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1. Learning Graph Convolutional Networks for Multi-Label Recognition and Applications

Author: Yanwen Guo, Peng Wang, Xiu-Shen Wei, and Zhao-Min Chen
Subjects: Word embedding, business.industry, Computer science, Applied Mathematics, Pattern recognition, Directed graph, Facial recognition system, Convolutional neural network, Graph, Set (abstract data type), Computational Theory and Mathematics, Artificial Intelligence, Graph (abstract data type), Computer Vision and Pattern Recognition, Artificial intelligence, Representation (mathematics), business, Classifier (UML), Software
Abstract: The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model label dependencies to improve recognition performance. To capture and explore such important information, we propose Graph Convolutional Networks based models for multi-label recognition, where directed graphs are constructed over classes and information is propagated between classes to learn inter-dependent class-level representations. Following this idea, we design two particular models that approach multi-label classification from different views. In our first model, the prior knowledge about the class dependencies is integrated into classifier learning. Specifically, we propose Classifier-Learning-GCN to map class-level semantic representations (\eg, word embedding) into classifiers that maintain the inter-class topology. In our second model, we decompose the visual representation of an image into a set of label-aware features and propose Prediction-Learning-GCN to encode such features into inter-dependent image-level prediction scores. Furthermore, we also present an effective correlation matrix construction approach to capture inter-class relationships and consequently guide information propagation among classes. Empirical results on generic multi-label recognition demonstrate that the effectiveness of both two proposed models. Moreover, the proposed methods also show advantages in other multi-label related applications.
Published: 2023
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2. Toward a No-Reference Quality Metric for Camera-Captured Images

Author: Xiongkuo Min, Ke Gu, Guangtao Zhai, Yutao Liu, and Runze Hu
Subjects: Source code, Computer science, business.industry, Image quality, media_common.quotation_subject, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Semantics, Computer Science Applications, Image (mathematics), Human-Computer Interaction, Support vector machine, Control and Systems Engineering, Metric (mathematics), Quality Score, Quality (business), Artificial intelligence, Electrical and Electronic Engineering, business, Software, Information Systems, media_common
Abstract: Existing no-reference (NR) image quality assessment (IQA) metrics are still not convincing for evaluating the quality of the camera-captured images. Toward tackling this issue, we, in this article, establish a novel NR quality metric for quantifying the quality of the camera-captured images reliably. Since the image quality is hierarchically perceived from the low-level preliminary visual perception to the high-level semantic comprehension in the human brain, in our proposed metric, we characterize the image quality by exploiting both the low-level image properties and the high-level semantics of the image. Specifically, we extract a series of low-level features to characterize the fundamental image properties, including the brightness, saturation, contrast, noiseness, sharpness, and naturalness, which are highly indicative of the camera-captured image quality. Correspondingly, the high-level features are designed to characterize the semantics of the image. The low-level and high-level perceptual features play complementary roles in measuring the image quality. To infer the image quality, we employ the support vector regression (SVR) to map all the informative features to a single quality score. Thorough tests conducted on two standard camera-captured image databases demonstrate the effectiveness of the proposed quality metric in assessing the image quality and its superiority over the state-of-the-art NR quality metrics. The source code of the proposed metric for camera-captured images is released at https://github.com/YT2015?tab=repositories.
Published: 2023
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3. Externalities of Accounting Disclosures: Evidence from the Federal Reserve

Author: Edward Xuejun Li, Gary Lind, Min Shen, and K. Ramesh
Subjects: History, Economics and Econometrics, Polymers and Plastics, business.industry, Monetary policy, Accounting, Track (rail transport), Industrial and Manufacturing Engineering, Economics, Business and International Management, business, Externality, Finance
Abstract: This study provides the first empirical evidence that the Federal Reserve (the Fed) systematically retrieves micro-level accounting reports to aid its understanding of the state of the macroeconomy. Using unique data identifying its direct access of corporate SEC filings, we show that the Fed tracks firms that are bellwethers and industry leaders, or that can engender systemic risk. The qualitative information in the Fed-accessed periodic reports explains the Fed’s GDP growth forecasts for up to four quarters, after controlling for contemporaneous aggregate earnings and other economic information. However, professional forecasts fail to incorporate such qualitative accounting information. In addition, this qualitative information is reflected in the tone of the ensuing FOMC meeting discussions as well as in Fed forecasts of key macro demand and supply factors. Overall, our evidence suggests important externalities of micro-level accounting reports, especially qualitative disclosures, on the central bank’s macroeconomic forecasts and, by extension, monetary policy. Data Availability: Data are available from public sources. JEL Classifications: E58; G20; M41; M45.
Published: 2023
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4. Identification of therapeutic targets and prognostic biomarkers among CC chemokines in the pancreatic adenocarcinoma microenvironment

Author: Tao Mao, Cui-Ping Zhang, Xinyuan Liu, Zibin Tian, Congcong Min, Qi Zhang, Xiaoyu Li, and Jing Guo
Subjects: business.industry, medicine, Cancer research, CC chemokine, Adenocarcinoma, Identification (biology), medicine.disease, business
Abstract: Pancreatic adenocarcinoma (PAAD) as one of the most aggressive and lethal malignant tumors is correlated with increased morbidity and mortality. Tumorigenesis, growth, and metastasis are affected by various cytokines. Among them, CC chemokines can modulate the infiltration of immune cells and recruit cancer-associated immune cells, which play an important role in the inhibition of tumor immunity and affect the clinical outcome of cancer patients. However, the therapeutic potential and prognostic value of CC chemokines in PAAD have not yet been elucidated. To do this, we comprehensively explore and analyze large amounts of data on the basis of ONCOMINE database, GEPIA, LinkedOmics, cBioPortal, GeneMANIA, UALCAN, jvenn, DAVID 6.8, TRRUST, TIMER, and TISIDB. We found the transcriptional levels of CCL5/7/13/15/18/19/20 in PAAD tissues were remarkably increased, whereas the transcriptional level of CCL17 was decreased. CCL20 expression had significantly been correlated with the tumor stage of PAAD patients. High expressions of CCL5, CCL7, CCL13, CCL18, and CCL20 were notably correlated with the prognosis of patients. Moreover, patients with CCL18 and CCL19 alterations showed a poor prognosis in both overall survival (OS) and disease-specific survival (DSS), and patients with CCL5 and CCL15 alterations also presented a poor prognosis in OS. The functions of the aberrantly expressed CC chemokines were mainly correlated with the cytokine-cytokine receptor interaction, chemokine signaling pathway, inflammatory response, and immune response. Our study shows that the key transcription factors for CC chemokines are RELA and NF-κB1. We also discovered significant associations between the expression levels of CC chemokines and six infiltrating immune cells including CD8+ T cells, CD4+ T cells, B cells, macrophages, neutrophils, and dendritic cells. Taken together, our study indicated the interaction between CC chemokines and PAAD and clarified the value of CC chemokines as potential therapeutic targets as well as prognostic markers for PAAD.
Published: 2023
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5. Learning Enhanced Acoustic Latent Representation for Small Scale Affective Corpus with Adversarial Cross Corpora Integration

Author: Chun-Min Chang and Chi-Chun Lee
Subjects: Domain adaptation, business.industry, Computer science, Scale (chemistry), computer.software_genre, Autoencoder, Human-Computer Interaction, Constraint (information theory), Adversarial system, Artificial intelligence, Representation (mathematics), Transfer of learning, business, computer, Feature learning, Software, Natural language processing
Abstract: Achieving robust cross contexts speech emotion recognition (SER) has become a critical next direction of research for wide adoption of SER technology. The core challenge is in the large variability of affective speech that is highly contextualized. Prior works have worked on this as a transfer learning problem that mostly focuses on developing domain adaptation strategy. However, many of the existing speech emotion corpora, even those considered as large scale, are still limited in size resulting in an unsatisfactory transfer result. On the other hand, directly collecting context-specific corpus often results in an even smaller data size leading to an inevitably non-robust accuracy. In order to mitigate this issue, we propose the concept of enhancing the affect-related variability when learning the in-context acoustic latent representation by integrating out-of-context emotion data. Specifically, we utilize adversarial autoencoder network as our backbone with multiple out-of-context emotion labels derived for each in-context samples that serve as an auxiliary constraint in learning the latent representation. We extensively evaluate our framework using three in-context databases with three out-of-context databases. In this work, we demonstrate not only an improved recognition accuracy but also a comprehensive analysis on the effectiveness of this representation learning strategy.
Published: 2023
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6. Privacy-Aware Laser Wireless Power Transfer for Aerial Multi-Access Edge Computing: A Colonel Blotto Game Approach

Author: Long Zhang, Zhu Han, Minghui Min, Yao Wang, Vishal Sharma, and Chao Guo
Subjects: business.industry, Computer science, Computer Networks and Communications, Distributed computing, Adversary, Grid, Computer Science Applications, symbols.namesake, User equipment, Nash equilibrium, Hardware and Architecture, Signal Processing, symbols, Wireless, Wireless power transfer, business, Edge computing, Vulnerability (computing), Information Systems
Abstract: Multi-access edge computing (MEC) has been recently considered in challenging environments lacking available terrestrial infrastructures by extending the computing resources to the air for further enhancing the computation capability of the new aerial user equipment (AUE). Additionally, wireless power transfer (WPT) is a promising solution to prolong the battery lifetime of energy-constrained wireless devices like AUEs. In this paper, we investigate the integration of laser-beamed WPT in the high-altitude platform (HAP) aided MEC systems for the HAP-connected AUEs. By discretizing the three-dimensional coverage space of the HAP, we present a multi-tier tile grid-based spatial structure to provide aerial locations for laser charging. With this setup, we identify a new privacy vulnerability caused by the openness during the air-to-air transmission of WPT signaling messages in the presence of a terrestrial adversary. A privacy-aware laser-powered aerial MEC framework is developed that addresses this vulnerability and enhances the location privacy of AUEs for laser WPT. Specifically, the interaction between the HAP as a defender and the adversary in their tile grid allocation as charging locations to AUEs is formulated as a Colonel Blotto game, which models the competition of the players for limited resources over multiple battlefields for a finite time horizon. Moreover, we derive the mixed-strategy Nash equilibria of the tile grid allocation game for both symmetric and asymmetric tile grids between the defender and the adversary. Simulations results show that the proposed framework significantly outperforms the design baselines with a given privacy protection level in terms of system-wide expected total utilities.
Published: 2023
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7. Predictors and long-term prognosis of early and late recurrence for patients undergoing hepatic resection of hepatocellular carcinoma: a large-scale multicenter study

Author: Han Wu, Wei-Min Gu, Timothy M. Pawlik, Wen-Tao Yan, Xin-Fei Xu, Hai-Bo Qiu, Wan Yee Lau, Chao Li, Jian-Hong Zhong, Ting-Hao Chen, Ya-Hao Zhou, Feng Shen, Lan-Qing Yao, Ming-Da Wang, Tian Yang, and Hong Wang
Subjects: medicine.medical_specialty, Scale (ratio), Hepatic resection, business.industry, medicine.disease, Term (time), Multicenter study, Hepatocellular carcinoma, Late Recurrence, medicine, General Earth and Planetary Sciences, Radiology, business, General Environmental Science
Published: 2023
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8. Privacy-Preserving Cross-Environment Human Activity Recognition

Author: Teo Sin Gee, Le Zhang, Min Wu, Chen Zhenghua, Wei Cui, and Bing Li
Subjects: Discrete wavelet transform, Modalities, business.industry, Computer science, SIGNAL (programming language), Machine learning, computer.software_genre, Computer Science Applications, Human-Computer Interaction, Privacy preserving, Activity recognition, Core (game theory), Control and Systems Engineering, Channel state information, Individual person, Artificial intelligence, Electrical and Electronic Engineering, business, computer, Software, Information Systems
Abstract: Recent studies have demonstrated the success of using the channel state information (CSI) from the WiFi signal to analyze human activities in a fixed and well-controlled environment. Those systems usually degrade when being deployed in new environments. A straightforward solution to solve this limitation is to collect and annotate data samples from different environments with advanced learning strategies. Although workable as reported, those methods are often privacy sensitive because the training algorithms need to access the data from different environments, which may be owned by different organizations. We present a practical method for the WiFi-based privacy-preserving cross-environment human activity recognition (HAR). It collects and shares information from different environments, while maintaining the privacy of individual person being involved. At the core of our approach is the utilization of the Johnson-Lindenstrauss transform, which is theoretically shown to be differentially private. Based on that, we further design an adversarial learning strategy to generate environment-invariant representations for HAR. →doWe demonstrate the effectiveness of the proposed method with different data modalities from two real-life environments. More specifically, on the raw CSI dataset, it shows 2.18% and 1.24% improvements over challenging baselines for two environments, respectively. Moreover, with the discrete wavelet transform features, it further yields 5.71% and 1.55% improvements, respectively.
Published: 2023
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9. Robust Unsupervised Feature Selection via Multi-Group Adaptive Graph Representation

Author: Xuelong Li, Min Zou, Dongjian He, Aihong Yuan, and Mengbo You
Subjects: Optimization problem, Source code, Computer science, business.industry, Iterative method, media_common.quotation_subject, Pattern recognition, Feature selection, Computer Science Applications, Matrix (mathematics), Computational Theory and Mathematics, Similarity (network science), Feature (computer vision), Artificial intelligence, Cluster analysis, business, Information Systems, media_common
Abstract: Unsupervised feature selection can play an important role in addressing the issue of processing massive unlabelled high-dimensional data in the domain of machine learning and data mining. This paper presents a novel unsupervised feature selection method, referred to as Multi-Group Adaptive Graph Representation (MGAGR). Different from existing methods, the relationship between features is explored via the global similarity matrix, which is reconstructed by local similarities of multiple groups. Specifically, the similarity of a feature compared to other features can be represented by the linear combination of all the local similarities. The local similarity of a representative group is given a large weight to reconstruct the global similarity. Besides, an iterative algorithm is given to solve the optimization problem, in which the global similarity matrix, its corresponding reconstruction weights and the self-representation matrix are iteratively improved. Experimental results on 8 benchmark datasets demonstrates that the proposed method outperforms the state-of-the-art unsupervised feature selection methods in terms of clustering performance. The source code is available at: https://github.com/misteru/MGAGR.
Published: 2023
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10. Multi-Dimensional Classification via Decomposed Label Encoding

Author: Min-Ling Zhang and Bin-Bin Jia
Subjects: Computer science, business.industry, Feature vector, Pattern recognition, Space (mathematics), Class (biology), Computer Science Applications, Computational Theory and Mathematics, Encoding (memory), Multi dimensional, Decomposition (computer science), Artificial intelligence, Benchmark data, business, Class variable, Information Systems
Abstract: In multi-dimensional classification (MDC), a number of class variables are assumed in the output space with each of them specifying the class membership w.r.t. one heterogeneous class space. One major challenge in learning from MDC examples lies in the heterogeneity of class spaces, where the modeling outputs from different class spaces are not directly comparable. To tackle this problem, we propose a new strategy named decomposed label encoding which enables modeling alignment for MDC in an encoded label space derived from one-vs-one (OvO) decomposition. Specifically, the original MDC output space is transformed into a ternary encoded label space by conducting OvO decomposition w.r.t. each class space. Then, the manifold structure in the feature space is exploited to enrich the labeling information in the encoded label space. Finally, the predictive model is induced by fitting the metric-aligned modeling outputs with enriched labeling information. Extensive experiments over twenty benchmark data sets clearly show the superiority of the proposed MDC strategy against state-of-the-art approaches.
Published: 2023
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11. Cohort Profile

Author: Katie M. O'Brien, Michael J. Orlich, Alpa V. Patel, Kim Robien, Alicja Wolk, Roger L. Milne, Susanna C. Larsson, Melissa A. Merritt, Brian D. Carter, Sven Sandin, Julie R. Palmer, Alan A. Arslan, Leo J. Schouten, Shelley S. Tworoger, Michael Jones, Anne Zeleniuch-Jacquotte, Nicolas Wentzensen, Sarah R. Irvin, Hilde Langseth, Britton Trabert, I-Min Lee, Maria Elena Martinez, Graham G. Giles, Lynn Rosenberg, Victoria A. Kirsh, Julie E. Buring, Mary K. Townsend, Rudolf Kaaks, James V. Lacey, Jian-Min Yuan, Kala Visvanathan, Giske Ursin, Jenny N. Poynter, Synnove F. Knutsen, Thomas E. Rohan, Ulrike Peters, Renée T. Fortner, Elisabete Weiderpass, V. Wendy Setiawan, Anthony J. Swerdlow, Woon-Puay Koh, Piet A. van den Brandt, Dale P. Sandler, Epidemiologie, and RS: GROW - R1 - Prevention
Subjects: Ovarian Neoplasms, Oncology, RISK, medicine.medical_specialty, Epidemiology, business.industry, MORTALITY, MEDLINE, General Medicine, Carcinoma, Ovarian Epithelial, medicine.disease, Cohort Studies, Online Only, Internal medicine, Cohort, Humans, Medicine, Female, business, Ovarian cancer, POWDER
Published: 2022
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12. Clinical characteristics of influenza with or without Streptococcus pneumoniae co-infection in children

Author: Ting-Yu Yen, Li-Min Huang, Li-Lun Chen, Yun-Chung Liu, Luan-Yin Chang, Tzu-Yun Hsing, Hsiao-Chi Lin, Fei-Pei Lai, Ping-Ing Lee, Yu-Cheng Liu, Chun-Yi Lu, and Jong-Min Chen
Subjects: medicine.medical_specialty, medicine.medical_treatment, medicine.disease_cause, Pneumococcal Infections, law.invention, law, Intensive care, Internal medicine, Influenza, Human, Streptococcus pneumoniae, medicine, Extracorporeal membrane oxygenation, Humans, Child, Retrospective Studies, Mechanical ventilation, medicine.diagnostic_test, Coinfection, business.industry, Medical record, Sodium, Bacterial Infections, General Medicine, Intensive care unit, C-Reactive Protein, Chest radiograph, business, Co infection
Abstract: Background/purpose Influenza is frequently complicated with bacterial co-infection. This study aimed to disclose the significance of Streptococcus pneumoniae co-infection in children with influenza. Methods We retrospectively reviewed medical records of pediatric patients hospitalized for influenza with or without pneumococcal co-infection at the National Taiwan University Hospital from 2007 to 2019. Clinical characteristics and outcomes were compared between patients with and without S. pneumoniae co-infection. Results There were 558 children hospitalized for influenza: 494 had influenza alone whereas 64 had S. pneumoniae co-infection. Patients with S. pneumoniae co-infection had older ages, lower SpO2, higher C-Reactive Protein (CRP), lower serum sodium, lower platelet counts, more chest radiograph findings of patch and consolidation on admission, longer hospitalization, more intensive care, longer intensive care unit (ICU) stay, more mechanical ventilation, more inotropes/vasopressors use, more surgical interventions including video-assisted thoracoscopic surgery (VATS) and extracorporeal membrane oxygenation (ECMO), and higher case-fatality rate. Conclusion Compared to influenza alone, patients with S. pneumoniae co-infection had more morbidities and mortalities. Pneumococcal co-infection is considered when influenza patients have lower SpO2, lower platelet counts, higher CRP, lower serum sodium, and more radiographic patches and consolidations on admission.
Published: 2022
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13. Cumulative exposure amount of PM2.5 in the ambient air is associated with coronary atherosclerosis - Serial coronary CT angiography study

