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Autolysosomal Dysfunction in Obesity-induced Metabolic Inflammation and Related Disorders

Current Obesity Reports 2025年第1期14卷 1-16页

作者： Cheong, Lenny Yi Tong Saipuljumri, Eka Norfaishanty Loi, Gavin Wen Zhao Zeng, Jialiu Lo, Chih Hung Lee Kong Chian School of Medicine Nanyang Technological University Singapore 308232 Singapore Program in Neuroscience & Cognitive Science University of Arizona Tucson 85721 AZ United States School of Biomedical Sciences The University of Queensland St Lucia 4072 QLD Australia Department of Biomedical and Chemical Engineering Syracuse University Syracuse 13244 NY United States Interdisciplinary Neuroscience Program Syracuse University Syracuse 13244 NY United States Department of Biology Syracuse University Syracuse 13244 NY United States

Purpose of Review: Obesity is a global health crisis affecting individuals across all age groups, significantly increasing the risk of metabolic disorders such as type 2 diabetes (T2D), metabolic dysfunction-associated fatty liver disease (MAFLD), and cardiovascular diseases. The World Health Organization reported in 2022 that 2.5 billion adults were overweight, with 890 million classified as obese, emphasizing the urgent need for effective interventions. A critical aspect of obesity’s pathophysiology is meta-inflammation—a chronic, systemic low-grade inflammatory state driven by excess adipose tissue, which disrupts metabolic homeostasis. This review examines the role of autolysosomal dysfunction in obesity-related metabolic disorders, exploring its impact across multiple metabolic organs and evaluating potential therapeutic strategies that target autophagy and lysosomal function. Recent Findings: Emerging research highlights the importance of autophagy in maintaining cellular homeostasis and metabolic balance. Obesity-induced lysosomal dysfunction impairs the autophagic degradation process, contributing to the accumulation of damaged organelles and toxic aggregates, exacerbating insulin resistance, lipotoxicity, and chronic inflammation. Studies have identified autophagic defects in key metabolic tissues, including adipose tissue, skeletal muscle, liver, pancreas, kidney, heart, and brain, linking autophagy dysregulation to the progression of metabolic diseases. Preclinical investigations suggest that pharmacological and nutritional interventions—such as AMPK activation, caloric restriction mimetics, and lysosomal-targeting compounds—can restore autophagic function and improve metabolic outcomes in obesity models. Summary: Autolysosomal dysfunction is a pivotal contributor to obesity-associated metabolic disorders, influencing systemic inflammation and metabolic dysfunction. Restoring autophagy and lysosomal function holds promise as a therapeutic strategy to miti

关键词： Autophagy function Lysosomal acidification Metabolic inflammation Neurodegeneration Neuroinflammation Therapeutic strategies

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Meter-scale heterostructure printing for high-toughness fiber electrodes in intelligent digital apparel

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Nature Communications 2025年第1期16卷 1-10页

作者： Lee, Gun-Hee Lee, Yunheum Seo, Hyeonyeob Jo, Kyunghyun Yeo, Jinwook Kim, Semin Bae, Jae-Young Kim, Chul Majidi, Carmel Kang, Jiheong Kang, Seung-Kyun Ryu, Seunghwa Park, Seongjun Medical Research Center Seoul National University Seoul South Korea Departments of Cogno-Mechatronics Engineering and Optics & Mechatronics Engineering Pusan National University Busan South Korea Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea Program of Brain and Cognitive Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea Department of Mechanical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea Graduate School of Semiconductor Technology Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea Department of Materials Science and Engineering Seoul National University Seoul South Korea Department of Mechanical Engineering Carnegie Mellon University Pittsburgh PA United States Department of Chemistry Seoul National University Seoul Seoul South Korea School of Transdisciplinary Innovations Seoul National University Seoul South Korea Department of Biomedical Science College of Medicine Seoul National University Seoul South Korea Interdisciplinary Program in Bioengineering College of Engineering Seoul National University Seoul South Korea Department of Transdisciplinary Medicine Seoul National University Hospital Seoul South Korea

