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Article ; Online: FedOSS: Federated Open Set Recognition via Inter-Client Discrepancy and Collaboration.

Zhu, Meilu / Liao, Jing / Liu, Jun / Yuan, Yixuan

2024 Volume 43, Issue 1, Page(s) 190–202

Abstract: Open set recognition (OSR) aims to accurately classify known diseases and recognize unseen diseases as the unknown class in medical scenarios. However, in existing OSR approaches, gathering data from distributed sites to construct large-scale centralized ...

Abstract	Open set recognition (OSR) aims to accurately classify known diseases and recognize unseen diseases as the unknown class in medical scenarios. However, in existing OSR approaches, gathering data from distributed sites to construct large-scale centralized training datasets usually leads to high privacy and security risk, which could be alleviated elegantly via the popular cross-site training paradigm, federated learning (FL). To this end, we represent the first effort to formulate federated open set recognition (FedOSR), and meanwhile propose a novel Federated Open Set Synthesis (FedOSS) framework to address the core challenge of FedOSR: the unavailability of unknown samples for all anticipated clients during the training phase. The proposed FedOSS framework mainly leverages two modules, i.e., Discrete Unknown Sample Synthesis (DUSS) and Federated Open Space Sampling (FOSS), to generate virtual unknown samples for learning decision boundaries between known and unknown classes. Specifically, DUSS exploits inter-client knowledge inconsistency to recognize known samples near decision boundaries and then pushes them beyond decision boundaries to synthesize discrete virtual unknown samples. FOSS unites these generated unknown samples from different clients to estimate the class-conditional distributions of open data space near decision boundaries and further samples open data, thereby improving the diversity of virtual unknown samples. Additionally, we conduct comprehensive ablation experiments to verify the effectiveness of DUSS and FOSS. FedOSS shows superior performance on public medical datasets in comparison with state-of-the-art approaches. The source code is available at https://github.com/CityU-AIM-Group/FedOSS.
MeSH term(s)	Humans ; Software ; Machine Learning ; Disease
Language	English
Publishing date	2024-01-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	622531-7
ISSN	1558-254X ; 0278-0062
ISSN (online)	1558-254X
ISSN	0278-0062
DOI	10.1109/TMI.2023.3294014
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: A Review of Chloride Penetration of Recycled Concrete with Enhancement Treatment and Service Life Prediction.

Wang, Yuanzhan / Liao, Jing / Zhang, Baohua

Materials (Basel, Switzerland)

2024 Volume 17, Issue 6

Abstract: The application of recycled coarse aggregate (RA) in structural concrete can save non-renewable resources and reduce land occupation. Developing comprehensive knowledge of chloride penetration and service life modeling of recycled coarse aggregate ... ...

Abstract	The application of recycled coarse aggregate (RA) in structural concrete can save non-renewable resources and reduce land occupation. Developing comprehensive knowledge of chloride penetration and service life modeling of recycled coarse aggregate concrete (RAC) is a prerequisite for practice. However, compared with the natural aggregate concrete (NAC), the inferior durability performance, especially chloride penetration resistance, of RAC hinders its application in structural concrete. Therefore, many RAC performance enhancement methods have been proposed. This paper presents a holistic review focused on the chloride penetration of RAC with/without enhancement methods and service life prediction. The current RAC performance enhancement methods are introduced. The improvement effect of the corresponding enhancement methods on the chloride penetration resistance of RAC are discussed and analyzed in turn. Based on the reviewed data on the chloride diffusion coefficient, the modification efficiencies of assorted enhancement methods are summarized. With the hope of promoting RAC application in structural concrete, the current literature on chloride-ingress-based service life prediction for RAC is also overviewed. In addition, the typical influencing factors on chloride transport properties are also discussed, i.e., RA quality. It can be concluded that enhancement techniques can effectively improve the chloride penetration resistance of RAC. The old mortar enhancement or removal methods can improve the chloride penetration resistance by 15-30%, depending on the specific treatment measures. The modification efficiency of the modifier material depends on the specific type and content of the incorporated substance, which ranges from approximately 5% to 95%. The estimated service life of RAC structures decreases with the increasing RA replacement ratio. Finally, concluding remarks are provided concerning future research on the chloride transport behavior of RAC.
Language	English
Publishing date	2024-03-15
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2487261-1
ISSN	1996-1944
ISSN	1996-1944
DOI	10.3390/ma17061349
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: 3D Question Answering.

Ye, Shuquan / Chen, Dongdong / Han, Songfang / Liao, Jing

IEEE transactions on visualization and computer graphics

2024 Volume 30, Issue 3, Page(s) 1772–1786

Abstract: Visual question answering (VQA) has experienced tremendous progress in recent years. However, most efforts have only focused on 2D image question-answering tasks. In this article, we extend VQA to its 3D counterpart, 3D question answering (3DQA), which ... ...

