Dynamic Risk Prediction via a Joint Frailty-Copula Model and IPD Meta-Analysis: Building Web Applications

Clinical risk prediction formulas for cancer patients can be improved by dynamically updating the formulas by intermediate events, such as tumor progression. The increased accessibility of individual patient data (IPD) from multiple studies has motivated the development of dynamic prediction formulas accounting for between-study heterogeneity. A joint frailty-copula model for overall survival and time to tumor progression has the potential to develop a dynamic prediction formula of death from heterogenous studies. However, the process of developing, validating, and publishing the prediction formula is complex, which has not been sufficiently described in the literature. In this article, we provide a tutorial in order to build a web-based application for dynamic risk prediction for cancer patients on the basis of the R packages joint.Cox and Shiny. We demonstrate the proposed methods using a dataset of breast cancer patients from multiple clinical studies. Following this tutorial, we demonstrate how one can publish web applications available online, which can be manipulated by any user through a smartphone or personal computer. After learning this tutorial, developers acquire the ability to build an online web application using their own datasets.     

Estimation of the Annual Effective Dose Due to the Ingestion of 210Pb and 210Po in Crops from a Site of Coal Mining and Processing in Southwest China

The exploitation of mineral resources may cause the environmental release of radionuclides and their introduction in the human trophic chain, affecting public health in the short and long term. A case study of the environmental radiation impact from coal mining and germanium processing was carried out in southwest China. The coal mines contain germanium and uranium and have been exploited for more than 40 years. The farmlands around the site of the coal mining and germanium processing have been contaminated by the solid waste and mine water to some extent since then. Samples of crops were collected from contaminated farmlands in the research area. The research area covers a radius of 5 km, in which there are two coal mines. ²¹⁰Pb and ²¹⁰Po were analyzed as the key radionuclides during the monitoring program. The average activity concentrations of ²¹⁰Pb and ²¹⁰Po in the crops were 1.38 and 1.32 Bq/kg in cereals, 4.07 and 2.19 Bq/kg in leafy vegetables and 1.63 and 1.32 Bq/kg in root vegetables. The annual effective doses due to the ingestion of ²¹⁰Pb and ²¹⁰Po in consumed crops were estimated for adult residents living in the research area. The average annual effective dose was 0.336 mSv/a, the minimum was 0.171 mSv/a and the maximum was 0.948 mSv/a. The results show that the crops grown on contaminated farmland contained an enhanced level of radioactivity concentration. The ingestion doses of local residents in the research area were significantly higher than the average level of 0.112 mSv/a in China, and the world average level of 0.042 mSv/a through ²¹⁰Pb and ²¹⁰Po in crop intake, respectively.     

Relationship between Apolipoprotein E Genotype and Lipoprotein Profile in Patients with Coronary Heart Disease

(1) Background: Apolipoprotein E(ApoE) plays a critical role in lipid transport. The specific allele of APOE being expressed is associated with the development of coronary heart disease (CHD), however the specific mechanisms by which ApoE drives disease are unclear. In this study, we investigated the relationship between APOE allele, lipoprotein metabolome, and CHD severity to provide evidence for the efficacy of clinical cholesterol-lowering therapy; (2) Methods: Blood samples were collected from 360 patients with CHD that were actively being treated with statins. The lipoprotein profile, including particle numbers, particle size, and lipoprotein composition concentrates, was measured by nuclear magnetic resonance (NMR) spectroscopy. The severity of CHD was determined by quantifying coronary angiography results using the Gensini scoring system; (3) Results: We found there was no significant difference in low-density lipoprotein cholesterol (LDL-C) levels among ε2+ (ε2 allele carriers, consisting of ε2/ε2 and ε2/ε3 genotypes), ε3 (consisting of ε3/ε3 and ε2/ε4 genotypes), and ε4+ (ε4 allele carriers, consisting of ε3/ε4 and ε4/ε4 genotypes) participants receiving statin treatment. Compared with the ε3 group, patients with the ε2+ genotype showed lower concentrations of total low-density lipoprotein (LDL), small-LDL, and middle-LDL particles, as well as a larger LDL size, higher very low-density lipoprotein (VLDL) composition concentrates, and higher intermediate density lipoprotein (IDL) composition concentrates. The ε4+ group showed higher concentrations of total LDL, small LDL particles, and LDL compositions with smaller LDL size. The higher level of small LDL concentration was associated with a high Gensini score (B = 0.058, p = 0.024). Compared with the ε3 group, the risk of increased branch lesions in the ε2+ group was lower (OR = 0.416, p = 0.027); (4) Conclusions: The specific allele of APOE being expressed can affect the severity of CHD by altering components of the lipoprotein profile, such as the concentration of small LDL and LDL size.     

