Porczak, A. E., & Feng, N. Y. (2025). Hibernation as a model for skeletal muscle preservation. Ann NY Acad Sci., 1549, 22–43. https://doi.org/10.1111/nyas.15389
In the originally published article, the arrow next to “Calpastatin” in Figure 4 incorrectly pointed down. The correct version of the figure is shown below. This has been corrected in the online version of the article.
{"title":"Correction to “Hibernation as a model for skeletal muscle preservation”","authors":"","doi":"10.1111/nyas.70176","DOIUrl":"10.1111/nyas.70176","url":null,"abstract":"<p>Porczak, A. E., & Feng, N. Y. (2025). Hibernation as a model for skeletal muscle preservation. <i>Ann NY Acad Sci</i>., 1549, 22–43. https://doi.org/10.1111/nyas.15389</p><p>In the originally published article, the arrow next to “Calpastatin” in Figure 4 incorrectly pointed down. The correct version of the figure is shown below. This has been corrected in the online version of the article.</p><p>We apologize for this error.</p><p></p>","PeriodicalId":8250,"journal":{"name":"Annals of the New York Academy of Sciences","volume":"1554 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://nyaspubs.onlinelibrary.wiley.com/doi/epdf/10.1111/nyas.70176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Calligraphy is the artistic expression of written language, similar to how songs artistically express verbal language. This article proposes a triangular-network model for neural processing of calligraphy, which builds on recent findings about the neurophysiology of writing and theories about neural processing of other art forms. This model emphasizes that calligraphy appreciation relies on both visual spatial features and temporal motoric features of the actual or mentally simulated writing process. While the appreciation of visual spatial features may engage neural mechanisms encoding other visual arts, the appreciation of temporal motoric features may engage a prediction-based mechanism-the brain actively predicts the writing movement, and the fulfillment or violation of these predictions generates pleasure, similar to how predictions may contribute to music appreciation. Furthermore, it is hypothesized that practicing calligraphy enriches the motoric representation of writing by directing attention to otherwise subconscious movement units, similar to how dancing is mindful movement. The triangular-network model provides a theoretical framework for neural encoding of calligraphy, generates testable predictions, and forges a link to neural encoding of other art forms.
{"title":"Triangular Network Model for the Neural Basis of Calligraphy.","authors":"Nai Ding","doi":"10.1111/nyas.70153","DOIUrl":"https://doi.org/10.1111/nyas.70153","url":null,"abstract":"Calligraphy is the artistic expression of written language, similar to how songs artistically express verbal language. This article proposes a triangular-network model for neural processing of calligraphy, which builds on recent findings about the neurophysiology of writing and theories about neural processing of other art forms. This model emphasizes that calligraphy appreciation relies on both visual spatial features and temporal motoric features of the actual or mentally simulated writing process. While the appreciation of visual spatial features may engage neural mechanisms encoding other visual arts, the appreciation of temporal motoric features may engage a prediction-based mechanism-the brain actively predicts the writing movement, and the fulfillment or violation of these predictions generates pleasure, similar to how predictions may contribute to music appreciation. Furthermore, it is hypothesized that practicing calligraphy enriches the motoric representation of writing by directing attention to otherwise subconscious movement units, similar to how dancing is mindful movement. The triangular-network model provides a theoretical framework for neural encoding of calligraphy, generates testable predictions, and forges a link to neural encoding of other art forms.","PeriodicalId":8250,"journal":{"name":"Annals of the New York Academy of Sciences","volume":"142 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145663976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Iron is essential for brain metabolism and cognitive functioning, but excessive levels during healthy and pathological aging can have detrimental effects. Although this notion was supported by several single studies, meta-analytic evidence in Alzheimer's disease (AD) is still scarce. Therefore, we performed a meta-analysis of 23 MRI experiments with, in total, 715 AD patients and 1130 healthy controls (HC). All studies employed iron sensitive markers in basal ganglia structures, thalamus, and hippocampus, together with the Mini-Mental-Status-Examination (MMSE) to quantify cognitive performance. In all regions of interest, significantly higher iron levels were present in people with AD compared to HC, with the most pronounced effects in the putamen followed by the caudate. Importantly, only globus pallidus iron levels were negatively correlated with MMSE performance in AD patients. Our results provide unique evidence that increases in iron levels, especially within basal ganglia structures, which provide a hub for cognitive information processing, are a characteristic hallmark of AD.
