Model Assessment Method Research Articles

The value of multiparameter combinations for predicting difficult airways by ultrasound

BackgroundBased on the upper airway anatomy and joint function parameters examined by ultrasound, a multiparameter ultrasound model for difficult airway assessment (ultrasound model) was established, and we evaluated its ability to predict difficult airways.MethodsA prospective case-cohort study of difficult airway prediction in adult patients undergoing elective surgery with endotracheal intubation under general anesthesia, and ultrasound phantom examination for difficult airway assessment before anesthesia, including hyomental distance, tongue thickness, mandibular condylar mobility, mouth opening, thyromental distance, and modified Mallampati tests, was performed. Receiver operating characteristic (ROC) curve analysis was used to evaluate the effectiveness of the ultrasound model and conventional airway assessment methods in predicting difficult airways.ResultsWe successfully enrolled 1000 patients, including 51 with difficult laryngoscopy (DL) and 26 with difficult tracheal intubation (DTI). The area under the ROC curve (AUC) for the ultrasound model to predict DL was 0.84 (95% confidence interval [CI]: 0.82–0.87), and the sensitivity and specificity were 0.75 (95% CI: 0.60–0.86) and 0.82 (95% CI: 0.79–0.84), respectively. The AUC for predicting DTI was 0.89 (95% CI: 0.87–0.91), and the sensitivity and specificity were 0.85 (95% CI: 0.65–0.96) and 0.81 (95% CI: 0.78–0.83), respectively. Compared with mouth opening, thyromental distance, and modified Mallampati tests, the ultrasound model predicted a greater AUC for DL (P < 0.05). Compared with mouth opening and modified Mallampati tests, the ultrasound model predicted a greater AUC for DTI (P < 0.05).ConclusionsThe ultrasound model has good predictive performance for difficult airways.Trial registrationThis study is registered on chictr.org.cn (ChiCTR-ROC-17013258); principal investigator: Jianling Xu; registration date: 06/11/2017).

Development of the Architecture and Reconfiguration Methods for the Smart, Self-Reconfigurable Manufacturing System

Over recent decades, the demand for smarter and more intelligent manufacturing systems has increased in order to meet the growing requirements of customers. Manufacturing systems are termed as smart manufacturing systems (SMSs); these systems are capable of fully integrated autonomous operation. Specifically, the concept of autonomous systems and functions has been adopted for next generation manufacturing systems (NGMSs). Among these NGMSs, the fractal manufacturing system (FrMS) exhibits several characteristics that are similar to those of SMSs. Therefore, in this paper, a smart, self-reconfigurable manufacturing system (SSrMS) based on the FrMS is proposed. The proposed SSrMS architecture was designed for realizing self-reconfiguration functions based on the FrMS concept. SSrMS exhibits a fractal structure, which enables the distribution of control features; this also constitutes the fundamental basis of autonomous operation and reconfiguration between each fractal. SSrMS architecture includes the use of big data, digital facilities, and simulations. Furthermore, we introduce three reconfiguration methods to conduct system reconfiguration, which are a goal decision model, a negotiation model, and a sustainability assessment method. The goal decision model was developed to determine a goal of each fractal to achieve the system’s goal. In other words, each fractal can decide a goal to achieve the system’s goal, such as maximizing productivity or profit, or minimizing cost, and others. The negotiation model was adopted to perform partial process optimization by reassigning tasks and resources between the fractals, based on the goal of coping with the changes in the system’s condition. The sustainability assessment method was designed to simultaneously evaluate sustainability with respect to the system’s goals. The proposed architecture of SSrMS with goal decision model, negotiation model, and sustainability assessment method has the features of self-optimization, self-organization, and self-reconfiguration in order to achieve fully autonomous operations for the manufacturing system. The proposed architecture including three methods are expected to provide a fundamental study of the autonomous operations. The main findings of in this study is the development of a new architecture for fully autonomous operations of the smart manufacturing system with reconfiguration methods of goal-oriented manufacturing processes.

