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1. CC-DCNet: Dynamic Convolutional Neural Network with Contrastive Constraints for Identifying Lung Cancer Subtypes on Multi-modality Images

Author

Jin, Yuan, Ma, Gege, Chen, Geng, Lyu, Tianling, Egger, Jan, Lyu, Junhui, Zhang, Shaoting, and Zhu, Wentao

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

The accurate diagnosis of pathological subtypes of lung cancer is of paramount importance for follow-up treatments and prognosis managements. Assessment methods utilizing deep learning technologies have introduced novel approaches for clinical diagnosis. However, the majority of existing models rely solely on single-modality image input, leading to limited diagnostic accuracy. To this end, we propose a novel deep learning network designed to accurately classify lung cancer subtype with multi-dimensional and multi-modality images, i.e., CT and pathological images. The strength of the proposed model lies in its ability to dynamically process both paired CT-pathological image sets as well as independent CT image sets, and consequently optimize the pathology-related feature extractions from CT images. This adaptive learning approach enhances the flexibility in processing multi-dimensional and multi-modality datasets and results in performance elevating in the model testing phase. We also develop a contrastive constraint module, which quantitatively maps the cross-modality associations through network training, and thereby helps to explore the "gold standard" pathological information from the corresponding CT scans. To evaluate the effectiveness, adaptability, and generalization ability of our model, we conducted extensive experiments on a large-scale multi-center dataset and compared our model with a series of state-of-the-art classification models. The experimental results demonstrated the superiority of our model for lung cancer subtype classification, showcasing significant improvements in accuracy metrics such as ACC, AUC, and F1-score.

Published
2024

2. VisConductor: Affect-Varying Widgets for Animated Data Storytelling in Gesture-Aware Augmented Video Presentation

Author

Femi-Gege, Temiloluwa, Brehmer, Matthew, and Zhao, Jian

Subjects
Computer Science - Human-Computer Interaction
Abstract

Augmented video presentation tools provide a natural way for presenters to interact with their content, resulting in engaging experiences for remote audiences, such as when a presenter uses hand gestures to manipulate and direct attention to visual aids overlaid on their webcam feed. However, authoring and customizing these presentations can be challenging, particularly when presenting dynamic data visualization (i.e., animated charts). To this end, we introduce VisConductor, an authoring and presentation tool that equips presenters with the ability to configure gestures that control affect-varying visualization animation, foreshadow visualization transitions, direct attention to notable data points, and animate the disclosure of annotations. These gestures are integrated into configurable widgets, allowing presenters to trigger content transformations by executing gestures within widget boundaries, with feedback visible only to them. Altogether, our palette of widgets provides a level of flexibility appropriate for improvisational presentations and ad-hoc content transformations, such as when responding to audience engagement. To evaluate VisConductor, we conducted two studies focusing on presenters (N = 11) and audience members (N = 11). Our findings indicate that our approach taken with VisConductor can facilitate interactive and engaging remote presentations with dynamic visual aids. Reflecting on our findings, we also offer insights to inform the future of augmented video presentation tools., Comment: To appear in ACM ISS'24

Published
2024

3. Decoding the glycoproteome: a new frontier for biomarker discovery in cancer.

Author

He, Kai, Baniasad, Maryam, Kwon, Hyunwoo, Caval, Tomislav, Xu, Gege, Lebrilla, Carlito, Hommes, Daniel, and Bertozzi, Carolyn

Subjects
Biomarker, Cancer, Glycoproteomics, Screening, Humans, Artificial Intelligence, Neoplasms, Biomarkers, Tumor, Glycoproteins, Liquid Biopsy, Proteome
Abstract

Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive nature and the implications in precision cancer management. Recently, liquid biopsy has been further expanded to profile glycoproteins, which are the products of post-translational modifications of proteins and play key roles in both normal and pathological processes, including cancers. The advancements in chemical and mass spectrometry-based technologies and artificial intelligence-based platforms have enabled extensive studies of cancer and organ-specific changes in glycans and glycoproteins through glycomics and glycoproteomics. Glycoproteomic analysis has emerged as a promising tool for biomarker discovery and development in early detection of cancers and prediction of treatment efficacy including response to immunotherapies. These biomarkers could play a crucial role in aiding in early intervention and personalized therapy decisions. In this review, we summarize the significant advance in cancer glycoproteomic biomarker studies and the promise and challenges in integration into clinical practice to improve cancer patient care.

Published
2024

4. Discovery and Clinical Proof-of-Concept of RLY-2608, a First-in-Class Mutant-Selective Allosteric PI3Kα Inhibitor That Decouples Antitumor Activity from Hyperinsulinemia.

Author

Giordanetto, Fabrizio, Hamilton, Erika, Harris, Katherine, Holliday, Michael, Hunter, Tamieka, Iskandar, Amanda, Ji, Yongli, Larivée, Alexandre, LaRochelle, Jonathan, Lescarbeau, André, Llambi, Fabien, Lormil, Brenda, Mader, Mary, Mar, Brenton, Martin, Iain, McLean, Thomas, Michelsen, Klaus, Pechersky, Yakov, Puente-Poushnejad, Erika, Raynor, Kevin, Rogala, Dipali, Samadani, Ramin, Schram, Alison, Shortsleeves, Kelley, Swaminathan, Sweta, Tajmir, Shahein, Tan, Gege, Tang, Yong, Valverde, Roberto, Wehrenberg, Bryan, Wilbur, Jeremy, Williams, Bret, Zeng, Hongtao, Zhang, Hanmo, Walters, W, Wolf, Beni, Shaw, David, Bergstrom, Donald, Watters, James, Varkaris, Andreas, Pazolli, Ermira, Gunaydin, Hakan, Wang, Qi, Pierce, Levi, Boezio, Alessandro, Bulku, Artemisa, DiPietro, Lucian, Fridrich, Cary, Fortin, Pascal, Kipp, D, Frost, Adam, and Fraser, James

Subjects
Humans, Female, Phosphoinositide-3 Kinase Inhibitors, Cryoelectron Microscopy, Breast Neoplasms, Class I Phosphatidylinositol 3-Kinases, Hyperinsulinism, DNA
Abstract