{"title":"Globus Pallidus Iron Relates to Cognitive Impairment in Alzheimer's Disease: Evidence From MRI-Based Meta-Analysis.","authors":"Marthe Mieling,Clara Wiskow,Nico Bunzeck","doi":"10.1111/nyas.70078","DOIUrl":"https://doi.org/10.1111/nyas.70078","url":null,"abstract":"Iron is essential for brain metabolism and cognitive functioning, but excessive levels during healthy and pathological aging can have detrimental effects. Although this notion was supported by several single studies, meta-analytic evidence in Alzheimer's disease (AD) is still scarce. Therefore, we performed a meta-analysis of 23 MRI experiments with, in total, 715 AD patients and 1130 healthy controls (HC). All studies employed iron sensitive markers in basal ganglia structures, thalamus, and hippocampus, together with the Mini-Mental-Status-Examination (MMSE) to quantify cognitive performance. In all regions of interest, significantly higher iron levels were present in people with AD compared to HC, with the most pronounced effects in the putamen followed by the caudate. Importantly, only globus pallidus iron levels were negatively correlated with MMSE performance in AD patients. Our results provide unique evidence that increases in iron levels, especially within basal ganglia structures, which provide a hub for cognitive information processing, are a characteristic hallmark of AD.","PeriodicalId":8250,"journal":{"name":"Annals of the New York Academy of Sciences","volume":"1 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145663930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The global mental health crisis has escalated to unprecedented levels, with stress, anxiety, and depression posing major public health concerns. Conventional interventions have shown limited success in addressing these multifaceted issues, prompting researchers to explore alternative solutions. Yoga Nidra (YN), a meditative practice, has gained momentum over the past decade as a potential holistic approach to mental health care. Yet, its clinical effectiveness remains inadequately understood. This systematic review and meta‐analysis rigorously assessed YN's impact on stress, anxiety, and depression. A comprehensive search of seven databases and one trial database yielded 814 articles, of which 73 studies involving 5201 participants met the inclusion criteria. Between‐group meta‐analyses revealed significant benefits of YN for stress (Hedge's g : −0.80 with active comparator, −1.70 with no comparator), anxiety (active: −1.35, no comparator: −1.43), and depression (active: −0.69, no comparator: −0.92). Within‐group analyses supported these effects, reinforcing YN's therapeutic potential. However, given the low methodological quality and variability in intervention delivery, these moderate‐to‐large effects should be interpreted cautiously, as they likely reflect inflated estimates. Despite these limitations, YN shows potential in managing mental health symptoms, underscoring the need for high‐quality, standardized research to establish its efficacy as a viable clinical intervention.
{"title":"Effects of Yoga Nidra on Stress, Anxiety, and Depression: A Systematic Review and Meta‐Analysis","authors":"Shashank Ghai, Pawel Odyniec, Ishan Ghai","doi":"10.1111/nyas.70149","DOIUrl":"https://doi.org/10.1111/nyas.70149","url":null,"abstract":"The global mental health crisis has escalated to unprecedented levels, with stress, anxiety, and depression posing major public health concerns. Conventional interventions have shown limited success in addressing these multifaceted issues, prompting researchers to explore alternative solutions. Yoga Nidra (YN), a meditative practice, has gained momentum over the past decade as a potential holistic approach to mental health care. Yet, its clinical effectiveness remains inadequately understood. This systematic review and meta‐analysis rigorously assessed YN's impact on stress, anxiety, and depression. A comprehensive search of seven databases and one trial database yielded 814 articles, of which 73 studies involving 5201 participants met the inclusion criteria. Between‐group meta‐analyses revealed significant benefits of YN for stress (Hedge's <jats:italic>g</jats:italic> : −0.80 with active comparator, −1.70 with no comparator), anxiety (active: −1.35, no comparator: −1.43), and depression (active: −0.69, no comparator: −0.92). Within‐group analyses supported these effects, reinforcing YN's therapeutic potential. However, given the low methodological quality and variability in intervention delivery, these moderate‐to‐large effects should be interpreted cautiously, as they likely reflect inflated estimates. Despite these limitations, YN shows potential in managing mental health symptoms, underscoring the need for high‐quality, standardized research to establish its efficacy as a viable clinical intervention.","PeriodicalId":8250,"journal":{"name":"Annals of the New York Academy of Sciences","volume":"1 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145650835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bearing