Detecting deformation mechanisms of metals from acoustic emission signals through knowledge-driven unsupervised learning.

Timely detection of deformation mechanisms in metallic structural materials is essential for early-warning alerts on potential damages and fractures. Acoustic emission (AE) technologies are commonly used for this purpose due to their non-destructive nature. However, traditional methods often struggle with distinguishing AE signals associated with multiple co-existing deformation mechanisms.

Exploring Psychological Trends in Populations With Chronic Obstructive Pulmonary Disease During COVID-19 and Beyond: Large-Scale Longitudinal Twitter Mining Study.

Background: Chronic obstructive pulmonary disease (COPD) ranks among the leading causes of global mortality, and COVID-19 has intensified its challenges. Beyond the evident physical effects, the long-term psychological effects of COVID-19 are not fully understood.

Objective: This study aims to unveil the long-term psychological trends and patterns in populations with COPD throughout the COVID-19 pandemic and beyond via large-scale Twitter mining.

IU-Net: A dual-path U-Net with rich information interaction for medical image segmentation.

Although the U-shape networks have achieved remarkable performances in many medical image segmentation tasks, they rarely model the sequential relationship of hierarchical layers. This weakness makes it difficult for the current layer to effectively utilize the historical information of the previous layer, leading to unsatisfactory segmentation results for lesions with blurred boundaries and irregular shapes. To solve this problem, we propose a novel dual-path U-Net, dubbed IU-Net.

Multilabel Feature Selection via Shared Latent Sublabel Structure and Simultaneous Orthogonal Basis Clustering.

Multilabel feature selection solves the dimension distress of high-dimensional multilabel data by selecting the optimal subset of features. Noisy and incomplete labels of raw multilabel data hinder the acquisition of label-guided information. In existing approaches, mapping the label space to a low-dimensional latent space by semantic decomposition to mitigate label noise is considered an effective strategy.

Barriers and Implications of 5G Technology Adoption for Hospitals in Western China: Integrated Interpretive Structural Modeling and Decision-Making Trial and Evaluation Laboratory Analysis.

Background: 5G technology is gaining traction in Chinese hospitals for its potential to enhance patient care and internal management. However, various barriers hinder its implementation in clinical settings, and studies on their relevance and importance are scarce.

Objective: This study aimed to identify critical barriers hampering the effective implementation of 5G in hospitals in Western China, to identify interaction relationships and priorities of the above-identified barriers, and to assess the intensity of the relationships and cause-and-effect relations between the adoption barriers.

Clinical predictions of COVID-19 patients using deep stacking neural networks.

The coronavirus disease 2019 (COVID-19) pandemic, which emerged in late 2019, has caused millions of infections and fatalities globally, disrupting various aspects of human society, including socioeconomic, political, and educational systems. One of the key challenges during the COVID-19 pandemic is accurately predicting the clinical development and outcome of the infected patients. In response, scientists and medical professionals globally have mobilized to develop prognostic strategies such as risk scores, biomarkers, and machine learning models to predict the clinical course and outcomes of COVID-19 patients.

A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients.

Objective: Based on ultrasound (US) images, this study aimed to detect and quantify calcifications of thyroid nodules, which are regarded as one of the most important features in US diagnosis of thyroid cancer, and to further investigate the value of US calcifications in predicting the risk of lymph node metastasis (LNM) in papillary thyroid cancer (PTC).

Methods: Based on the DeepLabv3+ networks, 2992 thyroid nodules in US images were used to train a model to detect thyroid nodules, of which 998 were used to train a model to detect and quantify calcifications. A total of 225 and 146 thyroid nodules obtained from two centers, respectively, were used to test the performance of these models.

MS-Net: Learning to assess the malignant status of a lung nodule by a radiologist and her peers.

Background: Automatically assessing the malignant status of lung nodules based on CTscan images can help reduce the workload of radiologists while improving their diagnostic accuracy.