Abstract	Visual question answering (VQA) has experienced tremendous progress in recent years. However, most efforts have only focused on 2D image question-answering tasks. In this article, we extend VQA to its 3D counterpart, 3D question answering (3DQA), which can facilitate a machine's perception of 3D real-world scenarios. Unlike 2D image VQA, 3DQA takes the color point cloud as input and requires both appearance and 3D geometrical comprehension to answer the 3D-related questions. To this end, we propose a novel transformer-based 3DQA framework "3DQA-TR", which consists of two encoders to exploit the appearance and geometry information, respectively. Finally, the multi-modal information about the appearance, geometry, and linguistic question can attend to each other via a 3D-linguistic Bert to predict the target answers. To verify the effectiveness of our proposed 3DQA framework, we further develop the first 3DQA dataset "ScanQA", which builds on the ScanNet dataset and contains over 10 K question-answer pairs for 806 scenes. To the best of our knowledge, ScanQA is the first large-scale dataset with natural-language questions and free-form answers in 3D environments that is fully human-annotated. We also use several visualizations and experiments to investigate the astonishing diversity of the collected questions and the significant differences between this task from 2D VQA and 3D captioning. Extensive experiments on this dataset demonstrate the obvious superiority of our proposed 3DQA framework over state-of-the-art VQA frameworks and the effectiveness of our major designs. Our code and dataset will be made publicly available to facilitate research in this direction. The code and data are available at http://shuquanye.com/3DQA_website/.
Language	English
Publishing date	2024-01-30
Publishing country	United States
Document type	Journal Article
ISSN	1941-0506
ISSN (online)	1941-0506
DOI	10.1109/TVCG.2022.3225327
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Impact Exploration of Spatiotemporal Feature Derivation and Selection on Machine Learning-Based Predictive Models for Post-Embolization Cerebral Aneurysm Recanalization.

Liao, Jing / Misaki, Kouichi / Sakamoto, Jiro

Cardiovascular engineering and technology

2024

Abstract: Purpose: To enhance the performance of machine learning (ML) models for the post-embolization recanalization of cerebral aneurysms, we evaluated the impact of hemodynamic feature derivation and selection method on six ML algorithms.: Methods: We ... ...

Abstract	Purpose: To enhance the performance of machine learning (ML) models for the post-embolization recanalization of cerebral aneurysms, we evaluated the impact of hemodynamic feature derivation and selection method on six ML algorithms. Methods: We utilized computational fluid dynamics (CFD) to simulate hemodynamics in 66 cerebral aneurysms from 65 patients, including 57 stable and nine recanalized aneurysms. We derived a total of 107 features for each aneurysm, encompassing four clinical features, 12 morphological features, and 91 hemodynamic features. To investigate the influence of feature derivation and selection methods on the ML models, we employed two derivation methods, simplified and fully derived, in combination with four selection methods: all features, statistically significant analysis, stepwise multivariate logistic regression analysis (stepwise-LR), and recursive feature elimination (RFE). Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC) on both the training and testing datasets. Results: The AUROC values on the testing dataset exhibited a wide-ranging spectrum, spanning from 0.373 to 0.863. Fully derived features and the RFE selection method demonstrated superior performance in intra-model comparisons. The multi-layer perceptron (MLP) model, trained with RFE-selected fully derived features, achieved the best performance on the testing dataset, with an AUROC value of 0.863 (95% CI: 0.684- 1.000). Conclusion: Our study demonstrated the importance of feature derivation and selection in determining the performance of ML models. This enabled the development of accurate decision-making models without the need to invade the patient.
Language	English
Publishing date	2024-05-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	2543111-0
ISSN	1869-4098 ; 1869-408X
ISSN (online)	1869-4098
ISSN	1869-408X
DOI	10.1007/s13239-024-00721-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Preparation, Characterization and Gelation of a Fungal Nano Chitin Derived from

Liao, Jing / Huang, Huihua

Polymers

2022 Volume 14, Issue 3

Abstract: Nano chitin is a promising biocompatible material with wide applications. In this work, a fungal-derived nano chitin was prepared ... ...

Abstract	Nano chitin is a promising biocompatible material with wide applications. In this work, a fungal-derived nano chitin was prepared from
Language	English
Publishing date	2022-01-25
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527146-5
ISSN	2073-4360 ; 2073-4360
ISSN (online)	2073-4360
ISSN	2073-4360
DOI	10.3390/polym14030474
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Social support, social strain and declines in verbal memory: sex-specific associations based on 16-year follow-up of the English Longitudinal Study of Ageing cohort.

Scholes, Shaun / Liao, Jing

Aging & mental health

2022 Volume 27, Issue 4, Page(s) 780–788

Abstract: Objectives: Previous investigations of cognitive aging have mainly focused on structural aspects of social relations (e.g. network size and composition), thereby neglecting the role of qualitative aspects of social relations. The current longitudinal ... ...

