A modified partial likelihood score method for Cox regression with covariate error under the internal validation design

We develop a new method for covariate error correction in the Cox survival regression model, given a modest sample of internal validation data. Unlike most previous methods for this setting, our method can handle covariate error of arbitrary form. Asymptotic properties of the estimator are derived. In a simulation study, the method was found to perform very well in terms of bias reduction and confidence interval coverage. The method is applied to data from the Health Professionals Follow‐Up Study (HPFS) on the effect of diet on incidence of Type II diabetes.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151375/1/biom13012_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151375/2/biom13012-sup-0001-SuppData.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151375/3/biom13012.pd

Survival analysis with functions of mis-measured covariate histories: the case of chronic air pollution exposure in relation to mortality in the Nurses\u27 Health Study

Environmental epidemiologists are often interested in estimating the effect of functions of time-varying exposure histories, such as the 12-month moving average, in relation to chronic disease incidence or mortality. The individual exposure measurements that comprise such an exposure history are usually mis-measured, at least moderately, and, often, more substantially. To obtain unbiased estimates of Cox model hazard ratios for these complex mis-measured exposure functions, an extended risk set regression calibration (RRC) method for Cox models is developed and applied to a study of long-term exposure to the fine particulate matter ($PM_{2.5}$) component of air pollution in relation to all-cause mortality in the Nurses\u27 Health Study. Simulation studies under several realistic assumptions about the measurement error model and about the correlation structure of the repeated exposure measurements were conducted to assess the finite sample properties of this new method, and found that the method has good performance in terms of finite sample bias reduction and nominal confidence interval coverage. User-friendly software has been developed and is available to the general public on the senior author\u27s website

Investment decision analysis of international megaprojects based on cognitive linguistic cloud models

The investment decision analysis of international megaprojects is a major area of interest. The choice of interna tional megaprojects usually depends on the multi-discipline knowledge from experts. Besides, experts may not be able to provide accurate or crisp evaluations such as deterministic numbers on each criterion because of the complexity of the decision problem. In this case, natural evaluation language, either single linguistic variable or multiple linguistic variables, is a good expression tool for experts to sharing their opinions freely and flexibly. To this end, this paper introduces a cognitive linguistic cloud model for the investment decision analysis of international megaprojects as a decision support system and provides a survey of the cloud model. Afterwards, the technique to tackle multi-granularity of cognitive linguistic information is proposed to capture personalized semantics. In addition, operators of the cognitive linguistic model are proposed to aggregate natural language. The proposed approach has the advantages of more accurate utilization of experts’ knowledge, reducing uncertainties, and more effective operations of cognitive clouds for decision analysis in comparing with the state of the art. Finally, a case study about the investment of international megaprojects is given to show the flexibility and understandability of the cognitive linguistic model

Mechanism of Oral Tolerance Induction to Therapeutic Proteins

Oral tolerance is defined as the specific suppression of humoral and / or cellular immune responses to an antigen by administration of the same antigen through the oral route. Due to its absence of toxicity, easy administration, and antigen specificity, oral tolerance is a very attractive approach to prevent unwanted immune responses that cause a variety of diseases or that complicate treatment of a disease. Many researchers have induced oral tolerance to efficiently treat autoimmune and inflammatory diseases in different animal models. However, clinical trials yielded limited success. Thus, understanding the mechanisms of oral tolerance induction to therapeutic proteins is critical for paving the way for clinical development of oral tolerance protocols. This review will summarize progress on understanding the major underlying tolerance mechanisms and contributors, including antigen presenting cells, regulatory T cells, cytokines, and signaling pathways. Potential applications, examples for therapeutic proteins and disease targets, and recent developments in delivery methods are discussed

Hesitant Fuzzy Linguistic Analytic Hierarchical Process With Prioritization, Consistency Checking, and Inconsistency Repairing

Analytic hierarchy process (AHP), as one of the most important methods to tackle multiple criteria decision-making problems, has achieved much success over the past several decades. Given that linguistic expressions are much closer than numerical values or single linguistic terms to a human way of thinking and cognition, this paper investigates the AHP with comparative linguistic expressions. After providing the snapshot of classical AHP and its fuzzy extensions, we propose the framework of hesitant fuzzy linguistic AHP, which shows how to yield a decision for qualitative decision-making problems with complex linguistic expressions. First, the comparative linguistic expressions over criteria or alternatives are transformed into hesitant fuzzy linguistic elements and then the hesitant fuzzy linguistic preference relations (HFLPRs) are constructed. Considering that HFLPRs may be inconsistent, we conduct consistency checking and improving processes after obtaining priorities from the HFLPRs based on a linear programming method. Regarding the consistency-improving process, we develop a new way to establish a perfectly consistent HFLPR. The procedure of the hesitant fuzzy linguistic AHP is given in stepwise. Finally, a numerical example concerning the used-car management in a lemon market is given to illustrate the ef ciency of the proposed hesitant fuzzy linguistic AHP method.This work was supported in part by the National Natural Science Foundation of China under Grant 71771156, in part by the 2019 Sichuan Planning Project of Social Science under Grant SC18A007, in part by the 2019 Soft Science Project of Sichuan Science and Technology Department under Grant 2019JDR0141, and in part by the Project of Innovation at Sichuan University under Grant 2018hhs-43