Insulin resistance, leptin and adiponectin in lean and hypothyroid children and adolescents with obesity

BackgroundObesity usually complicates hypothyroidism. Adipokines like leptin and adiponectin secreted by adipose tissue modulate insulin resistance (IR), appetite, and obesity. The association between adipokines, IR, and thyroid hormone has not been sufficiently studied in children. We investigated leptin and adiponectin as well as IR and their association with thyroid hormone in both lean and hypothyroid children and adolescents with obesity.MethodsThe study included 30 lean hypothyroid, 30 hypothyroid children and adolescents with obesity, and 30 healthy lean children as the control group. Serum thyroid-stimulating hormone (TSH), free triiodothyronine (fT3), free thyroxine (fT4), fasting blood glucose, fasting insulin, homeostatic model assessment method of insulin resistance (HOMA-IR), leptin, and adiponectin levels were estimated in all participants.ResultsFasting insulin, HOMA-IR, and leptin levels were significantly elevated in hypothyroid children compared to the control group; more in hypothyroid children with obesity. In contrast, adiponectin levels were significantly lower in the hypothyroid children with obesity compared to the lean hypothyroid children and controls. HOMA-IR was positively correlated to TSH and BMI but inversely correlated with fT3 and fT4 in hypothyroid children. There was no correlation between IR and either leptin or adiponectin levels. Leptin and adiponectin levels correlated well with BMI in hypothyroid children and adolescents with obesity.ConclusionInsulin resistance and leptin levels are increased in hypothyroid children and adolescents; more in those with obesity. IR is not related to leptin and adiponectin levels, however, leptin and adiponectin levels correlate well with BMI in hypothyroid children and adolescents with obesity.ImpactInsulin resistance (IR) and leptin levels increase in hypothyroid children and adolescent; more with obesity. IR is not related to leptin and adiponectin levels, however leptin and adiponectin levels correlated well with BMI in hypothyroid children and adolescents with obesity.

Probing Photoreceptor Outer Segment Phagocytosis by the RPE In Vivo: Models and Methodologies.

In the vertebrate retina, the light-sensitive photoreceptor rods and cones constantly undergo renewal by generating new portions of the outer segment and shedding their distal, spent tips. The neighboring RPE provides the critical function of engulfing the spent material by phagocytosis. RPE phagocytosis of shed rod outer segment fragments is a circadian process that occurs in a burst of activity shortly after daily light onset with low activity at other times, a rhythm that has been reported for many species and over 50 years. In this review, we compare studies on the rhythm and quantity of RPE phagocytosis using different in vivo model systems and assessment methods. We discuss how measurement methodology impacts the observation and analysis of RPE phagocytosis. Published studies on RPE phagocytosis investigating mice further suggest that differences in genetic background and housing conditions may affect results. Altogether, a comparison between RPE phagocytosis studies performed using differing methodology and strains of the same species is not as straightforward as previously thought.

Virus Elimination by Direct-Acting Antiviral Agents Impacts Glucose Homeostasis in Chronic Hepatitis C Patients.

Background and AimsChronic hepatitis C virus (HCV) infection is associated with dysregulation of glucose homeostasis, including insulin resistance (IR) and type 2 diabetes. However, independent risk factors associated with IR in chronic HCV-infected patients have not been detailly elucidated. Previous data regarding the impact of HCV elimination by direct-acting antiviral agents (DAAs) on glucose homeostasis is insufficient and controversial. This study aimed to analyze the independent factors associated with IR and to evaluate the changes in glucose homeostasis in chronic HCV-infected patients treated with DAAs therapies.MethodsWe screened 704 patients with chronic HCV infection who underwent treatment with interferon-free DAAs. Patients’ baseline characteristics, biochemical and virological data were collected. The outcome measurements were their IR and β-cell function assessed by the homeostasis model assessment (HOMA) method at baseline and 12-weeks post-treatment.ResultsHigh IR (HOMA-IR ≥ 2.5) was observed in 35.1% of the patients. Multivariable logistic regression analysis revealed that body mass index (BMI) >25 kg/m2, treatment experience, elevated baseline levels of alanine aminotransferase (ALT) and triglyceride, as well as Fibrosis-4 score >3.25 were independently associated with high IR. In patients who achieved sustained virological response (SVR), no significant change in mean HOMA-IR was observed from baseline to 12-weeks post-treatment (2.74 ± 2.78 to 2.54 ± 2.20, p = 0.128). We observed a significant improvement in β-cell secretion stress from 121.0 ± 110.1 to 107.6 ± 93.0 (p = 0.015). Subgroup analysis revealed that SVR was associated with a significant reduction in mean HOMA-IR in patients with baseline HOMA-IR ≥ 2.5 (5.31 ± 3.39 to 3.68 ± 2.57, p < 0.001), HCV genotype 1 (3.05 ± 3.11 to 2.62 ± 2.05, p = 0.027), and treatment experience (4.00 ± 3.37 to 3.01 ± 2.49, p = 0.039).ConclusionsThere were several independent factors associated with IR in patients with chronic HCV infection, including obesity, treatment experience, high serum ALT and triglyceride levels, as well as advanced hepatic fibrosis. After viral elimination by DAAs, we observed a significant reduction in mean HOMA-IR in patients with baseline high IR, HCV genotype 1, and treatment experience.