Abstract	Objectives: Previous investigations of cognitive aging have mainly focused on structural aspects of social relations (e.g. network size and composition), thereby neglecting the role of qualitative aspects of social relations. The current longitudinal study examined sex-specific differences in verbal memory decline by measures of perceived relationship quality (social support/strain) by relationship type. Method: In the English Longitudinal Study of Ageing (ELSA), 10,109 participants aged 50-89 years were assessed at wave 1 (baseline: 2002-03) and followed to wave 9 (2017-18). Verbal memory was assessed by immediate and delayed word-recall tasks. Social support/strain was measured by relationship type (spouse; children; family; friends). Random effects within-between (REWB) modelling was used to separate between- and within-person effects. We estimated associations between social support/strain and (1) baseline levels of memory (main effects), and (2) rate of decline in memory (interaction with time-since-baseline). Results: Longitudinal associations were most prominent for men, specific to relationship type, and showed between- rather than within-person effects. Among men, higher spousal strain was associated with faster memory decline (β Conclusion: Between-person differences in social support/strain were modestly associated with memory decline, especially among men.
MeSH term(s)	Male ; Female ; Humans ; Longitudinal Studies ; Follow-Up Studies ; Aging/psychology ; Social Support ; Memory Disorders
Language	English
Publishing date	2022-06-23
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1474804-6
ISSN	1364-6915 ; 1360-7863
ISSN (online)	1364-6915
ISSN	1360-7863
DOI	10.1080/13607863.2022.2089628
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Zs.A 5081: Show issues

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Article: Construction of hydrogels based on the chitin from Hericium erinaceus residue: role of molecular weight

Liao, Jing / Huang, Huihua

Cellulose. 2022 Mar., v. 29, no. 4

2022

Abstract: Chitin hydrogels with different molecular weight (Mw) were prepared by the ultrasonic degradation of chitin from Hericium erinaceus residue. The effects of Mw on the properties of the prepared chitin hydrogels were investigated. Results showed that the ... ...

Abstract	Chitin hydrogels with different molecular weight (Mw) were prepared by the ultrasonic degradation of chitin from Hericium erinaceus residue. The effects of Mw on the properties of the prepared chitin hydrogels were investigated. Results showed that the Mw of chitin was decreasing with the prolongation of the ultrasonic time. As the decrease of Mw, the gel strength of the chitin hydrogels weakened, whereas the swelling degree was enhanced. In addition, the formation of chitin hydrogels was through cross-linking the smallest nano-scale solid domains, whose size was not affected by the Mw of chitin, whereas the gel yield of the chitin hydrogels was decreasing as the Mw of chitin decreased. Therefore, decreasing the Mw of chitin reduced the amount of effective chitin that can participate in the cross-linking reaction, thereby reducing the gel yield and the cross-linking density of the chitin hydrogels, resulting in the weakened gel strength but enhanced swelling degree.
Keywords	Hericium ; cellulose ; chitin ; crosslinking ; gel strength ; hydrogels ; molecular weight ; ultrasonics
Language	English
Dates of publication	2022-03
Size	p. 2211-2222.
Publishing place	Springer Netherlands
Document type	Article
ZDB-ID	1496831-9
ISSN	1572-882X ; 0969-0239
ISSN (online)	1572-882X
ISSN	0969-0239
DOI	10.1007/s10570-022-04439-3
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields.

Zhang, Jingbo / Li, Xiaoyu / Wan, Ziyu / Wang, Can / Liao, Jing

IEEE transactions on visualization and computer graphics

2024 Volume PP

Abstract: Text-driven 3D scene generation is widely applicable to video gaming, film industry, and metaverse applications that have a large demand for 3D scenes. However, existing text-to-3D generation methods are limited to producing 3D objects with simple ... ...

Abstract	Text-driven 3D scene generation is widely applicable to video gaming, film industry, and metaverse applications that have a large demand for 3D scenes. However, existing text-to-3D generation methods are limited to producing 3D objects with simple geometries and dreamlike styles that lack realism. In this work, we present Text2NeRF, which is able to generate a wide range of 3D scenes with complicated geometric structures and high-fidelity textures purely from a text prompt. To this end, we adopt NeRF as the 3D representation and leverage a pre-trained text-to-image diffusion model to constrain the 3D reconstruction of the NeRF to reflect the scene description. Specifically, we employ the diffusion model to infer the text-related image as the content prior and use a monocular depth estimation method to offer the geometric prior. Both content and geometric priors are utilized to update the NeRF model. To guarantee textured and geometric consistency between different views, we introduce a progressive scene inpainting and updating strategy for novel view synthesis of the scene. Our method requires no additional training data but only a natural language description of the scene as the input. Extensive experiments demonstrate that our Text2NeRF outperforms existing methods in producing photo-realistic, multi-view consistent, and diverse 3D scenes from a variety of natural language prompts. Our code and model will be available upon acceptance.
Language	English
Publishing date	2024-02-05
Publishing country	United States
Document type	Journal Article
ISSN	1941-0506
ISSN (online)	1941-0506
DOI	10.1109/TVCG.2024.3361502
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Learning A Single Network for Robust Medical Image Segmentation with Noisy Labels.

