Energy expenditure of type-specific sedentary behaviors estimated using sensewear mini armband: a metabolic chamber validation study among adolescents

SenseWear Mini Armband, an accelerometer with multiple physiological sensors, could be a practical means to estimate energy expenditure (EE) of children and adolescents, but its validity reported for these age groups has not been consistent within the literature. EE of twenty-six healthy Chinese 12-year-old adolescents was measured simultaneously using both SenseWear Mini Armband (SWMA) and metabolic chamber (MC) during a 16-hour stay in a MC. SWMA systematically underestimated the adolescents’ EE during sedentary behaviors, resting metabolic rate (RMR), basal metabolic rate (BMR), and total EE, with the absolute error rate ranging from 14.85% to 28.65%. The SWMA significantly underestimated EE compared with MC in Chinese adolescents. However, the amount of error can be reduced by applying correction equation proposed in this study

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

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

OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing

Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes. Optimal retinal image quality is mandated for accurate medical diagnoses and automated analyses. Herein, we leveraged the Optimal Transport (OT) theory to propose an unpaired image-to-image translation scheme for mapping low-quality retinal CFPs to high-quality counterparts. Furthermore, to improve the flexibility, robustness, and applicability of our image enhancement pipeline in the clinical practice, we generalized a state-of-the-art model-based image reconstruction method, regularization by denoising, by plugging in priors learned by our OT-guided image-to-image translation network. We named it as regularization by enhancing (RE). We validated the integrated framework, OTRE, on three publicly available retinal image datasets by assessing the quality after enhancement and their performance on various downstream tasks, including diabetic retinopathy grading, vessel segmentation, and diabetic lesion segmentation. The experimental results demonstrated the superiority of our proposed framework over some state-of-the-art unsupervised competitors and a state-of-the-art supervised method.Comment: Accepted as a conference paper to The 28th biennial international conference on Information Processing in Medical Imaging (IPMI 2023

Near-net forming complex shaped Zr-based bulk metallic glasses by high pressure die casting

Forming complex geometries using the casting process is a big challenge for bulk metallic glasses (BMGs), because of a lack of time of the window for shaping under the required high cooling rate. In this work, we open an approach named the “entire process vacuum high pressure die casting” (EPV-HPDC), which delivers the ability to fill die with molten metal in milliseconds, and create solidification under high pressure. Based on this process, various Zr-based BMGs were prepared by using industrial grade raw material. The results indicate that the EPV-HPDC process is feasible to produce a glassy structure for most Zr-based BMGs, with a size of 3 mm × 10 mm and with a high strength. In addition, it has been found that EPV-HPDC process allows complex industrial BMG parts, some of which are hard to be formed by any other metal processes, to be net shaped precisely. The BMG components prepared by the EVP-HPDC process possess the advantages of dimensional accuracy, efficiency, and cost compared with the ones formed by other methods. The EVP-HPDC process paves the way for the large-scale application of BMGs

Survival and Clinicopathological Significance of SIRT1 Expression in Cancers: A Meta-Analysis

Background: Silent information regulator 2 homolog 1 (SIRT1) is an evolutionarily conserved enzymes with nicotinamide adenine dinucleotide (NAD)+-dependent deacetylase activity. SIRT1 is involved in a large variety of cellular processes, such as genomic stability, energy metabolism, senescence, gene transcription, and oxidative stress. SIRT1 has long been recognized as both a tumor promoter and tumor suppressor. Its prognostic role in cancers remains controversial.Methods: A meta-analysis of 13,138 subjects in 63 articles from PubMed, EMBASE, and Cochrane Library was performed to evaluate survival and clinicopathological significance of SIRT1 expression in various cancers.Results: The pooled results of meta-analysis showed that elevated expression of SIRT1 implies a poor overall survival (OS) of cancer patients [Hazard Ratio (HR) = 1.566, 95% CI: 1.293–1.895, P < 0.0001], disease free survival (DFS) (HR = 1.631, 95% CI: 1.250–2.130, P = 0.0003), event free survival (EFS) (HR = 2.534, 95% CI: 1.602–4.009, P = 0.0001), and progress-free survival (PFS) (HR = 3.325 95% CI: 2.762–4.003, P < 0.0001). Elevated SIRT1 level was associated with tumor stage [Relative Risk (RR) = 1.299, 95% CI: 1.114–1.514, P = 0.0008], lymph node metastasis (RR = 1.172, 95% CI: 1.010–1.360, P = 0.0363), and distant metastasis (RR = 1.562, 95% CI: 1.022–2.387, P = 0.0392). Meta-regression and subgroup analysis revealed that ethnic background has influence on the role of SIRT1 expression in predicting survival and clinicopathological characteristics of cancers. Overexpression of SIRT1 predicted a worse OS and higher TNM stage and lymphatic metastasis in Asian population especially in China.Conclusion: Our data suggested that elevated expression of SIRT1 predicted a poor OS, DFS, EFS, PFS, but not for recurrence-free survival (RFS) and cancer-specific survival (CCS). SIRT1 overexpression was associated with higher tumor stage, lymph node metastasis, and distant metastasis. SIRT1-mediated molecular events and biological processes could be an underlying mechanism for metastasis and SIRT1 is a therapeutic target for inhibiting metastasis, leading to good prognosis

Available Transfer Capability Calculation Constrained with Small-Signal Stability Based on Adaptive Gradient Sampling

Due to the nonsmoothness of the small-signal stability constraint, calculating the available transfer capability (ATC) limited by small-signal stability rigorously through the nonlinear programming is quite difficult. To tackle this challenge, this paper proposes a sequential quadratic programming (SQP) method combined with gradient sampling (GS) in a dual formulation. The highlighted feature is the sample size of the gradient changes dynamically in every iteration, yielding an adaptive gradient sampling (AGS) process. Thus, the computing efficiency is greatly improved owing to the decrease and the parallelization of gradient evaluation, which dominates the computing time of the whole algorithm. Simulations on an IEEE 10-machine 39-bus system and an IEEE 54-machine 118-bus system prove the effectiveness and high efficiency of the proposed method