Author: Byoung Kwon Lee, Seung Pyo Lee, Hye Kyung Kim, Minkwan Kim, Hyo Eun Park, Su Yeon Choi, Jin Young Min, Shinae Kang, Jung Hye Kim, Heesun Lee, and Kyoung-Bok Min
Subjects: medicine.medical_specialty, business.industry, Fine particulate, Cardiovascular health, Cardiovascular risk factors, Cumulative Exposure, Coronary ct angiography, Ambient air, Internal medicine, Coronary artery calcification, Cardiology, Medicine, Radiology, Nuclear Medicine and imaging, Cardiology and Cardiovascular Medicine, business, Coronary atherosclerosis
Abstract: Background We investigated the change of coronary atherosclerosis with long-term exposure to fine particulate matter of aerodynamic diameter Methods Subjects undergoing serial CCTAs between January 2007 and December 2017 (n = 3,127) were analyzed. Each individual's cumulative amount of PM2.5 exposure between the two CCTAs was evaluated by Kriging interpolation and zonal analysis, considering the time interval between the two CCTAs. The main outcome was progression of coronary artery calcium (CAC) with additional semiquantitative analysis on the changes in the severity and composition of atherosclerotic plaques. Results The CAC scores increased by 30.8 Agatston units per-year under a median PM2.5 concentration 24.9μg/m3 and tended to increase with the cumulative amount of PM2.5 exposure (r = 0.321, p Conclusions Cumulative exposure to air pollution is associated with the progression of diffuse coronary calcification, the importance of which may be more significant than other traditional cardiovascular risk factors. Further investigations into the causality between PM2.5 and coronary atherosclerosis are warranted to improve global cardiovascular health.
Published: 2022
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14. CircZNF236 facilitates malignant progression in oral squamous cell carcinoma by sequestering miR-145-5p

Author: Min Hu, Qing Lu, Yanhai Che, and Hongze Che
Subjects: stomatognathic diseases, Cancer Research, Oncology, business.industry, Cancer research, Medicine, Basal cell, General Medicine, Malignant progression, Mir 145 5p, business
Abstract: BackgroundA number of non-coding circular RNAs (circRNAs) have recently been implicated in the modulation of gene expression in cancer models. We therefore sought to explore if circZNF236 has a role in oral squamous cell carcinoma (OSCC).MethodsWe first examined circZNF236 expression in 32 pairs of OSCC and noncancerous tissues. We then investigated a functional role for circZNF236 using knockdown and overexpression approaches in OSCC cancer cell lines. Cell counting kit-8, wound healing, Transwell, and flow cytometry were employed to assess circZNF236 function in vitro. The association between circZNF236 and miR-145-5p, or that between miR-145-5p and malignant brain tumour domain containing 1 (MBTD1) was predicted by bioinformatics and demonstrated by dual-luciferase reporter assays, RNA pull-down assays as well as RNA immunoprecipitation (RIP) assays. A mouse OSCC xenograft model was employed to demonstrate the impacts of circZNF236 inhibition on tumor development in vivo. ResultsOSCC tissues and cells had higher levels of circZNF236 expression compared with normal controls. Furthermore, high circZNF2361 levels in patients with OSCC correlated with a poor prognosis. CircZNF236 silencing decreased the malignant properties of OSCC cells and suppressed OSCC tumor formation in the mouse model. We then noticed that miR-145-5p can be regulated by circZNF236, and that circZNF2361 promoted OSCC development by absorbing miR-145-5p and consequently upregulating MBTD1 expression.ConclusionCircZNF236 modulates OSCC via an miR-145-5p/MBTD1 axis. These results support the potential of circZNF236 as a treatment target for OSCC.
Published: 2023
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15. Computation Migration Oriented Resource Allocation in Mobile Social Clouds

Author: Min Huang, Fuliang Li, Bo Yi, Qiang He, and Xingwei Wang
Subjects: Edge device, Computer Networks and Communications, Computer science, business.industry, Distributed computing, Computation, Evolutionary algorithm, Cloud computing, Computer Science Applications, Resource (project management), Hardware and Architecture, Decomposition (computer science), Resource allocation, business, Mobile device, Software, Information Systems
Abstract: The rapid growth of mobile device (e.g., smart phone and bracelet) has spawned a lot of new applications, during which the requirements of applications are increasing, while the capacities of some mobile devices are still limited. Such contradiction drives the emergency of computation migration among mobile edge devices, which is a lack of research currently. In this work, we focus on addressing the computation migration oriented resource allocation problem among mobile edge devices. Specifically, we first construct a framework for Mobile Social Cloud(MSC), in which the mobile devices with rich resources are abstracted as resource suppliers and those resource-lacking devices are abstracted as resource demanders. Then, a mathematical model is formulated and an evolutionary algorithm is proposed to effectively solve this model based on decomposition, dominance and genetic operations. Moreover, the parallel computing is introduced to further improve the efficiency of the proposed algorithm. The experimental results indicate that the proposed algorithm outperforms the other state-of-the-art methods and it improves the calculation efficiency by about 178% (2 cores) and 262% (3 cores) by introducing parallel computing.
Published: 2023
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16. VerSA: Verifiable Secure Aggregation for Cross-Device Federated Learning

Author: Changhee Hahn, Junbeom Hur, Min-jae Kim, and Hodong Kim
Subjects: Correctness, business.industry, Computer science, Distributed computing, Cryptography, Verifiable secret sharing, Pseudorandom generator, Electrical and Electronic Engineering, Encryption, business, Versa, Protocol (object-oriented programming), Federated learning
Abstract: In privacy-preserving cross-device federated learning, users train a global model on their local data and submit encrypted local models, while an untrusted central server aggregates the encrypted models to obtain an updated global model. Prior work has demonstrated how to verify the correctness of aggregation in such a setting. However, such verification relies on strong assumptions, such as a trusted setup among all users under unreliable network conditions, or it suffers from expensive cryptographic operations, such as bilinear pairing. In this paper, we scrutinize the verification mechanism of prior work and propose a model recovery attack, demonstrating that most local models can be leaked within a reasonable time (e.g., 98% of encrypted local models are recovered within 21 h). Then, we propose VerSA, a verifiable secure aggregation protocol for cross-device federated learning. VerSA does not require any trusted setup for verification between users while minimizing the verification cost by enabling both the central server and users to utilize only a lightweight pseudorandom generator to prove and verify the correctness of model aggregation. We experimentally confirm the efficiency of VerSA under diverse datasets, demonstrating that \textsc{VerSA} is orders of magnitude faster than verification in prior work.
Published: 2023
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17. Embedding Refinement Framework for Targeted Aspect-Based Sentiment Analysis

Author: Lin Gui, Jiachen Du, Rongdi Yin, Bin Liang, Ruifeng Xu, Yulan He, and Min Yang
Subjects: Commonsense knowledge, Computer science, business.industry, Deep learning, Sentiment analysis, Context (language use), computer.software_genre, QA76, Sparse coefficient, Human-Computer Interaction, Benchmark (computing), Embedding, Artificial intelligence, business, computer, Software, Word (computer architecture), Natural language processing
Abstract: The state-of-the-art approaches to Targeted Aspect-Based Sentiment Analysis (TABSA) are mostly deep learning models based on attention mechanisms. One problem in most previous studies is that embeddings of targets and aspects are either pre-trained from large external corpora or randomly initialized. We argue that affective commonsense knowledge and words indicative of sentiment could be used to learn better target and aspect embeddings. We therefore propose an embedding refinement framework called RAEC (Refining Affective Embedding from Context), in which sentiment concepts extracted from affective commonsense knowledge and word relative location information are incorporated to derive context-affective embeddings. Furthermore, a sparse coefficient vector is exploited in refining the embeddings of targets and aspects separately. In this way, embeddings of targets and aspects can capture the highly relevant affective words. Experimental results on two benchmark datasets show that our framework can be easily integrated with existing embedding-based TABSA models and achieves state-of-the-art results compared to models relying on pre-trained word embeddings or built on other embedding refinement methods.
Published: 2023
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18. Prevalence, incidence, and risk factors of malignancy in patients with rheumatoid arthritis: a nationwide cohort study from Korea

Author: Su-Jin Moon and Kyung Min Ko
Subjects: medicine.medical_specialty, education.field_of_study, business.industry, Thyroid disease, Incidence (epidemiology), Population, General Medicine, Malignancy, medicine.disease, Lower risk, Internal medicine, Relative risk, Cohort, medicine, business, education, Cohort study
Abstract: Background/Aims: This study aims to evaluate the incidence of malignancy in patients with rheumatoid arthritis (RA) and to investigate risk factors for such in a nationwide, population-based cohort.Methods: In a large, prospective, observational cohort study, 5,077 patients with RA were enrolled from July 2009 to December 2011 and followed until February 2017. Standardized incidence ratios (SIRs) for malignancy were calculated using age- and sex-specific cancer rates in the Korean general population. Poisson regression was used to identify the risk of incident malignancy.Results: The cohort included 5,023 participants with RA contributing 16,689 person-years of follow-up. A total of 148 malignancies were recorded. The risks of stomach cancer (SIR, 0.41; 95% confidence interval [CI], 0.21 to 0.74), colon cancer (SIR, 0.13; 95% CI, 0.03 to 0.37), and lung cancer (SIR, 0.35; 95% CI, 0.14 to 0.72) were lower in RA patients than in the general population. Poisson regression modeling demonstrated that the malignancy risk was more than two-fold greater in patients with thyroid disease than in those without thyroid disease. Hydroxychloroquine therapy was associated with a reduced risk (relative risk, 0.39; 95% CI, 0.189 to 0.801) of malignancy development.Conclusions: The overall risk of malignancy in patients with RA is decreased relative to in the general population. In particular, stomach, colon, and lung cancers in Korean RA patients are less common, while brain and central nervous system cancers in male RA patients are more frequent. The patients with thyroid disease and longer RA disease duration were at increased risk for developing malignancy, while hydroxychloroquine users were at lower risk.
Published: 2023
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19. Gemini: Enabling Multi-Tenant GPU Sharing Based on Kernel Burst Estimation

Author: En-Te Lin, Yu-Min Chou, Jerry Chou, and Hung-Hsin Chen
Subjects: Multitenancy, Computer Networks and Communications, business.industry, Computer science, Cloud computing, Computer Science Applications, Scheduling (computing), Software, Computer architecture, Hardware and Architecture, Kernel (statistics), Synchronization (computer science), Resource allocation, Resource management (computing), business, Information Systems
Published: 2023
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20. Impact of Sarcopenia on Outcomes of Minimally Invasive Cardiac Surgery

Author: Min Ho Ju, Chee-Hoon Lee, Hyung Gon Je, and Mi Hee Lim
Subjects: Pulmonary and Respiratory Medicine, Body surface area, medicine.medical_specialty, business.industry, medicine.medical_treatment, General Medicine, Perioperative, musculoskeletal system, medicine.disease, Intensive care unit, law.invention, Cardiac surgery, body regions, Quartile, law, Median sternotomy, Internal medicine, Sarcopenia, medicine, Minimally invasive cardiac surgery, Surgery, Cardiology and Cardiovascular Medicine, business, human activities
Abstract: Sarcopenia is an objective marker of poor outcome following cardiac surgery through median sternotomy. However, the clinical impact of sarcopenia after minimally invasive cardiac surgery (MICS) has not been well established. This study aimed to analyze the influence of sarcopenia on the early and late outcomes following MICS. We retrospectively examined 1248 patients who underwent MICS via right mini-thoracotomy or upper sternotomy between February 2009 and April 2020. Patients older than 65 years who underwent preoperative computed tomography were enrolled. Sarcopenia was defined as the lowest sex-specific quartile of the body surface area adjusted psoas muscle area. The early operative and late survival outcomes were compared, and the predictor of late composite outcome was analyzed using Cox proportional regression model. Early and late composite outcomes in both groups were also compared. A total of 367 patients were classified into the sarcopenia (n = 92) or non-sarcopenia (n = 275) group. Patients in the sarcopenia group were older, and had lower preoperative hemoglobin and albumin levels. They had higher estimated surgical mortality, but similar early mortality and major morbidity. The survival and late composite outcome were comparable between the two groups. Sarcopenia was neither an independent predictor of late death nor a composite outcome in the multivariable model. After MICS, patients with sarcopenia displayed comparable perioperative outcomes and survival. The clinical impact of sarcopenia on postoperative outcomes might be alleviated by MICS and early recovery protocol after MICS. MICS could be a reasonable approach for elderly patients with sarcopenia.
Published: 2023
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21. Initial Invasive or Conservative Strategy for Stable Coronary Disease

