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Article ; Online: Meta-analysis: clinical features and treatments of lung cancer in combined pulmonary fibrosis and emphysema.

Zhou, Jiayu / Jiang, Yu

Sarcoidosis, vasculitis, and diffuse lung diseases : official journal of WASOG

2023 Volume 40, Issue 4, Page(s) e2023045

Abstract: Background and aim: There are many epidemiological pieces of evidence that show combined pulmonary fibrosis and emphysema (CPFE) patients have an increased risk of lung cancer. We conducted a systematic review of all published data to define the ... ...

Abstract	Background and aim: There are many epidemiological pieces of evidence that show combined pulmonary fibrosis and emphysema (CPFE) patients have an increased risk of lung cancer. We conducted a systematic review of all published data to define the characteristics and treatments of lung cancer that develops in CPFE by performing a meta-analysis. Methods: Databases(including PubMed, Medline, CNKI, VIP, etc.) were searched to find original articles that related to lung cancer in CPFE(CPFE-LC) patients and a meta-analysis was used to analyze the included 15 articles. Stata17.0 software was performed for this meta-analysis. Results: Fifteen original studies that assessed 5933 patients were included in this meta-analysis. In the pooled data, people with CPFE-LC were elderly(70.58 years) and heavy smokers( 0.959, 45.793 pack-years), with a male predominance(0.959). Most lung cancer in CPFE was located in the lower lobe(0.533) and obvious areas of pulmonary fibrosis(0.516). Highest prevalence of cellular subtypes of lung cancer in CPFE was squamous carcinoma(SQCC, 0.437) and chemotherapy was the main treatment(0.387). The mortality rate was 0.720(95%CI: 0.657-0.783) and the 5-year survival rate was 0.250(95%CI: 0.133-0.368). The main cause of death was infection(0.268) and respiratory failure was the main cause of death after surgery(0.392). Conclusions: Lung cancer in CPFE, most commonly SQCC, presents in elderly heavy smokers with a male, located in the lower lobe of the lung and the areas of fibrosis predominance. Chemotherapy is the main treatment and the optimal treatment remains to be explored.
Language	English
Publishing date	2023-12-20
Publishing country	Italy
Document type	Journal Article
ZDB-ID	1339192-6
ISSN	2532-179X ; 1124-0490
ISSN (online)	2532-179X
ISSN	1124-0490
DOI	10.36141/svdld.v40i4.14433
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Article: Study of Dynamic Failure Behavior of a Type of PC/ABS Composite.

Zhou, Jiayu / Xia, Zhaodong / Ma, Dongfang / Wang, Huanran

Materials (Basel, Switzerland)

2024 Volume 17, Issue 8

Abstract: PC/ABS composites are commonly used in airbag covers. In this paper, uniaxial tensile experiments of a PC/ABS composite at different temperatures and strain rates were conducted. The results showed that the temperature and loading rate affect the ... ...

Abstract	PC/ABS composites are commonly used in airbag covers. In this paper, uniaxial tensile experiments of a PC/ABS composite at different temperatures and strain rates were conducted. The results showed that the temperature and loading rate affect the mechanical properties of the PC/ABS composite. As the temperature increases, the yield stress decreases and the strain at the moment of fracture increases, but the strain rate at the same temperature has a relatively small effect on the mechanical properties, which are similar to ductile materials. The experimental results were applied to the Abaqus model which considered thermal effects and the exact Johnson-Cook constitutive parameters were calculated by applying the inverse method. Based on the constitutive model and the failure analysis findings acquired by DIC, the uniaxial tensile test at the room temperature and varied strain rates were simulated and compared to the test results, which accurately reproduced the test process. The experiment on target plate intrusion of the PC/ABS composite was designed, and a finite-element model was established to simulate the experimental process. The results were compared with the experiments, which showed that the constitutive and the failure fracture strains were valid.
Language	English
Publishing date	2024-04-10
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2487261-1
ISSN	1996-1944
ISSN	1996-1944
DOI	10.3390/ma17081728
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Article: Understanding Deep Gradient Leakage via Inversion Influence Functions.