Ye, Shuquan / Xu, Yan / Chen, Dongdong / Han, Songfang / Liao, Jing

IEEE transactions on medical imaging

2024 Volume PP

Abstract: Robust segmenting with noisy labels is an important problem in medical imaging due to the difficulty of acquiring high-quality annotations. Despite the enormous success of recent developments, these developments still require multiple networks to ... ...

Abstract	Robust segmenting with noisy labels is an important problem in medical imaging due to the difficulty of acquiring high-quality annotations. Despite the enormous success of recent developments, these developments still require multiple networks to construct their frameworks and focus on limited application scenarios, which leads to inflexibility in practical applications. They also do not explicitly consider the coarse boundary label problem, which results in sub-optimal results. To overcome these challenges, we propose a novel Simultaneous Edge Alignment and Memory-Assisted Learning (SEAMAL) framework for noisy-label robust segmentation. It achieves single-network robust learning, which is applicable for both 2D and 3D segmentation, in both Set-HQ-knowable and Set-HQ-agnostic scenarios. Specifically, to achieve single-model noise robustness, we design a Memory-assisted Selection and Correction module (MSC) that utilizes predictive history consistency from the Prediction Memory Bank to distinguish between reliable and non-reliable labels pixel-wisely, and that updates the reliable ones at the superpixel level. To overcome the coarse boundary label problem, which is common in practice, and to better utilize shape-relevant information at the boundary, we propose an Edge Detection Branch (EDB) that explicitly learns the boundary via an edge detection layer with only slight additional computational cost, and we improve the sharpness and precision of the boundary with a thinning loss. Extensive experiments verify that SEAMAL outperforms previous works significantly.
Language	English
Publishing date	2024-04-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	622531-7
ISSN	1558-254X ; 0278-0062
ISSN (online)	1558-254X
ISSN	0278-0062
DOI	10.1109/TMI.2024.3389776
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Article ; Online: Causal relationships between peripheral immune cells and Alzheimer's disease: a two-sample Mendelian randomization study.

Liao, Jing / Zhang, Yongquan / Tang, Zhanhong / Liu, Pinjing / He, Luoyi

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology

2024

Abstract: Objective: Previous research suggests that peripheral immune cells may play a role in the development of Alzheimer's disease (AD). Our study aims to determine if the composition of peripheral immune cells directly contributes to the occurrence of AD.: ...

Abstract	Objective: Previous research suggests that peripheral immune cells may play a role in the development of Alzheimer's disease (AD). Our study aims to determine if the composition of peripheral immune cells directly contributes to the occurrence of AD. Methods: We utilized a two-sample Mendelian randomization (MR) approach to examine the association between peripheral immune cells and AD.The primary analysis method used was the inverse variance weighted (IVW) method, and we also conducted analyses using MR Egger, weighted median, simple mode, and weighted mode methods to ensure the accuracy of the results.Heterogeneity and horizontal pleiotropy were evaluated using Cochran's Q statistics and the MR Egger intercept, respectively. Results: The study found a significant correlation between increased IgD + CD24- AC cells (Odds Ratio [OR] = 1.03, 95% Confidence Interval [CI] = 1.01-1.06, P = 0.0172), increased CD4 + %leukocyte (OR = 1.08, 95% CI = 1.02-1.14, P = 0.0086), and increased CD4 + CD8dim AC cells (OR = 1.06, 95% CI = 1.01-1.11, P = 0.0218), with an increased susceptibility to AD. Conversely, an increase in EM DN (CD4-CD8-) %T cells (OR = 0.95, 95% CI = 0.92-0.99, P = 0.0164) and an increase in DN (CD4-CD8-) AC cells (OR = 0.93, 95% CI = 0.88-0.99, P = 0.0145) were associated with a protective effect against AD. Conclusion: Our findings establish a causal link between peripheral immune cells and AD. This study is the first to examine the relationship between peripheral immune cells and AD using MR, offering valuable insights for early diagnosis and treatment decisions.
Language	English
Publishing date	2024-01-25
Publishing country	Italy
Document type	Journal Article
ZDB-ID	2016546-8
ISSN	1590-3478 ; 1590-1874
ISSN (online)	1590-3478
ISSN	1590-1874
DOI	10.1007/s10072-024-07324-y
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