Author: Maron D. J., Hochman J. S., Reynolds H. R., Bangalore S., O'Brien S. M., Boden W. E., Chaitman B. R., Senior R., Lopez-Sendon J., Alexander K. P., Lopes R. D., Shaw L. J., Berger J. S., Newman J. D., Sidhu M. S., Goodman S. G., Ruzyllo W., Gosselin G., Maggioni A. P., White H. D., Bhargava B., Min J. K., John Mancini G. B., Berman D. S., Picard M. H., Kwong R. Y., Ali Z. A., Mark D. B., Spertus J. A., Krishnan M. N., Elghamaz A., Moorthy N., Hueb W. A., Demkow M., Mavromatis K., Bockeria O., Peteiro J., Miller T. D., Szwed H., Doerr R., Keltai M., Selvanayagam J. B., Gabriel Steg P., Held C., Kohsaka S., Mavromichalis S., Kirby R., Jeffries N. O., Harrell F. E., Rockhold F. W., Broderick S., Bruce Ferguson T., Williams D. O., Harrington R. A., Stone G. W., Rosenberg Y, ISCHEMIA Research Group: Joseph Ricci, A Tello Montoliu, A I Robero Aniorte, Abbey Mulder, Abhay A Laddu, Abhinav Goyal, Abhishek Dubey, Abhishek Goyal, Abigail Knighton, Abraham Oomman, Adam J Jaskowiak, Adam Kolodziej, Adam Witkowski, Adnan Hameed, Adriana Anesini, Afshan Hussain, Agne Juceviciene, Agne Urboniene, Agnes Jakal, Agnieszka Szramowska, Ahmad Khairuddin, Ahmed Abdel-Latif, Ahmed Adel, Ahmed Aljzeeri, Ahmed Kamal, Ahmed Talaat, Aimee Mann, Aira Contreras, Ajit Kumar, V K Kumar, Akemi Furukawa, Akshay Bagai, Akvile Smigelskaite, Alain Furber, Alain Rheault, Alaine Melanie Loehr, Alan Rosen, Albert Varga, Albertina Qelaj, Alberto Barioli, Aldo Russo, Alec Moorman, Alejandro Gisbert, Aleksandra Fratczak, Aleksandras Laucevicius, Alena Kuleshova, Alessandro Sionis, Alexander A Sirker, Alexander M Chernyavskiy, Alexandra Craft, Alexandra Vazquez, Alexandre Ciappina Hueb, Alexandre S Colafranseschi, Alexandre Schaan de Quadros, Alexandre Tognon, Ali Alghamdi, Alice Manica Muller, Aline Nogueira Rabaça, Aline Peixoto Deiro, Alison Hallam, Allegra Stone, Allison Schley, Almudena Castro, Alvaro Rabelo Ales, Amanda Germann, Amanda O'Malley, Amar Uxa, Amarachi Ojajuni, Amarino C Oliveira Jr, Amber B Hull, Ambuj Roy, Amer Zarka, Amir Janmohamed, Ammani Brown, Ammy Malinay, Amparo Martinez Monzonis, Amy J Richards, Amy Iskandrian, Amy Ollinger, Ana D Djordjevic-Dikic, Ana Fernández Martínez, Ana Gomes Almeida, Ana Paula Batista, Ana Rita Francisco, Ana S Mladenovic, Ana Santana, Anam Siddiqui, Anastasia M Kuzmina-Krutetskaya, Andras Vertes, Andre S Sousa, Andre Gabriel, André Schmidt, Andrea M Lundeen, Andrea Bartykowszki, Andrea Lorimer, Andrea Mortara, Andrea Pascual, Andreia Coelho, Andreia Rocha, Andrés García-Rincón, Andrew G Howarth, Andrew J Moriarty, Andrew Docherty, Andrew Starovoytov, Andrew Zurick, Andrzej Łabyk, Andrzej Swiatkowski, Andy Lam, Anelise Kawakami, Angela Hoye, Angela Kim, Angelique Smit, Angelo Nobre, Anil V Shah, Anja Ljubez, Anjali Anand, Ankush Sachdeva, Ann Greenberg, Ann Luyten, Ann Ostrander, Anna Di Donato, Anna Cichocka-Radwan, Anna Fojt, Anna Plachcinska, Anna Proietti, Anna Teresinska, Anne Marie Webb, Anne Cartwright, Anne Heath, Anne Mackin, Anong Amaritakomol, Anong Chaiyasri, Anoop Chauhan, Anoop Mathew, Anthony Gemignani, Anto Luigi Andres, Antonia Vega, Antonietta Hansen, Antonino Ginel Iglesias, Antonio Carlos Carvalho, Antonio Di Chiara, Antonio Serra Peñaranda, Antonio Carvalho, Antonio Colombo, Antonio Fiarresga, Anupama Rao, Aquiles Valdespino-Estrada, Araceli Boan, Areef Ishani, Ariel Diaz, Arijit Ghosh, Arintaya Prommintikul, Arline Roberts, Arnold H Seto, Arnold P Good, Arshed Quyyumi, Arthur J Labovitz, Arthur Kerner, Arturo S Campos-Santaolalla, Arunima Misra, Ashok Mukherjee, Ashok Seth, Ashraf Seedhom, Asim N Cheema, Asker Ahmed, Atul Mathur, Atul Verma, Audrey W Leong, Axel Åkerblom, Axelle Fuentes, Aynun Naher, Badhma Valaiyapathi, Baljeet Kaur, Bandula Guruge, Barbara Brzezińska, Barbara Nardi, Bartosz Czarniak, Bebek Singh, Begoña Igual, Bela Merkely, Belen Cid Alvarez, Benjamin J Spooner, Benjamin J W Chow, Benjamin Cheong, Benoy N Shah, Bernard de Bruyne, Bernardas Valecka, Bernhard Jäger, Beth A Archer, Beth Abramson, Beth Jorgenson, Bethany Harvey, Betsy O'Neal, Bev Atkinson, Bev Bozek, Bevin Lang, Bijulal Sasidharan, Bin Yang, Bin Zhang, Binoy Mannekkattukudy Kurian, Bjoern Goebel, Bob Hu, Bogdan A Popescu, Bogdan Crnokrak, Bolin Zhu, Bonnie J Kirby, Brandi D Zimbelman, Brandy Starks, Branko D Beleslin, Brenda Hart, Brian P Shapiro, Brian McCandless, Brianna Wisniewski, Brigham R Smith, Brooks Mirrer, Bruce McManus, Bruce Rutkin, Bruna Edilena Paulino, Bruna Maria Ascoli, Bryn Smith, Byron J Allen, C Michael Gibson, C Noel Bairey Merz, Calin Pop, Cameron Hague, Camila Thais de Ormundo, Candace Gopaul, Candice P Edillo, Carísi A Polanczyk, Carita Krannila, Carla Vicente, Carl-Éric Gagné, Carlo Briguori, Carlos Peña Gil, Carlos Alvarez, Carly Ohmart, Carmen C Beladan, Carmen Ginghina, Carol M Kartje, Caroline Alsweiler, Caroline Brown, Caroline Callison, Caroline Pinheiro, Caroline Rodgers, Caroline Spindler, Carolyn Corbett, Carrie Drum, Casey Riedberger, Catherine Bone, Catherine Fleming, Catherine Gordon, Catherine Jahrsdorfer, Catherine Lemay, Catherine Weick, Cathrine Patten, Cecilia Goletto, Cezary Kepka, Chandini Suvarna, Chang Xu, Chantale Mercure, Charle A Viljoen, Charlene Wiyarand, Charles Jia-Yin Hou, Charles Y Lui, Charles Cannan, Charles Cornet, Charlotte Pirro, Chataroon Rimsukcharoenchai, Chen Wang, Cheng-Ting Tsai, Chen-Yen Chien, Cheryl A Allardyce, Chester M Hedgepeth, Chetan Patel, Chiara Attanasio, Chih-Hsuan Yen, Chi-Ming Chow, Ching Min Er, Ching-Ching Ong, Cholenahally Nanjappa Manjunath, Chris Beck, Chris Buller, Christel Vassaliere, Christian Hamm, Christiano Caldeira, Christie Ballantyne, Christina Björklund, Christine R Hinton, Christine Bergeron, Christine Masson, Christine Roraff, Christine Shelley, Christophe Laure, Christophe Thuaire, Christopher Kinsey, Christopher McFarren, Christopher Spizzieri, Christopher Travill, Chun-Chieh Liu, Chung-Lieh Hung, Chunguang Li, Chun-Ho Yun, Chunli Xia, Ciarra Heard, Cidney Schultz, Clare Venn-Edmonds, Claudia P Hochberg, Claudia Wegmayr, Claudia Cortés, Claudia Escobar, Cláudia Freixo, Claudio T Mesquita, Clemens T Kadalie, Colin Berry, Constance Philander, Corine Thobois, Costantino Costantini, Courtney Page, Craig Atkinson, Craig Barr, Craig Paterson, Cristina Bare, Cynthia Baumann, Cynthia Burman, Dalisa Espinosa, Damien Collison, Dan Deleanu, Dan Elian, Dan Gao, Dana Oliver, Daniel P Vezina, Daniel O'Rourke, Daniele Komar, Danielle Schade, Darrel P Francis, Dastan Malaev, David A Bull, David E Winchester, David P Faxon, David Booth, David Cohen, David DeMets, David Foo, David Schlichting, David Taggart, David Waters, David Wohns, Davis Vo, Dawid Teodorczyk, Dawn Shelstad, Dawn Turnbull, Dayuan Li, Dean Kereiakes, Deborah O'Neill, Deborah Yip, Debra K Johnson, Debra Dees, Deepak L Bhatt, Deepika Gopal, Deepti Kumar, Deirdre Mattina, Deirdre Murphy, Delano R Small, Delsa K Rose, Dengke Jiang, Denis Carl Phaneuf, Denise Braganza, Denise Fine, Derek Cyr, Desiree Tobin, Diana Cukali, Diana Parra, Diane Camara, Diane Minshall Liu, Diego Adrián Vences, Diego Franca de Cunha, Dimitrios Stournaras, Dipti Patel, Dongze Li, Donna Exley, Dorit Grahl, Dragana Stanojevic, Duarte Cacela, Dwayne S G Conway, E Pinar Bermudez, Eapen Punnoose, Edgar L Tay, Edgar Karanjah, Edoardo Verna, Eduardo Hernandez-Rangel, Edward D Nicol, Edward O McFalls, Edward T Martin, Edyta Kaczmarska, Ekaterina I Lubinskaya, Elena A Demchenko, Elena Refoyo Salicio, Eli Feen, Elihú Durán-Cortés, Elisabeth M Janzen, Elise L Hannemann, Elise van Dongen, Elissa Restelli Piloto, Eliza Kaplan, Elizabeta Srbinovska Kostovska, Elizabeth Capasso-Gulve, Elizabeth Congdon, Elizabeth Ferguson, Elizaveta V Zbyshevskaya, Ellen Magedanz, Ellie Fridell, Ellis W Lader, Elvin Kedhi, Emanuela Racca, Emilie Tachot, Emily DeRosa, Encarnación Alonso-Álvarez, Eric Nicollet, Eric Peterson, Erick Alexánderson Rosas, Erick Donato Morales, Erin Orvis, Ermina Moga, Estelle Montpetit, Estevao Figueiredo, Eugene Passamani, Eugenia Nikolsky, Eunice Yeoh, Evgeniy I Kretov, Ewa Szczerba, Ewelina Wojtala, Expedito Eustáquio Ribeiro Silva, F Marin Ortuño, Fabio R Farias, Fabio Fimiani, Fabrizio Rolfo, Fa-Chang Yu, Fadi Hage, Fadi Matar, Fahim Haider Jafary, Fang Feng, Fang Liu, Fatima Ranjbaran, Fatima Rodriguez, Fausto J Pinto, Fauzia Rashid, Federica Ramani, Fei Wang, Fernanda Igansi, Filipa Silva, Filippo Ottani, Fiona Haines, Firas Al Solaiman, Flávia Egydio, Flavio Lyra, Florian Egger, Fran Farquharson, Frances Laube, Francesc Carreras Costa, Francesca de Micco, Francesca Bianchini, Francesca Pezzetta, Francesca Pietrucci, Francesco Orso, Francesco Pisano, Francis Burt, Francisca Patuleia Figueiras, Francisco Fernandez-Aviles, Francois Pierre Mongeon, Frans Van de Werf, Franziska Guenther, Fraser N Witherow, Fred Mohr, Frederico Dall'Orto, Fumiyuki Otsuka, G De La Morena, G Karthikeyan, Gabor Dekany, Gabor Kerecsen, Gabriel Galeote, Gabriel Grossmann, Gabriel Vorobiof, Gabriela Sanchez de Souza, Gabriela Guzman, Gabriela Zeballos, Gabriele Gabrielli, Gabriele Jakl-Kotauschek, Gail A Shammas, Gail Brandt, Gang Chen, Gary E Lane, Gary J Luckasen, Gautam Sharma, Gelmina Mikolaitiene, Gennie Yee, Georg Nickenig, George E Revtyak, George J Juang, Gerald Fletcher, Gerald Leonard, Gerard Patrick Devlin, Gerard Esposito, Gergely Ágoston, Gervasio Lamas, Geza Fontos, Ghada Mikhail, Gia Cobb, Gian Piero Perna, Gianpiero Leone, Giles Roditi, Gilles Barone-Rochette, Girish Mishra, Giuseppe Tarantini, Glenda Wong, Glenn S Hamroff, Glenn Rayos, Gong Cheng, Gonzalo Barge-Caballero, Goran Davidović, Goran Stankovic, Gordana Stevanovic, Grace Jingyan Wang, Grace M Young, Graceanne Wayser, Graciela Scaro, Graham S Hillis, Graham Wong, Grazyna Anna Szulczyk, Gregor Simonis, Gregory Kumkumian, Gretchen Ann Peichel, Grzegorz Gajos, Gudrun Steinmaurer, Guilherme G Rucatti, Guilherme Portugal, Guilhermina Cantinho Lopes, Guillem Pons Lladó, Gunnar Frostfelt, Gurpreet S Wander, Gurpreet Gulati, Gustavo Pucci, Hafidz Abd Hadi, Haibo Zhang, Haitao Wang, Halina Marciniak, Han Chen, Hanan Kerr, Hani Najm, Hanna Douglas, Hannah Phillips, Hao Dai, Haojian Dong, Haqeel Jamil, Harikrishnan Sivadasanpillai, Harry Suryapranata, Hassan Reda, Hayley Pomeroy, Heather Barrentine, Heather Golden, Heather Hurlburt, Heidi Wilson, Helen C Tucker, Helene Abergel, Hemalata Siddaram, Hermine Osseni, Herwig Schuchlenz, Hesong Zeng, Hicham Skali, Hilda Solomon, Hollie Horton, Holly Hetrick, Holly Little, Holly Park, Hongjie Chi, Hossam Mahrous, Howard A Levite, Hristo Pejkov, Huajun Li, Hugo Bloise-Adames, Hugo Marques, Hui Zhong, Hui-Min Zhang, Humayrah Hashim, Hung-I Yeh, Hussien El Fishawy, Ian Webb, Iftikhar Kullo, Igor O Grazhdankin, Ihab Hamzeh, Ikraam Hassan, Ikuko Ueda, Ileana L Pina, Ilona Tamasauskiene, Ilse Bouwhuis, Imran Arif, Ina Wenzelburger, Inês Zimbarra Cabrita, Ines Rodrigues, Inga H Robbins, Inga Soveri, Ingela Schnittger, Iqbal Karimullah, Ira M Dauber, Iram Rehman, Irena Peovska Mitevska, Irene Marthe Lang, Irina Subbotina, Irma Kalibataite-Rutkauskiene, Irni Yusnida, Isabel Estela Carvajal, Isabella C Palazzo, Isabelle Hogan, Isabelle Roy, Ishba Syed, Ishita Tejani, Ivan A Naryshkin, Ivana Jankovic, Iwona Niedzwiecka, J David Knight, Jacek Kusmierek, Jackie M White, Jackie Chow, Jacob Udell, Jacqueline E Tamis-Holland, Jacqueline Fannon, Jacquelyn A Quin, Jacquelyn Do, Jaekyeong Heo, Jakub Maksym, James E Davies, James H O'Keefe Jr, James J Jang, James Cha, James Harrison, James Hirsch, James Stafford, James Tatoulis, Jamie Rankin, Jan Henzel, Jan Orga, Jana Tancredi, Janaina Oliveira, Jane Burton, Jane Eckstein, Jane Marucci, Janet P Knight, Janet Blount, Janet Halliday, Janetta Kourzenkova, Janitha Raj, Jan-Malte Sinning, Jaqueline Pozzibon, Jaroslaw Drozdz, Jaroslaw Karwowski, Jason D Glover, Jason Loh Kwok, Jason T Call, Jason Linefsky, Jassira Gomes, Jati Anumpa, Javier J Garcia, Javier Courtis, Jay Meisner, K Jayakumar, Jayne Scales, Jean E Denaro, Jean Michel Juliard, Jean Ho, Jeanette K Stansborough, Jean-Michel Juliard, Jeanne Russo, Jeannette J M Schoep, Jeet Thambyrajah, Jeff Leimberger, Jeffery A Breall, Jeffrey A Kohn, Jeffrey C Milliken, Jeffrey Anderson, Jeffrey Blume, Jeffrey Kanters, Jeffrey Lorin, Jeffrey Moses, Jelena J Stepanovic, Jelena Celutkiene, Jelena Djokic, Jelena Stojkovic, Jenne M Jose, Jenne Manchery, Jennifer A Mull, Jennifer H Czerniak, Jennifer L Stanford, Jennifer Gillis, Jennifer Horst, Jennifer Isaacs, Jennifer Langdon, Jennifer Thomson, Jennifer Tomfohr, Jennifer White, Jen-Yuan Kuo, Jeremy Rautureau, Jerome Fleg, Jessica Berg, Jessica Rodriguez, Jessica Waldron, Jhina Patro, Jia Li, Jiajia Mao, Jiamin Liu, Jian'an Wang, Jianhua Li, Jianxin Zhang, Jie Qi, Jihyun Lyo, Jill Marcus, Jim Blankenship, Jing Zhang, Jingjing Liu, Jing-Yao Fan, Jiun-Yi Li, Jiwan Pradhan, Jiyan Chen, J M Rivera Caravaca, Jo Evans, Joan Garcia Picart, Joan Hecht, Joanna Jaroch, Joanna Zalewska, Joanne Kelly, Joanne Taaffe, João Reynaldo Abbud, João V Vitola, Joaquín V Peñafiel, Jocelyne Benatar, Jody Bindeman, Joe Sabik, Joel Klitch, Johann Christopher, Johannes Aspberg, John D Friedman, John F Beltrame, John F Heitner, John Joseph Graham, John R Davies, John Doan, John Kotter, John Kurian, John Mukai, John Pownall, Jolanta Sobolewska, Jon Kobashigawa, Jonathan L Goldberg, Jonathan W Bazeley, Jonathan Byrne, Jonathan Himmelfarb, Jonathan Leipsic, Jonean Thorsen, Jorge F Trejo Gutierrez, Jorge Escobedo, Jorik Timmer, José A Ortega-Ramírez, José Antonio Marin-Neto, Jose D Salas, Jose Enrique Castillo, Jose Francisco Saraiva, José J Cuenca-Castillo, Jose L Diez, José Luis Narro Villanueva, José Luiz da Vieira, José M Flores-Palacios, Jose Ramon Gonzalez, Jose Seijas Amigo, Jose Fragata, Josep Maria Padró, Josheph F X McGarvey Jr, Joseph Hannan, Joseph Sacco, Joseph Sweeny, Joseph Wiesel, Josephine D Abraham, Joshua P Loh, Joy Burkhardt, Joyce R White, Joyce Riestenberg-Smith, Judit Sebo, Judith L Meadows, Judith Wright, Judy Mae Foltz, Judy Hung, Judy Otis, Juergen Stumpf, Jui-Peng Tsai, Julia S Dionne, Julia de Aveiro Morata, Julie Bunke, Julie Morrow, Julio César Figal, Jun Fujita, Jun Jiang, Junhua Li, Junqing Yang, Juntima Euathrongchit, Jyotsna Garg, K Manjula Rani, K Preethi, Kaatje Goetschalckx, Kai Eggers, Kamalakar Surineni, Kanae Hirase, T R Kapilamoorthy, Karen Calfas, Karen Gratrix, Karen Hallett, Karen Hultberg, Karen Nugent, Karen Petrosyan, Karen Swan, Karolina Kryczka, Karolina Wojtczak-Soska, Karolina Wojtera, Karsten Lenk, Karthik Ramasamy, Katarzyna Łuczak, Katarzyna Malinowska, Kate Pointon, Kate Robb, Katherine Martin, Kathleen Claes, Kathryn Carruthers, Kathy E Siegel, Katia Drouin, Katie Fowler-Lehman, Kavita Rawat, Kay Rowe, Keiichi Fukuda, Keith A A Fox, Ken Mahaffey, Kendra Unterbrink, Kenneth Giedd, Kerrie Van Loo, Kerry Lee, Kerstin Bonin, Kevin R Bainey, Kevin T Harley, Kevin Anstrom, Kevin Chan, Kevin Croce, Kevin Landolfo, Kevin Marzo, Keyur Patel, Khaled Abdul-Nour, Khaled Alfakih, Khaled Dajani, Khaled Ziada, Khaula Baloch, Khrystyna Kushniriuk, Kian-Keong Poh, Kim F Ireland, Kim Holland, Kimberly Ann Byrne, Kimberly E Halverson, Kimberly Elmore, Kimberly Miller-Cox, Kiran Reddy, Kirsten J Quiles, Kirsty Abercrombie, Klaus Matschke, Konrad Szymczyk, Koo Hui Chan, Kotiboinna Preethi, Kozhaya Sokhon, Krissada Meemuk, Kristian Thygesen, Kristin M Salmi, Kristin Newby, Kristina Wippler, Kristine Arges, Kristine Teoh, Krystal Etherington, Krystyna Łoboz-Grudzień, Krzysztof W Reczuch, Krzysztof Bury, Krzysztof Drzymalski, Krzysztof Kukuła, Kuo-Tzu Sung, Kurt Huber, Ladda Douangvila, Lance Sullenberger, Larissa Miranda Trama, Laszlone Matics, Laura Drew, Laura Flint, Laura Keinaite, Laura Sarti, Laurel Kolakaluri, Lawrence M Phillips, Lawrence Friedman, Lawrence Phillips, Lazar Velicki, Leah Howell, Leandro C Maranan, Leanne Cox, Ledjalem Daba, Lei Zhang, Lekshmi Dharmarajan, Leo Bockeria, Leonardo Pizzol Caetano, Leonardo Bridi, Leonid L Bershtein, Leszek Sokalski, Li Hai Yan, Li Li, Lia Nijmeijer, Lidia Sousa, Lihong Xu, Lihua Zhang, Lili Zhang, Lilia Schiavi, Lilian Mazza Barbosa, Lillian L Khor, Lina Felix-Stern, Linda L Hall, Linda M Hollenweger, Linda Arcand, Linda Davidson-Ray, Linda Schwarz, Lindsey N Sikora, Lingping Chi, Lino Patricio, Liping Zhang, Lisa Chaytor, Lisa Hatch, Lisa McCloy, Lisa Wong, Liselotte Persson, Lixin Jiang, Liz Low, Ljiljana Pupic, Loïc Bière, Lorenzo Monti, Lori Christensen, Lori Pritchard, Loriane Black, Lori-Ann Desimone, Lori-Ann Larmand, Lorraine McGregor, Louise Morby, Louise Thomson, Luc Harvey, Luciana de Pádua Baptista, Lucilla Garcia, Ludivine Eliahou, Ludmila Helmer, Luis F Smidt, Luis Bernanrdes, Luis Guzman, Luiz A Carvalho, Luyang Xiong, Lynette L Teo, Lynn M Neeson, Lynne Winstanley, M Barbara Srichai-Parsia, M Quintana Giner, M Sowjanya Reddy, M Valdés Chávarri, M Grazia Rossi, Maarten Simoons, Maayan Konigstein, Maciej Lesiak, Maciej Olsowka, Mafalda Selas, Magalie Corfias, Magdalena Madero Rovalo, Magdalena Łanocha, Magdalena Miller, Magdalena Misztal-Teodorczyk, Magdalena Rantinella, Magdy Abdelhamid, Magnolia Jimenez, Mahboob Alam, Mahevamma Mylarappa, Mahfouz El Shahawy, Mahmoud Mohamed, Mahmud Al-Bustami, Majo X Joseph, Malgorzata Frach, Małgorzta Celińska-Spodar, Malte Helm, Manas Chacko, Mandy Murphy, Manitha Vinod, Manjula Rani, Manu Dhawan, Manuela Mombelli, Marcel Weber, Marcello Galvani, Marcelo Jamus Rodrigues, Marcia F Dubin, Marcia F Werner Bayer, Marcin Szkopiak, Marco Antonio Monsalve, Marco Bizzaro Santos, Marco Magnoni, Marco Marini, Marco Sicuro, Marco Zenati, Marcos Valério Coimbra Resende, Marek Roik, Margalit Bentzvi, Margaret Gilsenan, Margaret Iraola, Margot C Quinn, Maria A Alfonso, Maria Antonieta Pereira Moraes, María Dolores Martínez-Ruíz, María Fernanda Canales, Maria Inês Caetano, Maria P Corral, Maria Pérez García, Maria Victoria Actis, Maria Aguirre, Maria Andreasson, Maria Aprile, Maria Colton, Maria Eugenia Martin, Maria Lasala, Maria Lorenzo, Maria Posada, Maria Shier, Maria Thottam, Mariana V Furtado, Mariana Yumi Okada, Marianna D A Dracoulakis, Marianne De Andrade, Mariano Rubio, Marie Essermark, Marielle Scherrer-Crosbie, Marija T Petrovic, Marija Zdravkovic, Marilyn Black, Marina Garcia, Mario J Garcia, Mariola Szulik, Marisa Orgera, Mark A de Belder, Mark Harbinson, Mark Hyun, Mark Peterson, Mark Xavier, Marlowe Mosley, Marta Capinha, Marta Marcinkiewicz-Siemion, Marta Swiderek, Martha Meyer, Martina Ceseri, Martina Tricoli, Marvin Kronenberg, Mary Williams, Mary Ann Champagne, Mary Colleen Rogge, Mary R Soltau, Mary Streif, Massimo Villella, Massoud Leesar, Matei Claudia, Mateusz Solecki, Matías Nicolás Mungo, Matthew Wall Jr, Matthew Budoff, Matthew Jezior, Matthew Luckie, Matthias Friedrich, Mauren P Haeffner, Maximilian Tscharre, Max-Paul Winter, Mayana Almeida, Mayil S Krishnam, Mayuri Patel, Meenakshi Mishra, Megan Manocchia, Meghana Kakade, Melanie J Munro, Melissa D Chaplin, Melissa LeFevre, Mervyn Andiapen, Michael A Gibson, Michael B Rubens, Michael C Turner, Michael D Shapiro, Michael W Lee, Michael Berlowitz, Michael Davidson, Michael Mack, Michael McDaniel, Michael Mumma, Michal Wlodarczyk, Michel G Khouri, Michel S Slama, Michele Rawlins, Michelle M Bonner, Michelle M Seib, Michelle Chang, Michelle Crowder, Michelle Dixon, Michelle Mayon, Michelle McEvoy, Michelle Yee, Miguel M Fernandes, Miguel Nobre Menezes, Miguel Souto Bayarri, Miguel Barrero, Mikhail T Torosoff, Milan R Dobric, Milan Dobric, Milica Nikola Dekleva, Milind Avdhoot Gadkari, Millie Gomez, Min Tun Kyaw, Miriam Brooks, Miroslav Stevo Martinovic, Mitchel B Lustre, Mohammad Tariq Vakani, Mohammad El-Hajjar, Mohammed Al-Amoodi, Mohammed Hussain, Mohammed Saleem, Moisés Blanco-Calvo, Moisés Jiménez-Santos, Mona Bhatia, Monica Rosca, Monika Laukyte, Montserrat Gracida Blanca, Montserrat Vila Perales, Mouaz H Al-Mallah, Moysés de Oliveira Filho, Mpiko Ntsekhe, Muhamed Saric, Mulei Chen, Myriam Brousseau, Myrthes Emy Takiuti, Nada Cemerlic-Adjic, Nadia Asif, Nadia Gakou, Nafisa Hussain, Nana O Katamadze, Nancy L Clapp, Nancy Aedy, Nandita Nataraj, Nanette K Wenger, Naomi Uchida, Nasrul Ismail, Natalia S Oliveira, Natalia de Carvalho Maffei, Natalie Spitzer, Natasha C Putnam, Naved Aslam, Neamat Mowafy, Neeraj Pandit, Neeraj Parakh, Nevena Garcevic, Ngaire Meadows, Nhi N Tran, Nicholas Danchin, Nicki Lakeman, Nicola Johnston, Nicolas W Shammas, Nicole Saint Vrestil, Nicole Deming, Nier Zhong, Niket Patel, Nikola N Boskovic, Nikolaos Karogiannis, Nikos Werner, Nina Johnston, Ning Zhang, Ning Zhou, Niree Hindoyan, Nirmal Kumar, Nitika Chadha, Nitish Naik, Nodira Aripova, Noloyiso Mtana, Nona A Eskelson, Noor Syamira Mokhtar, Noppon Taksaudom, Nor Asiah Basri, Nora Marchelletta, Norma Hogg, Nungshi Jungla, Nuno Ferreira, Oksana A Lubyanaya, Olga B Nikolaeva, Olga Cañavate, Olga Sobrino, Olga Walesiak, Olga Walter, Olga Zdończyk, Olivia J Lim, Olivia Anaya, Olivia Mancilla, Olivier Dubourg, Olugbenga Bello, Omar Almousalli, Omar Thompson, Oni Olurinde, Or Harel, Osama Raheem, Oscar Méndiz, Óscar Prada-Delgado, Oz Shapira, P Christian Schulze, Pachara Panpunuan, Pal Maurovich-Horvat, Pallav Garg, Paloma Moraga, Pam Singh, Pamela Julian, Pamela Ouyang, Pamela Sigel, Pamela Woodard, Panpan Zhou, Paola Emanuela Poggio, Paola Smanio, Paolo Calabro, Paramjit Jeetley, Pascal Goube, Patricia K Nguyen, Patricia Alarie, Patricia Arakelian, Patricia Arsenault, Patricia Blaise, Patricia Brito, Patricia Cowper, Patricia Endsley, Patricia Mieses, Patrick B Alexander, Patrick Donnelly, Patrick Wilmot, Patrycja Lebioda, Paul C Gordon, Paul Der Mesropian, Paul Galiwango, Paul Hauptman, Paul Kennedy, Paula Beardsley, Paula García-González, Paulo Cury Rezende, Paulo Ricardo Caramori, Pavel S Kozlov, Pedro Canas Silva, Pedro Gabriel Melo Barros E Silva, Pedro Píccaro de Oliveira, Pedro Carvalho, Pedro Modas, Pedro Rio, Peeyush Jain, Peiyu He, Peter A McCullough, Peter H Stone, Peter M Pollak, Peter Douglass, Peter Henriksen, Peter OKane, Peter Ong, Philip Jones, Philip Rogal, Philippe Généreux, Philippe Menasche, Philippe Rheault, Phoebe Goold, Pierre Gervais, Pierre Michaud, Pilar Calvillo, Ping Chai, Piotr Jakubowski, Piotr Pruszczyk, Piotr Slomka, Piyamitr Sritara, Poay-Huan Loh, Poonam Sonawane, Pouneh Samadi, Pragnesh P Parikh, Prakash Deedwania, Pranav M Patel, Praneeth Polamuri, Pratiksha Sharma, Precilia Vasquez, Preeti Kamath, Prince Thomas, Priyadarshani Arambam, Puja K Mehta, Purvez Grant, Pushpa Naik, Qi Zhong, Qian Zhao, Qiang Zhou, Qianqian Yuan, Qin Yu, Qingxian Li, Qiulan Xie, Qiutang Zeng, R J Vindhya, R James Gerlach, Rachel King, Rada Vučić, Radmila Lyubarova, Radoslaw Pracon, 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Jeffrey Kanters, Jeffrey Lorin, Jeffrey Moses, Jelena J Stepanovic, Jelena Celutkiene, Jelena Djokic, Jelena Stojkovic, Jenne M Jose, Jenne Manchery, Jennifer A Mull, Jennifer H Czerniak, Jennifer L Stanford, Jennifer Gillis, Jennifer Horst, Jennifer Isaacs, Jennifer Langdon, Jennifer Thomson, Jennifer Tomfohr, Jennifer White, Jen-Yuan Kuo, Jeremy Rautureau, Jerome Fleg, Jessica Berg, Jessica Rodriguez, Jessica Waldron, Jhina Patro, Jia Li, Jiajia Mao, Jiamin Liu, Jian'an Wang, Jianhua Li, Jianxin Zhang, Jie Qi, Jihyun Lyo, Jill Marcus, Jim Blankenship, Jing Zhang, Jingjing Liu, Jing-Yao Fan, Jiun-Yi Li, Jiwan Pradhan, Jiyan Chen, J M Rivera Caravaca, Jo Evans, Joan Garcia Picart, Joan Hecht, Joanna Jaroch, Joanna Zalewska, Joanne Kelly, Joanne Taaffe, João Reynaldo Abbud, João V Vitola, Joaquín V Peñafiel, Jocelyne Benatar, Jody Bindeman, Joe Sabik, Joel Klitch, Johann Christopher, Johannes Aspberg, John D Friedman, John F Beltrame, John F Heitner, John Joseph Graham, John R Davies, John Doan, John Kotter, John Kurian, John Mukai, John Pownall, Jolanta Sobolewska, Jon Kobashigawa, Jonathan L Goldberg, Jonathan W Bazeley, Jonathan Byrne, Jonathan Himmelfarb, Jonathan Leipsic, Jonean Thorsen, Jorge F Trejo Gutierrez, Jorge Escobedo, Jorik Timmer, José A Ortega-Ramírez, José Antonio Marin-Neto, Jose D Salas, Jose Enrique Castillo, Jose Francisco Saraiva, José J Cuenca-Castillo, Jose L Diez, José Luis Narro Villanueva, José Luiz da Vieira, José M Flores-Palacios, Jose Ramon Gonzalez, Jose Seijas Amigo, Jose Fragata, Josep Maria Padró, Josheph F X McGarvey Jr, Joseph Hannan, Joseph Sacco, Joseph Sweeny, Joseph Wiesel, Josephine D Abraham, Joshua P Loh, Joy Burkhardt, Joyce R White, Joyce Riestenberg-Smith, Judit Sebo, Judith L Meadows, Judith Wright, Judy Mae Foltz, Judy Hung, Judy Otis, Juergen Stumpf, Jui-Peng Tsai, Julia S Dionne, Julia de Aveiro Morata, Julie Bunke, Julie Morrow, Julio César Figal, Jun Fujita, Jun Jiang, Junhua Li, Junqing Yang, Juntima Euathrongchit, Jyotsna Garg, K Manjula Rani, K Preethi, Kaatje Goetschalckx, Kai Eggers, Kamalakar Surineni, Kanae Hirase, T R Kapilamoorthy, Karen Calfas, Karen Gratrix, Karen Hallett, Karen Hultberg, Karen Nugent, Karen Petrosyan, Karen Swan, Karolina Kryczka, Karolina Wojtczak-Soska, Karolina Wojtera, Karsten Lenk, Karthik Ramasamy, Katarzyna Łuczak, Katarzyna Malinowska, Kate Pointon, Kate Robb, Katherine Martin, Kathleen Claes, Kathryn Carruthers, Kathy E Siegel, Katia Drouin, Katie Fowler-Lehman, Kavita Rawat, Kay Rowe, Keiichi Fukuda, Keith A A Fox, Ken Mahaffey, Kendra Unterbrink, Kenneth Giedd, Kerrie Van Loo, Kerry Lee, Kerstin Bonin, Kevin R Bainey, Kevin T Harley, Kevin Anstrom, Kevin Chan, Kevin Croce, Kevin Landolfo, Kevin Marzo, Keyur Patel, Khaled Abdul-Nour, Khaled Alfakih, Khaled Dajani, Khaled Ziada, Khaula Baloch, Khrystyna Kushniriuk, Kian-Keong Poh, Kim F Ireland, Kim Holland, Kimberly Ann Byrne, Kimberly E Halverson, Kimberly Elmore, Kimberly Miller-Cox, Kiran Reddy, Kirsten J Quiles, Kirsty Abercrombie, Klaus Matschke, Konrad Szymczyk, Koo Hui Chan, Kotiboinna Preethi, Kozhaya Sokhon, Krissada Meemuk, Kristian Thygesen, Kristin M Salmi, Kristin Newby, Kristina Wippler, Kristine Arges, Kristine Teoh, Krystal Etherington, Krystyna Łoboz-Grudzień, Krzysztof W Reczuch, Krzysztof Bury, Krzysztof Drzymalski, Krzysztof Kukuła, Kuo-Tzu Sung, Kurt Huber, Ladda Douangvila, Lance Sullenberger, Larissa Miranda Trama, Laszlone Matics, Laura Drew, Laura Flint, Laura Keinaite, Laura Sarti, Laurel Kolakaluri, Lawrence M Phillips, Lawrence Friedman, Lawrence Phillips, Lazar Velicki, Leah Howell, Leandro C Maranan, Leanne Cox, Ledjalem Daba, Lei Zhang, Lekshmi Dharmarajan, Leo Bockeria, Leonardo Pizzol Caetano, Leonardo Bridi, Leonid L Bershtein, Leszek Sokalski, Li Hai Yan, Li Li, Lia Nijmeijer, Lidia Sousa, Lihong Xu, Lihua Zhang, Lili Zhang, Lilia Schiavi, Lilian Mazza Barbosa, Lillian L Khor, Lina Felix-Stern, Linda L Hall, Linda M Hollenweger, Linda Arcand, Linda Davidson-Ray, Linda Schwarz, Lindsey N Sikora, Lingping Chi, Lino Patricio, Liping Zhang, Lisa Chaytor, Lisa Hatch, Lisa McCloy, Lisa Wong, Liselotte Persson, Lixin Jiang, Liz Low, Ljiljana Pupic, Loïc Bière, Lorenzo Monti, Lori Christensen, Lori Pritchard, Loriane Black, Lori-Ann Desimone, Lori-Ann Larmand, Lorraine McGregor, Louise Morby, Louise Thomson, Luc Harvey, Luciana de Pádua Baptista, Lucilla Garcia, Ludivine Eliahou, Ludmila Helmer, Luis F Smidt, Luis Bernanrdes, Luis Guzman, Luiz A Carvalho, Luyang Xiong, Lynette L Teo, Lynn M Neeson, Lynne Winstanley, M Barbara Srichai-Parsia, M Quintana Giner, M Sowjanya Reddy, M Valdés Chávarri, M Grazia Rossi, Maarten Simoons, Maayan Konigstein, Maciej Lesiak, Maciej Olsowka, Mafalda Selas, Magalie Corfias, Magdalena Madero Rovalo, Magdalena Łanocha, Magdalena Miller, Magdalena Misztal-Teodorczyk, Magdalena Rantinella, Magdy Abdelhamid, Magnolia Jimenez, Mahboob Alam, Mahevamma Mylarappa, Mahfouz El Shahawy, Mahmoud Mohamed, Mahmud Al-Bustami, Majo X Joseph, Malgorzata Frach, Małgorzta Celińska-Spodar, Malte Helm, Manas Chacko, Mandy Murphy, Manitha Vinod, Manjula Rani, Manu Dhawan, Manuela Mombelli, Marcel Weber, Marcello Galvani, Marcelo Jamus Rodrigues, Marcia F Dubin, Marcia F Werner Bayer, Marcin Szkopiak, Marco Antonio Monsalve, Marco Bizzaro Santos, Marco Magnoni, Marco Marini, Marco Sicuro, Marco Zenati, Marcos Valério Coimbra Resende, Marek Roik, Margalit Bentzvi, Margaret Gilsenan, Margaret Iraola, Margot C Quinn, Maria A Alfonso, Maria Antonieta Pereira Moraes, María Dolores Martínez-Ruíz, María Fernanda Canales, Maria Inês Caetano, Maria P Corral, Maria Pérez García, Maria Victoria Actis, Maria Aguirre, Maria Andreasson, Maria Aprile, Maria Colton, Maria Eugenia Martin, Maria Lasala, Maria Lorenzo, Maria Posada, Maria Shier, Maria Thottam, Mariana V Furtado, Mariana Yumi Okada, Marianna D A Dracoulakis, Marianne De Andrade, Mariano Rubio, Marie Essermark, Marielle Scherrer-Crosbie, Marija T Petrovic, Marija Zdravkovic, Marilyn Black, Marina Garcia, Mario J Garcia, Mariola Szulik, Marisa Orgera, Mark A de Belder, Mark Harbinson, Mark Hyun, Mark Peterson, Mark Xavier, Marlowe Mosley, Marta Capinha, Marta Marcinkiewicz-Siemion, Marta Swiderek, Martha Meyer, Martina Ceseri, Martina Tricoli, Marvin Kronenberg, Mary Williams, Mary Ann Champagne, Mary Colleen Rogge, Mary R Soltau, Mary Streif, Massimo Villella, Massoud Leesar, Matei Claudia, Mateusz Solecki, Matías Nicolás Mungo, Matthew Wall Jr, Matthew Budoff, Matthew Jezior, Matthew Luckie, Matthias Friedrich, Mauren P Haeffner, Maximilian Tscharre, Max-Paul Winter, Mayana Almeida, Mayil S Krishnam, Mayuri Patel, Meenakshi Mishra, Megan Manocchia, Meghana Kakade, Melanie J Munro, Melissa D Chaplin, Melissa LeFevre, Mervyn Andiapen, Michael A Gibson, Michael B Rubens, Michael C Turner, Michael D Shapiro, Michael W Lee, Michael Berlowitz, Michael Davidson, Michael Mack, Michael McDaniel, Michael Mumma, Michal Wlodarczyk, Michel G Khouri, Michel S Slama, Michele Rawlins, Michelle M Bonner, Michelle M Seib, Michelle Chang, Michelle Crowder, Michelle Dixon, Michelle Mayon, Michelle McEvoy, Michelle Yee, Miguel M Fernandes, Miguel Nobre Menezes, Miguel Souto Bayarri, Miguel Barrero, Mikhail T Torosoff, Milan R Dobric, Milan Dobric, Milica Nikola Dekleva, Milind Avdhoot Gadkari, Millie Gomez, Min Tun Kyaw, Miriam Brooks, Miroslav Stevo Martinovic, Mitchel B Lustre, Mohammad Tariq Vakani, Mohammad El-Hajjar, Mohammed Al-Amoodi, Mohammed Hussain, Mohammed Saleem, Moisés Blanco-Calvo, Moisés Jiménez-Santos, Mona Bhatia, Monica Rosca, Monika Laukyte, Montserrat Gracida Blanca, Montserrat Vila Perales, Mouaz H Al-Mallah, Moysés de Oliveira Filho, Mpiko Ntsekhe, Muhamed Saric, Mulei Chen, Myriam Brousseau, Myrthes Emy Takiuti, Nada Cemerlic-Adjic, Nadia Asif, Nadia Gakou, Nafisa Hussain, Nana O Katamadze, Nancy L Clapp, Nancy Aedy, Nandita Nataraj, Nanette K Wenger, Naomi Uchida, Nasrul Ismail, Natalia S Oliveira, Natalia de Carvalho Maffei, Natalie Spitzer, Natasha C Putnam, Naved Aslam, Neamat Mowafy, Neeraj Pandit, Neeraj Parakh, Nevena Garcevic, Ngaire Meadows, Nhi N Tran, Nicholas Danchin, Nicki Lakeman, Nicola Johnston, Nicolas W Shammas, Nicole Saint Vrestil, Nicole Deming, Nier Zhong, Niket Patel, Nikola N Boskovic, Nikolaos Karogiannis, Nikos Werner, Nina Johnston, Ning Zhang, Ning Zhou, Niree Hindoyan, Nirmal Kumar, Nitika Chadha, Nitish Naik, Nodira Aripova, Noloyiso Mtana, Nona A Eskelson, Noor Syamira Mokhtar, Noppon Taksaudom, Nor Asiah Basri, Nora Marchelletta, Norma Hogg, Nungshi Jungla, Nuno Ferreira, Oksana A Lubyanaya, Olga B Nikolaeva, Olga Cañavate, Olga Sobrino, Olga Walesiak, Olga Walter, Olga Zdończyk, Olivia J Lim, Olivia Anaya, Olivia Mancilla, Olivier Dubourg, Olugbenga Bello, Omar Almousalli, Omar Thompson, Oni Olurinde, Or Harel, Osama Raheem, Oscar Méndiz, Óscar Prada-Delgado, Oz Shapira, P Christian Schulze, Pachara Panpunuan, Pal Maurovich-Horvat, Pallav Garg, Paloma Moraga, Pam Singh, Pamela Julian, Pamela Ouyang, Pamela Sigel, Pamela Woodard, Panpan Zhou, Paola Emanuela Poggio, Paola Smanio, Paolo Calabro, Paramjit Jeetley, Pascal Goube, Patricia K Nguyen, Patricia Alarie, Patricia Arakelian, Patricia Arsenault, Patricia Blaise, Patricia Brito, Patricia Cowper, Patricia Endsley, Patricia Mieses, Patrick B Alexander, Patrick Donnelly, Patrick Wilmot, Patrycja Lebioda, Paul C Gordon, Paul Der Mesropian, Paul Galiwango, Paul Hauptman, Paul Kennedy, Paula Beardsley, Paula García-González, Paulo Cury Rezende, Paulo Ricardo Caramori, Pavel S Kozlov, Pedro Canas Silva, Pedro Gabriel Melo Barros E Silva, Pedro Píccaro de Oliveira, Pedro Carvalho, Pedro Modas, Pedro Rio, Peeyush Jain, Peiyu He, Peter A McCullough, Peter H Stone, Peter M Pollak, Peter Douglass, Peter Henriksen, Peter OKane, Peter Ong, Philip Jones, Philip Rogal, Philippe Généreux, Philippe Menasche, Philippe Rheault, Phoebe Goold, Pierre Gervais, Pierre Michaud, Pilar Calvillo, Ping Chai, Piotr Jakubowski, Piotr Pruszczyk, Piotr Slomka, Piyamitr Sritara, Poay-Huan Loh, Poonam Sonawane, Pouneh Samadi, Pragnesh P Parikh, Prakash Deedwania, Pranav M Patel, Praneeth Polamuri, Pratiksha Sharma, Precilia Vasquez, Preeti Kamath, Prince Thomas, Priyadarshani Arambam, Puja K Mehta, Purvez Grant, Pushpa Naik, Qi Zhong, Qian Zhao, Qiang Zhou, Qianqian Yuan, Qin Yu, Qingxian Li, Qiulan Xie, Qiutang Zeng, R J Vindhya, R James Gerlach, Rachel King, Rada Vučić, Radmila Lyubarova, Radoslaw Pracon, Raewyn Fisher, Rafael Beyar, Rafael Diaz, Rafael Selgas, Raffaele Bugiardini, Raffaele Fanelli, Raisa Kavalakkat, V S Rajalekshmi, Rajat S Barua, Rajeev Menon, Rajesh Gopalan Nair, Rajesh Francis, Rajiv Narang, Rakesh Yadav, Ralph Alan Huston, T Ramakrishnan, Ramesh de Silva, Rami El Mahmoud, Ramiro Carvalho, Ramon de Jesús-Pérez, Ramona Stevens, Ran Leng, Ranjan Kachru, Ranjit Kumar Nath, Raquel Sanchez, Raven R Dwyer, Raven Lee, Ray Wyman, Raymond C Wong, Raymond W Little, Raymundo Ocaranza Sanchez, Rebecca J Wimmer, Rebecca Bariciano, Rebecca Otis, Rebekah R Herrmann, Reem Yunis, Reinette Hampson, Renato Abdala Karam, Renee C Hessian, Renee Kaneshiro, Reshma Ravindran, Reto Andreas Gamma, Reyna Bhandari, Reza Arsanjani, Ricardo L Lopes, Ricardo Mendes Oliveira, Ricardo Costa, Richa Bhatt, Richard F Davies, Richard H J Trimlett, Richard Goldweit, Rik Hermanides, Rine Nakanishi, Rinu R Sidh, Risha Patel, Rita Coram, Rizwan A Siddiqui, Rob S Beanlands, Robert J Hamburger, Robert K Riezebos, Robert M Donnino, Robert Bojar, Robert Chilton, Robert Guyton, Robert Henderson, Robert Kornberg, Robert Leber, Robert Mao, Robert Stenberg, Roberta P Santos, Roberto René Favaloro, Roberto Amati, Rodolfo G S D Lima, Rodrigo J Cerci, Rogerio Tumelero, Rohit Tandon, Roma Tewari, Romalisa Miranda-Peats, Ron Wald, Ronald A Mastouri, Ronald G Morford, Ronald G Schwartz, Ronald P Pedalino, Rongrong Hu, Ronnell A Hansen, Ronny A Cohen, Rory Hachamovitch, Rosa Homem, Rosa Sandonato, Rosane Laimer, Rosann Gans, Roxanne Yost, Roy Mathew, Rubén Baleón-Espinosa, Ruben Ramos, Rubine Gevorgyan, Rui Ferreira, Rui Jing, Ruth Pérez-Fernández, S K Dwivedi, S Ramakrishnan, Saadat Khan, Sabahat Bokhari, Sabu Thomas, Sadath Lubna, Sajeeda Parveen Khan, Sajeev Chakanalil Govindan, Saket Girotra, Saleem Kassam, Sallie Canada, Salvador Cruz-Flores, Samaa Mohamed, Samantha Ly, Sameh El Kaffas, Samia Massalha, Sampoornima Setty, Samuel Nwosu, Sandeep Seth, Sandeep Singh, Sander R Niehe, Sandra M Rivest, Sandra S Zier, Sandra Ahoud, Sandy Carr, Sanjay Ganapathi, Sanjay Shetty, Sanjeev Sharma, Santa Jimenez, Santhosh Satheesh, Santiago A Garcia, Sara Fernandez, Sara Karlsson, Sara Salkind, Sara Temiyasathit, Sarah Medina Rodriguez, Sarah Beaudry, Sarah Hadjih, Sarah Williams, Sarah Zahrani, Sarju Ralhan, Sasa Hinic, Sasko Kedev, Satinder Singh, Satoshi Yasuda, Satvic Cholenahally Manjunath, Sau Lee, Scott M Kaczkowski, Scott Kinlay, Sean W Hayes, Sebastian Sobczak, Senait Asier, Sergey A Sayganov, Seth I Sokol, Shaheen Pandie, Shaiful Azmi Yahaya, Shamir Mehta, Shao-Ping Nie, Sharad Chandra, Sharder Islam, Sharon Tai, Sheetal Rupesh Karwa, Sheri Ussery, Sheromani Bajaj, Sherron C Crook, Shigeyuki Nishimura, Shintaro Nakano, Shirin Heydari, Shiv Kumar Choudhary, Shivali Patel, Shobana Ganesan, Shruti Pandey, Shuyang Zhang, Shweta Hande, Siddharth Gadage, Sik-Yin V Tan, Silvia Zottis Poletti, Silvia Riera, Silvia Valbuena, Simon Walsh, Simona Maspoli, Simone Savaris, Si-Ting Feng, So Yang Cho, Solomon Yakubov, Songlin Zhu, Songtao Wang, Sonia Guerrero, Sonika Gupta, Sonja Salinger Martinovic, Sonya Brons, Sorin Brener, Sothinathan Gurunathan, Souheil Saba, Soundarya Nayak, Sowjanya Reddy, Srinivasa Potluri, Sriram Sudarshan, Srun Kuanprasert, Stacie Van Oosterhout, Stamatios Lerakis, Stanley E Cobos, Stefan C Bertog, Stefan M Simović, Stefan Weikl, Stefano Di Marco, Stefano Provasoli, Stephanie A Tirado, Stephanie C Boer, Stephanie M Lane, Stephanie Ferket, Stephanie Kelly, Stephanie Wasmiller, Stephen H McKellar, Stephen P Hoole, Stephen Fremes, Stephen Preston, Steve Leung, Steven A Fein, Steven J Lindsay, Steven P Sedlis, Steven Giovannone, Steven Michael, Steven Weitz, Stijn van Vugt, Subhash Banerjee, Sudhir Naik, Suellen Hosino, Sukie Desire, Sukit Yamwong, Suku T Thambar, Sulagna Mookherjee, Suman Singh, Sundeep Mishra, Sunil Kumar Verma, Supap Kulthawong, Supatchara Khwakhong, Surendra Naik, Suresh Babu, Surin Woragidpoonpol, Suryaprakash Narayanappa, Susan Derbyshire, Susan Gent, Susan Mathus, Susan Milbrandt, Susan Moore, Susan Regan, Susan Stinson, Susan Webber, Susana Silva, Susanna Stevens, Susanne Gruensfelder, Suthara Aramcharoen, Suvarna Kolhe, Suzana Tavares, Suzanne Arnold, Suzanne Welsh, Svetlana Apostolovic, Swapna Kunhunny, Ta-Chuan Hung, Taissa Zappernick, Tali Sharir, Talita Silva, Tamara Colaiácovo Soares, Tapan Umesh Pillay, Tarun K Mittal, Tatiana Trifonova, Tauane Bello Duarte, Tauqir Huk, Téodora Dutoiu, Terrance Chua, Terry Weyand, Thabitha Charles, Theodoros Kofidis, Theresa McCreary, Thierry Lefevre, Thippeekaa Arumairajah, Thitipong Tepsuwan, Thomas J Mulhearn, Thomas M Meyer, Thomas P Rocco, Thomas R Downes, Thomas Crain, Thomas Haldis, Thomas Mathew, Thomas Redick, Thounaojam Indira Devi, Thuraia Nageh, Tia Cauthren, Tiago Silva, Tiffany Little, Tijana Andric, Tina Harding, Titus Lau, Tiziana Formisano, Tiziano Moccetti, Tomasz Ciurus, Tomasz Mazurek, Tomasz Tarchalski, Toshiyuki Nagai, Tri Tran, Tricia Youn, Trish Tucker, Trudie Milner, Tuhina Bose, Tushar Kotecha, Udo Sechtem, Uma S Valeti, Umberto Cucchini, Umesh Badami, Upendra Kaul, V K Bahl, V S Narain, Valentina Casali, Valeria Godoy, Valerie Robesyn, Vamshi P Priya, Vandana Yadav, Vera McKinney, Veronica De Lenges, Veronica Tinnirello, Vicente Miro, Victor Navarro, Victoria Gumerova, Victoria Hernandez, Vidya Seeratan, Vijay Kumar, Vikentiy Y Kozulin, Viktoria Bulkley, Vilmar Veiga Jr, Vincent Setang, C P Vineeth, Virginai Pubull Nuñez, Virginia Fernández-Figares, Vitor Gomes, Viviana Gabriel, Viviane Dos Santos, Viviane Almeida, Vlad A Iliescu, Vladan Mudrenovic, Vladimir Dzavik, Vojislav L Giga, Walter Enrique Mogrovejo, Wan Xian Chan, Wanda C Marfori, Wanda Parker, Warangkana Mekara, Wassim Nona, Wayne Old, Wayne Pennachi, Weerachai Nawarawong, Wei Chen, Wei Su, Weibing Xing, Wei-Ren Lan, Wenda Crawford, Wendy L Stewart, Wendy Drewes, Wenhua Lin, William B Abernethy, William D Salerno, William F Fearon, William Vergoni, William Weintraub, Winnie C Sia, Wlodzimierz J Musial, Xacobe Flores-Ríos, Xavier Garcia-Moll Marimon, Xi Su, Xiang Ma, Xiangqiong Gu, Xiao Wang, Xiaomei Li, Xiaowei Yao, Xin Fu, Xin Su, Xin Zeng, Xinchun Yang, Xiuhong Li, Xuehua Fang, Xutong Wang, Yaming Geng, Yan Yan, Yanek Pépin-Dubois, Yanfu Wang, Yang Wang, Yanmeng Tian, Yaping Huang, Yechen Han, Yesenia Zambrano, Yi-Hsuan Yang, Ying Tung Sia, Yining Yang, Yitong Ma, Yolayfi Peralta, Yongjian Wu, Yu Kunwu, Yu Zhao, Yudong Peng, Yueh-Hung Lin, Yulan Zhao, Yumei Dong, Yunhai Zhao, Yutthaphan Wannasopha, Yvonne Taul, Zakir Sahul, Zalina Kudzoeva, Zbigniew Kalarus, Zeljko Z Markovic, Zhen Huang, Zheng Ji, Zhenyu Liu, Zhou Yue, Zhulin Zhang, Zhuxi Li, Zile Singh Meharwal, Ziliang Bai, Zixiang Yu, Zohra Huda, Zoltan Davidovits
Subjects: Male, Cardiac Catheterization, Computed Tomography Angiography, medicine.medical_treatment, Myocardial Ischemia, Coronary Disease, Coronary Artery Disease, Kaplan-Meier Estimate, 030204 cardiovascular system & hematology, Coronary Angiography, ISCHEMIA Research Group, law.invention, Angina, Coronary artery disease, 0302 clinical medicine, Randomized controlled trial, law, Cardiovascular Disease, Myocardial Revascularization, 030212 general & internal medicine, Coronary Artery Bypass, 11 Medical and Health Sciences, Cardiac catheterization, General Medicine, Middle Aged, humanities, Cardiovascular Diseases, Cardiology, Female, Human, medicine.medical_specialty, Ischemia, Article, 03 medical and health sciences, Geriatric cardiology, Percutaneous Coronary Intervention, General & Internal Medicine, Internal medicine, medicine, Humans, Angina, Unstable, Aged, business.industry, Coronary Artery Bypa, Percutaneous coronary intervention, Bayes Theorem, medicine.disease, Heart failure, Quality of Life, business
Abstract: BACKGROUND: Among patients with stable coronary disease and moderate or severe ischemia, whether clinical outcomes are better in those who receive an invasive intervention plus medical therapy than in those who receive medical therapy alone is uncertain. METHODS: We randomly assigned 5179 patients with moderate or severe ischemia to an initial invasive strategy (angiography and revascularization when feasible) and medical therapy or to an initial conservative strategy of medical therapy alone and angiography if medical therapy failed. The primary outcome was a composite of death from cardiovascular causes, myocardial infarction, or hospitalization for unstable angina, heart failure, or resuscitated cardiac arrest. A key secondary outcome was death from cardiovascular causes or myocardial infarction. RESULTS: Over a median of 3.2 years, 318 primary outcome events occurred in the invasive-strategy group and 352 occurred in the conservative-strategy group. At 6 months, the cumulative event rate was 5.3% in the invasive-strategy group and 3.4% in the conservative-strategy group (difference, 1.9 percentage points; 95% confidence interval [CI], 0.8 to 3.0); at 5 years, the cumulative event rate was 16.4% and 18.2%, respectively (difference, -1.8 percentage points; 95% CI, -4.7 to 1.0). Results were similar with respect to the key secondary outcome. The incidence of the primary outcome was sensitive to the definition of myocardial infarction; a secondary analysis yielded more procedural myocardial infarctions of uncertain clinical importance. There were 145 deaths in the invasive-strategy group and 144 deaths in the conservative-strategy group (hazard ratio, 1.05; 95% CI, 0.83 to 1.32). CONCLUSIONS: Among patients with stable coronary disease and moderate or severe ischemia, we did not find evidence that an initial invasive strategy, as compared with an initial conservative strategy, reduced the risk of ischemic cardiovascular events or death from any cause over a median of 3.2 years. The trial findings were sensitive to the definition of myocardial infarction that was used. (Funded by the National Heart, Lung, and Blood Institute and others; ISCHEMIA ClinicalTrials.gov number, NCT01471522.).
Published: 2020
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22. Sex difference after acute myocardial infarction patients with a history of current smoking and long-term clinical outcomes: Results of KAMIR Registry