Zhang, Haobo / Hong, Junyuan / Deng, Yuyang / Mahdavi, Mehrdad / Zhou, Jiayu

Advances in neural information processing systems

2024 Volume 36, Page(s) 3921–3944

Abstract: Deep Gradient Leakage (DGL) is a highly effective attack that recovers private training images from gradient vectors. This attack casts significant privacy challenges on distributed learning from clients with sensitive data, where clients are required to ...

Abstract	Deep Gradient Leakage (DGL) is a highly effective attack that recovers private training images from gradient vectors. This attack casts significant privacy challenges on distributed learning from clients with sensitive data, where clients are required to share gradients. Defending against such attacks requires but lacks an understanding of
Language	English
Publishing date	2024-01-25
Publishing country	United States
Document type	Journal Article
ZDB-ID	1012320-9
ISSN	1049-5258
ISSN	1049-5258
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Article ; Online: Resilient and Communication Efficient Learning for Heterogeneous Federated Systems.

Zhu, Zhuangdi / Hong, Junyuan / Drew, Steve / Zhou, Jiayu

Proceedings of machine learning research

2023 Volume 162, Page(s) 27504–27526

Abstract: The rise of Federated Learning (FL) is bringing machine learning to edge computing by utilizing data scattered across edge devices. However, the heterogeneity of edge network topologies and the uncertainty of wireless transmission are two major ... ...

Abstract	The rise of Federated Learning (FL) is bringing machine learning to edge computing by utilizing data scattered across edge devices. However, the heterogeneity of edge network topologies and the uncertainty of wireless transmission are two major obstructions of FL's wide application in edge computing, leading to prohibitive convergence time and high communication cost. In this work, we propose an FL scheme to address both challenges simultaneously. Specifically, we enable edge devices to learn self-distilled neural networks that are readily prunable to arbitrary sizes, which capture the knowledge of the learning domain in a nested and progressive manner. Not only does our approach tackle system heterogeneity by serving edge devices with varying model architectures, but it also alleviates the issue of connection uncertainty by allowing transmitting part of the model parameters under faulty network connections, without wasting the contributing knowledge of the transmitted parameters. Extensive empirical studies show that under system heterogeneity and network instability, our approach demonstrates significant resilience and higher communication efficiency compared to the state-of-the-art.
Language	English
Publishing date	2023-03-25
Publishing country	United States
Document type	Journal Article
ISSN	2640-3498
ISSN (online)	2640-3498
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Dynamic Augmentation Data Selection for Few-shot Text Classification.

Liu, Guangliang / Yuan, Owen / Jin, Lifeng / Zhou, Jiayu

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing

2023 Volume 2022, Page(s) 4870–4881

Abstract: Data augmentation has been a popular method for fine-tuning pre-trained language models to increase model robustness and performance. With augmentation data coming from modifying gold train data (in-sample augmentation) or being harvested from general ... ...

Abstract	Data augmentation has been a popular method for fine-tuning pre-trained language models to increase model robustness and performance. With augmentation data coming from modifying gold train data (in-sample augmentation) or being harvested from general domain unlabeled data (out-of-sample augmentation), the quality of such data is the key to successful fine-tuning. In this paper, we propose a dynamic data selection method to select effective augmentation data from different augmentation sources according to the model's learning stage, by identifying a set of augmentation samples that optimally facilitates the learning process of the most current model. The method firstly filters out augmentation samples with noisy pseudo labels through a curriculum learning strategy, then estimates the effectiveness of reserved augmentation data by its influence scores on the current model at every update, allowing the data selection process tightly tailored to model parameters. And the two-stage augmentation strategy considers in-sample augmentation and out-of-sample augmentation in different learning stages. Experiments with both kinds of augmentation data on a variety of sentence classification tasks show that our method outperforms strong baselines, proving the effectiveness of our method. Analysis confirms the dynamic nature of the data effectiveness and the importance of model learning stages in utilization of augmentation data.
Language	English
Publishing date	2023-04-14
Publishing country	United States
Document type	Journal Article
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Article ; Online: How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts.

Wang, Haotao / Hong, Junyuan / Zhou, Jiayu / Wang, Zhangyang

Transactions on machine learning research

2023 Volume 2023

Abstract: Increasing concerns have been raised on deep learning fairness in recent years. Existing fairness-aware machine learning methods mainly focus on the fairness of in-distribution data. However, in real-world applications, it is common to have distribution ... ...