UNLABELLED: PIK3CA (PI3Kα) is a lipid kinase commonly mutated in cancer, including ∼40% of hormone receptor-positive breast cancer. The most frequently observed mutants occur in the kinase and helical domains. Orthosteric PI3Kα inhibitors suffer from poor selectivity leading to undesirable side effects, most prominently hyperglycemia due to inhibition of wild-type (WT) PI3Kα. Here, we used molecular dynamics simulations and cryo-electron microscopy to identify an allosteric network that provides an explanation for how mutations favor PI3Kα activation. A DNA-encoded library screen leveraging electron microscopy-optimized constructs, differential enrichment, and an orthosteric-blocking compound led to the identification of RLY-2608, a first-in-class allosteric mutant-selective inhibitor of PI3Kα. RLY-2608 inhibited tumor growth in PIK3CA-mutant xenograft models with minimal impact on insulin, a marker of dysregulated glucose homeostasis. RLY-2608 elicited objective tumor responses in two patients diagnosed with advanced hormone receptor-positive breast cancer with kinase or helical domain PIK3CA mutations, with no observed WT PI3Kα-related toxicities. SIGNIFICANCE: Treatments for PIK3CA-mutant cancers are limited by toxicities associated with the inhibition of WT PI3Kα. Molecular dynamics, cryo-electron microscopy, and DNA-encoded libraries were used to develop RLY-2608, a first-in-class inhibitor that demonstrates mutant selectivity in patients. This marks the advance of clinical mutant-selective inhibition that overcomes limitations of orthosteric PI3Kα inhibitors. See related commentary by Gong and Vanhaesebroeck, p. 204 . See related article by Varkaris et al., p. 227 . This article is featured in Selected Articles from This Issue, p. 201.

Published
2024

5. On Designing Multi-UAV aided Wireless Powered Dynamic Communication via Hierarchical Deep Reinforcement Learning

Author

Zhao, Ze Yu, Che, Yue Ling, Luo, Sheng, Luo, Gege, Wu, Kaishun, and Leung, Victor C. M.

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Artificial Intelligence
Abstract

This paper proposes a novel design on the wireless powered communication network (WPCN) in dynamic environments under the assistance of multiple unmanned aerial vehicles (UAVs). Unlike the existing studies, where the low-power wireless nodes (WNs) often conform to the coherent harvest-then-transmit protocol, under our newly proposed double-threshold based WN type updating rule, each WN can dynamically and repeatedly update its WN type as an E-node for non-linear energy harvesting over time slots or an I-node for transmitting data over sub-slots. To maximize the total transmission data size of all the WNs over T slots, each of the UAVs individually determines its trajectory and binary wireless energy transmission (WET) decisions over times slots and its binary wireless data collection (WDC) decisions over sub-slots, under the constraints of each UAV's limited on-board energy and each WN's node type updating rule. However, due to the UAVs' tightly-coupled trajectories with their WET and WDC decisions, as well as each WN's time-varying battery energy, this problem is difficult to solve optimally. We then propose a new multi-agent based hierarchical deep reinforcement learning (MAHDRL) framework with two tiers to solve the problem efficiently, where the soft actor critic (SAC) policy is designed in tier-1 to determine each UAV's continuous trajectory and binary WET decision over time slots, and the deep-Q learning (DQN) policy is designed in tier-2 to determine each UAV's binary WDC decisions over sub-slots under the given UAV trajectory from tier-1. Both of the SAC policy and the DQN policy are executed distributively at each UAV. Finally, extensive simulation results are provided to validate the outweighed performance of the proposed MAHDRL approach over various state-of-the-art benchmarks., Comment: 13 pages, 10 figures; Submitted for possible journal publishing

Published
2023

6. FaceStudio: Put Your Face Everywhere in Seconds

Author

Yan, Yuxuan, Zhang, Chi, Wang, Rui, Zhou, Yichao, Zhang, Gege, Cheng, Pei, Yu, Gang, and Fu, Bin

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

This study investigates identity-preserving image synthesis, an intriguing task in image generation that seeks to maintain a subject's identity while adding a personalized, stylistic touch. Traditional methods, such as Textual Inversion and DreamBooth, have made strides in custom image creation, but they come with significant drawbacks. These include the need for extensive resources and time for fine-tuning, as well as the requirement for multiple reference images. To overcome these challenges, our research introduces a novel approach to identity-preserving synthesis, with a particular focus on human images. Our model leverages a direct feed-forward mechanism, circumventing the need for intensive fine-tuning, thereby facilitating quick and efficient image generation. Central to our innovation is a hybrid guidance framework, which combines stylized images, facial images, and textual prompts to guide the image generation process. This unique combination enables our model to produce a variety of applications, such as artistic portraits and identity-blended images. Our experimental results, including both qualitative and quantitative evaluations, demonstrate the superiority of our method over existing baseline models and previous works, particularly in its remarkable efficiency and ability to preserve the subject's identity with high fidelity., Comment: Project homepage: https://icoz69.github.io/facestudio/

Published
2023

7. GeV Variability Properties of TeV Blazars Detected by Fermi-LAT

Author

Wang, Gege, Xiao, Hubing, Fan, Junhui, and Zhang, Xin

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Astrophysics of Galaxies
Abstract

Variability is a prominent observational feature of blazars. The high-energy radiation mechanism of jets has always been important but still unclear. In this work, we performed a detailed analysis using Fermi-LAT data across 15 years and obtained GeV light curve information for 78 TeV blazars detected by Fermi. We provided annual GeV fluxes and corresponding spectral indices for the 78 TeV blazars and thorough monthly GeV fluxes for a subsample of 41 bright blazars. Our results suggest a strong correlation between the ${\gamma}$-ray photon index and $\log L_{\rm \gamma}$ for the flat spectrum radio quasars (FSRQs) and high-energy-peaked BL Lacs (HBLs). 14 sources in our sample show significant GeV outbursts/flares above the relatively stable, low-flux light curve, with 6 of them showing a clear sharp peak profile in their 5-day binned light curves. We quantified the variability utilizing the fractional variability parameter $F_{\mathrm{var}}$, and found that the flux of the FSRQs showed significantly stronger variability than that of the BL Lacs. The 41 bright blazars in this work are best fit by a log-normal flux distribution. We checked the spectral behavior and found 11 out of the 14 sources show a 'bluer-when-brighter (BWB)' trend, suggesting this spectral behavior for these TeV blazars at the GeV band arises from the mechanism that the synchrotron-self Compton (SSC) process dominates the GeV emission. Our research offers a systematic analysis of the GeV variability properties of TeV blazars and serves as a helpful resource for further associated blazar studies., Comment: 22 pages, 9 figures, accepted for publication in ApJS

Published
2023

8. GraphDreamer: Compositional 3D Scene Synthesis from Scene Graphs

Author

Gao, Gege, Liu, Weiyang, Chen, Anpei, Geiger, Andreas, and Schölkopf, Bernhard

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics, Computer Science - Machine Learning
Abstract