surface defect detection is critical for industrial equipment reliability, but existing deep learning methods suffer from low accuracy for small targets, high computational complexity, and limited edge device deployment. This paper proposes an efficient defect detection algorithm based on the StarNet‐MEIS‐FDConv‐detection transformer (SMF‐DETR). The algorithm employs element‐level multiplication operations in the backbone network to achieve high‐dimensional feature mapping, effectively reducing computational complexity while improving feature extraction capability. The multiscale edge information selection mechanism processes features at different resolutions simultaneously to improve small defect detection. Frequency domain dynamic convolution adapts to different frequency components for optimal feature extraction while maintaining computational efficiency. Experiments on custom bearing defect datasets show that SMF‐DETR achieves 96.2% mean average precision@50 (mAP@50) and 98.1% accuracy, improving baseline performance by 3.1% and 2.9%, respectively. The model also reduces computational cost by 57.7% and model size by 37.1%. Processing speeds reach 97.3 frames per second (FPS) on desktop systems and 58.1 FPS on embedded RK3588 platforms, meeting industrial real‐time detection requirements. Finally, experimental validation was conducted on the publicly available bearing defect‐detection dataset and the PASCAL visual object classes dataset, demonstrating the algorithm's versatility and generalization capabilities.
{"title":"SMF‐DETR: An Efficient Lightweight Detection Transformer for Real‐Time Bearing Surface Defect Detection","authors":"Min Gao, Xiaoping Kang, Kun Zhou, Teng Xie","doi":"10.1111/nyas.70156","DOIUrl":"https://doi.org/10.1111/nyas.70156","url":null,"abstract":"Bearing surface defect detection is critical for industrial equipment reliability, but existing deep learning methods suffer from low accuracy for small targets, high computational complexity, and limited edge device deployment. This paper proposes an efficient defect detection algorithm based on the StarNet‐MEIS‐FDConv‐detection transformer (SMF‐DETR). The algorithm employs element‐level multiplication operations in the backbone network to achieve high‐dimensional feature mapping, effectively reducing computational complexity while improving feature extraction capability. The multiscale edge information selection mechanism processes features at different resolutions simultaneously to improve small defect detection. Frequency domain dynamic convolution adapts to different frequency components for optimal feature extraction while maintaining computational efficiency. Experiments on custom bearing defect datasets show that SMF‐DETR achieves 96.2% mean average precision@50 (mAP@50) and 98.1% accuracy, improving baseline performance by 3.1% and 2.9%, respectively. The model also reduces computational cost by 57.7% and model size by 37.1%. Processing speeds reach 97.3 frames per second (FPS) on desktop systems and 58.1 FPS on embedded RK3588 platforms, meeting industrial real‐time detection requirements. Finally, experimental validation was conducted on the publicly available bearing defect‐detection dataset and the PASCAL visual object classes dataset, demonstrating the algorithm's versatility and generalization capabilities.","PeriodicalId":8250,"journal":{"name":"Annals of the New York Academy of Sciences","volume":"45 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2025-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The perception of aberrant data (PAD) is an essential cognitive ability in human socialization, yet the underlying dual processing mechanisms remain underexplored. Based on dual processing theory, this study uses electroencephalogram (EEG) time-frequency analysis to investigate the mediating role and representational patterns of neural oscillatory activity in automatic processes (APs) and controlled processes (CPs). The results indicated that during the PAD task, β oscillations in the frontal-parietal regions exhibited clear event-related desynchronization in the AP mode, whereas β oscillations displayed prominent event-related synchronization in the CP mode. The brain network excitation mediated by β oscillations was closely followed by brain network inhibition mediated by α oscillations, allowing for effective separation of the dual processing modes in PAD tasks through the β-kα index (p < 0.001). Moreover, in the PAD task, the AP mode was primarily attributed to the efficient communication mediated by cross-frequency phase coherence between β and α oscillations, as well as information integration mediated by intersite phase coherence in the frontal-parietal regions. This study provides a framework for a comprehensive understanding of the dual processing neural mechanisms behind PAD, with promising applications in the study of pathophysiological mechanisms in neurodegenerative diseases and clinical interventions.