Author: Sung Jin Hong, Seunghwan Kim, Jung Sun Kim, Yong Hoon Kim, Donghoon Choi, Young Guk Ko, Ae-Young Her, Yangsoo Jang, Chul Min Ahn, Myung Ho Jeong, Byeong Keuk Kim, and Myeong Ki Hong
Subjects: medicine.medical_specialty, business.industry, Hazard ratio, Female group, General Medicine, Disease, medicine.disease, Confidence interval, Internal medicine, medicine, Cardiology, Male group, Stent thrombosis, Myocardial infarction, Risk factor, Cardiology and Cardiovascular Medicine, business
Abstract: Background: The contribution of sex as an independent risk factor for cardiovascular disease still remains controversial. The present study investigated the impact of sex on long-term clinical outcomes in Korean acute myocardial infarction (AMI) patients with a history of current smoking on admission after drug-eluting stents (DESs). Methods: A total of 12,565 AMI patients (male: n = 11767 vs. female: n = 798) were enrolled. Major adverse cardiac events (MACEs) comprising all-cause death, recurrent myocardial infarction (Re-MI), and any repeat revascularization were the primary outcomes that were compared between the two groups. Probable or definite stent thrombosis (ST) was the secondary outcome. Results: After adjustment, the early (30 days) cumulative incidences of MACEs (adjusted hazard ratio [aHR]: 1.457; 95% confidence interval [CI]: 1.021–2.216; p = 0.035) and all-cause death (aHR: 1.699; 95% CI: 1.074–2.687; p = 0.023) were significantly higher in the female group than in the male group. At 2 years, the cumulative incidences of all-cause death (aHR: 1.561; 95% CI: 1.103–2.210; p = 0.012) and Re-MI (aHR: 1.880; 95% CI: 1.089–2.974; p = 0.022) were significantly higher in the female group than in the male group. However, the cumulative incidences of ST were similar between the two groups (aHR: 1.207; 95% CI: 0.583–2.497; p = 0.613). Conclusions: The female group showed worse short-term and long-term clinical outcomes compared with the male group comprised of Korean AMI patients with a history of current smoking after successful DES implantation. However, further studies are required to confirm these results.
Published: 2022
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23. Joint Observation and Transmission Scheduling in Agile Satellite Networks

