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1. The Villages at 995 East Santa Clara St, San Jose: Energy & Emission Report

Author

Duarte Roa, Carlos, Raftery, Paul, Webster, Brett, Hu, Xinxin, and Ackerly, Katie

Subjects
Energy efficiency, affordable housing, whole building energy simulation, demand response, resilience
Abstract

Our study uses EnergyPlus simulations to examine whole-building demand and energy end-use profiles for different design options and then uses these outputs to evaluate cost and carbon impacts of each scenario in Xendee, a modeling platform designed to “right size” and balance investments in distributed energy resources (DER). Our results show that efficiency measures are key to meet the ambitious performance metrics for this project; however, most of the technology potential occurs for heating, ventilation and air conditioning (HVAC) or domestic hot water (DHW) loads which are a relatively small portion of a mid-rise multifamily building’s overall energy use. The most meaningful strategies to reduce or shift loads for this building include DHW load shifting, energy recovery ventilation, dynamic ventilation, and ceiling fans. Envelope strategies improve overall annual building performance but become an issue when lower heat loss increases cooling during the critical afternoon peak. Compared to efficient, packaged air source heat pumps, a hydronic heating and cooling system (also serving DHW loads) with thermal energy storage has the best energy performance, highest load shifting capability, and best thermal resilience during outages. But because heating and cooling demands are small and hydronic systems are expensive, the net benefits of thermal energy storage are not substantial. Sizing on-site generation and storage systems to cover the “worst case” outage conditions significantly drives up system size and cost. Even small deviations from 100% coverage (95%, or 99%) can dramatically reduce size and cost without a very meaningful change in resilience.

Published
2024

2. Harmonized Resilience at Roosevelt Village: How Futuristic Grid-Interactivity and Resilience Come Together in Senior Affordable Housing

Author

Webster, Brett, Hu, Xinxin, Ackerly, Katie, Merkel, GG, Raftery, Paul, Duarte Roa, Carlos, Heinzerling, David, and Johnson, David

Subjects
Affordable housing, resilience, grid-interactive, demand response
Abstract

To decarbonize the buildings and electricity sectors at a pace consistent with state and national climate goals, buildings must become grid resources, capable of morphing load profiles to accommodate variable renewable generation, facilitate cost-effective grid decarbonization, and help ensure reliable power system operation. Simultaneously, the changing climate poses increasing risks of extreme weather events and resulting power outages that developers, designers, and building operators must plan for. This yield s new questions around the best approaches to achieve these outcomes, what it costs, who benefits, and what barriers there are to widespread adoption.We seek to answer these questions by adopting a futuristic set of grid-interactive design requirements for a mid-rise affordable housing, mixed-use development: to sustain critical loads during an outage and to consume no grid electricity for residential end-uses between 4-9pm every day. We present an approach that achieves these requirements, working to decarbonize the grid and maximize resilience for the facility and its residents while also addressing shortfalls in housing supply. Our work includes detailed building energy and economic optimization combined with construction cost analysis and carbon impacts. We find that achieving the requirements comes at a ~5% cost premium, an amount not likely to be acceptable for affordable housing under current policies and industry standards. We then generalize our design into a replicable ‘recipe’ to optimize the integrated use of distributed energy resources and explore the challenges to widespread adoption. We suggest many barriers could be overcome with policy changes that would bring benefits to under-resourced populations and society as a whole.

Published
2024

7. GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection

Author

Hu, Xinxin, Chen, Haotian, Zhang, Junjie, Chen, Hongchang, Liu, Shuxin, Li, Xing, Wang, Yahui, and Xue, Xiangyang