Abstract	Increasing concerns have been raised on deep learning fairness in recent years. Existing fairness-aware machine learning methods mainly focus on the fairness of in-distribution data. However, in real-world applications, it is common to have distribution shift between the training and test data. In this paper, we first show that the fairness achieved by existing methods can be easily broken by slight distribution shifts. To solve this problem, we propose a novel fairness learning method termed CUrvature MAtching (CUMA), which can achieve robust fairness generalizable to unseen domains with unknown distributional shifts. Specifically, CUMA enforces the model to have similar generalization ability on the majority and minority groups, by matching the loss curvature distributions of the two groups. We evaluate our method on three popular fairness datasets. Compared with existing methods, CUMA achieves superior fairness under unseen distribution shifts, without sacrificing either the overall accuracy or the in-distribution fairness.
Language	English
Publishing date	2023-03-13
Publishing country	United States
Document type	Journal Article
ISSN	2835-8856
ISSN (online)	2835-8856
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning.

Hong, Junyuan / Wang, Haotao / Wang, Zhangyang / Zhou, Jiayu

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence

2023 Volume 37, Issue 7, Page(s) 7893–7901

Abstract: Federated learning (FL) emerges as a popular distributed learning schema that learns a model from a set of participating users without sharing raw data. One major challenge of FL comes with heterogeneous users, who may have distributionally different (or ...

Abstract	Federated learning (FL) emerges as a popular distributed learning schema that learns a model from a set of participating users without sharing raw data. One major challenge of FL comes with heterogeneous users, who may have distributionally different (or non-iid) data and varying computation resources. As federated users would use the model for prediction, they often demand the trained model to be robust against malicious attackers at test time. Whereas adversarial training (AT) provides a sound solution for centralized learning, extending its usage for federated users has imposed significant challenges, as many users may have very limited training data and tight computational budgets, to afford the data-hungry and costly AT. In this paper, we study a novel FL strategy: propagating adversarial robustness from rich-resource users that can afford AT, to those with poor resources that cannot afford it, during federated learning. We show that existing FL techniques cannot be effectively integrated with the strategy to propagate robustness among non-iid users and propose an efficient propagation approach by the proper use of batch-normalization. We demonstrate the rationality and effectiveness of our method through extensive experiments. Especially, the proposed method is shown to grant federated models remarkable robustness even when only a small portion of users afford AT during learning. Source code will be released.
Language	English
Publishing date	2023-06-26
Publishing country	United States
Document type	Journal Article
ISSN	2374-3468
ISSN (online)	2374-3468
DOI	10.1609/aaai.v37i7.25955
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation.

Rajendran, Suraj / Pan, Weishen / Sabuncu, Mert R / Chen, Yong / Zhou, Jiayu / Wang, Fei

Patterns (New York, N.Y.)

2024 Volume 5, Issue 2, Page(s) 100913

Abstract: In healthcare, machine learning (ML) shows significant potential to augment patient care, improve population health, and streamline healthcare workflows. Realizing its full potential is, however, often hampered by concerns about data privacy, diversity ... ...

Abstract	In healthcare, machine learning (ML) shows significant potential to augment patient care, improve population health, and streamline healthcare workflows. Realizing its full potential is, however, often hampered by concerns about data privacy, diversity in data sources, and suboptimal utilization of different data modalities. This review studies the utility of cross-cohort cross-category (C
Language	English
Publishing date	2024-01-17
Publishing country	United States
Document type	Journal Article ; Review
ISSN	2666-3899
ISSN (online)	2666-3899
DOI	10.1016/j.patter.2023.100913
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Article ; Online: Effect of Placental Transfusion on Long-Term Neurodevelopmental Outcomes in Premature Infants: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Wang, Zi-Ming / Zhou, Jia-Yu / Tang, Wan / Jiang, Ying-Ying / Wang, Rui / Wang, Lai-Shuan

Pediatric neurology

2024 Volume 154, Page(s) 20–25

Abstract: Background: The pathophysiology and the potential risks of placental transfusion (PT) differ substantially in preterm infants, necessitating specific studies in this population. This study aimed to evaluate the safety and efficacy of PT in preterm ... ...