{"title":"Dual Processing of Aberrant Data Perception: Evidence From EEG Oscillations.","authors":"Haihong Yu,Yitao Chen,Dandan Li,Wei Liu,Bo Dong,Guanxiong Pei","doi":"10.1111/nyas.70146","DOIUrl":"https://doi.org/10.1111/nyas.70146","url":null,"abstract":"The perception of aberrant data (PAD) is an essential cognitive ability in human socialization, yet the underlying dual processing mechanisms remain underexplored. Based on dual processing theory, this study uses electroencephalogram (EEG) time-frequency analysis to investigate the mediating role and representational patterns of neural oscillatory activity in automatic processes (APs) and controlled processes (CPs). The results indicated that during the PAD task, β oscillations in the frontal-parietal regions exhibited clear event-related desynchronization in the AP mode, whereas β oscillations displayed prominent event-related synchronization in the CP mode. The brain network excitation mediated by β oscillations was closely followed by brain network inhibition mediated by α oscillations, allowing for effective separation of the dual processing modes in PAD tasks through the β-kα index (p < 0.001). Moreover, in the PAD task, the AP mode was primarily attributed to the efficient communication mediated by cross-frequency phase coherence between β and α oscillations, as well as information integration mediated by intersite phase coherence in the frontal-parietal regions. This study provides a framework for a comprehensive understanding of the dual processing neural mechanisms behind PAD, with promising applications in the study of pathophysiological mechanisms in neurodegenerative diseases and clinical interventions.","PeriodicalId":8250,"journal":{"name":"Annals of the New York Academy of Sciences","volume":"422 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Skin cutaneous melanoma (SKCM), the most aggressive form of cutaneous malignancy globally, remains poorly understood in terms of its molecular drivers. Although the copper metabolism MURR1 domain (COMMD) protein family has been associated with oncogenesis, its functional relevance in SKCM is undefined. In this study, we identified COMMD4 as a prognostic biomarker of SKCM and showed that it is positively correlated with the adverse clinical outcomes of patients. COMMD4 gene knockout (COMMD4‐KO) impaired the proliferative, migratory, and invasive capacities of SKCM cells in vitro and suppressed xenograft tumor growth in vivo. Mechanistically, COMMD4‐KO induced G2/M phase arrest by disrupting p21‐CDK1‐cyclinB1 and impeded epithelial‐mesenchymal transition (EMT) by reversing the E/N cadherin switch. We also demonstrate that COMMD4 activates PI3K‐AKT signaling by binding PI3K‐p85 to release PI3K‐p110, thereby driving G2/M transition and EMT. Reactivation of PI3K‐AKT signaling in COMMD4‐KO cells rescued oncogenic phenotypes. By integrative Connectivity Map analysis and functional validation, we identified triamterene as a pharmacological inhibitor targeting the COMMD4‐PI3K‐AKT axis, which suppressed the progression of SKCM effectively in vitro and vivo. Our findings establish the COMMD4‐PI3K‐AKT axis as a novel and critical regulator of SKCM progression and repurpose triamterene as a promising therapeutic agent against SKCM.
{"title":"COMMD4 Drives Skin Cutaneous Melanoma Progression by Targeting PI3K‐p85 to Activate PI3K‐AKT","authors":"Xiaoqiang Liu, Luojia Liu, Qiaoling Wang, Lufan Xia, Fangqing Zuo, Jinrui Yang, Kaibang Zheng, Yunfan Tang, Jingjing Guo, Xiaoping Yu, Boye Qi, Hanghang Zhou, Ying Chen, Jiaping Zhang, Xuanfen Zhang","doi":"10.1111/nyas.70141","DOIUrl":"https://doi.org/10.1111/nyas.70141","url":null,"abstract":"Skin cutaneous melanoma (SKCM), the most aggressive form of cutaneous malignancy globally, remains poorly understood in terms of its molecular drivers. Although the copper metabolism MURR1 domain (COMMD) protein family has been associated with oncogenesis, its functional relevance in SKCM is undefined. In this study, we identified COMMD4 as a prognostic biomarker of SKCM and showed that it is positively correlated with the adverse clinical outcomes of patients. COMMD4 gene knockout (COMMD4‐KO) impaired the proliferative, migratory, and invasive capacities of SKCM cells in vitro and suppressed xenograft tumor growth in vivo. Mechanistically, COMMD4‐KO induced G2/M phase arrest by disrupting p21‐CDK1‐cyclinB1 and impeded epithelial‐mesenchymal transition (EMT) by reversing the E/N cadherin switch. We also demonstrate that COMMD4 activates PI3K‐AKT signaling by binding PI3K‐p85 to release PI3K‐p110, thereby driving G2/M transition and EMT. Reactivation of PI3K‐AKT signaling in COMMD4‐KO cells rescued oncogenic phenotypes. By integrative Connectivity Map analysis and functional validation, we identified triamterene as a pharmacological inhibitor targeting the