Water quality evolution mechanism modeling and health risk assessment based on stochastic hybrid dynamic systems

Water quality assessment analysis is an important technical means for water pollution prevention and control. In this research area, mechanism models and real observation data of water quality evolution are always used to perform water quality assessment. However, the existing water quality evolution mechanism modeling researches commonly use a single time-invariant model to model the water quality evolution process. It is inappropriate to directly describe the complex behavior of long-term water quality evolution with the existing models, since the evolution process contain different feature states, and the water quality evolution characteristics under these states are different. In addition, the existing water quality assessment methods are mostly methods for directly processing and calculating the observation data of water quality. This makes the existing methods difficult to effectively compensate for the contingency and randomness in the water quality evolution process, which leads to deviations and errors in performing the water quality assessment. Considering these deficiencies, this paper proposes a water quality evolution mechanism modeling and health risk assessment method based on stochastic hybrid dynamic systems (SHDS). Firstly, a hybrid water quality evolution mechanism (H-WQEM) model is established based on SHDS, and a hybrid improved fruit fly optimization algorithm (H-IFFOA) is proposed to identify the unknown parameters of the H-WQEM model. Then, an improved interacting multiple model extended Kalman filter algorithm (IIMM-EKF) is employed to estimate the probability distribution of the hybrid state of the H-WQEM model, including the probability distribution of different feature states of water bodies and the probability distribution of water quality indexes under these states. Finally, the health degree of water quality is proposed as an indicator to achieve a quantitative assessment of the water health risk status. Real observation data from a monitoring station at Baiyangwan in China is used to validate the effectiveness of the proposed method. The results show that the method can effectively describe the complex water quality evolution process, and reasonably assess the water quality health risk status.

Research on online teaching problems and countermeasures

Through the online teaching survey of polytechnic, it is found that there are few mature high-quality online courses in polytechnic at this stage, teachers’ teaching ability and teaching methods cannot meet the teaching needs, and students’ learning initiative and enthusiasm are not high. In response to the above problems, it is proposed to use information technology to explore how to promote the reform of vocational education in the aspects of quality curriculum construction, teaching model innovation, assessment and evaluation methods, and new forms of integrated teaching materials.

Association of serum 25-hydroxyvitamin D3 levels and insulin resistance with viral load and degree of liver fibrosis in Egyptian chronic HBV patients: a case-control study.

Aim of the studyTo assess serum 25-hydroxyvitamin D3 level and insulin resistance (IR) in hepatitis B virus (HBV) patients compared with controls and to evaluate the correlation with HBV viral load, severity of liver disease and degree of liver fibrosis.Material and methodsA case-control study. Sixty HBV patients and 60 controls were enrolled. Chemiluminescence was used to determine 25-hydroxyvitamin D3 levels. Insulin resistance was evaluated using the homeostasis model assessment method. Polymerase chain reaction was used to quantify HBV viral loads. Severity of liver disease was assessed by Child-Pugh scores. Transient elastography was used to evaluate the degree of liver fibrosis.Results25-Hydroxyvitamin D3 deficiency is more prevalent among HBV patients compared to controls. 25-Hydroxyvitamin D3 levels declined considerably as viral load rose (p < 0.001). 25-Hydroxyvitamin D3 level declined as liver fibrosis progressed (34.0 ±0.0 ng/ml in F1 vs. 12.67 ±8.0 ng/ml in F4) and the severity of the disease increased (22.75 ±6.36 ng/ml in Child A vs. 5.50 ±0.58 ng/ml in Child C). Insulin resistance is more prevalent among HBV patients compared to controls and it appeared to deteriorate progressively with boosting of the viral load, degree of fibrosis and severity of liver disease (p < 0.001).ConclusionsHBV patients had significantly lower 25-hydroxyvitamin D3 levels compared to healthy individuals and HBV infection is associated with IR. 25-Hydroxyvitamin D3 deficiency and IR were associated with HBV viral loads, severity of liver disease, and degree of liver fibrosis.