Intelligent digital apparel, which integrates electronic functionalities into clothing, represents the future of healthcare and ubiquitous control in wearable devices. Realizing such apparel necessitates developing meter-scale conductive fibers with high toughness, conductivity, stable conductance under deformation, and mechanical durability. In this study, we present a heterostructure printing method capable of producing meter-scale (~50 m) biphasic conductive fibers that meet these criteria. Our approach involves encapsulating deformable liquid metal particles (LMPs) within a functionalized thermoplastic polyurethane matrix. This encapsulation induces in situ assembly of LMPs during fiber formation, creating a heterostructure that seamlessly integrates the matrix’s durability with the LMPs’ superior electrical performance. Unlike rigid conductive materials, deformable LMPs offer stretchability and toughness with a low gauge factor. Through precise twisting using an engineered annealing machine, multiple fiber strands are transformed into robust, electrically stable meter-scale electrodes. This advancement enhances their practicality in various intelligent digital apparel applications, such as stretchable displays, wearable healthcare systems, and digital controls. © The Author(s) 2025.

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Competitor Activation and Semantic Interference: Evidence from Combined Phonological and Semantic Similarity 36

Competitor Activation and Semantic Interference: Evidence fr...

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36th Annual Meeting of the cognitive science Society, CogSci 2014

作者： Frazer, Alexandra K. O'Séaghdha, Padraig G. Munoz-Avila, Hector Roessler, Nicholas Department of Psychology and Cognitive Science Program Lehigh University United States Computer Science and Engineering and Cognitive Science Program Lehigh University United States

ISBN: (纸本)9780991196708

Incremental learning explanations state that semantic interference is driven by activation levels of competitors. To explore nonsemantic contributions to interference, we examined the combined and separate effects of facilitatory phonological form preparation and semantic relatedness in a blocked cyclic picture naming procedure. Phonological similarity was facilitatory when tested separately, but had little effect when tested with the other conditions. We found about twice as much interference in word sets that shared both meaning and form (e.g., cyclically name puffin, pigeon, and peacock) as in semantic-only sets. Thus, phonological similarity impacted interference when it was combined with a semantic attribute. A computational model that isolated the learning mechanism and eliminated carryover effects simulated this result and additionally showed cumulative interference over naming cycles. Together with other findings from our research group and in the literature, these results suggest that co-activation from a variety of sources can drive interference. © 2014 Proceedings of the 36th Annual Meeting of the cognitive science Society, CogSci 2014. All rights reserved.

关键词： Semantics

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Role of simulation models in understanding the generation of behavior in C. elegans

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Current Opinion in Systems Biology 2019年 13卷 93-101页

作者： Izquierdo, Eduardo J. Cognitive Science Program School of Informatics Computing and Engineering Indiana University United States

There has been a proliferation of studies using simulation models to achieve a system-level understanding of behavior in Caenorhabditis elegans. Here I discuss the different aims of these modeling approaches and review examples of each in the context of locomotion and spatial navigation in the worm. I argue that as the focus shifts from understanding sensorimotor transformations to understanding neuromechanical instantiations for how behaviors are generated, simulation models will become a more integral part of the experiment–theory cycle. I discuss the insights that simulation modeling provides that complement information obtained from experimental discovery methods. © 2018 Elsevier Ltd

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Does dynamic cursor control gain improve the performance of selection task in wearable computing?

Does dynamic cursor control gain improve the performance of ...

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9th IEEE International Symposium on Wearable Computers, ISWC 2005

作者： Hong, Ji-Young Chae, Haeng-Suk Yoo, Sang Kim, Moon-Ju Han, Kwang-Hee Cognitive Engineering Lab. Grad. Program in Cognitive Science Yonsei Univ. Korea Republic of

ISBN: (纸本)0769524192

Recent research investigating the use of a trackball mouse in a wearable computing context showed that users have difficulty moving the cursor to the correct location [1]. Dynamic gain control may make cursor not to slip out of the target [2]. In this study, we test the use of dynamic gain with a portable trackball mouse in improving users' performance in a pointing task Experimental results reveal that dynamic gain control improves the pointing task performance in terms of speed and index of performance [4] of hitting the targets. © 2005 IEEE.

