diagnostic de l'ecart par rapport a un modele en inference bayesienne parametrique. Nous exprimons une densite de reference comme un melange; puis la distribution de melange est perturbtee dans le but de generer des perturbations plausibles a priori. L'influence des perturbations est mesuree pour les estimations bayesiennes ainsi que pour leurs pertes moyennes a posteriori. Nous construisons une fonction d'influence additionnelle pour le facteur de Bayes comparant les modeles perturbes et ceux de reference, cela dans le but d'evaluer la plausibilite des perturbations a posteriori. Nous incorporons dans l'analyse l'effet de la perturbation sur differents estimateurs.