COMMD4‐PI3K‐AKT axis, which suppressed the progression of SKCM effectively in vitro and vivo. Our findings establish the COMMD4‐PI3K‐AKT axis as a novel and critical regulator of SKCM progression and repurpose triamterene as a promising therapeutic agent against SKCM.","PeriodicalId":8250,"journal":{"name":"Annals of the New York Academy of Sciences","volume":"15 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145583275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Various cellular processes, such as DNA repair and signal transduction, are regulated through ubiquitination and deubiquitination. Dysregulation of ubiquitination cascade enzymes and deubiquitinating enzymes leads to various diseases. Among them, deubiquitinating enzymes have been shown to be closely associated with cancer, cardiovascular disease, and metabolic diseases. Recent studies have found that deubiquitinating enzymes play an important role in controlling neuronal fate, synaptic development, and maintaining normal nervous system function. USP10, a member of the deubiquitinating enzyme family, regulates the progression of various diseases by acting on different substrates and modulating their functions. USP10 has been shown to regulate neurological diseases by mediating pathways such as immune response, oxidative stress, and apoptosis. This review provides a comprehensive overview of the molecular structure of USP10, identifies its substrate-binding sites, and summarizes its biological functions, particularly in relation to neurological diseases, including Alzheimer's disease, Parkinson's disease, glioblastoma, and ischemic stroke. USP10 promotes pathological progression in Alzheimer's disease and glioblastoma on the one hand, and exerts protective effects in Parkinson's disease and ischemic stroke on the other. Additionally, we summarize recent progress in the development and application of USP10 modulators and potential therapeutic strategies targeting USP10 in neurological disorders.
{"title":"USP10 in Neurological Disorders: Mechanistic Insights and Emerging Therapeutic Strategies.","authors":" Celemuge,Hongying Sun,Jia Zhang,Yang Yang,Jian Mao, Cheliger","doi":"10.1111/nyas.70144","DOIUrl":"https://doi.org/10.1111/nyas.70144","url":null,"abstract":"Various cellular processes, such as DNA repair and signal transduction, are regulated through ubiquitination and deubiquitination. Dysregulation of ubiquitination cascade enzymes and deubiquitinating enzymes leads to various diseases. Among them, deubiquitinating enzymes have been shown to be closely associated with cancer, cardiovascular disease, and metabolic diseases. Recent studies have found that deubiquitinating enzymes play an important role in controlling neuronal fate, synaptic development, and maintaining normal nervous system function. USP10, a member of the deubiquitinating enzyme family, regulates the progression of various diseases by acting on different substrates and modulating their functions. USP10 has been shown to regulate neurological diseases by mediating pathways such as immune response, oxidative stress, and apoptosis. This review provides a comprehensive overview of the molecular structure of USP10, identifies its substrate-binding sites, and summarizes its biological functions, particularly in relation to neurological diseases, including Alzheimer's disease, Parkinson's disease, glioblastoma, and ischemic stroke. USP10 promotes pathological progression in Alzheimer's disease and glioblastoma on the one hand, and exerts protective effects in Parkinson's disease and ischemic stroke on the other. Additionally, we summarize recent progress in the development and application of USP10 modulators and potential therapeutic strategies targeting USP10 in neurological disorders.","PeriodicalId":8250,"journal":{"name":"Annals of the New York Academy of Sciences","volume":"33 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145583373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study explores the brain–heart axis and its directional control dynamics across various sleep stages using electroencephalography and electrocardiogram data from publicly available whole‐night recordings of 50 healthy individuals. Utilizing a validated functional brain–heart interplay (BHI) mathematical model, we identified a decrease in central control over peripheral neural activity regulating heartbeat dynamics during non‐REM sleep. In contrast, an increase in sympathovagal activity influencing cortical function was observed during deep sleep, particularly in Non‐REM3, compared to light sleep and REM phases. These results indicate a dynamic shift in the functional balance of the brain–heart axis and related BHI throughout sleep stages, characterized by predominant central control during wakefulness and enhanced bodily neuro‐cardiac‐autonomic regulation during deep sleep.