As pretrained text-to-image diffusion models become increasingly powerful, recent efforts have been made to distill knowledge from these text-to-image pretrained models for optimizing a text-guided 3D model. Most of the existing methods generate a holistic 3D model from a plain text input. This can be problematic when the text describes a complex scene with multiple objects, because the vectorized text embeddings are inherently unable to capture a complex description with multiple entities and relationships. Holistic 3D modeling of the entire scene further prevents accurate grounding of text entities and concepts. To address this limitation, we propose GraphDreamer, a novel framework to generate compositional 3D scenes from scene graphs, where objects are represented as nodes and their interactions as edges. By exploiting node and edge information in scene graphs, our method makes better use of the pretrained text-to-image diffusion model and is able to fully disentangle different objects without image-level supervision. To facilitate modeling of object-wise relationships, we use signed distance fields as representation and impose a constraint to avoid inter-penetration of objects. To avoid manual scene graph creation, we design a text prompt for ChatGPT to generate scene graphs based on text inputs. We conduct both qualitative and quantitative experiments to validate the effectiveness of GraphDreamer in generating high-fidelity compositional 3D scenes with disentangled object entities., Comment: CVPR 2024 (18 pages, 11 figures, https://graphdreamer.github.io/)

Published
2023

12. A Multi-Scale Spatial Transformer U-Net for Simultaneously Automatic Reorientation and Segmentation of 3D Nuclear Cardiac Images

Author

Ni, Yangfan, Zhang, Duo, Ma, Gege, Lu, Lijun, Huang, Zhongke, and Zhu, Wentao

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

Accurate reorientation and segmentation of the left ventricular (LV) is essential for the quantitative analysis of myocardial perfusion imaging (MPI), in which one critical step is to reorient the reconstructed transaxial nuclear cardiac images into standard short-axis slices for subsequent image processing. Small-scale LV myocardium (LV-MY) region detection and the diverse cardiac structures of individual patients pose challenges to LV segmentation operation. To mitigate these issues, we propose an end-to-end model, named as multi-scale spatial transformer UNet (MS-ST-UNet), that involves the multi-scale spatial transformer network (MSSTN) and multi-scale UNet (MSUNet) modules to perform simultaneous reorientation and segmentation of LV region from nuclear cardiac images. The proposed method is trained and tested using two different nuclear cardiac image modalities: 13N-ammonia PET and 99mTc-sestamibi SPECT. We use a multi-scale strategy to generate and extract image features with different scales. Our experimental results demonstrate that the proposed method significantly improves the reorientation and segmentation performance. This joint learning framework promotes mutual enhancement between reorientation and segmentation tasks, leading to cutting edge performance and an efficient image processing workflow. The proposed end-to-end deep network has the potential to reduce the burden of manual delineation for cardiac images, thereby providing multimodal quantitative analysis assistance for physicists., Comment: 17 pages, 7 figures

Published
2023

13. Model Inversion Attack via Dynamic Memory Learning

Author

Qi, Gege, Chen, YueFeng, Mao, Xiaofeng, Hui, Binyuan, Li, Xiaodan, Zhang, Rong, and Xue, Hui

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Model Inversion (MI) attacks aim to recover the private training data from the target model, which has raised security concerns about the deployment of DNNs in practice. Recent advances in generative adversarial models have rendered them particularly effective in MI attacks, primarily due to their ability to generate high-fidelity and perceptually realistic images that closely resemble the target data. In this work, we propose a novel Dynamic Memory Model Inversion Attack (DMMIA) to leverage historically learned knowledge, which interacts with samples (during the training) to induce diverse generations. DMMIA constructs two types of prototypes to inject the information about historically learned knowledge: Intra-class Multicentric Representation (IMR) representing target-related concepts by multiple learnable prototypes, and Inter-class Discriminative Representation (IDR) characterizing the memorized samples as learned prototypes to capture more privacy-related information. As a result, our DMMIA has a more informative representation, which brings more diverse and discriminative generated results. Experiments on multiple benchmarks show that DMMIA performs better than state-of-the-art MI attack methods.

Published
2023

14. Classification of lung cancer subtypes on CT images with synthetic pathological priors

Author

Zhu, Wentao, Jin, Yuan, Ma, Gege, Chen, Geng, Egger, Jan, Zhang, Shaoting, and Metaxas, Dimitris N.

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

The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between the same case's CT images and its pathological images, we innovatively developed a pathological feature synthetic module (PFSM), which quantitatively maps cross-modality associations through deep neural networks, to derive the "gold standard" information contained in the corresponding pathological images from CT images. Additionally, we designed a radiological feature extraction module (RFEM) to directly acquire CT image information and integrated it with the pathological priors under an effective feature fusion framework, enabling the entire classification model to generate more indicative and specific pathologically related features and eventually output more accurate predictions. The superiority of the proposed model lies in its ability to self-generate hybrid features that contain multi-modality image information based on a single-modality input. To evaluate the effectiveness, adaptability, and generalization ability of our model, we performed extensive experiments on a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to compare our model and a series of state-of-the-art (SOTA) classification models. The experimental results demonstrated the superiority of our model for lung cancer subtypes classification with significant accuracy improvements in terms of accuracy (ACC), area under the curve (AUC), and F1 score., Comment: 16 pages, 7 figures

Published
2023
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15. Calpeptin improves the cognitive function in Alzheimer's disease‐like complications of diabetes mellitus rats by regulating TXNIP/NLRP3 inflammasome

Author

Luyao Qiao, Shouqin Yi, Tianpei Li, Xin Pan, Gege Wang, Xu Liu, Min Li, Jun Min, Huahui Le, and Zhenyu Tang

Subjects
Alzheimer's disease‐like complications of DM, Calpain‐2, TXNIP/NLRP3 inflammasome, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract

ABSTRACT Aims Diabetes mellitus (DM) is closely associated with Alzheimer's disease (AD), and is considered an accelerator of AD. Our previous study has confirmed that the Calpain inhibitor Calpeptin may alleviate AD‐like complications of diabetes mellitus. This work further investigated its underlying mechanism. Materials and Methods Diabetes mellitus rat model was constructed by a high‐fat and high‐sugar diet combined with streptozotocin, followed by the administration of Calpeptin. Moreover, rats were micro‐injected with LV‐TXNIP‐OE/vector into the CA1 region of the hippocampus one day before streptozotocin injection. The Morris water maze test assessed the spatial learning and memory ability of rats. Immunohistochemistry and western blotting detected the expression of the pericyte marker PDGFRβ, tight junction proteins occludin and ZO‐1, calpain‐1, calpain‐2, APP, Aβ, Aβ‐related, and TXNIP/NLRP3 inflammasome‐related proteins. Immunofluorescence staining examined the blood vessel density and neurons in the hippocampus. Evans blue extravasation and fluorescence detected the permeability of the blood–brain barrier (BBB) in rats. Additionally, the oxidative stress markers and inflammatory‐related factors were assessed by enzyme‐linked immunosorbent assay. Results Calpeptin effectively reduced the expression of Calpain‐2 and TXNIP/NLRP3 inflammasome‐related proteins, improved the decreased pericyte marker (PDGFR‐β) and cognitive impairment in hippocampus of DM rats. The neuronal loss, microvessel density, permeability of BBB, Aβ accumulation, inflammation, and oxidative stress injury in the hippocampus of DM rats were also partly rescued by calpeptin treatment. The influence conferred by calpeptin treatment was reversed by TXNIP overexpression. Conclusions These data demonstrated that calpeptin treatment alleviated AD‐like symptoms in DM rats through regulating TXNIP/NLRP3 inflammasome. Thus, calpeptin may be a potential drug to treat AD‐like complications of diabetes mellitus.