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Along with the rapid evolution of mobile communication technologies, such as 5G, there has been a drastically increase in telecom fraud, which significantly dissipates individual fortune and social wealth. In recent years, graph mining techniques are gradually becoming a mainstream solution for detecting telecom fraud. However, the graph imbalance problem, caused by the Pareto principle, brings severe challenges to graph data mining. This is a new and challenging problem, but little previous work has been noticed. In this paper, we propose a Graph ATtention network with COst-sensitive BOosting (GAT-COBO) for the graph imbalance problem. First, we design a GAT-based base classifier to learn the embeddings of all nodes in the graph. Then, we feed the embeddings into a well-designed cost-sensitive learner for imbalanced learning. Next, we update the weights according to the misclassification cost to make the model focus more on the minority class. Finally, we sum the node embeddings obtained by multiple cost-sensitive learners to obtain a comprehensive node representation, which is used for the downstream anomaly detection task. Extensive experiments on two real-world telecom fraud detection datasets demonstrate that our proposed method is effective for the graph imbalance problem, outperforming the state-of-the-art GNNs and GNN-based fraud detectors. In addition, our model is also helpful for solving the widespread over-smoothing problem in GNNs. The GAT-COBO code and datasets are available at https://github.com/xxhu94/GAT-COBO.

Published
2023

8. Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection

Author

Hu, Xinxin, Chen, Haotian, Chen, Hongchang, Liu, Shuxin, Li, Xing, Zhang, Shibo, Wang, Yahui, and Xue, Xiangyang

Subjects
Computer Science - Social and Information Networks, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

With the rapid development of mobile networks, the people's social contacts have been considerably facilitated. However, the rise of mobile social network fraud upon those networks, has caused a great deal of distress, in case of depleting personal and social wealth, then potentially doing significant economic harm. To detect fraudulent users, call detail record (CDR) data, which portrays the social behavior of users in mobile networks, has been widely utilized. But the imbalance problem in the aforementioned data, which could severely hinder the effectiveness of fraud detectors based on graph neural networks(GNN), has hardly been addressed in previous work. In this paper, we are going to present a novel Cost-Sensitive Graph Neural Network (CSGNN) by creatively combining cost-sensitive learning and graph neural networks. We conduct extensive experiments on two open-source realworld mobile network fraud datasets. The results show that CSGNN can effectively solve the graph imbalance problem and then achieve better detection performance than the state-of-the-art algorithms. We believe that our research can be applied to solve the graph imbalance problems in other fields. The CSGNN code and datasets are publicly available at https://github.com/xxhu94/CSGNN.

Published
2023

9. Will a single two-level atom simultaneously scatter two photons?

Author

Masters, Luke, Hu, Xinxin, Cordier, Martin, Maron, Gabriele, Pache, Lucas, Rauschenbeutel, Arno, Schemmer, Max, and Volz, Jürgen

Subjects
Quantum Physics
Abstract

The interaction of light with a single two-level emitter is the most fundamental process in quantum optics, and is key to many quantum applications. As a distinctive feature, two photons are never detected simultaneously in the light scattered by the emitter. This is commonly interpreted by saying that a single two-level quantum emitter can only absorb and emit single photons. However, it has been theoretically proposed that the photon anti-correlations can be thought to arise from quantum interference between two possible two-photon scattering amplitudes, which one refers to as coherent and incoherent. This picture is in stark contrast to the aforementioned one, in that it assumes that the atom even has two different mechanisms at its disposal to scatter two photons at the same time. Here, we validate the interference picture by experimentally verifying the 40-year-old conjecture that, by spectrally rejecting only the coherent component of the fluorescence light of a single two-level atom, the remaining light consists of photon pairs that have been simultaneously scattered by the atom. Our results offer fundamental insights into the quantum-mechanical interaction between light and matter and open up novel approaches for the generation of highly non-classical light fields., Comment: 9 pages, 7 figures, updated Methods section

Published
2022

10. MDIINet: A Few-Shot Semantic Segmentation Network by Exploiting Multi-dimensional Information Interaction

Author

Chen, Hao, Qiu, XinTao, Zhou, Xiaogen, Wang, Tao, Hu, Xinxin, Zhang, Qing, Liu, Hongjun, Wen, Yongyao, Zhang, Pei, Zhang, Xinlin, Tong, Tong, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Si, Zhanjun, editor, and Guo, Jiayang, editor

Published
2024
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11. Insider Threat Defense Strategies: Survey and Knowledge Integration

Author

Song, Chengyu, Zhang, Jingjing, Ma, Linru, Hu, Xinxin, Zheng, Jianming, Yang, Lin, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cao, Cungeng, editor, Chen, Huajun, editor, Zhao, Liang, editor, Arshad, Junaid, editor, Asyhari, Taufiq, editor, and Wang, Yonghao, editor

