BACKGROUND: Overt hepatic encephalopathy (HE) should be predicted preoperatively to identify suitable candidates for transjugular intrahepatic portosystemic shunt (TIPS) instead of first-line treatment. This study aimed to construct a 3D assessment-based model to predict post-TIPS overt HE.
METHODS: In this multi-center cohort study, 487 patients who underwent TIPS were subdivided into a training dataset (390 cases from three hospitals) and an external validation dataset (97 cases from another two hospitals). Candidate factors included clinical, vascular, and 2D and 3D data. Combining the least absolute shrinkage and operator method, support vector machine, and probability calibration by isotonic regression, we constructed four predictive models: clinical, 2D, 3D, and combined models. Their discrimination and calibration were compared to identify the optimal model, with subgroup analysis performed.
RESULTS: The 3D model showed better discrimination than did the 2D model (training: 0.719 vs. 0.691; validation: 0.730 vs. 0.622). The model combining clinical and 3D factors outperformed the clinical and 3D models (training: 0.802 vs. 0.735 vs. 0.719; validation: 0.816 vs. 0.723 vs. 0.730; all p < 0.050). Moreover, the combined model had the best calibration. The performance of the best model was not affected by the total bilirubin level, Child-Pugh score, ammonia level, or the indication for TIPS.
CONCLUSIONS: 3D assessment of the liver and the spleen provided additional information to predict overt HE, improving the chance of TIPS for suitable patients. 3D assessment could also be used in similar studies related to cirrhosis.

UNASSIGNED: This study presented a new model for optimal assignment of human resources to 3-level defined clinics to improve the management of diabetes.
UNASSIGNED: First, the data of population and prevalence of diabetes and data about complications were gathered. Then, the number of needed visits was calculated for different classes of diabetic people using guidelines. On the supply side, the maximum number of available visits for a given year by a given specialty was calculated. Two scenarios were considered. The first scenario calculated the number of needed specialties to cover the guideline needs, while the second real-world scenario used human resource data to optimize the assignment of human resources to different levels of clinics.
UNASSIGNED: The highest and lowest required specialties per year are 2780 General practitioners (GPs) and 492 gastroenterologists. Seven hundred forty-one endocrinologists or internists are required each year to cover all the needs. The highest and lowest number of the available specialties were 4967 GPs and 35 nutritionists. 81% of cities can cover basic services, while even the lowest level of coverage is not possible in 19% of districts.
UNASSIGNED: The present study\'s findings advise the policymakers to train human resources based on available evidence and distribute the human resources based on an evidence-based model. This could be achieved using the private section resources.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s40200-021-00939-4.

To establish an optimal model for photo aesthetic assessment, in this paper, an internal metric called the disentanglement-measure (D-measure) is introduced, which reflects the disentanglement degree of the final layer FC (full connection) nodes of convolutional neural network (CNN). By combining the F-measure with the D-measure to obtain an FD measure, an algorithm of determining the optimal model from many photo score prediction models generated by CNN-based repetitively self-revised learning (RSRL) is proposed. Furthermore, the aesthetics features of the model regarding the first fixation perspective (FFP) and the assessment interest region (AIR) are defined by means of the feature maps so as to analyze the consistency with human aesthetics. The experimental results show that the proposed method is helpful in improving the efficiency of determining the optimal model. Moreover, extracting the FFP and AIR of the models to the image is useful in understanding the internal properties of these models related to the human aesthetics and validating the external performances of the aesthetic assessment.

To effectively use the fishery count data containing zero values, Setipinna taty in the coastal waters of south inshore of Zhejiang in China from 2017 to 2019 was used in this study. Environmental factors, such as water temperature, water depth, and salinity, were selected to establish models and compare based on the generalized additive model (GAM) of the Tweedie distribution (Tweedie-GAM) and two-stage GAM, Ad hoc method, and generalized additive mixed model (GAMM). The results showed that each station accounted for a higher proportion of zero values and the two-stage GAM model had a higher deviation interpretation rate, and GAM I and GAM II had 19.6% and 60.4% deviation interpretation rates. The cross-validation results showed that the performance evaluation of the two-stage GAM model was the best and showed the highest R2 value, the lowest average absolute error, and the relatively small root mean square error. This study found that the abundance of S. taty in the south inshore of Zhejiang was highest at around 21°C and 18°C in spring and autumn, and the abundance reached the highest at a water depth of about 20 m. In spatial distribution, the high value of the abundance of S. taty was mostly distributed in the coastal waters in the south of 28°N. In future research, models should be fitted and compared for different sampling zero-value ratios, and more environmental factors should be included to accurately find an optimal model and provide references for the conservation of fishery resources.