关键词： Wearable computers

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Multifunctionality in embodied agents: Three levels of neural reuse 40

Multifunctionality in embodied agents: Three levels of neura...

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40th Annual Meeting of the cognitive science Society: Changing Minds, CogSci 2018

作者： Candadai, Madhavun Izquierdo, Eduardo J. Cognitive Science Program Indiana University BloomingtonIN47406 United States Cognitive Science Program School of Informatics Computing and Engineering Indiana University BloomingtonIN47406 United States

ISBN: (纸本)9780991196784

The brain in conjunction with the body is able to adapt to new environments and perform multiple behaviors through reuse of neural resources and transfer of existing behavioral traits. Although mechanisms that underlie this ability are not well understood, they are largely attributed to neuromodulation. In this work, we demonstrate that an agent can be multifunctional using the same sensory and motor systems across behaviors, in the absence of modulatory mechanisms. Further, we lay out the different levels at which neural reuse can occur through a dynamical filtering of the brain-body-environment system's operation: structural network, autonomous dynamics, and transient dynamics. Notably, transient dynamics reuse could only be explained by studying the brain-body-environment system as a whole and not just the brain. The multifunctional agent we present here demonstrates neural reuse at all three levels. © 2018 Proceedings of the 40th Annual Meeting of the cognitive science Society, CogSci 2018. All rights reserved.

关键词： Neural networks

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A NEWBORN EMBODIED TURING TEST FOR COMPARING OBJECT SEGMENTATION ACROSS ANIMALS AND MACHINES 12

A NEWBORN EMBODIED TURING TEST FOR COMPARING OBJECT SEGMENTA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Garimella, Manjulata Pak, Denizhan Wood, Justin N. Wood, Samantha M.W. Department of Informatics School of Informatics Computing & Engineering Department of Psychological & Brain Sciences Cognitive Science Program Program in Neuroscience

Newborn brains rapidly learn to solve challenging object perception tasks, including segmenting objects from backgrounds and recognizing objects across new viewing situations. Conversely, modern machine learning (ML) algorithms are "data hungry," requiring more training data than brains to reach similar performance levels. How do we close this learning gap between brains and machines? Here, we introduce a new benchmark-a Newborn Embodied Turing Test (NETT) for object segmentation-in which newborn animals and machines are raised in the same environments and tested with the same tasks, permitting direct comparison of their learning. First, newborn chicks were raised in controlled environments containing a single object rotating on a single background, then their recognition performance was tested across new backgrounds and viewpoints. Second, we performed "digital twin" experiments in which artificial agents were reared and tested in virtual environments that mimicked the rearing and testing conditions of the chicks. We inserted a variety of ML "brains" into the artificial agents and measured whether those algorithms learned common object recognition behavior as chicks. All newborn chicks solved this one-shot object segmentation task, successfully learning background-invariant object representations that generalized across new backgrounds and viewpoints. In contrast, none of the artificial agents solved the task, instead learning background-dependent representations that failed to generalize across new backgrounds and viewpoints. This digital twin design exposes core limitations in current ML algorithms in developing brain-like object perception. Our NETT is publicly available for comparing ML algorithms with newborn chicks. We argue that NETT benchmarks can help researchers build embodied AI systems that learn as efficiently and robustly as newborn brains. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Animals

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Accessing the viscera: Technologies for interoception research

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Current Opinion in Neurobiology 2025年 93卷 103050-103050页