Published
2024
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13. CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers

Author

Zhang, Jiaming, Liu, Huayao, Yang, Kailun, Hu, Xinxin, Liu, Ruiping, and Stiefelhagen, Rainer

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Scene understanding based on image segmentation is a crucial component of autonomous vehicles. Pixel-wise semantic segmentation of RGB images can be advanced by exploiting complementary features from the supplementary modality (X-modality). However, covering a wide variety of sensors with a modality-agnostic model remains an unresolved problem due to variations in sensor characteristics among different modalities. Unlike previous modality-specific methods, in this work, we propose a unified fusion framework, CMX, for RGB-X semantic segmentation. To generalize well across different modalities, that often include supplements as well as uncertainties, a unified cross-modal interaction is crucial for modality fusion. Specifically, we design a Cross-Modal Feature Rectification Module (CM-FRM) to calibrate bi-modal features by leveraging the features from one modality to rectify the features of the other modality. With rectified feature pairs, we deploy a Feature Fusion Module (FFM) to perform sufficient exchange of long-range contexts before mixing. To verify CMX, for the first time, we unify five modalities complementary to RGB, i.e., depth, thermal, polarization, event, and LiDAR. Extensive experiments show that CMX generalizes well to diverse multi-modal fusion, achieving state-of-the-art performances on five RGB-Depth benchmarks, as well as RGB-Thermal, RGB-Polarization, and RGB-LiDAR datasets. Besides, to investigate the generalizability to dense-sparse data fusion, we establish an RGB-Event semantic segmentation benchmark based on the EventScape dataset, on which CMX sets the new state-of-the-art. The source code of CMX is publicly available at https://github.com/huaaaliu/RGBX_Semantic_Segmentation., Comment: Accepted to IEEE Transactions on Intelligent Transportation Systems (T-ITS). The source code of CMX is publicly available at https://github.com/huaaaliu/RGBX_Semantic_Segmentation

Published
2022

16. The relationship between negative life events and mental health of overseas Chinese left-behind children: A moderated mediation model

Author

Liao, Chuanjing, Zhang, Ke, Lin, Yingying, Han, Li, and Hu, Xinxin

Subjects
Overseas Chinese -- Psychological aspects -- Social aspects -- Health aspects, Life change events -- Research, Children -- Social aspects -- Psychological aspects -- Health aspects, Social networks -- Psychological aspects -- Health aspects, Psychology and mental health
Abstract

To examine the factors influencing the mental health and sociality of overseas Chinese left-behind children, and to identify the protective mechanisms of their mental health, the current study investigated 568 overseas Chinese left-behind children. The age distribution of the sample ranges from 10 to 16 years, with an average age of 13.51 years and a standard deviation of 1.11. Using the Adolescent Self-Rating Life Events Checklist, Perceived Social Support Scale, General Self-Efficacy Scale and Mental Health Inventory of Middle-school Students, results showed that: (1) Perceived social support played a moderate mediate role in the relationship between negative life events and mental health. (2) The effect of negative life events on perceived social support was moderated by general self-efficacy. Negative life events of overseas Chinese left-behind children with higher self-efficacy were more likely to experience negative mental health. Conclusion: Perceived social support plays a moderating mediating role in the relationship between negative life events and mental health of overseas Chinese left-behind children., Author(s): Chuanjing Liao [sup.1] , Ke Zhang [sup.1] , Yingying Lin [sup.2] , Li Han [sup.3] , Xinxin Hu [sup.4] Author Affiliations: (1) https://ror.org/020hxh324, grid.412899.f, 0000 0000 9117 1462, School [...]

