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Background In the PROSELICA, a randomized controlled trial (RCT) comparing cabazitaxel 20 mg/m2 (C20) versus 25 mg/m2 (C25) in metastatic castration‐resistant prostate cancer (mCRPC), one‐variable‐at‐a‐time subgroup analysis suggested possible heterogeneity in treatment effect (HTE) of C25 versus C20 among study participants. Novel predictive HTE analysis approaches may provide an in‐depth understanding of such results. Methods We analyzed patient‐level data from 1200 patients with mCRPC who were randomized in the PROSELICA trial. Outcomes included overall survival (OS) and progression‐free survival (PFS). Using baseline characteristics, patients were stratified into quartiles based on either quantitative baseline risk of poor outcome (risk modeling) or predicted individualized treatment effect (ITE) using a causal survival forest algorithm (effect modeling). Treatment effects were measured as differences in restricted mean survival time (RMST). Results For risk modeling, the OS effect of C25 increased with risk quartiles: −0.07 months (95% CI, −1.60 to 1.46) in the lowest risk quartile and 1.67 months (95% CI, 0.25 to 3.10) in the highest risk quartile. For effect modeling, the OS effect ranged from −0.17 months (95% CI, −3.01 to 2.68) in the lowest ITE quartile to 0.57 months (95% CI, −2.27 to 3.41) in the highest ITE quartile. Both approaches demonstrated greater C25 benefit in patients with extensive previous treatment and baseline disease burden. PFS effects remained consistent across all quartiles. Conclusions The OS effect of C25 versus C20 may vary based on baseline characteristics in post‐docetaxel mCRPC. Patients with extensive treatment history and disease burden may benefit more from C25.

The process of accomplishing strategic objectives by concentrating on effects as opposed to attrition-based destruction is known as effects-based operations, or EBO. Finding important nodes hi an adversary network is a critical step hi the EBO process for a successful implementation, hi this paper, propose a network-based method to identify the most influential nodes by combining network centrality and optimization. To determine the node influence, the adversary's network structure is analyzed ushig degree and between centralities. Given the dynamic nature of the adversary network structure and the centrality results, an optimization model that takes resource constraints hito account chooses the key nodes. Our findings demonstrate that various network properties, such as between and degree centralities, influence the priorities of nodes as targets, and that ushig an optimization model yields better priorities with decreasing marginal properties. There is a discussion of the implications for theory and sensible decision-making.

Shape memory materials have been playing an important role in a wide range of bioengineering applications. At the same time, recent developments of graphene-based nanostructures, such as nanoribbons, have demonstrated that, due to the unique properties of graphene, they can manifest superior electronic, thermal, mechanical, and optical characteristics ideally suited for their potential usage for the next generation of diagnostic devices, drug delivery systems, and other biomedical applications. One of the most intriguing parts of these new developments lies in the fact that certain types of such graphene nanoribbons can exhibit shape memory effects. In this paper, we apply machine learning tools to build an interatomic potential from DFT calculations for highly ordered graphene oxide nanoribbons, a material that had demonstrated shape memory effects with a recovery strain up to 14.5% for 2D layers. The graphene oxide layer can shrink to a metastable phase with lower constant lattice through the application of an electric field, and returns to the initial phase through an external mechanical force. The deformation leads to an electronic rearrangement and induces magnetization around the oxygen atoms. DFT calculations show no magnetization for sufficiently narrow nanoribbons, while the machine learning model can predict the suppression of the metastable phase for the same narrower nanoribbons. We can improve the prediction accuracy by analyzing only the evolution of the metastable phase, where no magnetization is found according to DFT calculations. The model developed here allows also us to study the evolution of the phases for wider nanoribbons, that would be computationally inaccessible through a pure DFT approach. Moreover, we extend our analysis to realistic systems that include vacancies and boron or nitrogen impurities at the oxygen atomic positions. Finally, we provide a brief overview of the current and potential applications of the materials exhibiting shape memory effects in bioengineering and biomedical fields, focusing on data-driven approaches with machine learning interatomic potentials.