作者： Pang, Karen K.L. Mondal, Rajib Sahasrabudhe, Atharva Anikeeva, Polina Department of Brain and Cognitive Sciences Massachusetts Institute of Technology United States K. Lisa Yang Brain-Body Center Massachusetts Institute of Technology United States McGovern Institute for Brain Research Massachusetts Institute of Technology United States Research Laboratory of Electronics Massachusetts Institute of Technology United States MIT-Harvard Graduate Program in Health Sciences and Technology United States Department of Materials Science and Engineering Massachusetts Institute of Technology United States

Interoception, or the perception and regulation of body signals by the central nervous system, is critical for maintaining homeostasis and coordination of behaviors. Deciphering the mechanisms of interoception requires identifying pathways and decoding of diverse signals across the brain-body axis. These studies are enabled by tools to modulate and record physiological processes in the brain and visceral organs. While numerous advanced neurotechnologies are well-established in the brain, these techniques often offer limited utility for other organs, such as the gastrointestinal tract, heart, liver, or bladder. In this review, we highlight recent advances in technologies for recording and modulation of visceral organ physiology in small animals in vivo, with a focus on implantable bioelectronic organ interfaces that can be deployed in behaving animals. We discuss how such interfaces are made possible through innovations in materials and electronics and outline unmet technological challenges in interoception research. © 2025 Elsevier Ltd

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What does functional connectivity tell us about the behaviorally-functional connectivity of a multifunctional neural circuit?

What does functional connectivity tell us about the behavior...

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2022 Conference on Artificial Life, ALIFE 2022

作者： Izquierdo, Eduardo J. Candadai, Madhavun Cognitive Science Program Indiana University Luddy School of Informatics Computing and Engineering Indiana University United States

What insights can statistical analysis of the time series recordings of neurons and brain regions during behavior give about the neural basis of behavior? With the increasing amount of whole-brain imaging data becoming available, the importance of addressing this unanswered theoretical challenge has become increasingly urgent. We propose a computational neuroethology approach to begin to address this challenge. We evolve dynamical recurrent neural networks to be capable of performing multiple tasks. We then analyze the neural activity using popular network neuroscience tools, specifically functional connectivity using Pearson’s correlation, mutual information, and transfer entropy. We compare the results from these tools against a series of informational lesions, as a way to reveal their degree of approximation to the ground-truth. Our initial analysis reveals an overwhelming large gap between the insights gained from statistical inference of the functionality of the circuits based on neural activity and the actual functionality of the circuits as revealed by mechanistic interventions. © Artificial Life Conference Proceedings 2022.

关键词： Recurrent neural networks

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Multi-focus attention network for efficient deep reinforcement learning 31

Multi-focus attention network for efficient deep reinforceme...

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31st AAAI Conference on Artificial Intelligence, AAAI 2017

作者： Choi, Jinyoung Lee, Beom-Jin Zhang, Byoung-Tak Interdisciplinary Program in Cognitive Science Seoul National University Korea Republic of School of Computer Science and Engineering Seoul National University Korea Republic of

ISBN: (纸本)9781577357865

Deep reinforcement learning (DRL) has shown incredible performance in learning various tasks to the human level. However, unlike human perception, current DRL models connect the entire low-level sensory input to the state-action values rather than exploiting the relationship between and among entities that constitute the sensory input. Because of this difference, DRL needs vast amount of experience samples to learn. In this paper, we propose a Multi-focus Atten-tion Network (MANet) which mimics human ability to spatially abstract the low-level sensory input into multiple entities and attend to them simultaneously. The proposed method first divides the low-level input into several segments which we refer to as partial states. After this segmentation, parallel attention layers attend to the partial states relevant to solving the task. Our model estimates state-action values using these attended partial states. In our experiments, MANet attains highest scores with significantly less experience samples. Additionally, the model shows higher performance compared to the Deep Q-network and the single attention model as benchmarks. Furthermore, we extend our model to attentive communication model for performing multi-agent cooperative tasks, in multi-agent cooperative task experiments, our model shows 20% faster learning than existing state-of-the-art model. © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Benchmarking

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