Based on data from the China National Health Survey, we aimed to examine the association between body height and cardiometabolic disease (CMD) in a large adult population from Shaanxi province, and further to test whether this association was hinged upon other population characteristics.
This population-based study was conducted in 2014 in Shaanxi Province, China. Utilizing a multi-stage stratified cluster sampling method, total 5,905 adults with complete data were eligible for analysis, and 1,151 (19.5%) of them had CMD. Of 1,151 CMD patients, 895 (15.1%) had one disorder and 256 (4.4%) had ≥2 disorders.
Using the bi-directional stepwise method and all-subsets regression, five factors-age, body mass index, family histories of CMD, exercise, and height-constituted the optimal model when predicting CMD risk. Restricted cubic spline regression showed a reduced tendency towards CMD with the increase of body height, with per 10 cm increment in body height corresponding to 14% reduced risk. Ordinal Logistic regression supported the contribution of body height on both continuous and categorical scales to CMD risk before and after adjustment, yet this contribution was significantly confounded by exercise and education, especially by exercise, which can explain 65.4% of total impact. For example, short stature was associated with an increased risk of CMD after multivariable adjustment not including exercise and education (odds ratio, 95% confidence interval, P: 1.42, 1.21 to 1.66, <0.001), and tall stature was associated with a reduced risk (0.77, 0.64 to 0.92, 0.003).
Our findings indicate short stature was a risk factor, yet tall stature was a protective factor for CMD in Chinese. Notably, the prediction of short and tall stature for CMD may be mediate in part by exercise.

For the inverse finite element method (iFEM), an inappropriate scheme of strain senor distribution would cause severe degradation of the deformation reconstruction accuracy. The robustness of the strain⁻displacement transfer relationship and the accuracy of reconstruction displacement are the two key factors of reconstruction accuracy. Previous research studies have been focused on single-objective optimization for the robustness of the strain⁻displacement transfer relationship. However, researchers found that it was difficult to reach a mutual balance between robustness and accuracy using single-objective optimization. In order to solve this problem, a bi-objective optimal model for the scheme of sensor distribution was proposed for this paper, where multi-objective particle swarm optimization (MOPSO) was employed to optimize the robustness and the accuracy. Initially, a hollow circular beam subjected to various loads was used as a case to perform the static analysis. Next, the optimization model was established and two different schemes of strain sensor were obtained correspondingly. Finally, the proposed schemes were successfully implemented in both the simulation calculation and the experiment test. It was found that the results from the proposed optimization model in this paper proved to be a promising tool for the selection of the scheme of strain sensor distribution.

Rectangle hyperbola model (RH), nonrectangle hyperbola model (NRH), exponential model (EM), modified rectangle hyperbola model (MRH), and modified exponential model (MEM) were applied respectively for modeling the photosynthesis-light response curves (PLC) based on four types of curves (photosynthesis-light response curve of inhibition, PLCi, photosynthesis-light response curve of saturation, PLCs, photosynthesis-light response curve of unsaturation, PLCu andphotosynthesis-light response curve in weak light, PLCw) in canopy of 15 years old planted Larix olgensis tree in the Maoershan Forest Farm, Heilongjiang Province. The major photosynthetic physiological indexes including maximum net photosynthetic rate (Pn max), dark respiration rate (Rd), light compensation point (LCP), light saturation point at saturated light intensity (LSP) and apparent quantum yield (AQY) were calculated. All the five candidate models were comprehensively compared by the model goodness-of-fit and the precision estimations of photosynthetic physiological indexes in the four types of curves. The results showed that MEM model was only suitable for fitting the PLCi. MRH model showed the best goodness-of-fit for PLCi and PLCs(Ra2was 0.9986 and 0.9978, respectively). Meanwhile, NRH model expressed the best fitting result in PLCu and PLCw (Ra2 was 0.9996 and 0.9963, respectively). MRH had the lowest mean absolute value of relative error (MAPE) when evaluating Pn max in different types of curves (0.1%以黑龙江省帽儿山林场15年生人工长白落叶松为研究对象,采用直角双曲线模型(RH)、非直角双曲线模型(NRH)、指数模型(EM)、修正直角双曲线模型(MRH)和修正指数模型(MEM)分别对4种不同光响应特征的光合作用-光响应曲线(光抑制型光响应曲线,PLCi;光饱和型光响应曲线,PLCs;未饱和型光响应曲线,PLCu;弱光环境下植被的光响应曲线,PLCw)进行拟合,计算出光饱和时的最大净光合速率(Pn max)、暗呼吸速率(Rd)、光补偿点(LCP)、光饱和点(LSP)及表观量子效率(AQY)等重要的光合生理指标,综合对比5个候选模型对不同响应曲线的拟合优度和对光合生理指标的估计精度.结果表明: MEM模型仅适用于拟合光抑制型曲线,MRH对光抑制型曲线和光饱和型曲线的拟合效果最好(Ra2分别为0.9986和0.9978),NRH最适合拟合未饱和型曲线和弱光环境型曲线(Ra2分别为0.9996和0.9963).在所有类型曲线中,MRH模型估计Pn max时,平均相对误差绝对值(MAPE)最低(0.1%

Understanding the growth behavior of microorganisms using modeling and optimization techniques is an active area of research in the fields of biochemical engineering and systems biology. In this paper, we propose a general modeling framework, based on Monod model, to model the growth of microorganisms. Utilizing the general framework, we formulate an optimal control problem with the objective of maximizing a long-term cellular goal and solve it analytically under various constraints for the growth of microorganisms in a two substrate batch environment. We investigate the relation between long term and short term cellular goals and show that the objective of maximizing cellular concentration at a fixed final time is equivalent to maximization of instantaneous growth rate. We then establish the mathematical connection between the generalized framework and optimal and cybernetic modeling frameworks and derive generalized governing dynamic equations for optimal and cybernetic models. We finally illustrate the influence of various constraints in the cybernetic modeling framework on the optimal growth behavior of microorganisms by solving several dynamic optimization problems using genetic algorithms.