Published
2024
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16. The effect of career compromise on nurses’ turnover intention: the mediating role of job satisfaction

Author

Zhanghao Xie, Zhongqing Chen, Waner Wang, Jiangfeng Pu, Gege Li, Jiehao Zhuang, Xuanhao Fan, Ziyi Xiong, Hanxi Chen, Yuemei Liang, Peng Xu, and Huigen Huang

Subjects
Nurses, Career compromise, Job satisfaction, Turnover intention, Mediating effect, Nursing, RT1-120
Abstract

Abstract Aim We aimed to examine the relationships among nurses’ career compromise, job satisfaction, and turnover intention and the mediating role of job satisfaction in the relationship between career compromise and turnover intention. Background Nurses are prone to career compromise when there is a discrepancy between the reality of their job and their personal career expectations due to personal or family factors. High levels of career compromise may have a significant impact on turnover intentions through low job satisfaction, thereby affecting the stability of the nursing workforce. There is a paucity of research on the impact of nurses’ career compromise on turnover intentions. Method This was a cross-sectional research design of nurses in hospitals in Guangdong Province using a convenience sampling method. The Career Compromise Scale (CCS), Job Satisfaction Scale (JSS), and Turnover Intention Scale (TIS) were used. The obtained data were analyzed through descriptive statistics, Spearman’s correlation coefficient, and the mediating effect of perceived organizational support was tested through the PROCESS macro mediation model in SPSS. Results Data from 821 nurses who met the inclusion criteria were analyzed. The total score of nurses’ turnover intention was 14.68 ± 4.32. Spearman’s correlation analysis showed that career compromise was negatively correlated with job satisfaction (r = -0.594, p

Published
2024
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17. METTL3-mediated m6A modification of SIRT1 mRNA affects the progression of diabetic cataracts through cellular autophagy and senescence

Author

Su Dong, Jiajia Zhang, Yushan Fu, Gege Tang, Jianfeng Chen, Dawei Sun, Yanhua Qi, and Nan Zhou

Subjects
m6A, METTL3, SIRT1, Diabetic cataract, Autophagy, Cellular senescence, Medicine
Abstract

Abstract Background The increasing incidence of diabetes mellitus has established diabetic cataracts (DC) as a significant worldwide public health issue. The mechanisms underlying DC remain unknown, and effective prevention and treatment strategies are lacking. Accordingly, we aimed to explore the role and mechanism behind N6-methyladenosine (m6A) in DC progression. Methods Methyltransferase-like 3 (METTL3), p21, Beclin1, LC3, and p62 expression levels were measured in human tissues. This study assessed total m6A levels and common m6A-regulated biomarkers in both in vitro and in vivo DC models. Autophagy flux was detected in vitro through Ad-mCherry-GFP-LC3B and Monodansylcadaverine (MDC) staining. Cellular senescence was assessed utilizing the senescence-associated β-galactosidase (SA-β-Gal) assay. Furthermore, the effect of METTL3 on SIRT1 mRNA modification was demonstrated, and its mechanism was elucidated using RT-qPCR, western blot, RNA stability assays, and RIP analysis. Results METTL3, p21, and p62 expression levels were elevated in lens epithelial cells (LECs) from DC patients, while Beclin1 and LC3 levels were reduced. Silencing METTL3-mediated m6A modifications restored high-glucose-induced autophagy inhibition and prevented premature senescence in LECs. Notably, SIRT1720 and Metformin significantly enhanced autophagosome generation and delayed cellular senescence. The m6A-reading protein YTHDF2 bound to m6A modifications, and YTHDF2 silencing significantly reduced METTL3-mediated SIRT1 inactivation. Conclusions METTL3 induces senescence in DC by destabilizing SIRT1 mRNA in an m6A-YTHDF2-dependent manner. The METTL3-YTHDF2-SIRT1 axis is a key target and potential pathogenic mechanism in DC.

Published
2024
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18. Influence of smoking status on the relationship between serum selenium and cause-specific mortality in US adults

Author

Gege Jiang, Shangqi Sun, Liqin Huang, Guanfeng Xie, and Jing Xiong

Subjects
Serum selenium, Mortality, NHANES, Nonlinear association, Smoking, Medicine, Science
Abstract

Abstract Selenium, a crucial antioxidant in the body, has been linked to all-cause and cause-specific mortality. However, the relationship between selenium and mortality in the general population remains unclear. A total of 5449 participants in the National Health and Nutrition Examination Survey (NHANES) (2003–2004, 2011–2016) were analyzed to track participant mortality until December 31, 2019. The COX proportional hazard model, Kaplan‒Meier survival analysis and restricted cubic spline regression analysis were used to investigate the associations. Subgroup analysis was conducted on the basis of age (≤ 60, > 60), sex (male, female), and smoking status (nonsmoker, former smoker, and current smoker). The second quartile was associated with lower all-cause mortality and noncardiovascular mortality (HR and 95% CI 0.61,0.45–0.83;0.59,0.42–0.83, respectively). The third quartile was associated with lower cardiovascular-related mortality (HR and 95% CI 0.49, 0.32–0.76). Elevated serum selenium concentrations were associated with lower all-cause mortality, noncardiovascular mortality (range ≤ 129.82 μg/L), and cardiovascular mortality (range ≤ 129.08 μg/L). Subgroup analysis revealed a positive correlation between the serum selenium concentration (range ≥ 129.82 μg/L) and all-cause mortality among the subgroup of current smokers (p

Published
2024
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19. Robust identification of perturbed cell types in single-cell RNA-seq data

Author

Phillip B. Nicol, Danielle Paulson, Gege Qian, X. Shirley Liu, Rafael Irizarry, and Avinash D. Sahu

Subjects
Science
Abstract

Abstract Single-cell transcriptomics has emerged as a powerful tool for understanding how different cells contribute to disease progression by identifying cell types that change across diseases or conditions. However, detecting changing cell types is challenging due to individual-to-individual and cohort-to-cohort variability and naive approaches based on current computational tools lead to false positive findings. To address this, we propose a computational tool, scDist, based on a mixed-effects model that provides a statistically rigorous and computationally efficient approach for detecting transcriptomic differences. By accurately recapitulating known immune cell relationships and mitigating false positives induced by individual and cohort variation, we demonstrate that scDist outperforms current methods in both simulated and real datasets, even with limited sample sizes. Through the analysis of COVID-19 and immunotherapy datasets, scDist uncovers transcriptomic perturbations in dendritic cells, plasmacytoid dendritic cells, and FCER1G+NK cells, that provide new insights into disease mechanisms and treatment responses. As single-cell datasets continue to expand, our faster and statistically rigorous method offers a robust and versatile tool for a wide range of research and clinical applications, enabling the investigation of cellular perturbations with implications for human health and disease.