八种黄酮类天然产物对高危型HPV16/18 E6蛋白的分子对接研究

宫颈癌以其高发病率和高死亡率严重危害女性健康,传统治疗手段有效率低且治疗过程给患者带来极大痛苦。近年来,许多天然植物来源化合物已被确定为治疗和预防宫颈癌的有希望的药物来源,但天然产物治疗癌症的具体作用机制尚未明确。因此,本文选取了8种黄酮类天然小分子抑制剂,分别与高危型HPV16/18 E6蛋白重要位点LxxLL疏水口袋进行分子对接研究,以期探索天然产物抗宫颈癌的作用机制。对接分析显示,这些天然产物均与HPV18E6蛋白LxxLL疏水口袋产生较强相互作用,与HPV16 E6蛋白对接时,木犀草素则比其他7种黄酮类化合物结合得更为深入,这些相互作用可能有助于p53功能的恢复。对接分析有助于理解蛋白质-配体相互作用的分子机制,为设计治疗HPV感染的新药提供依据。     

On Some Extension of Intuitionistic Fuzzy Synthetic Measures for Two Reference Points and Entropy Weights

In this paper, a novel Double Intuitionistic Fuzzy Synthetic Measure (DIFSM), based on intuitionistic fuzzy values for handling multi-criteria decision-making problems used to rank alternatives, is presented. In the studies, intuitionistic fuzzy sets (IFSs) represented uncertain, imprecise information or human judgment. The intuitionistic fuzzy sets can also reflect the approval, rejection, and hesitation of decision-makers. The degrees of satisfiability and non-satisfiability and uncertainty of each alternative with respect to a set of criteria are described by membership functions, non-membership functions, and hesitancy indexes, respectively. The aggregation algorithm DIFSM is inspired by Hellwig’s method based on two reference points: ideal point (pattern) and anti-ideal point (anti-pattern), measuring distances between the alternative and ideal point and distance between the ideal and anti-ideal point. The proposed methods take into consideration the entropy-based weights of criteria. An illustrative example is given to demonstrate the practicality and effectiveness of the proposed approach. Additionally, the comparative analysis results, using the DIFSM and the Intuitionistic Fuzzy TOPSIS-based framework, are presented.     

Dynamic Multiscale Information Spillover among Crude Oil Time Series

This study investigated information spillovers across crude oil time series at different time scales, using a network combined with a wavelet transform. It can detect the oil price, which plays an important role in the dynamic process of spillovers, and it can also analyze the dynamic feature of systematic risk based on entropy at different scales. The results indicate that the network structure changes with time, and the important roles of an oil price can be identified. WTI and Brent act as important spillover transmitters, and other prices are important spillover receivers at a scale. With the increase in time scale, both the number of neighbors and the importance of spillovers of Brent and WTI as spillover transmitters show downward trends. The importance for spillovers of China–Shengli and Dubai as spillover receivers shows a downward trend. This paper provides new evidence for explaining WTI and Brent as global benchmark oil prices. In addition, systematic risk is time-varying, and it is smaller at short-term scale than at long-term scale. The trend of systematic risk is also discussed when typical oil-related events occur. This paper provides a new perspective for exploring dynamic spillovers and systematic risk that offers important implications for policymakers and market investors.     