Stochastic model assessment of low-cost devices considering the impacts of multipath effects and atmospheric delays

A proper stochastic model is urgently required in Global Navigation Satellite System (GNSS) data processing, whereas deficient knowledge of stochastic model often occurs, especially for low-cost devices such as low-cost receivers and smartphones. The stochastic modeling for low-cost devices considering the impacts of multipath effects and atmospheric delays is systematically studied in this paper. Firstly, two stochastic model assessment methods for low-cost devices are deduced. Secondly, the stochastic modeling including measurement precision and physical correlation for the low-cost receiver and smartphone is comprehensively investigated with single-differenced residuals by using different baseline lengths. The results show that the measurement precisions of short baseline and medium-long baseline for the low-cost receiver are worse than zero baseline due to the systematic errors. The code measurement precisions are similar between short baseline and medium-long baseline, whereas the phase measurement precisions of medium-long baseline are larger than short baseline. Moreover, the measurement precisions of the smartphone are much worse than the low-cost receiver and the C/N0-dependent function is more suitable for the smartphone measurements. For the physical correlation, the measurements of zero baseline are free of cross correlation, but the cross correlation can exist in short baseline and medium-long baseline. The phase measurements of the low-cost receiver are free of temporal correlation for zero baseline, but the code measurements show a certain temporal correlation. Meanwhile, the temporal correlations of the low-cost receiver can exist in short baseline and they are larger than zero baseline due to the multipath effects. In the medium-long baseline, the temporal correlations of code measurements are similar with short baseline both for the low-cost receiver and smartphone, whereas the temporal correlations of phase measurements become larger than short baseline. It can be concluded that the unmodeled errors such as multipath effects and atmospheric delays should be taken into account in the realistic stochastic model assessment including measurement precision and physical correlation for the low-cost devices. Meanwhile, different baseline lengths may consider the baseline-specific stochastic model better in realistic precise positioning.

Spatial Assessment of Jerusalem Artichoke’s Potential as an Energy Crop in the Marginal Land of the Shaanxi Province, China

As a foodstuff crop, Jerusalem artichoke has a promising prospect for providing sustainable feed-stock sources for bioenergy development. Due to relatively limited cultivated land resources in China, it is crucial to evaluate Jerusalem artichoke’s potential production capacity in marginal land. Based on Jerusalem artichoke’s growth and photosynthetic characteristics, the agricultural production systems simulator model (APSIM) and multi-factor integrated assessment method were integrated to provide an operational method for comprehensively evaluating the marginal land resources suitable for developing the plantation of Jerusalem artichoke in the Shaanxi province, China. The results showed that 0.73 million ha of marginal land was suitable for Jerusalem artichoke cultivation in the Shaanxi province, and 5.4 million ha of marginal land was fairly suitable for Jerusalem artichoke cultivation, with the yield reaching 44,289 kg/ha and 38,861 kg/ha, respectively. The suitable land resources are mainly located in Yan’an (0.18 million ha), Hanzhong (0.13 million ha), and Baoji (0.08 million ha), most of which are moderate dense grassland (accounting for 50.6% of suitable land), dense grassland (accounting for 16.2% of suitable land), shrubland (accounting for 14.7% of suitable land), and sparse forest land (accounting for 9.18% of suitable land). The findings of this study can be used to establish targeted policies for Jerusalem artichoke development in China and other countries, particularly those along the Silk Road.