Published
2023
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19. Capturing Omni-Range Context for Omnidirectional Segmentation

Author

Yang, Kailun, Zhang, Jiaming, Reiß, Simon, Hu, Xinxin, and Stiefelhagen, Rainer

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Convolutional Networks (ConvNets) excel at semantic segmentation and have become a vital component for perception in autonomous driving. Enabling an all-encompassing view of street-scenes, omnidirectional cameras present themselves as a perfect fit in such systems. Most segmentation models for parsing urban environments operate on common, narrow Field of View (FoV) images. Transferring these models from the domain they were designed for to 360-degree perception, their performance drops dramatically, e.g., by an absolute 30.0% (mIoU) on established test-beds. To bridge the gap in terms of FoV and structural distribution between the imaging domains, we introduce Efficient Concurrent Attention Networks (ECANets), directly capturing the inherent long-range dependencies in omnidirectional imagery. In addition to the learned attention-based contextual priors that can stretch across 360-degree images, we upgrade model training by leveraging multi-source and omni-supervised learning, taking advantage of both: Densely labeled and unlabeled data originating from multiple datasets. To foster progress in panoramic image segmentation, we put forward and extensively evaluate models on Wild PAnoramic Semantic Segmentation (WildPASS), a dataset designed to capture diverse scenes from all around the globe. Our novel model, training regimen and multi-source prediction fusion elevate the performance (mIoU) to new state-of-the-art results on the public PASS (60.2%) and the fresh WildPASS (69.0%) benchmarks., Comment: Accepted to CVPR2021

Published
2021

21. Real-time Fusion Network for RGB-D Semantic Segmentation Incorporating Unexpected Obstacle Detection for Road-driving Images

Author

Sun, Lei, Yang, Kailun, Hu, Xinxin, Hu, Weijian, and Wang, Kaiwei

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Semantic segmentation has made striking progress due to the success of deep convolutional neural networks. Considering the demands of autonomous driving, real-time semantic segmentation has become a research hotspot these years. However, few real-time RGB-D fusion semantic segmentation studies are carried out despite readily accessible depth information nowadays. In this paper, we propose a real-time fusion semantic segmentation network termed RFNet that effectively exploits complementary cross-modal information. Building on an efficient network architecture, RFNet is capable of running swiftly, which satisfies autonomous vehicles applications. Multi-dataset training is leveraged to incorporate unexpected small obstacle detection, enriching the recognizable classes required to face unforeseen hazards in the real world. A comprehensive set of experiments demonstrates the effectiveness of our framework. On Cityscapes, Our method outperforms previous state-of-the-art semantic segmenters, with excellent accuracy and 22Hz inference speed at the full 2048x1024 resolution, outperforming most existing RGB-D networks., Comment: Accepted by IEEE Robotics and Automation Letters (RA-L); 8 Figures, 3 Tables; Code is available at https://github.com/AHupuJR/RFNet

Published
2020

24. DS-PASS: Detail-Sensitive Panoramic Annular Semantic Segmentation through SwaftNet for Surrounding Sensing

Author

Yang, Kailun, Hu, Xinxin, Chen, Hao, Xiang, Kaite, Wang, Kaiwei, and Stiefelhagen, Rainer

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics, Electrical Engineering and Systems Science - Image and Video Processing, Electrical Engineering and Systems Science - Signal Processing
Abstract

Semantically interpreting the traffic scene is crucial for autonomous transportation and robotics systems. However, state-of-the-art semantic segmentation pipelines are dominantly designed to work with pinhole cameras and train with narrow Field-of-View (FoV) images. In this sense, the perception capacity is severely limited to offer higher-level confidence for upstream navigation tasks. In this paper, we propose a network adaptation framework to achieve Panoramic Annular Semantic Segmentation (PASS), which allows to re-use conventional pinhole-view image datasets, enabling modern segmentation networks to comfortably adapt to panoramic images. Specifically, we adapt our proposed SwaftNet to enhance the sensitivity to details by implementing attention-based lateral connections between the detail-critical encoder layers and the context-critical decoder layers. We benchmark the performance of efficient segmenters on panoramic segmentation with our extended PASS dataset, demonstrating that the proposed real-time SwaftNet outperforms state-of-the-art efficient networks. Furthermore, we assess real-world performance when deploying the Detail-Sensitive PASS (DS-PASS) system on a mobile robot and an instrumented vehicle, as well as the benefit of panoramic semantics for visual odometry, showing the robustness and potential to support diverse navigational applications., Comment: 8 pages, 10 figures