The present study defined a simplified physiologically based pharmacokinetic (PBPK) model for nicotine and its primary metabolite cotinine in humans, based on metabolic parameters determined in vitro using relevant liver microsomes, coefficients derived in silico, physiological parameters derived from the literature, and an established rat PBPK model. The model consists of an absorption compartment, a metabolizing compartment, and a central compartment for nicotine and three equivalent compartments for cotinine. Evaluation of a rat model was performed by making comparisons with predicted concentrations in blood and in vivo experimental pharmacokinetic values obtained from rats after oral treatment with nicotine (1.0 mg/kg, a no-observed-adverse-effect level) for 14 days. Elimination rates of nicotine in vitro were established from data from rat liver microsomes and from human pooled liver microsomes. Human biomonitoring data (17 ng nicotine and 150 ng cotinine per mL plasma 1 h after smoking) from pooled five male Japanese smokers (daily intake of 43 mg nicotine by smoking) revealed that these blood concentrations could be calculated using a human PBPK model. These results indicate that a simplified PBPK model for nicotine/cotinine is useful for a forward dosimetry approach in humans and for estimating blood concentrations of other related compounds resulting from exposure to low chemical doses.

In recent years, the influence of knowledge spillover among cluster enterprises on the innovation performance has become a hot topic in the field of cluster innovation. This article reviews the unique context of cluster. It believes that it has two aspects of positive and negative effect when standing in the different starting points of knowledge overflow. The effect of active and purposeful knowledge spillover on cluster innovation performance has a unique mechanism. Our samples are focused on \"innovative enterprise\" as the core of industrial cluster. It chose five batches of \"innovative companies\" list of survey questionnaire released in 2008 -2012 by the Ministry of Science and Technology National Ministries and other commissions, and in consideration of the particular industry characteristics, it has established a structural equation model with 275 effective samples collected and screened at last. It demonstrates the process how the conscious knowledge spillover has its effect on innovation performance in

目前迅速發展的語料庫語言學面臨眾多研究方法的挑戰。與語料庫語言學本身相關的方法論問題──資料的散布離差、詞種頻率／亂度、與資料隱含的方向性問題等，直接影響語料相關性指標的計算；忽視語料庫的資料取樣結構也與之後的統計分析結果直接相關。歷史語言學與學習者語料庫研究等領域應用語料庫語言學時，也有方法論的問題。本文詳細討論以上所提到的問題，並具體提出實例演示相對應的解決方法

Objectives: Previous studies of performance have focused on either organizational or individual-level analysis. Multilevel theory has been used to explore the effect of the method of compensation on the quality and quantity of individual physician services after hospital global budgeting (HGB) was implemented in Taiwan. Methods: We used a sample of convenience of 33 regional hospitals that participated in HGB and surveyed managers and physicians with structured questionnaires in 2008. A total of 210 valid questionnaires from 24 hospitals were returned. We conducted hierarchical linear modeling analyses and demonstrated that both individual-level and organizational-level factors were associated with physician performance. Results: The effect of the variable compensation level on quality of physicians' service, productivity in the outpatient department (OPD) and in the inpatient department (IPD) was not significant (0.250, 0.171, 0.253, all p＞.05). The ratio of beds to physicians negatively affected individual

Objectives: Previous studies of performance have focused on either organizational or individual-level analysis. Multilevel theory has been used to explore the effect of the method of compensation on the quality and quantity of individual physician services after hospital global budgeting (HGB) was implemented in Taiwan. Methods: We used a sample of convenience of 33 regional hospitals that participated in HGB and surveyed managers and physicians with structured questionnaires in 2008. A total of 210 valid questionnaires from 24 hospitals were returned. We conducted hierarchical linear modeling analyses and demonstrated that both individual-level and organizational-level factors were associated with physician performance. Results: The effect of the variable compensation level on quality of physicians' service, productivity in the outpatient department (OPD) and in the inpatient department (IPD) was not significant (0.250, 0.171, 0.253, all p>.05). The ratio of beds to physicians negatively affected individual