Published
2024
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20. Comprehensive Review and Future Trend Outlook on Energy Utilization Technologies in Low-Carbon Energy Systems

Author

Zhang Chuan, Hu Peiyu, Yin Gege, Yan Wubin, Xu Shiwei, Cao Ruming, Feng Yinan, Zhao Mingsheng, Yang Lei, and Jin Zhijun

Subjects
energy system, low-carbon transition, primary energy, secondary energy, end-use energy utilization, electrification, nontechnical factors, Engineering (General). Civil engineering (General), TA1-2040
Abstract

Energy decarbonization is essential for achieving carbon neutrality. To facilitate the low-carbon transition of the energy system, extensive utilization of low-carbon energy technologies is crucial across various sectors, including primary energy supply, energy processing and conversion, and end-use consumption. This study provides a comprehensive overview of the current status and future trends of key energy utilization technologies from the perspective of low-carbon energy system analysis and optimization. We analyze the application prospects and layout challenges of energy utilization technologies in China’s path toward carbon neutrality from multiple dimensions. Our study highlights the following points: Wind and solar energy, as the fastest-growing renewable energy sources, will continue to play a crucial role in energy supply. The utilization of biomass resources requires systematic evaluation of different conversion technologies, optimizing the allocation of biomass resources across industries. Utilization technologies of secondary energies such as hydrogen and energy storage face challenges related to materials, performance, and lifespan. Overcoming these technical bottlenecks is necessary to achieve low-cost and scalable system applications. Future end-use energy utilization technologies will focus on electrification, which depends on the secure and stable transformation and expansion of electricity distribution systems. Achieving low-carbon transformation in the energy system requires balancing the coordinated development of different energy technologies while addressing non-technical factors such as market dynamics, institutional frameworks, and societal acceptance, ultimately accelerating the decarbonization process.

Published
2024
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21. Duration of exposure to compound daytime-nighttime high temperatures and changes in population exposure in China under global warming

Author

Gege Nie, Jun Yang, Yuqing Zhang, Xiangming Xiao, Jianhong (Cecilia) Xia, Xiaoyu Cai, and Chunli Li

Subjects
History of scholarship and learning. The humanities, AZ20-999, Social Sciences
Abstract

Abstract Global warming has led to the enhancement of diurnal (daytime-nighttime) compound heat waves, which can severely affect the population’s physical health and productive life; this is particularly the case for vulnerable populations that are more susceptible to psychological and physiological harm with the future normalization of heatwave scenarios. In this study, we attributed the changes in population exposure under diurnal compound high temperatures (HTs) and extremely high temperatures (EHTs) to the influence of climatic (exposure duration) and population factors and their combined influence to determine the relationship between exposure duration and the changes in exposure of vulnerable populations. Diurnal compound HT and EHT covered a land area of 4,016,800 and 1,984,200 km2, respectively. The heat-exposed area spread step-like in all directions, centering on the 24-h high-exposure area with a strong gradient effect. The area of prolonged exposure shifted from the Yellow-Huai-Huai Plain across the geographical boundary between the south and the north to the middle and lower reaches of the Yangtze River Plain. Meanwhile, the average maximum value of vulnerable population exposure reached 186,800 and 93,100 person-h, with high values in Zhengzhou, Chongqing, Wuhan, Chengdu, Beijing, and other cities. Changes in vulnerable population exposure were dominated first by demographic factors and then by climatic factors, with the dominance of climatic factors evident in areas of prolonged exposure. Under prolonged exposure to daytime-nighttime compound EHT, changes in the exposure of child and older populations were dominated by climatic factors with a contribution rate of 87.16% and 87.43%, respectively. Therefore, suitable cooling measures should be proposed for vulnerable populations under prolonged exposure, focusing more on the physical and mental health of children and older adults exposed to compound EHT.

Published
2024
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22. Robust Automatic Speech Recognition via WavAugment Guided Phoneme Adversarial Training

Author

Qi, Gege, Chen, Yuefeng, Mao, Xiaofeng, Jia, Xiaojun, Duan, Ranjie, Zhang, Rong, and Xue, Hui

Subjects
Computer Science - Sound, Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Developing a practically-robust automatic speech recognition (ASR) is challenging since the model should not only maintain the original performance on clean samples, but also achieve consistent efficacy under small volume perturbations and large domain shifts. To address this problem, we propose a novel WavAugment Guided Phoneme Adversarial Training (wapat). wapat use adversarial examples in phoneme space as augmentation to make the model invariant to minor fluctuations in phoneme representation and preserve the performance on clean samples. In addition, wapat utilizes the phoneme representation of augmented samples to guide the generation of adversaries, which helps to find more stable and diverse gradient-directions, resulting in improved generalization. Extensive experiments demonstrate the effectiveness of wapat on End-to-end Speech Challenge Benchmark (ESB). Notably, SpeechLM-wapat outperforms the original model by 6.28% WER reduction on ESB, achieving the new state-of-the-art.

Published
2023

23. PDS-MAR: a fine-grained Projection-Domain Segmentation-based Metal Artifact Reduction method for intraoperative CBCT images with guidewires

Author

Lyu, Tianling, Wu, Zhan, Ma, Gege, Jiang, Chen, Zhong, Xinyun, Xi, Yan, Chen, Yang, and Zhu, Wentao

Subjects
Physics - Medical Physics, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Since the invention of modern CT systems, metal artifacts have been a persistent problem. Due to increased scattering, amplified noise, and insufficient data collection, it is more difficult to suppress metal artifacts in cone-beam CT, limiting its use in human- and robot-assisted spine surgeries where metallic guidewires and screws are commonly used. In this paper, we demonstrate that conventional image-domain segmentation-based MAR methods are unable to eliminate metal artifacts for intraoperative CBCT images with guidewires. To solve this problem, we present a fine-grained projection-domain segmentation-based MAR method termed PDS-MAR, in which metal traces are augmented and segmented in the projection domain before being inpainted using triangular interpolation. In addition, a metal reconstruction phase is proposed to restore metal areas in the image domain. The digital phantom study and real CBCT data study demonstrate that the proposed algorithm achieves significantly better artifact suppression than other comparing methods and has the potential to advance the use of intraoperative CBCT imaging in clinical spine surgeries., Comment: 19 Pages

Published
2023
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24. Learning CO$_2$ plume migration in faulted reservoirs with Graph Neural Networks

Author

Ju, Xin, Hamon, François P., Wen, Gege, Kanfar, Rayan, Araya-Polo, Mauricio, and Tchelepi, Hamdi A.