Published
2019

25. ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation

Author

Hu, Xinxin, Yang, Kailun, Fei, Lei, and Wang, Kaiwei

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Compared to RGB semantic segmentation, RGBD semantic segmentation can achieve better performance by taking depth information into consideration. However, it is still problematic for contemporary segmenters to effectively exploit RGBD information since the feature distributions of RGB and depth (D) images vary significantly in different scenes. In this paper, we propose an Attention Complementary Network (ACNet) that selectively gathers features from RGB and depth branches. The main contributions lie in the Attention Complementary Module (ACM) and the architecture with three parallel branches. More precisely, ACM is a channel attention-based module that extracts weighted features from RGB and depth branches. The architecture preserves the inference of the original RGB and depth branches, and enables the fusion branch at the same time. Based on the above structures, ACNet is capable of exploiting more high-quality features from different channels. We evaluate our model on SUN-RGBD and NYUDv2 datasets, and prove that our model outperforms state-of-the-art methods. In particular, a mIoU score of 48.3\% on NYUDv2 test set is achieved with ResNet50. We will release our source code based on PyTorch and the trained segmentation model at https://github.com/anheidelonghu/ACNet., Comment: Accepted to be published in 2019 IEEE International Conference on Image Processing (ICIP 2019), Sep 22-25, 2019, Taipei. IEEE Copyright notice added, 5 pages, 5 figures

Published
2019

33. Daphnetin: A Novel Anti-Helicobacter pylori Agent.

Author

Wang, Genzhu, Pang, Jing, Hu, Xinxin, Nie, Tongying, Lu, Xi, Li, Xue, Wang, Xiukun, Lu, Yun, Yang, Xinyi, Jiang, Jiandong, Li, Congran, Xiong, Yan Q, and You, Xuefu

Subjects
Cell Line, Transformed, Cell Membrane, Epithelial Cells, Humans, Helicobacter pylori, DNA Damage, Clarithromycin, Metronidazole, Umbelliferones, Bacterial Proteins, Anti-Bacterial Agents, Microbial Sensitivity Tests, Bacterial Adhesion, Cell Death, Gene Expression Regulation, Bacterial, colonization, daphnetin, mechanism of action, Cell Line, Transformed, Gene Expression Regulation, Bacterial, Other Chemical Sciences, Genetics, Other Biological Sciences, Chemical Physics
Abstract

BackgroundAntibiotic-resistant H. pylori was increasingly found in infected individuals, which resulted in treatment failure and required alternative therapeutic strategies. Daphnetin, a coumarin-derivative compound, has multiple pharmacological activities.MethodsThe mechanism of daphnetin on H. pylori was investigated focusing on its effect on cell morphologies, transcription of genes related to virulence, adhesion, and cytotoxicity to human gastric epithelial (GES-1) cell line.ResultsDaphnetin showed good activities against multidrug resistant (MDR) H. pylori clinical isolates, with minimal inhibitory concentration (MIC) values ranging from 25 to 100 μg/mL. In addition, daphnetin exposure resulted in H. pylori morphological changes. Moreover, daphnetin caused increased translocation of phosphatidylserine (PS), DNA damage, and recA expression, and RecA protein production vs. control group. Of great importance, daphnetin significantly decreased H. pylori adhesion to GES-1 cell line vs. control group, which may be related to the reduced expression of colonization related genes (e.g., babA and ureI).ConclusionsThese results suggested that daphnetin has good activity against MDR H. pylori. The mechanism(s) of daphnetin against H. pylori were related to change of membrane structure, increase of DNA damage and PS translocation, and decrease of H. pylori attachment to GES-1 cells.

Published
2019

38. Development and characterisation of [18F]TTDP, a novel T cell immunoglobulin and ITIM domain tracer, in humanised mice and non-human primates.