Green suppler selection by an integrated method with stochastic acceptability analysis and MULTIMOORA

In the process of supplier selection for green supply chain management, uncertain information may appear in alternatives’ performances or experts’ preferences. The stochastic multicriteria acceptability analysis (SMAA) is a beneficial technique to tackling the uncertain information in such a problem and the MULTIMOORA is a robust technique to aggregate alternatives’ utilities. This study dedicates to proposing an SMAA-MULTIMOORA method by considering the advantages of both methods. The integrated method can accept uncertain information as inputs. The steps of the SMAA-MULTIMOORA are illustrated. A case study about the selection of green suppliers is given to show the validity and robustness of the SMAA-MULTIMOORA method

Hospitality brand management by a score-based q-rung ortho pair fuzzy V.I.K.O.R. method integrated with the best worst method

Hospitality brand management is a primary concern in the hotel industry and the evaluation of brands can be considered as a decision-making problem with multiple criteria. The evaluation information of brands may be uncertain sometimes. The q-rung orthopair fuzzy set (q-R.O.F.S.), which represents the preference degree of a person from the positive and negative aspects, has turned out to be an efficient tool in depicting uncertainty and vagueness in the decision-making process. This article dedicates to presenting an integrated multiple criteria decision-making method with q-R.O.F.S.. Firstly, a score function of the q-R.O.F.S. is proposed to solve the deficiencies of two existing score functions. Then, a weight-determining method based on the additive consistency of the preference relation is developed. A decision-making method integrating the score function, the best worst method and the VIsekriterijumska optimizacija I KOmpromisno Resenje (V.I.K.O.R.) which means multiple criteria compromise optimisation in English) method is further proposed. Finally, a case study regarding the hospitality brand management is provided to show the applicability and validity of the proposed method

Mild Moxibustion Decreases the Expression of Prokineticin 2 and Prokineticin Receptor 2 in the Colon and Spinal Cord of Rats with Irritable Bowel Syndrome

It has been proven that prokineticin 2 (PK2) and its receptor PKR2 play an important role in hyperalgesia, while mild moxibustion can relieve visceral hypersensitivity in a rat model of irritable bowel syndrome (IBS). The goal of the present study was to determine the effects of mild moxibustion on the expression of PK2 and PKR2 in colon and spinal cord in IBS rat model, which was induced by colorectal distension using inflatable balloons. After mild moxibustion treatment, abdominal withdrawal reflex (AWR) scores were assessed by colorectal distension; protein and mRNA expression of PK2 and PKR2 in rat colon and spinal cord was determined by immunohistochemistry and fluorescence quantitative PCR. Compared with normal rats, the AWR scores of rats and the expressions of PK2/PKR2 proteins and mRNAs in colon and spinal cord tissue were significantly increased in the model group; compared with the model group, the AWR scores of rats and the expressions of PK2/PKR2 proteins and mRNAs in colon and spinal cord tissue were significantly decreased in the mild moxibustion group. These findings suggest that the analgesia effect of mild moxibustion may be associated with the reduction of the abnormally increased expression of the PK2/PKR2 proteins and mRNAs in the colon and spinal cord

High-Fidelity Clothed Avatar Reconstruction from a Single Image

This paper presents a framework for efficient 3D clothed avatar reconstruction. By combining the advantages of the high accuracy of optimization-based methods and the efficiency of learning-based methods, we propose a coarse-to-fine way to realize a high-fidelity clothed avatar reconstruction (CAR) from a single image. At the first stage, we use an implicit model to learn the general shape in the canonical space of a person in a learning-based way, and at the second stage, we refine the surface detail by estimating the non-rigid deformation in the posed space in an optimization way. A hyper-network is utilized to generate a good initialization so that the convergence o f the optimization process is greatly accelerated. Extensive experiments on various datasets show that the proposed CAR successfully produces high-fidelity avatars for arbitrarily clothed humans in real scenes