{"title":"Cardiovascular Activity Predominantly Modulates Brain Dynamics in Non‐REM Sleep Transitions","authors":"Vincenzo Catrambone, Ugo Faraguna, Gaetano Valenza","doi":"10.1111/nyas.70138","DOIUrl":"https://doi.org/10.1111/nyas.70138","url":null,"abstract":"This study explores the brain–heart axis and its directional control dynamics across various sleep stages using electroencephalography and electrocardiogram data from publicly available whole‐night recordings of 50 healthy individuals. Utilizing a validated functional brain–heart interplay (BHI) mathematical model, we identified a decrease in central control over peripheral neural activity regulating heartbeat dynamics during non‐REM sleep. In contrast, an increase in sympathovagal activity influencing cortical function was observed during deep sleep, particularly in Non‐REM3, compared to light sleep and REM phases. These results indicate a dynamic shift in the functional balance of the brain–heart axis and related BHI throughout sleep stages, characterized by predominant central control during wakefulness and enhanced bodily neuro‐cardiac‐autonomic regulation during deep sleep.","PeriodicalId":8250,"journal":{"name":"Annals of the New York Academy of Sciences","volume":"142 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Weakly supervised semantic segmentation plays a pivotal role in domains such as autonomous driving and medical image analysis. However, existing approaches often rely on limited semantic cues from single images or paired samples, leading to underutilized intraclass information and entangled interclass features—both of which significantly impair segmentation performance. To address these challenges, we propose a novel dual‐dimensional contrastive learning (D2CL) framework that explores fine‐grained feature attributes both across and within views, thereby promoting intraclass compactness and interclass discriminability in the feature space. Specifically, the interclass prototype contrastive learning module constructs a cross‐view dynamic prototype memory bank and imposes a contrastive loss to enhance category‐level distinctiveness. In parallel, the intraclass pixel contrastive learning module focuses on pixel‐wise variations within the same category from a single view, enabling the model to capture more refined semantic details and better handle intraclass diversity. Extensive experiments conducted on the PASCAL VOC 2012 and MS COCO 2014 datasets demonstrate that D2CL consistently boosts the performance of multiple baseline models. For instance, the mean intersection over union of the baseline model SEAM is improved from 64.5% to 67.7%, while another model AMN sees an increase from 69.6% to 71.8%, highlighting the general applicability and effectiveness of our method.
{"title":"D2CL: A Dual‐Dimensional Contrastive Learning Method for Enhancing the Performance of Weakly Supervised Semantic Segmentation","authors":"Qihang Jia, Xiangfu Ding, Na Tian, Wencang Zhao","doi":"10.1111/nyas.70130","DOIUrl":"https://doi.org/10.1111/nyas.70130","url":null,"abstract":"Weakly supervised semantic segmentation plays a pivotal role in domains such as autonomous driving and medical image analysis. However, existing approaches often rely on limited semantic cues from single images or paired samples, leading to underutilized intraclass information and entangled interclass features—both of which significantly impair segmentation performance. To address these challenges, we propose a novel dual‐dimensional contrastive learning (D2CL) framework that explores fine‐grained feature attributes both across and within views, thereby promoting intraclass compactness and interclass discriminability in the feature space. Specifically, the interclass prototype contrastive learning module constructs a cross‐view dynamic prototype memory bank and imposes a contrastive loss to enhance category‐level distinctiveness. In parallel, the intraclass pixel contrastive learning module focuses on pixel‐wise variations within the same category from a single view, enabling the model to capture more refined semantic details and better handle intraclass diversity. Extensive experiments conducted on the PASCAL VOC 2012 and MS COCO 2014 datasets demonstrate that D2CL consistently boosts the performance of multiple baseline models. For instance, the mean intersection over union of the baseline model SEAM is improved from 64.5% to 67.7%, while another model AMN sees an increase from 69.6% to 71.8%, highlighting the general applicability and effectiveness of our method.","PeriodicalId":8250,"journal":{"name":"Annals of the New York Academy of Sciences","volume":"101 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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