Author

Wang, Jing, Hu, Xinxin, Wang, Yueqi, A, Rong, Li, Xiaoqian, Sun, Ying, Guan, Zhengqi, Li, Xiaona, Wu, Yongyi, Wang, Jiannan, Zhao, Fangyu, Liu, Yang, Wang, Hongbin, Yu, Hong, Wang, Tianyi, Zhu, Mengyuan, Li, Xinyu, Zhang, Duoyi, Chen, Wei, and Han, Zhaoguo

Subjects
POSITRON emission tomography, NON-small-cell lung carcinoma, MEDICAL sciences, TREATMENT effectiveness, TUMOR-infiltrating immune cells, T cells
Abstract

Purpose: The T cell immunoglobulin and ITIM domain (TIGIT) blockade immunotherapy response is directly associated with individual differences of TIGIT expression on tumour-infiltrating lymphocytes (TILs) in tumour immune microenvironment (TIME) of non-small cell lung cancer (NSCLC). Here, we developed a TIGIT-targeted PET tracer to evaluate its feasibility in predicting immunotherapy efficacy, aiming to manage NSCLC patients accurately. Methods: We synthesised a 18F-labeled TIGIT-targeted D-peptide, [18F]TTDP, and investigated the specificity of [18F]TTDP both to murine TIGIT and human TIGIT by a series of in vitro and in vivo assays. [18F]TTDP PET imaging was performed in humanised immune system (HIS) mice models bearing NSCLC patient-derived xenografts (PDXs) to evaluate the predictive value of FDA-approved combination immunotherapy of atezolizumab plus tiragolumab. Lastly, rhesus macaque was applied for [18F] TTDP PET to explore the tracer's in vivo distribution and translational potential in non-human primates. Results: [18F]TTDP showed high specificity for both murine TIGIT and human TIGIT in vitro and in vivo. The HIS NSCLC PDX platform was successfully established for [18F]TTDP PET imaging, and tumour uptake of [18F]TTDP was significantly correlated with the TIGIT expression of TILs in the TIME. [18F]TTDP PET imaging, in predicting treatment response to the combination immunotherapy in NSCLC HIS-PDX models, showed a sensitivity of 83.33% and a specificity of 100%. In addition, [18F]TTDP PET also showed cross-species consistency of the tracer biodistribution between non-human primate and murine animals, and no adverse events were observed. Conclusion: The combined implementation of the [18F]TTDP and HIS-PDX model creates a state-of-the-art preclinical platform that will impact the identification and validation of TIGIT-targeted PET image-guided diagnosis, treatment response prediction, beneficial patient screening, novel immunotherapies, and ultimately the outcome of NSCLC patients. We first provided in vivo biodistribution of [18F]TTDP PET imaging in rhesus macaque, indicating its excellent translational potential in the clinic. [ABSTRACT FROM AUTHOR]

Published
2025
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39. Coronary artery calcification detected on low‐dose computed tomography in high‐risk participants of an Australian lung cancer screening program: A prospective observational study.

Author

Bonney, Asha, Chua, Michelle, McCusker, Mark W., Pascoe, Diane, Joshi, Subodh B., Steinfort, Daniel, Marshall, Henry, Silver, Jeremy D., Xie, Cheng, Yang, Sally, Watson, Jack, Fogarty, Paul, Stone, Emily, Brims, Fraser, McWilliams, Annette, Hu, XinXin, Rofe, Christopher, Milner, Brad, Lam, Stephen, and Fong, Kwun M.

Subjects
CORONARY artery calcification, CORONARY artery disease, EARLY detection of cancer, MEDICAL screening, CARDIOVASCULAR diseases
Abstract

Background and Objectives: Coronary artery calcification (CAC) is a frequent additional finding on lung cancer screening (LCS) low‐dose computed tomography (LDCT). Cardiovascular disease (CVD) is a major cause of death in LCS participants. We aimed to describe prevalence of incidental CAC detected on LDCT in LCS participants without prior history of coronary artery disease (CAD), evaluate their CVD risk and describe subsequent investigation and management. Methods: Prospective observational nested cohort study including all participants enrolled at a single Australian site of the International Lung Screen Trial. Baseline LDCTs were reviewed for CAC, and subsequent information collected regarding cardiovascular health. 5‐year CVD risk was calculated using the AusCVD risk calculator. Results: 55% (226/408) of participants had CAC on LDCT and no prior history of CAD, including 23% with moderate–severe CAC. Mean age of participants with CAC was 65 years, 68% were male. 53% were currently smoking. Majority were high risk (51%) or intermediate risk (32%) of a cardiovascular event in 5 years. 21% of participants were re‐stratified to a higher CVD risk group when CAC detected on LCS was incorporated. Only 10% of participants with CAC received lifestyle advice (only 3% currently smoking received smoking cessation advice). 80% of participants at high‐risk did not meet guideline recommendations, with 47% of this group remaining without cholesterol lowering therapy. Conclusion: LCS with LDCT offers the potential to identify and communicate CVD risk in this population. This may improve health outcomes for high‐risk LCS participants and further personalize management once screening results are known. Almost 25% of screening participants had moderate–severe CAC detected on LDCT and no known history of coronary artery disease. Most of these participants were at high risk of CVD and were not meeting guideline recommendations for CVD care. LDCT detected CAC may be a useful tool to optimize CVD health. See relatededitorial [ABSTRACT FROM AUTHOR]