Subjects
Computer Science - Machine Learning, Physics - Atmospheric and Oceanic Physics
Abstract

Deep-learning-based surrogate models provide an efficient complement to numerical simulations for subsurface flow problems such as CO$_2$ geological storage. Accurately capturing the impact of faults on CO$_2$ plume migration remains a challenge for many existing deep learning surrogate models based on Convolutional Neural Networks (CNNs) or Neural Operators. We address this challenge with a graph-based neural model leveraging recent developments in the field of Graph Neural Networks (GNNs). Our model combines graph-based convolution Long-Short-Term-Memory (GConvLSTM) with a one-step GNN model, MeshGraphNet (MGN), to operate on complex unstructured meshes and limit temporal error accumulation. We demonstrate that our approach can accurately predict the temporal evolution of gas saturation and pore pressure in a synthetic reservoir with impermeable faults. Our results exhibit a better accuracy and a reduced temporal error accumulation compared to the standard MGN model. We also show the excellent generalizability of our algorithm to mesh configurations, boundary conditions, and heterogeneous permeability fields not included in the training set. This work highlights the potential of GNN-based methods to accurately and rapidly model subsurface flow with complex faults and fractures.

Published
2023

38. Optimizing Carbon Storage Operations for Long-Term Safety

Author

Wang, Yizheng, Zechner, Markus, Wen, Gege, Corso, Anthony Louis, Mern, John Michael, Kochenderfer, Mykel J., and Caers, Jef Karel

Subjects
Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Systems and Control, Physics - Fluid Dynamics
Abstract

To combat global warming and mitigate the risks associated with climate change, carbon capture and storage (CCS) has emerged as a crucial technology. However, safely sequestering CO2 in geological formations for long-term storage presents several challenges. In this study, we address these issues by modeling the decision-making process for carbon storage operations as a partially observable Markov decision process (POMDP). We solve the POMDP using belief state planning to optimize injector and monitoring well locations, with the goal of maximizing stored CO2 while maintaining safety. Empirical results in simulation demonstrate that our approach is effective in ensuring safe long-term carbon storage operations. We showcase the flexibility of our approach by introducing three different monitoring strategies and examining their impact on decision quality. Additionally, we introduce a neural network surrogate model for the POMDP decision-making process to handle the complex dynamics of the multi-phase flow. We also investigate the effects of different fidelity levels of the surrogate model on decision qualities.

Published
2023

39. TransAudio: Towards the Transferable Adversarial Audio Attack via Learning Contextualized Perturbations

Author

Gege, Qi, Chen, Yuefeng, Mao, Xiaofeng, Zhu, Yao, Hui, Binyuan, Li, Xiaodan, Zhang, Rong, and Xue, Hui

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

In a transfer-based attack against Automatic Speech Recognition (ASR) systems, attacks are unable to access the architecture and parameters of the target model. Existing attack methods are mostly investigated in voice assistant scenarios with restricted voice commands, prohibiting their applicability to more general ASR related applications. To tackle this challenge, we propose a novel contextualized attack with deletion, insertion, and substitution adversarial behaviors, namely TransAudio, which achieves arbitrary word-level attacks based on the proposed two-stage framework. To strengthen the attack transferability, we further introduce an audio score-matching optimization strategy to regularize the training process, which mitigates adversarial example over-fitting to the surrogate model. Extensive experiments and analysis demonstrate the effectiveness of TransAudio against open-source ASR models and commercial APIs.

Published
2023

40. Incidence of Cytomegalovirus Infection After Repeat Keratoplasty and Associated Rate of Graft Failure

Author

Yunxiao Zang, Yaning Zhao, Rongmei Peng, Gege Xiao, Xiaozhen Liu, Yi Qu, Xuanjun Zhang, Jiaxin Zhang, and Jing Hong

Subjects
Repeat keratoplasty, Cytomegalovirus, Corneal graft, Viral DNA, Ophthalmology, RE1-994
Abstract

Abstract Introduction The aim of this work was to compare the prognosis and characteristics of patients with Cytomegalovirus (CMV) infection (CMV+) with those of patients without virus infection (Virus–) undergoing repeat keratoplasty. Methods This prospective propensity score-matched cohort study enrolled patients who underwent repeat keratoplasty for graft failure at the Peking University Third Hospital between January 2016 and May 2022. Patients with prior viral keratitis before the first keratoplasty were excluded. The primary outcome measure was the graft failure rate. The secondary outcome measures included the anterior segment characteristics, intraocular pressure (IOP), and endothelial cell density. Results Ninety-four matched patient pairs were included. The graft failure rate in the CMV+ group (71%) was higher than that in the Virus– group (29%) (P

Published
2024
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42. Shenqi Fuzheng injection hinders non-small cell lung cancer cell growth by regulating the Bax/Bcl-2 signaling pathway

Author

Siqi Li, Tianyu Ma, Gege Li, Xu Cheng, Tao Wen, Yuxuan Wang, Hongtao Zhang, and Zhidong Liu

Subjects
Shenqi Fuzheng injection, Non-small cell lung cancer, Antitumor, Apoptosis, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Introduction Lung cancer (LC) is the most common solid tumor and is currently considered the primary cause of cancer-related deaths worldwide. In clinical efficacy studies, it was not difficult to find that the combination of SFI and chemotherapy could improve the general condition of patients, reduce the side effects of chemotherapy drugs, and have a cooperative antitumor effect in NSCLC patients. However, whether SFI can be used as a novel antitumor drug is still unknown. Methods First, meta-analysis aimed to explore the efficacy of SFI in NSCLC patients, and SFI was identified by ultra-performance liquid chromatography‒mass spectrometry (UPLC‒MS). Cell proliferation, migration, and invasion were explored by Cell Counting Kit-8 (CCK-8), scratch healing, and Transwell assays, respectively. Cell cycle and apoptosis assays were performed by flow cytometry. Transcriptome sequencing analysis was performed in four NSCLC cell lines. Differential expression analysis was used to identify potential targets. Apoptosis-related protein levels were detected by Western blotting assays. The effects of SFI in NSCLC were further investigated by mouse xenografts. Results SFI could markedly improve the chemotherapy efficacy of NSCLC patients. The main active ingredients include flavonoids and terpenoids, which can effectively exert antitumor effects. SFI could not only inhibit tumor cell proliferation and cell migration/invasion but also regulate the cell cycle and promote tumor cell apoptosis. In NSCLC, SFI could enhance the transcription level of the CHOP gene, thereby upregulating the expression of the proapoptotic proteins Bax and caspase 3, and inhibiting the expression of the antiapoptotic protein Bcl-2. SFI hindered the growth of mouse NSCLC xenografts in vivo. Conclusions SFI hindered tumor progression and might promote apoptosis by increasing the expression of Bax, caspase 3 and decreasing the level of Bcl-2 in NSCLC.