Purpose: Despite remarkable progress in the automatic diagnosis of pulmonary nodules by deep learning technologies, two significant problems remain outstanding. First, end-to-end deep learning solutions tend to neglect the empirical (semantic) features accumulated by radiologists and only rely on automatic features discovered by neural networks to provide the final diagnostic results, leading to questionable reliability, and interpretability.

A novel multi-attention, multi-scale 3D deep network for coronary artery segmentation.

Automatic segmentation of coronary arteries provides vital assistance to enable accurate and efficient diagnosis and evaluation of coronary artery disease (CAD). However, the task of coronary artery segmentation (CAS) remains highly challenging due to the large-scale variations exhibited by coronary arteries, their complicated anatomical structures and morphologies, as well as the low contrast between vessels and their background. To comprehensively tackle these challenges, we propose a novel multi-attention, multi-scale 3D deep network for CAS, which we call CAS-Net.

Learning from dermoscopic images in association with clinical metadata for skin lesion segmentation and classification.

Automatic segmentation and classification of lesions are two clinically significant tasks in the computer-aided diagnosis of skin diseases. Both tasks are challenging due to the nonnegligible lesion differences in dermoscopic images from different patients. In this paper, we propose a novel pipeline to efficiently implement skin lesions' segmentation and classification tasks, which consists of a segmentation network and a classification network.

Rethinking adversarial domain adaptation: Orthogonal decomposition for unsupervised domain adaptation in medical image segmentation.

Medical image segmentation methods based on deep learning have made remarkable progress. However, such existing methods are sensitive to data distribution. Therefore, slight domain shifts will cause a decline of performance in practical applications.

[The working principle and prototype construction of the Chinese vestibular prosthesis].

To complete the working principle design and prototype construction of the Chinese multichannel vestibular prosthesis （CMVP） with independent intellectual property rights, and verify its working performance, so as to lay the foundation for the clinical promotion and application of CMVP. On the basis of previous research, the working principle of CMVP was constructed based on the information encoding principle of vestibular nervous system, and the circuit was designed according to the principle. Then, appropriate electronic components and software systems were selected to construct a CMVP prototype according to the design.

A novel end-to-end deep learning solution for coronary artery segmentation from CCTA.

Purpose: Coronary computed tomographic angiography (CCTA) plays a vital role in the diagnosis of cardiovascular diseases, among which automatic coronary artery segmentation (CAS) serves as one of the most challenging tasks. To computationally assist the task, this paper proposes a novel end-to-end deep learning-based (DL) solution for automatic CAS.

Methods: Inspired by the Di-Vnet network, a fully automatic multistage DL solution is proposed.

Adaptive Multi-Factor Quantitative Analysis and Prediction Models: Vaccination, Virus Mutation and Social Isolation on COVID-19.

Different countries have adopted various control measures for the COVID-19 pandemic in different periods, and as the virus continues to mutate, the progression of the pandemic and preventive measures adopted have varied dynamically over time. Thus, quantitative analysis of the dynamic impact of different factors such as vaccination, mutant virus, social isolation, etc., on transmission and predicting pandemic progress has become a difficult task.

A comparative analysis on characteristics and mortalities of four key transmission populations on antiretroviral therapy: a retrospective cohort study in Northwest China.

Background: This study explored disparities in characteristics and mortalities among four major transmission groups on antiretroviral therapy in northwest China as well as the survival impact of each transmission route.

Methods: We first examined disparities in demographics and clinical characteristics of the four transmission populations. Kaplan Meier analysis was subsequently conducted to compare survival rates among all groups.

User Behaviors and User-Generated Content in Chinese Online Health Communities: Comparative Study.

Background: Online health communities (OHCs) have increasingly gained traction with patients, caregivers, and supporters globally. Chinese OHCs are no exception. However, user-generated content (UGC) and the associated user behaviors in Chinese OHCs are largely underexplored and rarely analyzed systematically, forfeiting valuable opportunities for optimizing treatment design and care delivery with insights gained from OHCs.