Author: Ben Liang, Min Sheng, He Lijun, and Jiandong Li
Subjects: Data collection, Computer Networks and Communications, business.industry, Computer science, Real-time computing, Initialization, Genetic algorithm, Relaxation (approximation), Electrical and Electronic Engineering, Transmission time, business, Resource management (computing), Integer programming, Software, Agile software development
Abstract: Compared with traditional observation satellites, agile earth observation satellites are capable of prolonging observation time windows (OTWs) for targets, which significantly alleviates observation conflicts, thereby facilitating imaging data collection. However, it also leads to more uncertainties in determining the start time to image targets within these longer OTWs for an agile satellite network (ASN) to collect imaging data. Furthermore, these collected data is offloaded only within short transmission time windows between data collectors and data sinks, thus resulting in a transmission scheduling problem. Toward this end, this paper investigates joint observation and transmission scheduling in ASNs, aiming at accommodating more imaging data to be collected and offloaded successfully. Specifically, we formulate the studied problem as integer linear programming (ILP) to maximize the weighted sum of scheduled imaging tasks. Then, we explore the hidden structure of this ILP and transform it into a special framework, which can be solved efficiently through semidefinite relaxation (SDR). To reduce computation complexity, we further propose a fast yet efficient algorithm by combining the advantages of the devised SDR method and a genetic algorithm with special population initialization. Finally, simulation results demonstrate that the proposed algorithm can significantly increase the weighted sum of scheduled tasks.
Published: 2022
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24. Preference Learning to Multifocus Image Fusion via Generative Adversarial Network