Published
2024
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43. Association between pain interference and motoric cognitive risk syndrome in older adults: a population-based cohort study

Author

Gege Li, Zijun He, Jinjing Hu, Chongwu Xiao, Weichao Fan, Zhuodong Zhang, Qiuru Yao, Jihua Zou, Guozhi Huang, and Qing Zeng

Subjects
Pain interference, Older adults, Motoric cognitive risk syndrome, Cohort, Geriatrics, RC952-954.6
Abstract

Abstract Objectives Motoric cognitive risk syndrome (MCR) is a pre-dementia condition characterized by subjective complaints in cognition and slow gait. Pain interference has previously been linked with cognitive deterioration; however, its specific relationship with MCR remains unclear. We aimed to examine how pain interference is associated with concurrent and incident MCR. Methods This study included older adults aged ≥ 65 years without dementia from the Health and Retirement Study. We combined participants with MCR information in 2006 and 2008 as baseline, and the participants were followed up 4 and 8 years later. The states of pain interference were divided into 3 categories: interfering pain, non-interfering pain, and no pain. Logistic regression analysis was done at baseline to examine the associations between pain interference and concurrent MCR. During the 8-year follow-up, Cox regression analysis was done to investigate the associations between pain interference and incident MCR. Results The study included 7120 older adults (74.6 ± 6.7 years; 56.8% females) at baseline. The baseline prevalence of MCR was 5.7%. Individuals with interfering pain had a significantly increased risk of MCR (OR = 1.51, 95% CI = 1.17–1.95; p = 0.001). The longitudinal analysis included 4605 participants, and there were 284 (6.2%) MCR cases on follow-up. Participants with interfering pain at baseline had a higher risk for MCR at 8 years of follow-up (HR = 2.02, 95% CI = 1.52–2.69; p

Published
2024
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44. The outcomes of corneal sight rehabilitating surgery in Stevens-Johnson syndrome: case series

Author

Rongmei Peng, Miaomiao Chi, Gege Xiao, Hongqiang Qu, Zhan Shen, Yinghan Zhao, and Jing Hong

Subjects
Stevens-Johnson syndrome, Keratoplasty, Keratolimbal allograft, Toxic epidermal necrolysis, Ocular SJS, Ophthalmology, RE1-994
Abstract

Abstract Purpose To summarize the outcomes of corneal sight rehabilitating surgery in Stevens-Johnson syndrome (SJS). Methods This is a retrospective analysis of a consecutive case series. Twenty-four eyes of 18 SJS patients were included in this study. The ocular parameters, surgical procedures, postoperative complications, and additional treatments of the cases were reviewed. Results A total of 29 corneal sight rehabilitating surgeries, which consists of 9 keratoplasties, 8 Keratolimbal allograft (KLAL) and 12 combined surgeries (keratoplasty and KLAL simultaneously) were performed on the 24 eyes. All patients were treated with glucocorticoid eyedrops and tacrolimus eyedrops for anti-rejection treatment without combining systemic immunosuppression, except two patients who were prescribed prednisone tablets for the management of systemic conditions. The mean follow-up period was 50.6 ± 28.1 months. The optimal visual acuity (VA) (0.74 ± 0.60 logarithm of the minimum angle of resolution [logMAR]) and endpoint VA (1.06 ± 0.82 logMAR) were both significantly better than the preoperative VA (1.96 ± 0.43 logMAR) (95% CI, p = 0.000). 57.1% patients (8/14) were no longer in the low vision spectrum, and 88.9% patients (8/9) were no longer blind. The mean epithelialization time was 7.1 ± 7.6 weeks. The success rate was 86.7%. Additional treatments for improving epithelialization included administration of serum eyedrops (n = 10), contact lens (n = 15), amniotic membrane transplantation (n = 6), and tarsorrhaphy (n = 8). Complications included delayed epithelialization (n = 4, over 12 weeks), glaucoma (n = 11), and severe allograft opacity (n = 4). Only one graft rejection was observed. Conclusions Keratoplasty and KLAL can remarkably enhance VA and improve low vision or even eliminate blindness for ocular complications of SJS. The outcome of the surgeries was correlated with the preoperative ocular situation and choice of operative methods.

Published
2024
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45. Effects of First-Time Experiences and Self-Regulation on College Students' Online Learning Motivation: Based on a National Survey during COVID-19

Author

Li, Gege, Luo, Heng, Lei, Jing, Xu, Shuxian, and Chen, Tianjiao

Abstract

The COVID-19 pandemic has forced many college students in developing countries to engage in online learning for the first time, and the sudden transit has raised concerns regarding students' competencies for, perception of, and attitude towards online learning. To address those concerns, this study measured three essential constructs of online learning (self-regulated learning, perceived presences, and learning motivation) based on a national survey in China (N = 12,826) and employed structural equation modeling to investigate their intertwined relationship. The study results reveal that (1) college students' academic achievement cannot effectively predict their self-regulated learning in an online learning context; (2) self-regulation can be further differentiated into general and task-specific strategies with a varying impact on three types of presences; (3) online learning motivation is best predicted by cognitive presence, followed by social presence and teaching presence; and (4) the path of task-specific self-regulated learning [right arrow] cognitive presence [right arrow] online learning motivation generates the largest positive compound effect. Implications for online teaching and learning practice are also discussed through the stakeholder perspectives of students, teachers, and platform developers.

Published
2022

46. Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges

Author

Batarseh, Feras A., Donti, Priya L., Drgoňa, Ján, Fletcher, Kristen, Hanania, Pierre-Adrien, Hatton, Melissa, Keshav, Srinivasan, Knowles, Bran, Kotsch, Raphaela, McGinnis, Sean, Mitra, Peetak, Philp, Alex, Spohrer, Jim, Stein, Frank, Tare, Meghna, Volkov, Svitlana, and Wen, Gege

Subjects
Computer Science - Artificial Intelligence
Abstract

Climate change is one of the most pressing challenges of our time, requiring rapid action across society. As artificial intelligence tools (AI) are rapidly deployed, it is therefore crucial to understand how they will impact climate action. On the one hand, AI can support applications in climate change mitigation (reducing or preventing greenhouse gas emissions), adaptation (preparing for the effects of a changing climate), and climate science. These applications have implications in areas ranging as widely as energy, agriculture, and finance. At the same time, AI is used in many ways that hinder climate action (e.g., by accelerating the use of greenhouse gas-emitting fossil fuels). In addition, AI technologies have a carbon and energy footprint themselves. This symposium brought together participants from across academia, industry, government, and civil society to explore these intersections of AI with climate change, as well as how each of these sectors can contribute to solutions.

