Topics on multiple hypotheses testing and generalized linear model

In applications such as studying drug adverse events (AE) in clinical trials and identifying differentially expressed genes in microarray experiments, the data of the experiments usually consists of frequency counts. In the analysis of such data, researchers often face multiple hypotheses testing based on discrete test statistics. Incorporating this discrete property of the data, several stepwise procedures, which allow to use the CDF of p-values to determine the testing threshold, are proposed for controlling familiwise error rate (FWER). It is shown that the proposed procedures strongly control the FWER and are more powerful than the existing ones for discrete data. Through some simulation studies and real data examples, the proposed procedures are shown to outperform the existing procedures in terms of the FWER control and power. An R package “MHTdiscrete” and a web application are developed for implementing the proposed procedures for discrete data. Many complex biomedical studies, such as clinical safety studies and genome-wide association studies, often involve testing multiple families of hypotheses. Most existing multiple testing methods cannot guarantee strong control of appropriate type 1 error rates suitable for such increasingly complex research questions. A novel two-stage procedure based on the recently developed idea of selective inference for clinical safety studies is introduced. In the first stage, some significant families are selected by using some family-level global test, which guarantees control of generalized familywise error rate (k-FWER) among the selected families. In the second stage, individual hypotheses are tested for each selected families by using some multiple testing procedure, which controls conditional false discovery rate (cFDR) based on the fact that the family is selected. By applying the proposed procedure to clinical safety studies, one can not only efficiently flag the significant clinical adverse events (AEs) but also select body systems of interest (BSoI) as extra information for further research. The simulation studies show that the proposed procedure can be more reliable than alternative methods such as Mehrotra and Heyse’s double FDR procedure in the setting of clinical safety. The proposed procedure for multiple families structure is implemented in the R package “MHTmult”. Categorical data arises in biomedical and healthcare experiments naturally. In many of these cases, the outcome variables of interest are the numbers of special events. At least one distinct special event category is observed, when the negative multinomial and extended negative multinomial or generalized inverse sampling scheme-based regression models are used. The new model, based on generalized inverse sampling scheme for several special events, is developed in this dissertation. This research is an adaption to the widely used multinomial logistic regression model. The resulting equations of the proposed model, corresponding to the natural log of the ratio of the expected responses, appears similar to the multinomial logistic regression. Using this expected response ratio of a category to that of the special category, the maximum likelihood estimator of the regression parameters can be computed by creating score equations and the Hessian matrix of the likelihood. The covariance matrix of estimators of the regression parameters for the new model can be estimated by inverting the Hessian matrix to develop the inference. This research also develops model diagnostics such as normality check with deviance and Pearson residuals, and likelihood based computations. The proposed model is implemented in the R package “mvlogit”

Simulation of local head loss of drip-irrigation tape with integrated in-line emitters as a function of cross section

Aim of study: To investigate how the cross section of a drip-irrigation tape affects local head loss.Area of study: The work was carried out in the laboratory of Irrigation hydraulics, College of Water Conservancy and Environment, Three Gorges University, Yichang, Hubei province.Material and methods: Tapes with six different wall thicknesses were studied experimentally to determine the relationship between cross-section deformation, wall thickness, and pressure. Based on the experimental results, we determined the factors that influence local head loss in drip-irrigation tapes by numerical simulation and dimensional analysis.Main results: The cross-sectional shape of the drip-irrigation tape varied with pressure: under low pressure, the cross section was nearly elliptical. The cross-sectional shape of the tape strongly influenced the local head loss, which was inversely proportional to the 0.867th power of the flattening coefficient of the drip irrigation tape. We expressed the local head loss of a drip-irrigation tape equipped with integrated in-line emitters by considering the deformation of the cross section. Under the conditions used in this study, when the cross section is circular, the ratio of local head loss to frictional head loss was about 10% but, when the cross section is elliptical, this ratio increased to 15%.Research highlights: The shape of the cross section of a drip-irrigation tape is nearly elliptical under low pressure. Local head loss is inversely proportional to the 0.867th power of that is the flatting coefficient of the drip-irrigation tape. Local head loss is about 1.5 times for elliptical tape than circular tape

PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers

Multiple instance learning (MIL) was a weakly supervised learning approach that sought to assign binary class labels to collections of instances known as bags. However, due to their weak supervision nature, the MIL methods were susceptible to overfitting and required assistance in developing comprehensive representations of target instances. While regularization typically effectively combated overfitting, its integration with the MIL model has been frequently overlooked in prior studies. Meanwhile, current regularization methods for MIL have shown limitations in their capacity to uncover a diverse array of representations. In this study, we delve into the realm of regularization within the MIL model, presenting a novel approach in the form of a Progressive Dropout Layer (PDL). We aim to not only address overfitting but also empower the MIL model in uncovering intricate and impactful feature representations. The proposed method was orthogonal to existing MIL methods and could be easily integrated into them to boost performance. Our extensive evaluation across a range of MIL benchmark datasets demonstrated that the incorporation of the PDL into multiple MIL methods not only elevated their classification performance but also augmented their potential for weakly-supervised feature localizations.Comment: The code is available in https://github.com/ChongQingNoSubway/PD

TetCNN: Convolutional Neural Networks on Tetrahedral Meshes

Convolutional neural networks (CNN) have been broadly studied on images, videos, graphs, and triangular meshes. However, it has seldom been studied on tetrahedral meshes. Given the merits of using volumetric meshes in applications like brain image analysis, we introduce a novel interpretable graph CNN framework for the tetrahedral mesh structure. Inspired by ChebyNet, our model exploits the volumetric Laplace-Beltrami Operator (LBO) to define filters over commonly used graph Laplacian which lacks the Riemannian metric information of 3D manifolds. For pooling adaptation, we introduce new objective functions for localized minimum cuts in the Graclus algorithm based on the LBO. We employ a piece-wise constant approximation scheme that uses the clustering assignment matrix to estimate the LBO on sampled meshes after each pooling. Finally, adapting the Gradient-weighted Class Activation Mapping algorithm for tetrahedral meshes, we use the obtained heatmaps to visualize discovered regions-of-interest as biomarkers. We demonstrate the effectiveness of our model on cortical tetrahedral meshes from patients with Alzheimer's disease, as there is scientific evidence showing the correlation of cortical thickness to neurodegenerative disease progression. Our results show the superiority of our LBO-based convolution layer and adapted pooling over the conventionally used unitary cortical thickness, graph Laplacian, and point cloud representation.Comment: Accepted as a conference paper to Information Processing in Medical Imaging (IPMI 2023) conferenc

Sulfur Flotation Performance Recognition Based on Hierarchical Classification of Local Dynamic and Static Froth Features

© 2018 IEEE. This paper proposes a flotation performance recognition system based on a hierarchical classification of froth images using both local dynamic and static features, which includes a series of functions in image extraction, processing, and classification. Within the integrated system, to identify the abnormal working condition with poor flotation performance (NB it could be significantly different with the dynamic features of the froth in abnormal working condition), it is functioned first with building up local dynamic features of froth image from the information including froth velocity, disorder degree, and burst rate. To enhance the dynamic feature extraction and matching, this system introduces a scale-invariant feature transform method to cope with froth motion and the noise induced by dust and illumination. For the performance subdividing under normal working conditions, bag-of-words (BoW) description is utilized to fill the semantic gap in performance recognition when images are directly described by global image features. Accordingly typical froth status words are extracted to form a froth status glossary so that the froth status words of each patch form the BoW description of an image. A Bayesian probabilistic model is built to establish a froth image classification reference with the BoW description of images as the input. An expectation-maximization algorithm is used for training the model parameters. Data obtained from a real plant are selected to verify the proposed approach. It is noted that the proposed system can reduce the negative effects of image noise, and has high accuracy in flotation performance recognition

NNMobile-Net: Rethinking CNN Design for Deep Learning-Based Retinopathy Research

Retinal diseases (RD) are the leading cause of severe vision loss or blindness. Deep learning-based automated tools play an indispensable role in assisting clinicians in diagnosing and monitoring RD in modern medicine. Recently, an increasing number of works in this field have taken advantage of Vision Transformer to achieve state-of-the-art performance with more parameters and higher model complexity compared to Convolutional Neural Networks (CNNs). Such sophisticated and task-specific model designs, however, are prone to be overfitting and hinder their generalizability. In this work, we argue that a channel-aware and well-calibrated CNN model may overcome these problems. To this end, we empirically studied CNN's macro and micro designs and its training strategies. Based on the investigation, we proposed a no-new-MobleNet (nn-MobileNet) developed for retinal diseases. In our experiments, our generic, simple and efficient model superseded most current state-of-the-art methods on four public datasets for multiple tasks, including diabetic retinopathy grading, fundus multi-disease detection, and diabetic macular edema classification. Our work may provide novel insights into deep learning architecture design and advance retinopathy research.Comment: Code will publish soon: https://github.com/Retinal-Research/NNMOBILE-NE