The Evolution and Disparities of Online Attitudes Toward COVID-19 Vaccines: Year-long Longitudinal and Cross-sectional Study.

Background: Due to the urgency caused by the COVID-19 pandemic worldwide, vaccine manufacturers have to shorten and parallel the development steps to accelerate COVID-19 vaccine production. Although all usual safety and efficacy monitoring mechanisms remain in place, varied attitudes toward the new vaccines have arisen among different population groups.

Objective: This study aimed to discern the evolution and disparities of attitudes toward COVID-19 vaccines among various population groups through the study of large-scale tweets spanning over a whole year.

Ms RED: A novel multi-scale residual encoding and decoding network for skin lesion segmentation.

Computer-Aided Diagnosis (CAD) for dermatological diseases offers one of the most notable showcases where deep learning technologies display their impressive performance in acquiring and surpassing human experts. In such the CAD process, a critical step is concerned with segmenting skin lesions from dermoscopic images. Despite remarkable successes attained by recent deep learning efforts, much improvement is still anticipated to tackle challenging cases, e.

Nuclear-localized eukaryotic translation initiation factor 1A is involved in mouse preimplantation embryo development.

Preimplantation embryo development is characterized by drastic nuclear reprogramming and dynamic stage-specific gene expression. Key regulators of this earliest developmental stage have not been revealed. In the present study, a "non-classical" nuclear-localization pattern of eIF1A was observed during early developmental stages of mouse preimplantation embryo before late-morula.

Particle hydrodynamic simulation of thrombus formation using velocity decay factor.

Background And Objective: Thrombus simulation plays an important role in many specialist areas in the field of medicine such as surgical education and training, clinical diagnosis and prediction, treatment planning, etc. Although a considerable number of methods have been developed to simulate various kinds of fluid flows, it remains a non-trivial task to effectively simulate thrombus because of its unique physiological properties in contrast to other types of fluids. To tackle this issue, this study introduces a novel method to model the formation mechanism of thrombus and its interaction with blood flow.

Understanding Concerns, Sentiments, and Disparities Among Population Groups During the COVID-19 Pandemic Via Twitter Data Mining: Large-scale Cross-sectional Study.

Background: Since the beginning of the COVID-19 pandemic in late 2019, its far-reaching impacts have been witnessed globally across all aspects of human life, such as health, economy, politics, and education. Such widely penetrating impacts cast significant and profound burdens on all population groups, incurring varied concerns and sentiments among them.

Objective: This study aims to identify the concerns, sentiments, and disparities of various population groups during the COVID-19 pandemic through a cross-sectional study conducted via large-scale Twitter data mining infoveillance.

Real-Time Shadow Detection From Live Outdoor Videos for Augmented Reality.

Simulating shadow interactions between real and virtual objects is important for augmented reality (AR), in which accurately and efficiently detecting real shadows from live videos is a crucial step. Most of the existing methods are capable of processing only scenes captured under a fixed viewpoint. In contrast, this article proposes a new framework for shadow detection in live outdoor videos captured under moving viewpoints.

Juvenile Granulosa Cell Tumors of the Ovary.

Objective: To explore the clinical and pathologic features of ovarian juvenile granulosa cell tumors (JGCTs).

Methods: Clinical data, histopathologic observations, immunohistochemical results, FOXL2 mutation status, and follow-up information of 7 JGCT cases were studied.

Results: The patients most commonly presented with abdominal distension and pain (5 cases), followed by precocious puberty (1 case) and a pelvic mass (1 case).

Akt plays indispensable roles during the first cell lineage differentiation of mouse.

The first cell lineage differentiation occurs during the development of mouse 8-cell embryo to blastocyst. Akt is a potent kinase whose role during blastocyst formation has not been elucidated. In the present study, immunofluorescence results showed that the Akt protein was specifically localized to the outer cells of the morula.