Author: Rencan Nie, Shishuang Yu, Min He, and Chengchao Wang
Subjects: Image fusion, Preference learning, Artificial Intelligence, Computer science, business.industry, Multi focus, Artificial intelligence, business, Generative adversarial network, Software
Published: 2022
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25. rPPG-Based Heart Rate Estimation Using Spatial-Temporal Attention Network

Author: Min Hu, Fei Qian, Dong Guo, Fuji Ren, Mingxing Jiang, and Xiaohua Wang
Subjects: Estimation, Artificial Intelligence, business.industry, Computer science, Attention network, Heart rate, Pattern recognition, Artificial intelligence, business, Software
Published: 2022
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26. The Role of Race on Acute Kidney Injury After Cardiac Surgery

Author: Xiaoting Wu, David Fitzgerald, PERForm Registry, Patricia F. Theurer, Gaetano Paone, Michael Heung, Alphonse DeLucia, Timothy A Dickinson, David Grix, Donald S. Likosky, Donald Nieter, Jeffrey Chores, and Min Zhang
Subjects: Male, Pulmonary and Respiratory Medicine, medicine.medical_specialty, medicine.medical_treatment, Renal function, Postoperative Complications, Risk Factors, Internal medicine, Odds Ratio, Humans, Medicine, Cardiac Surgical Procedures, Dialysis, Retrospective Studies, Body surface area, business.industry, Acute kidney injury, Odds ratio, Acute Kidney Injury, medicine.disease, Cardiac surgery, Creatinine, Propensity score matching, Cohort, Female, Surgery, Cardiology and Cardiovascular Medicine, business
Abstract: Acute kidney injury (AKI) frequently complicates cardiac surgery and is more common among Black patients. We evaluated determinants of race-based differences in AKI rates.Serum creatinine-based criteria were used to identify adult cardiac surgical patients having postoperative AKI in the Perfusion Measures and Outcomes (PERForm) Registry (July 1, 2014, to June 30, 2019). Patient characteristics, operative details, and outcomes were compared by race (Black vs White) after excluding patients with preoperative dialysis, missing preoperative or postoperative creatinine, or other races. A mixed effects model (adjusting for demographics, comorbidities, surgical factors) used hospital as a random effect to predict postoperative stage 2 or 3 AKI. Propensity score analyses were conducted to evaluate robustness of the primary analyses.The study cohort included 34 520 patients (8% Black). More Black patients than White patients were female (43% vs 27%, P.001), and had hypertension (93% vs 87%, P.001) and diabetes mellitus (51% vs 41%, P.001). Acute kidney injury of stage 2 or greater occurred in 1697 patients (5%), more often among Black than White patients (8% vs 5%, P.001). Intraoperatively, Black patients had lower nadir hematocrits (23 vs 26, P.001), and were more likely to be given transfusions (22% vs 14%, P.001). After adjustment, Black race (compared with White) independently predicted odds for postoperative AKI (adjusted odds ratio 1.50; 95% confidence interval, 1.26 to 1.78). The multivariable findings were similar in propensity score analyses.Despite accounting for differences in risk factors and intraoperative practices, Black patients had a 50% increased odds for having moderate-severe postoperative AKI compared with White patients. Additional evaluations are warranted to identify potential targets to address racial disparities in AKI outcomes.
Published: 2022
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27. Bridging the Gap Between Few-Shot and Many-Shot Learning via Distribution Calibration

Author: Shuo Yang, Tongliang Liu, Min Xu, and Songhua Wu
Subjects: Bridging (networking), Contextual image classification, Distribution (number theory), Computer science, business.industry, Applied Mathematics, Linear classifier, 0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 0906 Electrical and Electronic Engineering, Computational Theory and Mathematics, Margin (machine learning), Approximation error, Artificial Intelligence, Classifier (linguistics), Calibration, Artificial Intelligence & Image Processing, Artificial intelligence, Computer Vision and Pattern Recognition, business, Algorithm, Software
Abstract: A major gap between few-shot and many-shot learning is the data distribution empirically oserved by the model during training. In few-shot learning, the learned model can easily become over-fitted based on the biased distribution formed by only a few training examples, while the ground-truth data distribution is more accurately uncovered in many-shot learning to learn a well-generalized model. In this paper, we propose to calibrate the distribution of these few-sample classes to be more unbiased to alleviate such an over-fitting problem. The distribution calibration is achieved by transferring statistics from the classes with sufficient examples to those few-sample classes. After calibration, an adequate number of examples can be sampled from the calibrated distribution to expand the inputs to the classifier. Specifically, we assume every dimension in the feature representation from the same class follows a Gaussian distribution so that the mean and the variance of the distribution can borrow from that of similar classes whose statistics are better estimated with an adequate number of samples. Extensive experiments on three datasets, miniImageNet, tieredImageNet, and CUB, show that a simple linear classifier trained using the features sampled from our calibrated distribution can outperform the state-of-the-art accuracy by a large margin. Besides the favorable performance, the proposed method also exhibits high flexibility by showing consistent accuracy improvement when it is built on top of any off-the-shelf pretrained feature extractors and classification models without extra learnable parameters. The visualization of these generated features demonstrates that our calibrated distribution is an accurate estimation thus the generalization ability gain is convincing. We also establish a generalization error bound for the proposed distribution-calibration-based few-shot learning, which consists of the distribution assumption error, the distribution approximation error, and the estimation error. This generalization error bound theoretically justifies the effectiveness of the proposed method.
Published: 2022
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28. Extracutaneous mastocytoma of colon: a case report and literature review

Author: Min Su Chu and Eui Joong Kim
Subjects: Pathology, medicine.medical_specialty, medicine.diagnostic_test, business.industry, medicine.medical_treatment, Gastroenterology, Medicine (miscellaneous), chemical and pharmacologic phenomena, medicine.disease, Extracutaneous mastocytoma, Mast cell, Polypectomy, Benign tumor, medicine.anatomical_structure, Biopsy, Medicine, Colonoscopic Polypectomy, Radiology, Nuclear Medicine and imaging, business
Abstract: Extracutaneous mastocytoma is a rare benign tumor composed of mature mast cells and is located in tissues other than the skin. We report the case of a 61-year-old male who was diagnosed with extracutaneous mastocytoma via colonoscopic polypectomy and biopsy. To our knowledge, this was the first case of a solitary extracutaneous mastocytoma of the colon. We reported this case and reviewed the literature.
Published: 2022
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29. Web Service Network Embedding Based on Link Prediction and Convolutional Learning

Author: Yuan Zhuang, Xingquan Zhu, Min Shi, Yufei Tang, Jianxun Liu, and Maohua Lin
Subjects: Topic model, Information Systems and Management, Information retrieval, Computer Networks and Communications, business.industry, Computer science, Probabilistic logic, computer.software_genre, Semantics, Computer Science Applications, Software, Hardware and Architecture, Task analysis, Mashup, Web service, business, computer, Feature learning
Published: 2022
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30. Interhospital variability in health care–associated infections and payments after durable ventricular assist device implant among Medicare beneficiaries

Author: Donald S. Likosky, Guangyu Yang, Min Zhang, Preeti N. Malani, Michael D. Fetters, Raymond J. Strobel, Carol E. Chenoweth, Hechuan Hou, Francis D. Pagani, Ashraf Shaaban Abdel Aziz Abou El Ela, Paul C. Tang, Michael P. Thompson, Keith Aaronson, Supriya Shore, Thomas Cascino, Katherine B. Salciccioli, Jeffrey S. McCullough, Michelle Hou, Allison M. Janda, Michael R. Mathis, Tessa M.F. Watt, Michael J. Pienta, Alexander Brescia, Austin Airhart, Daniel Liesman, and Khalil Nassar
Subjects: Pulmonary and Respiratory Medicine, medicine.medical_specialty, medicine.medical_treatment, media_common.quotation_subject, Psychological intervention, 030204 cardiovascular system & hematology, Medicare, Health care associated, Article, Ventricular Function, Left, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, Registries, Aged, Retrospective Studies, media_common, Heart Failure, Cross Infection, business.industry, Incidence (epidemiology), Medicare beneficiary, Payment, Patient Discharge, United States, Confidence interval, Treatment Outcome, 030228 respiratory system, Ventricular assist device, Emergency medicine, Surgery, Heart-Assist Devices, Implant, Health Expenditures, Cardiology and Cardiovascular Medicine, business
Abstract: OBJECTIVE: The objective of this study was to investigate variations across hospitals in infection rates and associated costs, the latter reflected in 90-day Medicare payments. Despite high rates and expenditures of healthcare-associated infections associated with durable ventricular assist device implant, few studies have examined inter-hospital variation and associated costs. METHODS: Clinical data on 8688 patients who received primary durable ventricular assist devices from July 2008 to July 2017 from The Society of Thoracic Surgeons Interagency Registry for Mechanically Assisted Circulatory Support (Intermacs) hospitals (n=120) were merged with post-implant 90-day Medicare claims. Terciles of hospital-specific, risk-adjusted infection rates per 100 patient-months were estimated using Intermacs and associated with Medicare payments (among 5440 Medicare beneficiaries). Primary outcomes included infections within 90 days of implant and Medicare payments. RESULTS: There were 3982 infections identified among 27.8% (2,417/8,688) of patients developing an infection. The median (25(th), 75(th) percentile) adjusted incidence of infections (per 100 patient-months) across hospitals was 14.3 (9.3, 19.5) and varied by hospital (range 0.0 – 35.6). Total Medicare payments from implant to 90-days were 9.0% (absolute difference: $13,652) greater in high versus low infection tercile hospitals, p
Published: 2022
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31. Outcomes of daytime nurse practitioner–staffed versus resident-staffed nonsurgical intensive care units: A retrospective observational study

Author: Li-Ying Cheng, Hsiao-Chi Chang, Min-Tsun Liao, Ting-Tse Lin, Chun-Kai Chen, and Li-Ta Keng
Subjects: medicine.medical_specialty, APACHE II, Nurse practitioners, business.industry, Staffing, Retrospective cohort study, Apache II score, Length of Stay, Emergency Nursing, Critical Care Nursing, Intensive care unit, law.invention, Intensive Care Units, Health evaluation, law, Intensive care, Emergency medicine, medicine, Humans, Nurse Practitioners, Hospital Mortality, business, Retrospective Studies, APACHE
Abstract: BACKGROUND Rapid developments in medical care-such as monitoring devices, medications, and working hours restrictions for intensive care personnel-have dramatically increased the demand for intensive care physicians. Therefore, nurse practitioner (NP)-staffed care is becoming increasingly important. This study was aimed to compare the outcomes of daytime NP-staffed and daytime resident-staffed nonsurgical intensive care units (ICU). METHODS We retrospectively assessed patients admitted to a nonsurgical ICU from March 2017 to December 2017. We collected basic patient data, including age, sex, admission diagnosis, transferring unit, and Acute Physiology and Chronic Health Evaluation II (APACHE II) score. Primary endpoints were ICU mortality, hospital mortality, and 30-day mortality. Secondary endpoints were 48-h readmission, discharge to nonhome locations, and lengths of ICU and hospital stay. RESULTS A total of 838 subjects were analysed: 334 subjects in the NP-staffed group and 504 in the resident-staffed group. The NP-staffed group was more likely to come from inpatient units (38.3% vs 16.5% for resident-staffed group; p
Published: 2022
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32. Highly Efficient Full-Bridge Resonant AC/DC Converter Using T-Type Voltage Doubler

Author: Myeong-Hwan Kim, Nam-Gyeong Kim, Minsung Kim, Seung-Won Jo, and Min-Jae Kim
Subjects: Voltage doubler, Materials science, Control and Systems Engineering, business.industry, Electrical engineering, Full bridge, Electrical and Electronic Engineering, business, Dc converter
Published: 2022
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33. Burden of comorbidities and medication use in childbearing women with rheumatic diseases: a nationwide population-based study

Author: Min Kyung Chung, Hyunsun Lim, Jin Su Park, Jisoo Lee, and Chanhee Lee
Subjects: Male, medicine.medical_specialty, Arthritis, Rheumatoid, Adrenal Cortex Hormones, Rheumatic Diseases, Neoplasms, Internal medicine, Diabetes mellitus, Hyperlipidemia, Diabetes Mellitus, medicine, Humans, Lupus Erythematosus, Systemic, Spondylitis, Ankylosing, skin and connective tissue diseases, Ankylosing spondylitis, Medication use, business.industry, Anti-Inflammatory Agents, Non-Steroidal, Cancer, Odds ratio, medicine.disease, Comorbidity, Confidence interval, Antirheumatic Agents, Hypertension, Female, business
Abstract: Background/Aims: We aimed to estimate the prevalence of comorbidities and medication use in Korean women with rheumatic diseases (RDs) during their childbearing years.Methods: We included women aged 20 to 44 years with seropositive rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and ankylosing spondylitis (AS) (n = 41,547) and age-matched women without seropositive RA, SLE, and AS (n = 208,941) from the National Health Insurance Service-National Health Information Database (2009 to 2016). The prevalence of hypertension (HTN), hyperlipidemia (HLD), diabetes mellitus (DM), and cancer and the use of nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids (CSs), and disease-modifying anti-rheumatic drugs (DMARDs) were estimated.Results: Women of childbearing age with RDs were more likely to have at least one of the measured comorbidities than the controls (odds ratio [OR], 3.0; 95% confidence interval [CI], 2.9 to 3.1). The OR (95% CI) was 2.9 (2.8 to 3.0) for HTN, 2.8 (2.7 to 2.9) for HLD, 1.4 (1.4 to 1.5) for DM, and 1.3 (1.3 to 1.4) for cancer. The SLE group had the highest prevalence and odds of all four measured comorbidities. Almost all (97.9%) women of childbearing age with RDs were taking RD-related medications (NSAIDs, 81.6%; CSs, 77.8%; DMARDs, 87.3%). The RD group was 13.8 times more likely to take NSAIDs and 68.2 times more likely to take CSs than the controls. Use of NSAIDs was more prevalent in RA and AS than SLE, whereas use of CSs and DMARDs was more prevalent in RA and SLE than AS.Conclusions: Korean women with RDs have a greater burden of comorbidities and medication use during their childbearing years than women without RDs of the same age.
Published: 2022
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34. Diagnostic accuracy of the short-form Fonseca Anamnestic Index in relation to the Diagnostic Criteria for Temporomandibular Disorders

Author: Jie Lei, Adrian Ujin Yap, Kai-Yuan Fu, and Min-Juan Zhang
Subjects: Adult, medicine.medical_specialty, Adolescent, Pain, Diagnostic accuracy, Muscle disorder, Sensitivity and Specificity, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, Humans, Cutoff, Medicine, Medical diagnosis, Receiver operating characteristic, business.industry, Area under the curve, Mean age, 030206 dentistry, Middle Aged, Temporomandibular Joint Disorders, Predictive value, Female, Oral Surgery, business, Algorithms
Abstract: Statement of problem Screening for temporomandibular disorders (TMDs) is important in research and clinical practice. The short-form Fonseca Anamnestic Index (SFAI) was recently introduced but had only been validated for muscle disorders. Purpose The purpose of this clinical study was to determine the diagnostic accuracy of the SFAI and its discrete and pooled items in relation to the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) benchmark. Material and methods A total of 866 consecutive participants with TMDs and 57 TMD-free controls (aged ≥18 years) were recruited. The participants (n=923; mean age 32.8 ±13.3 years; women 79.2%) answered the FAI, and TMD diagnoses were derived based on the DC/TMD protocol and algorithms. The 5-item SFAI, which comprised 2 pain-related and 3 function-related TMD questions, was subsequently acquired and assessed with reference to the DC/TMD diagnoses. The receiver operating characteristics (ROC) was used to verify accuracy (area under the curve [AUC]) and the best cutoff points. Sensitivity, specificity, predictive values, and likelihood ratios were also examined. Results Pain-related (PT) and intra-articular (IT) TMDs were present in 48.3% (446/923) and 82.7% (763/923) of the participants, respectively. The SFAI demonstrated high accuracy for identifying all TMDs, PT, and IT (AUC of 0.97, 0.99, and 0.97, respectively). The best cutoff points were 12.5 for all TMDs/IT and 17.5 for PT. Sensitivity of the SFAI ranged from 90.7% to 97.5% while specificity varied from 93.0% to 96.5%, with the highest values for PT. As positive predictive values (99.4% to 99.5%) were greater than negative ones (41.7% to 83.3%), the SFAI was better at detecting the presence than the absence of TMDs. With reference to PT, the sensitivity, and specificity of the 2 discrete and pooled pain-related questions (questions 3 and 4), extended from 82.3% to 99.3% and 77.2% to 96.5% respectively. With regard to IT diagnoses, sensitivity and specificity ranged from 56.0% to 98.3% and 86.0% to 98.3% for the 3 discrete and pooled function-related items (questions 1, 2, and 5). Conclusions The SFAI presented high degrees of diagnostic accuracy in relation to the DC/TMD and can be used for screening TMDs. SFAI scores between 15 and 50 points should be used to identify the presence of TMDs, with scores ≥20 points specifying possible pain-related TMDs.
Published: 2022
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35. Dynamic impact of virome on colitis and colorectal cancer: Immunity, inflammation, prevention and treatment

Author: Zhihan Wang, Canhua Huang, Yingying Liu, Min Wu, and Kai Guo
Subjects: Inflammation, Cancer Research, biology, Virome, business.industry, Mechanism (biology), Cancer, Gut flora, Inflammatory Bowel Diseases, Colitis, medicine.disease, biology.organism_classification, Inflammatory bowel disease, Immune system, Immunity, Viruses, Immunology, Humans, Medicine, Human virome, Colorectal Neoplasms, business
Abstract: The gut microbiome includes a series of microorganism genomes, such as bacteriome, virome, mycobiome, etc. The gut microbiota is critically involved in intestine immunity and diseases, including inflammatory bowel disease (IBD) and colorectal cancer (CRC); however, the underlying mechanism remains incompletely understood. Clarifying the relationship between microbiota and inflammation may profoundly improve our understanding of etiology, disease progression, patient management, and the development of prevention and treatment. In this review, we discuss the latest studies of the influence of enteric viruses (i.e., commensal viruses, pathogenic viruses, and bacteriophages) in the initiation, progression, and complication of colitis and colorectal cancer, and their potential for novel preventative approaches and therapeutic application. We explore the interplay between gut viruses and host immune systems for its effects on the severity of inflammatory diseases and cancer, including both direct and indirect interactions between enteric viruses with other microbes and microbial products. Furthermore, the underlying mechanisms of the virome’s roles in gut inflammatory response have been explained to infer potential therapeutic targets with examples in specific clinical trials. Given that very limited literature has thus far discussed these various topics with the gut virome, we believe these extensive analyses may provide insight into the understanding of the molecular pathogenesis of IBD and CRC, which could help add the design of improved therapies for these important human diseases.
Published: 2022
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36. Frequency Principle in Broad Learning System