Generalized Linear Models to Identify the Impact of Road Geometric Design Features on Crash Frequency in Rural Roads

One of the first steps in improving the road network is to implement a traffic safety program and identify problematic road segments where local risk factors increase the likelihood of vehicular accidents. Numerous studies have attempted to identify both the geometric and traffic features of road segments that may lead to increased risk. The statistical models linking the number of accidents at road sites over a unit of time and the characteristics of these sites contribute to highlighting and quantifying the impact of the development of road infrastructure on the risk of accidents. The evaluation and selection of the best-fit model are usually carried out using an information criterion. The main purpose of this research is to propose a comparative assessment framework to support the evaluation task during the process of modeling crash frequency. Performance evaluation was conducted using additional approaches, specifically data splitting and rootograms. In this study, we focus on Korean rural road accident records. Six generalized linear models were applied, and their performance was compared to determine the best-fitting statistical model. According to each model assessment method used, the negative binomial hurdle model showed the best performance among all the investigated models.

A Study of STEAM Model Development and Assessment Method for Deep Learning: Through the Voice of Mimesis and Brontë

This research aimed to explore a humanities-based STEAM (H-STEAM) model for EFL undergraduates (n = 72) to improve deeper thoughts and language proficiency in a multidisciplinary setting. The H-STEAM focused on the integration of a literature text, Emily Brontë’s Wuthering Heights, and the philosophical analysis framework of mimesis as a humanities resource. For a specific teaching and learning method, various group activities based on project-based learning (PBL) were fabricated to enhance collaborative and conceptual learning both inside and outside of the classroom. Analyzing the relationship of Wuthering Heights and mimesis, learners shared and adopted peers’ opinions with openness. They could improve problem-solving ability, caring, communication skills, and self-reviewing practice when accumulating content knowledge and generating creative ideas. This study organized student-based assessment; self-assessment (SA) and peer-assessment (PA). SPSS 25 was conducted for the correlation and reliability analysis of SA and PA, and the evaluation of linguistic improvement. The results indicate that the H-STEAM facilitating PBL can be more workable through openness and community caring. The integration of collaborative and conceptual learning through PBL can empower learners’ autonomy and produce deeper thoughts, which can contribute to deep learning. Consequently, this study may suggest a path to develop H-STEAM for higher education.

A Novel and Highly Effective Bayesian Sampling Algorithm Based on the Auxiliary Variables to Estimate the Testlet Effect Models

In this paper, a new two-parameter logistic testlet response theory model for dichotomous items is proposed by introducing testlet discrimination parameters to model the local dependence among items within a common testlet. In addition, a highly effective Bayesian sampling algorithm based on auxiliary variables is proposed to estimate the testlet effect models. The new algorithm not only avoids the Metropolis-Hastings algorithm boring adjustment the turning parameters to achieve an appropriate acceptance probability, but also overcomes the dependence of the Gibbs sampling algorithm on the conjugate prior distribution. Compared with the traditional Bayesian estimation methods, the advantages of the new algorithm are analyzed from the various types of prior distributions. Based on the Markov chain Monte Carlo (MCMC) output, two Bayesian model assessment methods are investigated concerning the goodness of fit between models. Finally, three simulation studies and an empirical example analysis are given to further illustrate the advantages of the new testlet effect model and Bayesian sampling algorithm.

Plotting receiver operating characteristic and precision-recall curves from presence and background data.

The receiver operating characteristic (ROC) and precision–recall (PR) plots have been widely used to evaluate the performance of species distribution models. Plotting the ROC/PR curves requires a traditional test set with both presence and absence data (namely PA approach), but species absence data are usually not available in reality. Plotting the ROC/PR curves from presence‐only data while treating background data as pseudo absence data (namely PO approach) may provide misleading results.In this study, we propose a new approach to calibrate the ROC/PR curves from presence and background data with user‐provided information on a constant c, namely PB approach. Here, c defines the probability that species occurrence is detected (labeled), and an estimate of c can also be derived from the PB‐based ROC/PR plots given that a model with good ability of discrimination is available. We used five virtual species and a real aerial photography to test the effectiveness of the proposed PB‐based ROC/PR plots. Different models (or classifiers) were trained from presence and background data with various sample sizes. The ROC/PR curves plotted by PA approach were used to benchmark the curves plotted by PO and PB approaches.Experimental results show that the curves and areas under curves by PB approach are more similar to that by PA approach as compared with PO approach. The PB‐based ROC/PR plots also provide highly accurate estimations of c in our experiment.We conclude that the proposed PB‐based ROC/PR plots can provide valuable complements to the existing model assessment methods, and they also provide an additional way to estimate the constant c (or species prevalence) from presence and background data.