Antiseismic response research of horizontal residual heat removal pump in different seismic spectrum input directions

A million kilowatt horizontal residual heat removal pump is an essential part of the first loop residual heat removal system in nuclear power plants; it is the second most significant piece of nuclear power equipment. The residual heat removal pump of a nuclear power plant is examined by using a multiseismic spectrum, multiinput direction method to analyze its dynamic characteristics and responses. The aim of this analysis was to determine the seismic responses and possible actions to reduce damage to the integral structure. The favorable and unfavorable spectra are investigated as well. The research focuses on avoiding the damaging effects caused by earthquakes. The maximum value of seismic effect and the corresponding seismic input direction are determined, laying a speculative foundation for structural design and installation. Utilizing a response spectrum method, the antiseismic performance of a pump at SSE seismic load has been analyzed according to an algorithm using the square root of the sum of the squares. The result shows that the deformation of the impeller surface fitted with a wear ring decreases along the direction of flow in different input directions of the seismic spectrum. The largest deformation occurs at an angle of approximately 135 degrees; thus, antiseismic analysis should be conducted at this input angle to conservatively evaluate the antiseismic performance, and the installation angle designed for frequent earthquakes should avoid 135 degrees to decrease the deformation caused by the seismic force. Calculation results prove that the clearance between the rotor and the stator of the horizontal residual heat removal pump shows satisfactory seismic response performance that fulfills the requirements for antiseismic design according to the RCC-M standard; this may reduce seismic damage and avoid environmental disasters

Evolution of the spatiotemporal pattern of PM2.5 concentrations in China – a case study from the Beijing-Tianjin-Hebei region

Atmospheric haze pollution has become a global concern because of its severe effects on human health and the environment. The Beijing-Tianjin-Hebei urban agglomeration is located in northern China, and its haze is the most serious in China. The high concentration of PM2.5 is the main cause of haze pollution, and thus investigating the temporal and spatial characteristics of PM2.5 is important for understanding the mechanisms underlying PM2.5 pollution and for preventing haze. In this study, the PM2.5 concentration status in 13 cities from the Beijing-Tianjin-Hebei region was statistically analyzed from January 2016 to November 2016, and the spatial variation of PM2.5 was explored via spatial autocorrelation analysis. The research yielded three overall results. (1) The distribution of PM2.5 concentrations in this area varied greatly during the study period. The concentrations increased from late autumn to early winter, and the spatial range expanded from southeast to northwest. In contrast, the PM2.5 concentration decreased rapidly from late winter to early spring, and the spatial range narrowed from northwest to southeast. (2) The spatial dependence degree, by season from high to low, was in the order winter, autumn, spring, summer. Winter (from December to February of the subsequent year) and summer (from June to August) were, respectively, the highest and lowest seasons with regard to the spatial homogeneity of PM2.5 concentrations. (3) The PM2.5 concentration in the Beijing-Tianjin-Hebei region has significant spatial spillovers. Overall, cities far from Bohai Bay, such as Shijiazhuang and Hengshui, demonstrated a high-high concentration of PM2.5 pollution, while coastal cities, such as Chengde and Qinhuangdao, showed a low-low concentration

The association between parent-child relationship and problematic internet use among English- and Chinese-language studies: A meta-analysis

As past studies of the association between parent-child relationship and problematic internet use show mixed results and are influenced by many factors, this meta-analysis of 75 primary Chinese and English language studies from 1990 to 2021 with 110,601 participants (aged 6−25 years) explored (a) the overall association between parent-child relationship and problematic internet use, and (b) whether the association is affected by their types, country, measures, objects of the parent-child relationship, gender, age, year and publication types. We used funnel plots, Classic fail-safe N and Egger's test to test for publication bias and for moderation with the homogeneity tests. The results showed a negative association between quality of parent-child relationship and problematic internet use (r = −0.18, 95% CI = [−0.20, −0.15]). The moderation analysis found that compared with internet addiction tendency, the association between social media addiction and parent-child relationship was stronger