Author: C. L. Philip Chen, Hong-Tao Zhu, Long Chen, Guang-Yong Chen, and Min Gan
Subjects: Interpretation (logic), ComputingMilieux_THECOMPUTINGPROFESSION, Artificial neural network, Computer Networks and Communications, Computer science, business.industry, Process (computing), Machine learning, computer.software_genre, Flat organization, Computer Science Applications, Discriminative model, Artificial Intelligence, Frequency domain, Feature (machine learning), Learning, Neural Networks, Computer, Artificial intelligence, business, Focus (optics), computer, Software
Abstract: Deep neural networks have achieved breakthrough improvement in various application fields. Nevertheless, they usually suffer from a time-consuming training process because of the complicated structures of neural networks with a huge number of parameters. As an alternative, a fast and efficient discriminative broad learning system (BLS) is proposed, which takes the advantages of flat structure and incremental learning. The BLS has achieved outstanding performance in classification and regression problems. However, the previous studies ignored the reason why the BLS can generalize well. In this article, we focus on the interpretation from the viewpoint of the frequency domain. We discover the existence of the frequency principle in BLS, i.e., the BLS preferentially captures low-frequency components quickly and then fits the high frequencies during the incremental process of adding feature nodes and enhancement nodes. The frequency principle may be of great inspiration for expanding the application of BLS.
Published: 2022
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37. Transferable Feature Selection for Unsupervised Domain Adaptation

Author: Qingyao Wu, Yuzhong Ye, Hanrui Wu, Liu Dapeng, Min Lu, Yuguang Yan, Bi Chaoyang, and Michael K. Ng
Subjects: Domain adaptation, Source data, Computer science, business.industry, Feature selection, Machine learning, computer.software_genre, Computer Science Applications, Domain (software engineering), Task (project management), Computational Theory and Mathematics, Discriminative model, Feature (machine learning), Artificial intelligence, business, computer, Classifier (UML), Information Systems
Abstract: Domain adaptation aims at extracting knowledge from auxiliary source domains to assist the learning task in a target domain. In classification problems, since the distributions of the source and target domains are different, directly using source data to build a classifier for the target domain may hamper the classification performance on the target data. Fortunately, in many tasks, there can be some features that are transferable, i.e., the source and target domains share similar properties. On the other hand, it is common that the source data contain noisy features which may degrade the learning performance in the target domain. This issue, however, is barely studied in existing works. In this paper, we propose to find a feature subset that is transferable across the source and target domains. As a result, the domain discrepancy measured on the selected features can be reduced. Moreover, we seek to find the most discriminative features for classification. To achieve the above goals, we formulate a new sparse learning model that is able to jointly reduce the domain discrepancy and select informative features for classification. We develop two optimization algorithms to address the derived learning problem. Extensive experiments on real-world data sets demonstrate the effectiveness of the proposed method.
Published: 2022
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38. Post-traumatic avascular necrosis of tibial plafond following an ankle fracture

Author: Kyoung Min Lee, Ki Hyuk Sung, Ki Bum Kwon, Moon Seok Park, and Chin Youb Chung
Subjects: medicine.medical_specialty, medicine.anatomical_structure, business.industry, Fracture (geology), Medicine, Orthopedics and Sports Medicine, Surgery, Avascular necrosis, Ankle, business, Tibial plafond, medicine.disease
Published: 2022
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39. Successful management of spontaneous intracranial hypotension with epidural blood patch

Author: Thinzar Min, Amanda Ebejer, and Mithila Vijay
Subjects: Diplopia, Epidural blood patch, Photophobia, Cerebrospinal Fluid Leak, Nausea, business.industry, medicine.medical_treatment, Headache, Intracranial Hypotension, General Medicine, Middle Aged, Bed rest, Magnetic Resonance Imaging, Cerebrospinal fluid, Anesthesia, medicine, Humans, Female, medicine.symptom, business, Neck stiffness, Tinnitus, Blood Patch, Epidural
Abstract: Spontaneous intracranial hypotension (SIH) is characterised by postural headache and a cerebrospinal fluid (CSF) pressure of ≤6 cmH20 measured with the patient in the lateral decubitus position. Other symptoms include tinnitus, altered hearing, diplopia, photophobia, nausea and neck stiffness, and must not have occurred within a month of dural puncture. Symptoms typically remit after normalisation of CSF pressure or successful sealing of the CSF leak. An epidural blood patch (EBP) is a treatment option in those who have not responded to bed rest, fluids, non-steroidal anti-inflammatories or caffeine. We present a case of SIH successfully treated with both conservative measures and EBP. We compare our case with similar cases in the literature and summarise what is known about EBP for SIH to help clinicians take a more informed approach to managing such patients.
Published: 2023

40. Osmotic demyelination syndrome following slow correction of hyponatraemia

Author: Isabel Saunders, Aliya Mohd Ruslan, David M Williams, and Thinzar Min
Subjects: Aged, 80 and over, Saline Solution, Hypertonic, Demeclocycline, business.industry, Sodium, chemistry.chemical_element, General Medicine, Bacteriuria, medicine.disease, Hypertonic saline, Inappropriate ADH Syndrome, chemistry, Anesthesia, Urine osmolality, Medicine, Humans, Female, Fluid restriction, business, medicine.drug, Antidiuretic, Hormone, Demyelinating Diseases, Hyponatremia
Abstract: Hyponatraemia is the most common electrolyte disturbance observed in hospital inpatients. We report a 90-year-old woman admitted generally unwell following a fall with marked confusion. Examination revealed a tender suprapubic region, and investigations observed elevated inflammatory markers and bacteriuria. Admission investigations demonstrated a serum sodium of 110 mmol/L with associated serum osmolality 236 mmol/kg and urine osmolality 346 mmol/kg. She was treated for hyponatraemia secondary to syndrome of inappropriate antidiuretic hormone (SIADH) and urosepsis. However, her serum sodium failed to normalise despite fluid restriction, necessitating treatment with demeclocycline and hypertonic saline. Despite slow reversal of hyponatraemia over 1 month, the patient developed generalised seizures with pontine and thalamic changes on MRI consistent with osmotic demyelination syndrome (ODS). This case highlights the risk of ODS, a rare but devastating consequence of hyponatraemia treatment, despite cautious sodium correction.
Published: 2023

41. Association between Condylar Bone Density and Disk Displacement in the Temporomandibular Joint

Author: Min-Seok Chang, Sug-Joon Ahn, Min-Suk Heo, Il-Hyung Yang, Jeong-Ho Choi, and Jung-Sub An
Subjects: Male, Bone density, Endocrinology, Diabetes and Metabolism, medicine.medical_treatment, Joint Dislocations, Common method, Condyle, stomatognathic system, Bone Density, Temporomandibular Joint Disc, Bone quality, medicine, Humans, Radiology, Nuclear Medicine and imaging, Orthopedics and Sports Medicine, Displacement (orthopedic surgery), Reduction (orthopedic surgery), Retrospective Studies, Orthodontics, Temporomandibular Joint, medicine.diagnostic_test, business.industry, Mandibular Condyle, Magnetic resonance imaging, Temporomandibular Joint Disorders, Magnetic Resonance Imaging, Temporomandibular joint, medicine.anatomical_structure, Female, business
Abstract: Measuring bone density (BD) is a common method of determining bone quality; however, the relationship between condylar BD and the occurrence of temporomandibular joint (TMJ) disorders has not been investigated. To address this knowledge gap, we aimed to investigate condylar BD in terms of TMJ disk displacement (TMJ DD) using computed tomography (CT) and magnetic resonance imaging (MRI). We classified TMJ MRI results according to the position of the disk: normal disk position (Normal), anterior disk displacement with reduction (ADDR), and anterior disk displacement without reduction (ADDNR). After retrospectively evaluating 86 female condyles, we determined the total, cortical, and trabecular BD in the upper-joint portion of the condyle and the whole condyle using CT data. To standardize condylar BD, we calculated the BD ratios by dividing the condylar BD by the cervical axis BD. The Kruskal-Wallis test analyzed the differences in BD measurements in the TMJ DD patient groups and showed significant between-group differences in condylar BD. The total and trabecular BD was significantly higher in ADDNR condyles than in Normal or ADDR condyles (Normal = ADDRADDNR). However, there was no significant difference in the cortical BD among the three TMJ DD groups. The BD ratios showed a similar tendency with condylar BD. These results suggest that increased condylar BD - specifically total and trabecular BD - may be significantly associated with ADDNR condyles. Our findings will help clinicians determine the course of treatment for patients with disk-related TMJ disorders.
Published: 2022
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42. Successful treatment using targeted therapy, radiotherapy, and intrathecal chemotherapy in a patient with leptomeningeal metastasis with an epidermal growth factor receptor exon 20 insertion mutation: a case report

Author: Bailong Liu, Min Zhang, Qiuhui Ding, Lei Zhang, Liang Guo, Yunfei Ma, Hui Liu, and Min Liu
Subjects: Advanced and Specialized Nursing, Oncology, medicine.medical_specialty, business.industry, medicine.medical_treatment, medicine.disease, Primary tumor, Targeted therapy, Radiation therapy, Anesthesiology and Pain Medicine, Pemetrexed, Internal medicine, Ommaya reservoir, Medicine, Osimertinib, Radical surgery, business, Lung cancer, medicine.drug
Abstract: Leptomeningeal metastasis (LM) is a disastrous complication in lung cancer. LM patients with oncogene-addicted non-small cell lung cancer (NSCLC) have a relatively better prognosis than those with the wild-type counterpart; however, overall post-LM survival is short. Additionally, the high heterogenicity of the LM entity creates a treatment challenge, and to date, no standard strategy has been established. This article describes a female lung adenocarcinoma patient with a resistant epidermal growth factor receptor (EGFR) exon20ins mutation who developed LM only 11 months after radical surgery IIIA (pT1bN2). Intrathecal chemotherapy (ITC), whole-brain radiotherapy (WBRT) with a simultaneous integrated boost (SIB) followed by Osimertinib was initiated. The cerebrospinal fluid (CSF) cytology turned negative. The first remission lasted 6 months, then bone metastases occurred, and the LM progressed. An Ommaya reservoir was implanted. ITC with pemetrexed and anlotinib was administered. A CSF next-generation sequencing (NGS) examination revealed EGFR exon20ins (p. A767_V769 dup 1.5%), which was different from that of the primary tumor (p. V769_D770 ins ASV 17.48%). The CSF cytology then turned negative again; however, the patient succumbed to the disease in December 2020. The patient's post-LM overall survival (OS) time was 13.5 months. This case is novel and of great value. Clinicians should pay special attention to populations at high risk of developing LM. Early detection followed by active intervention, including ITC, RT, and systemic treatment, will result in a better prognosis. The NGS of CSF is fundamental to understanding the genetic profiles of LM and providing effective and precise treatment.
Published: 2022
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43. Medialized vs. lateralized humeral implant in reverse total shoulder arthroplasty: the comparison of outcomes in pseudoparalysis with massive rotator cuff tear

Author: Seung-Min Youn, Hee Sung Lee, Sung Min Rhee, and Yong Girl Rhee
Subjects: medicine.medical_specialty, Visual analogue scale, medicine.medical_treatment, Deltoid curve, Periprosthetic, Rotator Cuff Injuries, medicine, Humans, Orthopedics and Sports Medicine, Rotator cuff, Range of Motion, Articular, Aged, Retrospective Studies, Shoulder Joint, business.industry, Significant difference, General Medicine, Humerus, Middle Aged, Arthroplasty, Surgery, Treatment Outcome, medicine.anatomical_structure, Arthroplasty, Replacement, Shoulder, Implant, Range of motion, business
Abstract: Background With the increasing use of lateralized humeral implants in reverse total shoulder arthroplasty (RTSA), it is still unclear whether this design will be as advantageous as the medialized humeral implants in the setting of pseudoparalysis (PP). Methods A total of 51 eligible RTSA cases for PP signs due to massive rotator cuff deficiency were selected for the retrospective review. Twenty-five patients were allocated to group L in which the isolated humeral side lateralization was performed (neck-shaft angle of 145°), and 26 patients were allocated to group M (neck-shaft angle of 155°). Pre- and postoperative radiologic measurements including critical shoulder angle, acromiohumeral distance (AHD), lateral humeral offset (LHO), and deltoid wrapping offset (DWO), as well as range of motion (ROM) and clinical outcome scores, were compared. The mean age was 71.0 ± 6.5 years for group L and 70.3 ± 7.0 years for group M, and the overall mean follow-up period was 49.0 ± 13.5 months (range, 25.7-68.9). Results The preoperative radiologic measurements were similar, but the postoperative LHO and DWO were significantly larger for group L, whereas the postoperative AHD was larger for group M compared with group L, with the values being 39.8 ± 9.7 mm and 33.6 ± 10.4 mm, respectively (P = .034). For the ROM, active forward elevation did improve significantly for both, starting preoperatively with the active ranges of 39° ± 19° (passively 153° ± 24°) for group L and 42° ± 18° (passively 156° ± 11°) for group M—the final postoperative active ranges being 142° ± 16° and 133° ± 33°, respectively, without significant difference between the groups (P = .426). The postoperative recovery of PP for group L was 100% (25 of 25), whereas for group M, it was 96.2% (25 of 26). The final visual analog scale (VAS) scores and University of California Los Angeles (UCLA) shoulder scores improved significantly for both groups, but the differences between the groups at the final assessments were insignificant. The 4 complications were periprosthetic fracture, acromial stress fracture, periprosthetic joint infection, and neuropraxia, all of which were in group M, and scapular notching with higher grades were more prevalent for group M (26.9%, 7 of 26). Conclusions With RTSA, good outcomes and recovery were achieved in most cases of PP. The postoperative active elevation range and functional outcomes were not affected by medialization or lateralization of the humeral implant. Earlier recovery of motion was observed with the lateralized group, but no significant differences were seen in the final ROM between the groups.
Published: 2022
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44. Volume Doubling Times of Pulmonary Metastases in Patients With Bone and Soft-Tissue Sarcomas: Associations With Subsequent New Metastases and Survival After Metastasectomy

Author: Sang Min Lee, Joon Beom Seo, Seon-Ok Kim, Min Seon Kim, Yura Ahn, Kyung-Hyun Do, and Se Hoon Choi
Subjects: Adult, Male, medicine.medical_specialty, Lung Neoplasms, Volume Doubling Time, Soft Tissue Neoplasms, medicine, Humans, Radiology, Nuclear Medicine and imaging, In patient, Aged, Retrospective Studies, business.industry, Soft tissue sarcoma, Hazard ratio, Metastasectomy, Soft tissue, Sarcoma, Retrospective cohort study, General Medicine, Middle Aged, Prognosis, medicine.disease, Survival Rate, Complete Metastasectomy, Multiple Pulmonary Nodules, Female, Radiology, business
Abstract: Background: Pulmonary metastases of bone and soft tissue sarcoma are common and have a high recurrence rate after metastatectomy. Factors associated with post-metastatectomy recurrence are not well studied. Objective: To investigate the association of the volume doubling time (VDT) of pulmonary metastases with the subsequent development of new pulmonary nodules and survival after metastasectomy in patients with bone or soft tissue sarcoma. Methods: This retrospective study included patients with bone or soft tissue sarcoma who, between July 2020 and December 2020, underwent first complete metastasectomy of pulmonary nodules visualized on two sequential preoperative CT scans. Semi-automatic volumetric segmentation of the pulmonary metastases was performed on the two CT scans, and VDTs were calculated. VDT was compared between patients with and without subsequent new metastases after metastatectomy. Cox proportional hazards regression analyses were performed to determine risk factors for recurrence-free survival (RFS) after metastasectomy and for post-metastasectomy overall survival (OS). Results: Forty patients (21 women, 19 men; mean age, 51.1±14.3 years) were included. Of these patients, 23 (57.5%) developed new metastatic nodules after metastasectomy, and 10 (25%) died during follow-up. Median VDT was shorter in patients with, versus without, new metastases after metastasectomy (56 vs. 140 days, p=.002). Only four of 23 patients with new metastases had VDT ≥140 days. In multivariable analysis, older age [hazard ratio (HR), 1.06; p=.004), female sex (HR, 2.80; p=.03), and VDT
Published: 2022
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45. Mean and Variability of Lipid Measurements and Risk for Development of Subclinical Left Ventricular Diastolic Dysfunction

Author: Jiyun Park, Sang-Man Jin, Jae Hyeon Kim, Jiyeon Ahn, You-Bin Lee, Min Young Kim, Gyuri Kim, Kyu Yeon Hur, Mira Kang, Min Sun Choi, and Jeong Hoon Yang
Subjects: Adult, medicine.medical_specialty, Apolipoprotein B, Endocrinology, Diabetes and Metabolism, Diastole, Ventricular Function, Left, Ventricular Dysfunction, Left, Interquartile range, Internal medicine, Humans, Medicine, Longitudinal Studies, Apolipoproteins B, Subclinical infection, Ejection fraction, biology, business.industry, Hazard ratio, Stroke Volume, Cholesterol, LDL, Middle Aged, medicine.disease, Confidence interval, Cholesterol, Cardiology, biology.protein, lipids (amino acids, peptides, and proteins), business, Dyslipidemia
Abstract: Background: Subclinical left ventricular diastolic dysfunction (LVDD) is an emerging consequence of increased insulin resistance, and dyslipidemia is one of the few correctable risk factors of LVDD. This study evaluated the role of mean and visit-to-visit variability of lipid measurements in risk of LVDD in a healthy population.Methods: This was a 3.7-year (interquartile range, 2.1 to 4.9) longitudinal cohort study including 2,817 adults (median age 55 years) with left ventricular ejection fraction >50% who underwent an annual or biannual health screening between January 2008 and July 2016. The mean, standard deviation (SD), coefficient of variation (CV), variability independent of the mean (VIM), and average real variability of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein B (apoB), non-HDL-C, and triglycerides were obtained from three to six measurements during the 5 years preceding the first echocardiogram.Results: Among the 2,817 patients, 560 (19.9%) developed LVDD. The mean of no component of lipid measurements was associated with risk of LVDD. CV (hazard ratio [HR], 1.35; 95% confidence interval [CI], 1.10 to 1.67), SD (HR, 1.27; 95% CI, 1.03 to 1.57), and VIM (HR, 1.26; 95% CI, 1.03 to 1.55) of LDL-C and all the variability parameters of apoB were significantly associated with development of LVDD. The association between CV-LDL and risk of LVDD did not have significant interaction with sex, increasing/decreasing trend at baseline, or use of stain and/or lipid-modifying agents.Conclusion: The variability of LDL-C and apoB, rather than their mean, was associated with risk for LVDD.
Published: 2022
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46. Characterizing Sensorimotor-Related Area Abnormalities in Amyotrophic Lateral Sclerosis: An Intravoxel Incoherent Motion Magnetic Resonance Imaging Study