Abstract	Background: The pathophysiology and the potential risks of placental transfusion (PT) differ substantially in preterm infants, necessitating specific studies in this population. This study aimed to evaluate the safety and efficacy of PT in preterm infants from the perspective of long-term neurodevelopmental outcomes. Methods: We conducted a systematic literature search using placental transfusion, preterm infant, and its synonyms as search terms. Cochrane Central Register of Controlled Trials, Medline, and Embase were searched until March 07, 2023. Two reviewers independently identified, extracted relevant randomized controlled trials, and appraised the risk of bias. The extracted studies were included in the meta-analysis of long-term neurodevelopmental clinical outcomes using fixed-effects models. Results: A total of 5612 articles were identified, and seven randomized controlled trials involving 2551 infants were included in our meta-analysis. Compared with immediate cord clamping (ICC), PT may not impact adverse neurodevelopment events. No clear evidence was found of a difference in the risk of neurodevelopmental impairment (risk ratio [RR]: 0.89, 95% confidence interval [CI]: 0.76 to 1.03, P = 0.13, I Conclusions: From the perspective of long-term neurodevelopment, PT at preterm birth may be as safe as ICC. Future studies should focus on standardized, high-quality clinical trials and individual participant data to optimize cord management strategies for preterm infants after birth.
MeSH term(s)	Infant ; Infant, Newborn ; Humans ; Female ; Pregnancy ; Infant, Premature ; Umbilical Cord Clamping ; Premature Birth ; Placenta ; Randomized Controlled Trials as Topic
Language	English
Publishing date	2024-02-01
Publishing country	United States
Document type	Meta-Analysis ; Systematic Review ; Journal Article
ZDB-ID	639164-3
ISSN	1873-5150 ; 0887-8994
ISSN (online)	1873-5150
ISSN	0887-8994
DOI	10.1016/j.pediatrneurol.2024.01.018
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Article ; Online: Development and validation of a nomogram prediction model for ADHD in children based on individual, family, and social factors.

Gao, Ting / Yang, Lan / Zhou, Jiayu / Zhang, Yu / Wang, Laishuan / Wang, Yan / Wang, Tianwei

Journal of affective disorders

2024 Volume 356, Page(s) 483–491

Abstract: Objectives: A reliable, user-friendly, and multidimensional prediction tool can help to identify children at high risk for ADHD and facilitate early recognition and family management of ADHD. We aimed to develop and validate a risk nomogram for ADHD in ... ...

Abstract	Objectives: A reliable, user-friendly, and multidimensional prediction tool can help to identify children at high risk for ADHD and facilitate early recognition and family management of ADHD. We aimed to develop and validate a risk nomogram for ADHD in children aged 3-17 years in the United States based on clinical manifestations and complex environments. Methods: A total of 141,356 cases were collected for the prediction model. Another 54,444 cases from a new data set were utilized for performing independent external validation. The LASSO regression was used to control possible variables. A final risk nomogram for ADHD was established based on logistic regression, and the discrimination and calibration of the established nomogram were evaluated by bootstrapping with 1000 resamples. Results: A final risk nomogram for ADHD was established based on 13 independent predictors, including behavioral problems, learning disabilities, age, intellectual disabilities, anxiety symptoms, gender, premature birth, maternal age at childbirth, parent-child interaction patterns, etc. The C-index of this model was 0.887 in the training set, and 0.862 in the validation set. Internal and external validation proved that the model was reliable. Conclusions: A nomogram, a statistical prediction tool that assesses individualized ADHD risk for children is helpful for the early identification of children at high risk for ADHD and the construction of a conceptual model of society-family-school collaborative diagnosis, treatment, and management of ADHD.
MeSH term(s)	Humans ; Attention Deficit Disorder with Hyperactivity/diagnosis ; Attention Deficit Disorder with Hyperactivity/epidemiology ; Child ; Female ; Male ; Nomograms ; Adolescent ; Child, Preschool ; Risk Factors ; Reproducibility of Results ; United States ; Logistic Models ; Risk Assessment/statistics & numerical data
Language	English
Publishing date	2024-04-18
Publishing country	Netherlands
Document type	Journal Article ; Validation Study ; Research Support, Non-U.S. Gov't
ZDB-ID	135449-8
ISSN	1573-2517 ; 0165-0327
ISSN (online)	1573-2517
ISSN	0165-0327
DOI	10.1016/j.jad.2024.04.069
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