Analysis of executive transaction characteristics based on machine learning cross-validation

Executive trading is a kind of investment strategy that analyses and models the financial market with mathematical methods. Machine learning requires computer programs to improve their performance at specific tasks by learning on data sets. Both require the extraction of information from data, so with the success of machine learning in recent years, there has been a strong interest in the combination of executive trading and machine learning methods in both industry and academia. In this paper, based on machine learning is complete, a more complete model of data processing - build - generated forecast - trading strategy - back-test analysis “research framework, and discusses the structures of branching and merging model structure set up, based on the value relevance of the output value and supervision model selection criteria, independently of the back and trading strategy model assessment method and a series of important issues. This paper focuses on the innovative exploration of the application of the loss function, and puts forward several loss functions based on the portfolio point of view, among which the negative cosine loss function shows significant advantages and strong practicability in stability and gain. Corresponding empirical research also reveals the application value and limitation of other loss functions.

Effect of -55C/T Polymorphism of Uncoupling Protein 3 Gene on Risk for New-Onset Diabetes in Chinese Peritoneal Dialysis Patients: A Prospective Cohort Study

Background: A high-glucose load in therapy can cause new-onset diabetes (NOD) in peritoneal dialysis (PD) patients. Genetic variability may result in risk modulation. Objectives: This study aims to investigate the association between −55C/T polymorphism of uncoupling protein 3 (UCP3) gene and the risk of NOD in PD patients. Methods: Nondiabetic incident PD patients between May 2005 and January 2017 were recruited (n = 154). −55C/T polymorphism of the UCP3 was genotyped in all participants at baseline. The cohort of wild group (−55CC) and mutant group (−55CT or −55TT) was built based on the genotypic difference. Insulin resistance was evaluated by the homeostasis model assessment method (HOMA-IR) during the follow-up. Binary logistic regression was performed to explore the association between HOMA-IR and genotypes. Competitive risk analysis was used to analyze the impact of −55C/T polymorphism of UCP3 on risk for NOD. Results: The cohort was followed for up to 164.6 months (median: 58.3 months; interquartile range: 30.7 months). During the follow-up, 14 NODs occurred in the mutant group, while only 3 occurred in the wild group. Patients in the mutant group had higher HOMA-IR (Odd ratio: 2.210; 95% CI: 1.043–4.680; p = 0.038). Genotype with the variant T allele turned out to be an independent predictor for NOD morbidity (HR: 7.639; 95% CI: 1.798–32.451; p = 0.006). Conclusions: The variant of T allele of UCP3 −55C/T polymorphism was an independent predictor for NOD in PD patients. Early identification of the genotype may provide scientific basis for patients’ clinic management.

Childbearing age is correlated with components of metabolic syndrome and parameters of insulin resistance in Chinese menopausal women

Objective To assess the relationship between childbearing age and elements of metabolic syndrome (MS) among menopausal women in China. Materials and methods One thousand one hundred and forty-one subjects were divided into four interim average childbearing age (ACA) groups: ≥16 < 24 years old, ≥24 < 29 years old, ≥29 < 34 years old, and ≥34 years old. The group with the lowest prevalence of MS was used as a control. Body mass index, waist circumference (WC) and waist-to-hip ratio were assessed. Plasma glucose, lipids, glycated hemoglobin (HbA1c) and fasting insulin (FINS) were measured. Insulin resistance (IR) was assessed using the homeostasis model assessment (HOMA) method. Results The prevalence of MS in the 16–24 and 29–34 age groups was higher than that in the control group (p < .05). Compared with the control group, the odds ratios of MS were 1.431 for the 16-24 group (p < .05) and 1.553 for the 29-34 group (P < 0.01). ACA was correlated with FINS, HOMA-IR, WC, fasting plasma glucose, and triglycerides independent of age (p < .05). Conclusions Average childbearing age was correlated with parameters of IR and components of MS independent of age in Chinese menopausal women.

