Covariate Misclassification under Covariate-Adaptive Randomization: Understanding the Impact and Method for Bias Correction

Covariate-adaptive randomization has been frequently used in randomized controlled trials (RCTs) because it can well balance prognostic factors between treatment groups. However when a subject is assigned a wrong covariate value or misplaced in a wrong cohort during the randomization procedure, it may not only impact the balancing of the covariate, but also influence the treatment assignment based on the assigned cohort. Furthermore, it is preferred that covariates that are adjusted during the randomization procedure should also be adjusted for in the primary analysis. It is not clear whether a corrected covariate value, if it could be ascertained, or a misclassified covariate value should be used for the analysis, since the covariate value is tied both to the randomization procedure and analytic model. In this research, the impact of such misclassification on the type I error rate, power for hypothesis testing for the treatment effect and estimation bias of the treatment effect is explored under covariate-adaptive randomization in Aim 1. In Aim 2, a latent class model, the Continuous-time Hidden Markov Model (CTHMM) is used to estimate the misclassification issue with respect to both the estimation of misclassification probabilities and model diagnosis. An AIC based approach, which is calculated from a modified full data likelihood, is developed to test the assumption of misclassification. In Aim 3, a two-stage analysis strategy is proposed, which combines the CTHMM and the Misclassification Simulation-Extrapolation method (MCSIMEX), to correct the estimation bias of the perfectly measured variable caused by covariate misclassification. We apply the proposed analysis strategy to data from the Interventional Management of Stroke III trial to demonstrate the two-stage model

The preparation and properties of novel structural carbon foams derived from different mesophase pitches

As a novel porous multi-functional carbon material, carbon foams have high bulk thermal conductivity and low density, making them as excellent materials for thermal management systems applications, such as heat exchangers, space radiators, and thermal protection systems. In this paper, the carbon foams with high thermal conductivity, derived from three kinds of mesophase pitches, were fabricated by the process of foaming, carbonization and graphitization. The microstructures of the foams were examined by scanning electron microscopy. It was found that the pores were uniformly distributed, and the pore wall thickened with increasing foams’ density. The properties of the foams were studied, including compressive strength and thermal conductivity. The results showed that lower density and higher thermal conductivity were achieved for the foams using the two kinds of pitches with higher volatile components. The bulk thermal conductivity of carbon foams were up to 179 W/(m·K) and 201 W/(m·K), for the densities of 0.66 g/cm3 and 0.83 g/cm3, respectively. The foams’ compressive strength was in the range of 1.6 MPa to 3.4 MPa

What Are the New Challenges of the Current Cancer Biomarkers?

Biomarkers are emerging research filed in the past decade. Even though numerous biomarkers were reported, the efficiency of cancer therapy remains low. So the question emerges as to how much can we trust the current biomarkers on cancer therapy? In this upcoming chapter, we would like to illustrate the outcomes of classical cancer therapies on advanced pancreatic cancer disclosed successful, neutral and failed in clinical trials. The analysis will be carried on the perspective interdisciplinary on the biomarkers including anatomy, physiology, pharmacology, biochemistry, history path and development of pharmacy. Particular in-depth insight may open a window for new researches and lighting the therapies

Security Situation Prediction of Network Based on Lstm Neural Network

Part 2: AIInternational audienceAs an emerging technology that blocks network security threats, network security situation prediction is the key to defending against network security threats. In view of the single source of information and the lack of time attributes of the existing methods, we propose an optimal network security situation prediction model based on lstm neural network. We employ the stochastic gradient descent method as the minimum training loss to establish a network security situation prediction model, and give the model implementation algorithm pseudo code to further predict the future network security situation. The simulation experiments based on the data collected from Security Data dataset show that compared with other commonly used time series methods, the prediction accuracy of the model is higher and the overall situation of network security situation is more intuitively reflected, which provides a new solution for network security situation

Local versus Global Models for Just-In-Time Software Defect Prediction

Just-in-time software defect prediction (JIT-SDP) is an active topic in software defect prediction, which aims to identify defect-inducing changes. Recently, some studies have found that the variability of defect data sets can affect the performance of defect predictors. By using local models, it can help improve the performance of prediction models. However, previous studies have focused on module-level defect prediction. Whether local models are still valid in the context of JIT-SDP is an important issue. To this end, we compare the performance of local and global models through a large-scale empirical study based on six open-source projects with 227417 changes. The experiment considers three evaluation scenarios of cross-validation, cross-project-validation, and timewise-cross-validation. To build local models, the experiment uses the k-medoids to divide the training set into several homogeneous regions. In addition, logistic regression and effort-aware linear regression (EALR) are used to build classification models and effort-aware prediction models, respectively. The empirical results show that local models perform worse than global models in the classification performance. However, local models have significantly better effort-aware prediction performance than global models in the cross-validation and cross-project-validation scenarios. Particularly, when the number of clusters k is set to 2, local models can obtain optimal effort-aware prediction performance. Therefore, local models are promising for effort-aware JIT-SDP

A Novel Coarse-to-Fine Method of Ship Detection in Optical Remote Sensing Images Based on a Deep Residual Dense Network

Automatic ship detection in optical remote sensing images is of great significance due to its broad applications in maritime security and fishery control. Most ship detection algorithms utilize a single-band image to design low-level and hand-crafted features, which are easily influenced by interference like clouds and strong waves and not robust for large-scale variation of ships. In this paper, we propose a novel coarse-to-fine ship detection method based on discrete wavelet transform (DWT) and a deep residual dense network (DRDN) to address these problems. First, multi-spectral images are adopted for sea-land segmentation, and an enhanced DWT is employed to quickly extract ship candidate regions with missing alarms as low as possible. Second, panchromatic images with clear spatial details are used for ship classification. Specifically, we propose the local residual dense block (LRDB) to fully extract semantic feature via local residual connection and densely connected convolutional layers. DRDN mainly consists of four LRDBs and is designed to further remove false alarms. Furthermore, we exploit the multiclass classification strategy, which can overcome the large intra-class difference of targets and identify ships of different sizes. Extensive experiments demonstrate that the proposed method has high robustness in complex image backgrounds and achieves higher detection accuracy than other state-of-the-art methods