Multi-Sensor Scheduling Method Based on Joint Risk Assessment with Variable Weight

In multi-sensor cooperative detection systems, to reduce target threat risk caused by attack tasks and target loss risk induced by uncertain environmental factors, this paper proposes a multi-sensor scheduling method based on joint risk assessment with variable weight. Firstly, considering the target state and prior expert experience of sensor scheduling, this paper gives a new scheme of target threat risk. Then, by combining the given target threat risk and the target loss risk, this paper constructs a joint risk model to meet the diversity of risk assessment. Secondly, a variable-weighted joint risk assessment model is given based on the adaptive weight of target loss risk and target threat risk, and the optimization problem of multi-sensor scheduling is described to minimize the multi-step prediction of the variable-weighted joint risk model. Finally, this paper relaxes above the non-convex optimization problem as a subconvex problem and designs the scheme of multi-sensor scheduling, improving the rapidity and optimization of the sensor scheduling solution. The simulation results show that the proposed method can adaptively schedule sensors and accurately track targets by using minimum sensor resources.     

New Classification Method for Independent Data Sources Using Pawlak Conflict Model and Decision Trees

The research concerns data collected in independent sets—more specifically, in local decision tables. A possible approach to managing these data is to build local classifiers based on each table individually. In the literature, many approaches toward combining the final prediction results of independent classifiers can be found, but insufficient efforts have been made on the study of tables’ cooperation and coalitions’ formation. The importance of such an approach was expected on two levels. First, the impact on the quality of classification—the ability to build combined classifiers for coalitions of tables should allow for the learning of more generalized concepts. In turn, this should have an impact on the quality of classification of new objects. Second, combining tables into coalitions will result in reduced computational complexity—a reduced number of classifiers will be built. The paper proposes a new method for creating coalitions of local tables and generating an aggregated classifier for each coalition. Coalitions are generated by determining certain characteristics of attribute values occurring in local tables and applying the Pawlak conflict analysis model. In the study, the classification and regression trees with Gini index are built based on the aggregated table for one coalition. The system bears a hierarchical structure, as in the next stage the decisions generated by the classifiers for coalitions are aggregated using majority voting. The classification quality of the proposed system was compared with an approach that does not use local data cooperation and coalition creation. The structure of the system is parallel and decision trees are built independently for local tables. In the paper, it was shown that the proposed approach provides a significant improvement in classification quality and execution time. The Wilcoxon test confirmed that differences in accuracy rate of the results obtained for the proposed method and results obtained without coalitions are significant, with a p level = 0.005. The average accuracy rate values obtained for the proposed approach and the approach without coalitions are, respectively: 0.847 and 0.812; so the difference is quite large. Moreover, the algorithm implementing the proposed approach performed up to 21-times faster than the algorithm implementing the approach without using coalitions.     

Pruning Decision Rules by Reduct-Based Weighting and Ranking of Features

Methods and techniques of feature selection support expert domain knowledge in the search for attributes, which are the most important for a task. These approaches can also be used in the process of closer tailoring of the obtained solutions when dimensionality reduction is aimed not only at variables but also at learners. The paper reports on research where attribute rankings were employed to filter induced decision rules. The rankings were constructed through the proposed weighting factor based on the concept of decision reducts—a feature reduction mechanism embedded in the rough set theory. Classical rough sets operate only in discrete input space by indiscernibility relation. Replacing it with dominance enables processing real-valued data. Decision reducts were found for both numeric and discrete attributes, transformed by selected discretisation approaches. The calculated ranking scores were used to control the selection of decision rules. The performance of the resulting rule classifiers was observed for the entire range of rejected variables, for decision rules with conditions on continuous values, discretised conditions, and also inferred from discrete data. The predictive powers were analysed and compared to detect existing trends. The experiments show that for all variants of the rule sets, not only was dimensionality reduction possible, but also predictions were improved, which validated the proposed methodology.