Published
2025
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40. The protective role of gamma zone peripapillary atrophy in diabetic retinopathy: insights from deep learning and SS-OCT angiography.

Author

Li, Yingying, Hu, Xinxin, Ye, Xueqiong, Zhong, Qingya, Zhang, Xixi, Zhu, Jinglin, Jiang, Jiahui, Wang, Dandan, Zhang, Juntao, Ren, Weina, Zhao, Yitian, Lu, Qinkang, and Zhao, Na

Subjects
OPTICAL coherence tomography, CHOROID, TYPE 2 diabetes, DIABETIC retinopathy, DIGITAL photography
Abstract

Purpose: To explore the relationship between peripapillary atrophy (PPA) and diabetic retinopathy (DR), and to uncover potential mechanisms using swept-source optical coherence tomography (SS-OCT) angiography. Methods: This cross-sectional study included 845 patients with type 2 diabetes (T2DM), who underwent detailed systemic and ophthalmic evaluations. A state-of-the-art deep learning method was employed to precisely identify the parapapillary beta and gamma zones. Based on PPA characteristics, eyes were categorized into four groups: without beta or gamma zone (Group A), isolated beta zone (Group B), isolated gamma zone (Group C), and with both beta and gamma zone (Group D). Digital fundus photography was utilized to diagnose and stage DR severity, while SS-OCT angiography quantified retinal and choroidal vasculature. Results: Participants had a mean age of 66 ± 8.8 years, with 437 (51.7%) male. Beta and gamma PPA zones were observed in 574 (67.9%) and 256 (30.3%) eyes, respectively. Beta zone PPA was associated with older age, whereas gamma zone PPA was correlated with longer axial length (AL), lower vessel density, and reduced choroidal thickness. Adjusted analyses revealed that eyes with isolated (Group C) or concurrent (Group D) gamma zone PPA conferred significantly lower DR grade, independent of known risk factors including systemic diabetes risk factors and AL. Conclusion: This study finds that gamma zone PPA is linked to a reduced risk of developing DR. These results imply that the gamma zone may reflect progressive myopia-associated thinning and microvascular depletion in posterior ocular tissues, potentially contributing to structural resilience against DR. This novel insight offers a promising avenue for understanding the interplay between PPA and DR. [ABSTRACT FROM AUTHOR]

Published
2024
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43. School-Level Factors Related to Visual Arts Achievement for 4th-Graders: A Longitudinal Analysis

Author

Jiang, Ning, Rogers, Bradley D., Fan, Xumei, Hu, Xinxin, Lewis, Ashlee, and Cai, Bo

Abstract

A longitudinal analysis was used to investigate the relationships between school-level factors (poverty, location, size, ratings, and race/ethnicity) and visual arts achievement. Data were collected on 4th-grade students from 34 elementary schools that received Distinguished Arts Program grants in a southeastern state of the United States from 2011 to 2017. Students' individual multiple-choice visual arts scores were aggregated into school-level scores. The analysis revealed that school-level visual arts scores decreased over time from 2011 to 2017. Visual arts scores appeared to be significantly associated with school ratings, poverty, and race/ethnicity. The findings of the study may inform art educators, school administrators, and policy makers in their program offerings, planning, and targeted interventions around issues of art education.

Published
2021
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