Published
2022

47. Variability and Spectral Behavior of Gamma-ray Flares of 3C 279

Author

Wang, Gege, Fan, Junhui, Xiao, Hubing, and Cai, Jinting

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

3C 279 showed enhanced flux variations in Fermi-LAT {\gamma}-ray observations from January to June 2018. We present a detailed Fermi-LAT analysis to investigate the variability and spectral behaviors of 3C 279 during the {\gamma}-ray flares in 2018. In this work, we analyzed the {\gamma}-ray spectra and found that the spectra in either the flaring or quiescent states do not show any clear breaks (or cutoffs). This indicates that the dissipation region is outside the broad-line region, and the energy dissipation may be due to the inverse Compton process of scattering the dust torus infrared photons, this result is also consistent with that in Tolamatti et al. An external inverse Compton scattering of dusty torus (DT) photons is employed to calculate the broadband spectral energy distribution (SED). This model was further supported by the fact that we found flare decay timescale was consistent with the cooling time of relativistic electrons through DT photons. During the SED modeling, a relatively harder spectrum for the electron energy distribution (EED) is found and suggests these electrons may not be accelerated by the shock that happened in the dissipation region. Besides, the magnetic reconnection is also ruled out due to a low magnetization ratio. Thus, we suggest an injection of higher-energy electrons from outside the blob and raising the flare., Comment: 12 pages, 6 figures, published in the Publications of the Astronomical Society of the Pacific

Published
2022
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48. Genome-wide association study reveals the genetic basis of rice resistance to three herbicides

Author

Peizhou Xu, Yuhe Qin, Maosen Ma, Tengfei Liu, Fenhua Ruan, Le Xue, Jiying Cao, Guizong Xiao, Yun Chen, Hongyan Fu, Gege Zhou, Yonghua Xie, and Duo Xia

Subjects
rice, herbicide resistance, genome-wide association studies, genetic diversity, glufosinate, glyphosate, Plant culture, SB1-1110
Abstract

Crop resistance to herbicides is crucial for agricultural productivity and sustainability amidst escalating challenges of weed resistance. Uncovering herbicide resistant genes is particularly important for rice production. In this study, we tested the resistance to three commonly used herbicides: glufosinate, glyphosate and mesotrione of 421 diverse rice cultivars and employed genome-wide association studies (GWAS) to unravel the genetic underpinnings of resistance to these three herbicides in rice. We discovered that cultivated rice exhibited rich variation in resistance to the three herbicides, and the differences among subpopulations were significant. Six identified associations harboring candidate genes for resistance to these herbicides were significant. Among them, RGlu6 and RGly8 were the major QTL for resistance to glufosinate and glyphosate, respectively. The favorable alleles of RGlu6 and RGly8 were primarily present in japonica cultivars that originated from Europe, highlighting the geographic and genetic diversity of herbicide resistance and emphasizing the localized selection pressures in European rice varieties. Moreover, our findings might suggest that traditional target genes may not contain tolerant alleles in nature, and alternative mechanisms with novel loci associated with resistance may work. By mapping the genes for herbicide resistance, our results may help develop new strategies to combat the dual challenges on effective weed management and herbicide sustainability.

Published
2024
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49. Psychological resilience and intention to stay among nurses: the mediating role of perceived organizational support

Author

Jiangfeng Pu, Waner Wang, Gege Li, Zhanghao Xie, Xuanhao Fan, Ningjing Zhan, Yixuan Xu, and Huigen Huang

Subjects
nurses, psychological resilience, perceived organizational support, intention to stay, the mediation effect, Psychology, BF1-990
Abstract

BackgroundThis study aims to analyze the relationship between psychological resilience, perceived organizational support, and intention to stay among nurses. Additionally, it explores the mediating role of perceived organizational support in the relationship between psychological resilience and nurses’ intention to stay.MethodsA cross-sectional survey was conducted between August and September 2023, involving 1,402 nurses from five Grade 3A hospitals in Guangdong. The survey utilized several instruments, including the General Information Questionnaire (GIQ), the Chinese version of the Connor-Davidson Resilience Scale (CD-RISC), the Chinese version of the Perceived Organizational Support Scale (POSS), and the Chinese version of the Intention to Stay Scale (ITSS). The obtained data were analyzed using descriptive statistics and Pearson’s correlation coefficient, while the mediating effect of perceived organizational support was assessed using the PROCESS macro mediation model in SPSS.ResultsThe overall mean score for psychological resilience among nurses in the five Grade 3A hospitals in Guangdong was 60.54 ± 19.17, the overall mean score for perceived organizational support was 45.77 ± 11.49, and the mean score for intention to stay was 20.82 ± 4.65. The results of the statistical analysis revealed positive correlations between psychological resilience and intention to stay (r = 0.388, p

Published
2024
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50. Brain activation in older adults with hypertension and normotension during standing balance task: an fNIRS study

Author

Weichao Fan, Qing Zeng, Peng Zheng, Shuyang Wen, Gege Li, Tao Fan, Guozhi Huang, Manxu Zheng, and Qinglu Luo

Subjects
aging, hypertension, balance, standing, posture control, fNIRS, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

BackgroundHypertension (HT) is a common chronic disease in older adults. It not only leads to dizziness and other symptoms affecting balance in older adults with HT but also affects the hemodynamics of the cerebral cortex. At present, potential neural mechanisms of balance control in older adults with HT are still unclear. Therefore, this study aimed to explore the differences in the center of pressure (COP) and cerebral cortex activation between older adults with HT and normotension (NT) during standing balance tasks. This study May provide guidance for the early detection of the risk of falls among older adults with HT and the development of clinical rehabilitation strategies.MethodsIn this cross-sectional study, 30 older adults with NT (NT group) and 27 older adults with HT (HT group) were subjected to three conditions: task 1, standing with eyes open on a stable surface; task 2, standing with eyes closed on a stable surface; and task 3, standing with eyes open on the surface of the foam pad. Cortical hemodynamic reactions were measured using functional near-infrared spectroscopy, and COP parameters were measured using a force plate.ResultsThe mean velocity of the COP in the medial–lateral direction in the NT group was significantly higher than that in the HT group (F = 5.955, p = 0.018) during task 3. When proprioception was disturbed, the activation of the left premotor cortex and supplementary motor cortex in the HT group was significantly lower than that in the NT group (F = 14.381, p

Published
2024
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