Author: Jia-Hui Lin, Nao-Xin Huang, Chuanyin Zhan, Hua-Jun Chen, Li-Min Cai, Zhang-Yu Zou, Min-Xiong Zhou, and Yuan-Fen Liu
Subjects: medicine.diagnostic_test, business.industry, Amyotrophic Lateral Sclerosis, Water, Magnetic resonance imaging, medicine.disease, Magnetic Resonance Imaging, White matter, Premotor cortex, Motion, Diffusion Magnetic Resonance Imaging, medicine.anatomical_structure, Disease severity, medicine, Humans, Effective diffusion coefficient, Radiology, Nuclear Medicine and imaging, Amyotrophic lateral sclerosis, Nuclear medicine, business, Perfusion, Intravoxel incoherent motion
Abstract: Rationale and Objectives To investigate the microperfusion and water molecule diffusion alterations in sensorimotor-related areas in amyotrophic lateral sclerosis (ALS) using intravoxel incoherent motion (IVIM) magnetic resonance imaging. Materials and Methods IVIM data were obtained from 43 ALS patients and 31 controls. This study employed the revised ALS Functional Rating Scale (ALSFRS-R) in evaluating disease severity. IVIM-derived metrics were calculated, including diffusion coefficient (D), pseudo-diffusion coefficient, and perfusion fraction. Conventional apparent diffusion coefficient was also computed. Atlas-based analysis was conducted to detect between-group difference in these metrics in sensorimotor-related gray/white matter areas. Spearman correlation analysis was employed to establish correlation between various metrics and ALSFRS-R. Results ALS patients had perfusion fraction (× 10-3) reduction in the left presupplementary motor area (60.72 ± 16.15 vs. 71.15 ± 12.98, p = 0.016), right presupplementary motor area (61.35 ± 17.02 vs. 72.18 ± 14.22, p = 0.016), left supplementary motor area (55.73 ± 12.29 vs. 64.12 ± 9.17, p = 0.015), and right supplementary motor area (56.53 ± 11.93 vs. 63.67 ± 10.03, p = 0.020). Patients showed D (× 10-6 mm2/s) increase in a white matter tract projecting to the right ventral premotor cortex (714.20 ± 39.75 vs. 691.01 ± 24.53, p = 0.034). A negative correlation between D of right ventral premotor cortex tract and ALSFRS-R score was observed (r = -0.316, p = 0.039). Conclusion These findings suggest aberrant microperfusion and water molecule diffusion in the sensorimotor-related areas in ALS patients, which are associated with motor impairment in ALS.
Published: 2022
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47. Decorrelated Debiased Converted Measurement Kalman Filter for Phased Array Radar Tracking System

Author: Min-Hyun Cho and Min-Jea Tahk
Subjects: Phased array, business.industry, Computer science, Aerospace Engineering, Computer vision, Tracking system, Artificial intelligence, Kalman filter, Electrical and Electronic Engineering, business
Published: 2022
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48. Favorable Long-Term Outcomes with Autologous Stem Cell Transplantation for High-Risk Multiple Myeloma Patients with a Positive Result On 18F-FDG PET/CT at Baseline

Author: Sung-Hoon Jung, Jung-Joon Min, Seo-Yeon Ahn, Joon Ho Moon, Jung Min Lee, Ga-Young Song, Deok-Hwan Yang, Hee Jeong Cho, Chae Moon Hong, Hyeoung-Joon Kim, Juhyung Kim, Sang-Kyun Sohn, Je-Jung Lee, Jae-Sook Ahn, Dong Won Baek, Yu Kyung Chung, and Shin Young Jeong
Subjects: Cancer Research, medicine.medical_specialty, Chemotherapy, medicine.diagnostic_test, business.industry, Medical record, medicine.medical_treatment, Hematology, medicine.disease, Transplantation, Autologous stem-cell transplantation, Oncology, Positron emission tomography, medicine, Long term outcomes, Fdg pet ct, Radiology, business, Multiple myeloma
Abstract: Background 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is a useful tool for identifying high-risk features in patients with newly diagnosed multiple myeloma (NDMM). This study evaluated the role of autologous stem cell transplantation (ASCT) in patients presenting with positive results on PET/CT scans. Materials and Methods The medical records of 210 patients who underwent PET/CT at diagnosis were retrospectively reviewed. Eligible patients for transplantation proceeded to upfront ASCT with high-dose chemotherapy (HDT) after induction therapy with novel agents. Results The presence of a number of focal lesions (FL) >3 and extramedullary disease (EMD) occurred in 111 and 35 patients, respectively. ASCT was performed in 54 patients. Among patients with FL > 3, those treated with ASCT showed a prolonged 2-year progression-free survival (PFS) and overall survival (OS) rates compared to those not treated with ASCT (PFS, 60.2% vs. 23.5%, P 3, Revised International Staging System (R-ISS), and upfront ASCT were independent prognostic factors for PFS and OS. Conclusion Presenting FL > 3 on baseline PET/CT represents a high-risk feature in patients with NDMM. Frontline ASCT with HDT prolonged the survival of patients with FL > 3.
Published: 2022
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49. Diagnostic Performance of 2-D Shear Wave Elastography on the Evaluation of Hepatic Fibrosis with Emphasis on Impact of the Different Region-of-Interest Methods

Author: Ji Won Park, Kwanseop Lee, Young Chul Kim, Min-Jeong Kim, Eun Mi Kim, Sung Eun Kim, Eun Soo Kim, Hong Il Ha, and Sang Min Lee
Subjects: Liver Cirrhosis, Cirrhosis, Acoustics and Ultrasonics, Biophysics, Liver stiffness, Region of interest, mental disorders, medicine, Humans, Radiology, Nuclear Medicine and imaging, Retrospective Studies, Shear wave elastography, Radiological and Ultrasound Technology, Receiver operating characteristic, business.industry, medicine.disease, nervous system diseases, Liver, ROC Curve, nervous system, Elasticity Imaging Techniques, Nuclear medicine, business, Transient elastography, Hepatic fibrosis, psychological phenomena and processes, Significant fibrosis
Abstract: The purpose of this study was to evaluate the impact of the different region-of-interest (ROI) methods of 2-D shear wave elastography (2-D SWE) on hepatic fibrosis diagnosis. In this retrospective study, 83 patients who underwent 2-D SWE with three ROI methods (a circle ROI, a box ROI and multiple ROIs in an elastogram) and transient elastography (TE) were included. Liver stiffness (LS) was measured five times with each ROI method. These LS values were compared, and their correlation with those obtained from TE was evaluated. The LS values obtained using the three different ROI methods differed statistically (p0.001). However, the LS values obtained using each ROI method of 2-D SWE were highly correlated with those obtained using TE (r0.7, p0.001). All three ROI methods of 2-D SWE had high areas under the receiver operating characteristic curve in diagnosing significant fibrosis and cirrhosis (0.841, 0.820 and 0.840, respectively; 0.962, 0.946 and 0.945, respectively). There were no significant differences in area under the receiver operating characteristic curve among the ROI methods of 2-D SWE. Regardless of the ROI method, 2-D SWE had high performance in diagnosing hepatic fibrosis.
Published: 2022
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50. Whole Genome Sequence Analysis of the Plasma Proteome in Black Adults Provides Novel Insights Into Cardiovascular Disease

Author: Daniel H. Katz, Usman A. Tahir, Alexander G. Bick, Akhil Pampana, Debby Ngo, Mark D. Benson, Zhi Yu, Jeremy M. Robbins, Zsu-Zsu Chen, Daniel E. Cruz, Shuliang Deng, Laurie Farrell, Sumita Sinha, Alec A. Schmaier, Dongxiao Shen, Yan Gao, Michael E. Hall, Adolfo Correa, Russell P. Tracy, Peter Durda, Kent D. Taylor, Yongmei Liu, W. Craig Johnson, Xiuqing Guo, Jie Yao, Yii-Der Ida Chen, Ani W. Manichaikul, Deepti Jain, Claude Bouchard, Mark A. Sarzynski, Stephen S. Rich, Jerome I. Rotter, Thomas J. Wang, James G. Wilson, Pradeep Natarajan, Robert E. Gerszten, Namiko Abe, Gonçalo Abecasis, Francois Aguet, Christine Albert, Laura Almasy, Alvaro Alonso, Seth Ament, Peter Anderson, Pramod Anugu, Deborah Applebaum-Bowden, Kristin Ardlie, Dan Arking, Donna K. Arnett, Allison Ashley-Koch, Stella Aslibekyan, Tim Assimes, Paul Auer, Dimitrios Avramopoulos, Najib Ayas, Adithya Balasubramanian, John Barnard, Kathleen Barnes, R. Graham Barr, Emily Barron-Casella, Lucas Barwick, Terri Beaty, Gerald Beck, Diane Becker, Lewis Becker, Rebecca Beer, Amber Beitelshees, Emelia Benjamin, Takis Benos, Marcos Bezerra, Larry Bielak, Joshua Bis, Thomas Blackwell, John Blangero, Eric Boerwinkle, Donald W. Bowden, Russell Bowler, Jennifer Brody, Ulrich Broeckel, Jai Broome, Deborah Brown, Karen Bunting, Esteban Burchard, Carlos Bustamante, Erin Buth, Brian Cade, Jonathan Cardwell, Vincent Carey, Julie Carrier, April Carson, Cara Carty, Richard Casaburi, Juan P. Casas Romero, James Casella, Peter Castaldi, Mark Chaffin, Christy Chang, Yi-Cheng Chang, Daniel Chasman, Sameer Chavan, Bo-Juen Chen, Wei-Min Chen, Michael Cho, Seung Hoan Choi, Lee-Ming Chuang, Mina Chung, Ren-Hua Chung, Clary Clish, Suzy Comhair, Matthew Conomos, Elaine Cornell, Carolyn Crandall, James Crapo, L. Adrienne Cupples, Joanne Curran, Jeffrey Curtis, Brian Custer, Coleen Damcott, Dawood Darbar, Sean David, Colleen Davis, Michelle Daya, Mariza de Andrade, Lisa de las Fuentes, Paul de Vries, Michael DeBaun, Ranjan Deka, Dawn DeMeo, Scott Devine, Huyen Dinh, Harsha Doddapaneni, Qing Duan, Shannon Dugan-Perez, Ravi Duggirala, Jon Peter Durda, Susan K. Dutcher, Charles Eaton, Lynette Ekunwe, Adel El Boueiz, Patrick Ellinor, Leslie Emery, Serpil Erzurum, Charles Farber, Jesse Farek, Tasha Fingerlin, Matthew Flickinger, Myriam Fornage, Nora Franceschini, Chris Frazar, Mao Fu, Stephanie M. Fullerton, Lucinda Fulton, Stacey Gabriel, Weiniu Gan, Shanshan Gao, Margery Gass, Heather Geiger, Bruce Gelb, Mark Geraci, Soren Germer, Robert Gerszten, Auyon Ghosh, Richard Gibbs, Chris Gignoux, Mark Gladwin, David Glahn, Stephanie Gogarten, Da-Wei Gong, Harald Goring, Sharon Graw, Kathryn J. Gray, Daniel Grine, Colin Gross, C. Charles Gu, Yue Guan, Namrata Gupta, David M. Haas, Jeff Haessler, Michael Hall, Yi Han, Patrick Hanly, Daniel Harris, Nicola L. Hawley, Jiang He, Ben Heavner, Susan Heckbert, Ryan Hernandez, David Herrington, Craig Hersh, Bertha Hidalgo, James Hixson, Brian Hobbs, John Hokanson, Elliott Hong, Karin Hoth, Chao (Agnes) Hsiung, Jianhong Hu, Yi-Jen Hung, Haley Huston, Chii Min Hwu, Marguerite Ryan Irvin, Rebecca Jackson, Cashell Jaquish, Jill Johnsen, Andrew Johnson, Craig Johnson, Rich Johnston, Kimberly Jones, Hyun Min Kang, Robert Kaplan, Sharon Kardia, Shannon Kelly, Eimear Kenny, Michael Kessler, Alyna Khan, Ziad Khan, Wonji Kim, John Kimoff, Greg Kinney, Barbara Konkle, Charles Kooperberg, Holly Kramer, Christoph Lange, Ethan Lange, Leslie Lange, Cathy Laurie, Cecelia Laurie, Meryl LeBoff, Jiwon Lee, Sandra Lee, Wen-Jane Lee, Jonathon LeFaive, David Levine, Dan Levy, Joshua Lewis, Xiaohui Li, Yun Li, Henry Lin, Honghuang Lin, Xihong Lin, Simin Liu, Yu Liu, Ruth J.F. Loos, Steven Lubitz, Kathryn Lunetta, James Luo, Ulysses Magalang, Michael Mahaney, Barry Make, Ani Manichaikul, Alisa Manning, JoAnn Manson, Lisa Martin, Melissa Marton, Susan Mathai, Rasika Mathias, Susanne May, Patrick McArdle, Merry-Lynn McDonald, Sean McFarland, Stephen McGarvey, Daniel McGoldrick, Caitlin McHugh, Becky McNeil, Hao Mei, James Meigs, Vipin Menon, Luisa Mestroni, Ginger Metcalf, Deborah A. Meyers, Emmanuel Mignot, Julie Mikulla, Nancy Min, Mollie Minear, Ryan L. Minster, Braxton D. Mitchell, Matt Moll, Zeineen Momin, May E. Montasser, Courtney Montgomery, Donna Muzny, Josyf C. Mychaleckyj, Girish Nadkarni, Rakhi Naik, Take Naseri, Sergei Nekhai, Sarah C. Nelson, Bonnie Neltner, Caitlin Nessner, Deborah Nickerson, Osuji Nkechinyere, Kari North, Jeff O’Connell, Tim O’Connor, Heather Ochs-Balcom, Geoffrey Okwuonu, Allan Pack, David T. Paik, Nicholette Palmer, James Pankow, George Papanicolaou, Cora Parker, Gina Peloso, Juan Manuel Peralta, Marco Perez, James Perry, Ulrike Peters, Patricia Peyser, Lawrence S. Phillips, Jacob Pleiness, Toni Pollin, Wendy Post, Julia Powers Becker, Meher Preethi Boorgula, Michael Preuss, Bruce Psaty, Pankaj Qasba, Dandi Qiao, Zhaohui Qin, Nicholas Rafaels, Laura Raffield, Mahitha Rajendran, Vasan S. Ramachandran, D.C. Rao, Laura Rasmussen-Torvik, Aakrosh Ratan, Susan Redline, Robert Reed, Catherine Reeves, Elizabeth Regan, Alex Reiner, Muagututi’a Sefuiva Reupena, Ken Rice, Stephen Rich, Rebecca Robillard, Nicolas Robine, Dan Roden, Carolina Roselli, Jerome Rotter, Ingo Ruczinski, Alexi Runnels, Pamela Russell, Sarah Ruuska, Kathleen Ryan, Ester Cerdeira Sabino, Danish Saleheen, Shabnam Salimi, Sejal Salvi, Steven Salzberg, Kevin Sandow, Vijay G. Sankaran, Jireh Santibanez, Karen Schwander, David Schwartz, Frank Sciurba, Christine Seidman, Jonathan Seidman, Frédéric Sériès, Vivien Sheehan, Stephanie L. Sherman, Amol Shetty, Aniket Shetty, Wayne Hui-Heng Sheu, M. Benjamin Shoemaker, Brian Silver, Edwin Silverman, Robert Skomro, Albert Vernon Smith, Jennifer Smith, Josh Smith, Nicholas Smith, Tanja Smith, Sylvia Smoller, Beverly Snively, Michael Snyder, Tamar Sofer, Nona Sotoodehnia, Adrienne M. Stilp, Garrett Storm, Elizabeth Streeten, Jessica Lasky Su, Yun Ju Sung, Jody Sylvia, Adam Szpiro, Daniel Taliun, Hua Tang, Margaret Taub, Matthew Taylor, Simeon Taylor, Marilyn Telen, Timothy A. Thornton, Machiko Threlkeld, Lesley Tinker, David Tirschwell, Sarah Tishkoff, Hemant Tiwari, Catherine Tong, Russell Tracy, Michael Tsai, Dhananjay Vaidya, David Van Den Berg, Peter VandeHaar, Scott Vrieze, Tarik Walker, Robert Wallace, Avram Walts, Fei Fei Wang, Heming Wang, Jiongming Wang, Karol Watson, Jennifer Watt, Daniel E. Weeks, Joshua Weinstock, Bruce Weir, Scott T. Weiss, Lu-Chen Weng, Jennifer Wessel, Cristen Willer, Kayleen Williams, L. Keoki Williams, Carla Wilson, James Wilson, Lara Winterkorn, Quenna Wong, Joseph Wu, Huichun Xu, Lisa Yanek, Ivana Yang, Ketian Yu, Seyedeh Maryam Zekavat, Yingze Zhang, Snow Xueyan Zhao, Wei Zhao, Xiaofeng Zhu, Michael Zody, and Sebastian Zoellner
Subjects: Adult, Male, Proteomics, Aging, Whole genome sequence analysis, Proteome, Clinical Sciences, Black People, Disease, Computational biology, race and ethnicity, Cardiorespiratory Medicine and Haematology, Cardiovascular, Article, proteomics, cardiovascular disease, Physiology (medical), Genetics, 2.1 Biological and endogenous factors, Humans, Medicine, Aetiology, Lung, Heart Disease - Coronary Heart Disease, and Blood Institute TOPMed (Trans-Omics for Precision Medicine) Consortium†, business.industry, Prevention, Human Genome, National Heart, Genomics, Blood proteins, Genetic architecture, Heart Disease, Good Health and Well Being, Cardiovascular System & Hematology, Cardiovascular Diseases, Public Health and Health Services, Female, Cardiology and Cardiovascular Medicine, business, Biotechnology, Genome-Wide Association Study
Abstract: Background: Plasma proteins are critical mediators of cardiovascular processes and are the targets of many drugs. Previous efforts to characterize the genetic architecture of the plasma proteome have been limited by a focus on individuals of European descent and leveraged genotyping arrays and imputation. Here we describe whole genome sequence analysis of the plasma proteome in individuals with greater African ancestry, increasing our power to identify novel genetic determinants. Methods: Proteomic profiling of 1301 proteins was performed in 1852 Black adults from the Jackson Heart Study using aptamer-based proteomics (SomaScan). Whole genome sequencing association analysis was ascertained for all variants with minor allele count ≥5. Results were validated using an alternative, antibody-based, proteomic platform (Olink) as well as replicated in the Multi-Ethnic Study of Atherosclerosis and the HERITAGE Family Study (Health, Risk Factors, Exercise Training and Genetics). Results: We identify 569 genetic associations between 479 proteins and 438 unique genetic regions at a Bonferroni-adjusted significance level of 3.8×10 -11 . These associations include 114 novel locus-protein relationships and an additional 217 novel sentinel variant-protein relationships. Novel cardiovascular findings include new protein associations at the APOE gene locus including ZAP70 (sentinel single nucleotide polymorphism [SNP] rs7412-T, β=0.61±0.05, P =3.27×10 -30 ) and MMP-3 (β=-0.60±0.05, P =1.67×10 -32 ), as well as a completely novel pleiotropic locus at the HPX gene, associated with 9 proteins. Further, the associations suggest new mechanisms of genetically mediated cardiovascular disease linked to African ancestry; we identify a novel association between variants linked to APOL1-associated chronic kidney and heart disease and the protein CKAP2 (rs73885319-G, β=0.34±0.04, P =1.34×10 -17 ) as well as an association between ATTR amyloidosis and RBP4 levels in community-dwelling individuals without heart failure. Conclusions: Taken together, these results provide evidence for the functional importance of variants in non-European populations, and suggest new biological mechanisms for ancestry-specific determinants of lipids, coagulation, and myocardial function.
Published: 2022
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