Integrating Climatic and Physical Information in a Bayesian Hierarchical Model of Extreme Daily Precipitation

Extreme precipitation events are often localized, difficult to predict, and available records are often sparse. Improving frequency analysis and describing the associated uncertainty are essential for regional hazard preparedness and infrastructure design. Our primary goal is to evaluate incorporating Bayesian model averaging (BMA) within a spatial Bayesian hierarchical model framework (BHM). We compare results from two distinct regions in Oregon with different dominating rainfall generation mechanisms, and a region of overlap. We consider several Bayesian hierarchical models from relatively simple (location covariates only) to rather complex (location, elevation, and monthly mean climatic variables). We assess model predictive performance and selection through the application of leave-one-out cross-validation; however, other model assessment methods were also considered. We additionally conduct a comprehensive assessment of the posterior inclusion probability of covariates provided by the BMA portion of the model and the contribution of the spatial random effects term, which together characterize the pointwise spatial variation of each model’s generalized extreme value (GEV) distribution parameters within a BHM framework. Results indicate that while using BMA may improve analysis of extremes, model selection remains an important component of tuning model performance. The most complex model containing geographic and information was among the top performing models in western Oregon (with relatively wetter climate), while it performed among the worst in the eastern Oregon (with relatively drier climate). Based on our results from the region of overlap, site-specific predictive performance improves when the site and the model have a similar annual maxima climatology—winter storm dominated versus summer convective storm dominated. The results also indicate that regions with greater temperature variability may benefit from the inclusion of temperature information as a covariate. Overall, our results show that the BHM framework with BMA improves spatial analysis of extremes, especially when relevant (physical and/or climatic) covariates are used.

A General Three-Parameter Logistic Model With Time Effect.

Within the framework of item response theory, a new and flexible general three-parameter logistic model with response time (G3PLT) is proposed. The advantage of this model is that it can combine time effect, ability, and item difficulty to influence the correct-response probability. In contrast to the traditional response time models used in educational psychology, the new model incorporates the influence of the time effect on the correct-response probability directly, rather than linking them through a hierarchical method via latent and speed parameters as in van der Linden's model. In addition, the Metropolis–Hastings within Gibbs sampling algorithm is employed to estimate the model parameters. Based on Markov chain Monte Carlo output, two Bayesian model assessment methods are used to assess the goodness of fit between models. Finally, two simulation studies and a real data analysis are performed to further illustrate the advantages of the new model over the traditional three-parameter logistic model.

Osteocalcin and osteoprotegerin levels and their relationship with adipokines and proinflammatory cytokines in children with nonalcoholic fatty liver disease

ObjectivesTo investigate the relationship between osteocalcin and osteoprotegerin as bone markers and inflammatory biomarkers such as adiponectin, leptin, tumor necrosis factor alpha (TNF-α), and interleukin-6 (IL-6) in children with nonalcoholic fatty liver disease (NAFLD). MethodsThis study included 40 obese children with NAFLD as the patient group and 40 healthy obese children of matched age, sex and BMI as the control group. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), fasting blood glucose, fasting insulin, Homeostatic model assessment method of insulin resistance (HOMA-IR), lipid profile, osteocalcin, osteoprotegerin, adiponectin, leptin, TNF-α, and IL-6 were measured in all participants. ResultsChildren with NAFLD had a significant decrease in osteocalcin, osteoprotegerin and adiponectin level with a significant increase in TNF-α and IL-6 levels. We also found a significant positive correlation between osteocalcin level and adiponectin levels but a significant negative correlation of osteocalcin with each of leptin and TNF-α. However, there was a significant negative correlation between osteoprotegerin levels and both TNF-α and IL-6 levels. Moreover, adiponectin and TNF-α were significant predictors for osteocalcin, and IL-6 was a significant predictor for osteoprotegerin. ConclusionAdiponectin, leptin, TNF-α, and IL-6 have potential association with the changes of osteocalcin and osteoprotegerin levels in children with NAFLD.