检索结果-内蒙古大学图书馆

Crosstalk between adenine nucleotide transporter and mitochondrial swelling: experimental and computational approaches

引用

CELL BIOLOGY AND TOXICOLOGY 2023年第2期39卷 435-450页

作者： Chapa-Dubocq, Xavier R. Garcia-Baez, Jorge F. Bazil, Jason N. Javadov, Sabzali Univ Puerto Rico Dept Physiol Sch Med San Juan PR 00936 USA Michigan State Univ Dept Physiol E Lansing MI 48824 USA

Mitochondrial metabolism and function are modulated by changes in matrix Ca2+. Small increases in the matrix Ca2+ stimulate mitochondrial bioenergetics, whereas excessive Ca2+ leads to cell death by causing massive matrix swelling and impairing the structural and functional integrity of mitochondria. Sustained opening of the non-selective mitochondrial permeability transition pores (PTP) is the main mechanism responsible for mitochondrial Ca2+ overload that leads to mitochondrial dysfunction and cell death. Recent studies suggest the existence of two or more types of PTP, and adenine nucleotide translocator (ANT) and F0F1-ATP synthase were proposed to form the PTP independent of each other. Here, we elucidated the role of ANT in PTP opening by applying both experimental and computational approaches. We first developed and corroborated a detailed model of the ANT transport mechanism including the matrix (ANT(M)), cytosolic (ANT(C)), and pore (ANT(P)) states of the transporter. Then, the ANT model was incorporated into a simple, yet effective, empirical model of mitochondrial bioenergetics to ascertain the point when Ca2+ overload initiates PTP opening via an ANT switch-like mechanism activated by matrix Ca2+ and is inhibited by extra-mitochondrial ADP. We found that encoding a heterogeneous Ca2+ response of at least three types of PTPs, weakly, moderately, and strongly sensitive to Ca2+ , enabled the model to simulate Ca2+ release dynamics observed after large boluses were administered to a population of energized cardiac mitochondria. Thus, this study demonstrates the potential role of ANT in PTP gating and proposes a novel mechanism governing the cryptic nature of the PTP phenomenon.

关键词： Mitochondria computational modeling Calcium retention capacity Permeability transition pore Mitochondrial swelling, Adenine nucleotide translocase

来源：评论

学校读者我要写书评

暂无评论

Toward the Development of 6G System Level Simulators: Addressing the computational Complexity Challenge

引用

IEEE WIRELESS COMMUNICATIONS 2023年第6期30卷 160-168页

作者： Manalastas, Marvin bin Farooq, Muhammad Umar Asad Zaidi, Syed Muhammad Imran, Ali Univ Oklahoma Norman OK 73019 USA Univ Glasgow Glasgow Scotland

The advancement and standardization of the cellular network system require thorough investigation, analysis, and experimentation of novel protocols, architectures, and functionalities. In this regard, computer-aided tools or simulators allow the execution of these requirements with the much-needed controllability, reproducibility, cost efficiency, and convenience. Simulators have been proven beneficial since the dawn of the cellular network era and are likely to be critical for the upcoming 6G development. However, mimicking such a complex network requires developing intricate and, at the same time, practical simulators. One of the major concerns of the existing simulators is the computational complexity of modeling a realistic and complete network. This challenge is anticipated to exacerbate with the advent of 6G considering its scope and peculiarities. In this article, we analyze the computational complexity of incoming 6G simulators and provide solutions to mitigate this issue. The presented novel framework for future simulators aims to transform the traditional way of building network simulators to serve the unprecedented demand of 6G. The presented use case highlights the efficacy of the proposed framework where we show a 100-fold improvement in the run-time performance of the innovative architecture compared to traditional simulators.

关键词： 6G mobile communication Cellular networks computational modeling Architecture Buildings Computer architecture Transforms

来源：评论

学校读者我要写书评

暂无评论

The computational psychopathology of emotion

引用

PSYCHOPHARMACOLOGY 2023年第11期240卷 2231-2238页

作者： Erdman, Alon Eldar, Eran Hebrew Univ Jerusalem Dept Psychol IL-9190501 Jerusalem Israel Hebrew Univ Jerusalem Dept Cognit & Brain Sci IL-9190501 Jerusalem Israel

Mood and anxiety disorders involve recurring, maladaptive patterns of distinct emotions and moods. Here, we argue that understanding these maladaptive patterns first requires understanding how emotions and moods guide adaptive behavior. We thus review recent progress in computational accounts of emotion that aims to explain the adaptive role of distinct emotions and mood. We then highlight how this emerging approach could be used to explain maladaptive emotions in various psychopathologies. In particular, we identify three computational factors that may be responsible for excessive emotions and moods of different types: self-intensifying affective biases, misestimations of predictability, and misestimations of controllability. Finally, we outline how the psychopathological roles of these factors can be tested, and how they may be used to improve psychotherapeutic and psychopharmacological interventions.

关键词： Emotion Mood computational modeling Reinforcement learning Reward Psychopathology

来源：评论

学校读者我要写书评

暂无评论

A computational Model of Coupled Human Trust and Self-confidence Dynamics

引用

ACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION 2023年第3期12卷 1-29页

作者： Williams, Katherine J. Yuh, Madeleine S. Jain, Neera Purdue Univ 177 S Russell St W Lafayette IN 47907 USA

Autonomous systems that can assist humans with increasingly complex tasks are becoming ubiquitous. Moreover, it has been established that a human's decision to rely on such systems is a function of both their trust in the system and their own self-confidence as it relates to executing the task of interest. Given that both under- and over-reliance on automation can pose significant risks to humans, there is motivation for developing autonomous systems that could appropriately calibrate a human's trust or self-confidence to achieve proper reliance behavior. In this article, a computational model of coupled human trust and self-confidence dynamics is proposed. The dynamics are modeled as a partially observable Markov decision process without a reward function (POMDP/R) that leverages behavioral and self-report data as observations for estimation of these cognitive states. The model is trained and validated using data collected from 340 participants. Analysis of the transition probabilities shows that the proposed model captures the probabilistic relationship between trust, self-confidence, and reliance for all discrete combinations of high and low trust and self-confidence. The use of the proposed model to design an optimal policy to facilitate trust and self-confidence calibration is a goal of future work.

关键词： Human cognitive modeling human trust in automation human self-confidence computational modeling partially observable Markov decision process

来源：评论

学校读者我要写书评

暂无评论

computational design of catalysts for ammonia synthesis

引用

Nano Research Energy 2023年第3期2卷 56-65页

作者： Yining Zhang Sha Li Wei Zheng Xi Wang Chemistry and Chemical Engineering Guangdong Laboratory Shantou 515021China Institute of Molecular Plus Tianjin UniversityTianjin 300072China Department of Physics School of Physical Science and EngineeringBeijing Jiaotong UniversityBeijing 100044China

Ammonia plays a crucial role in agriculture and chemical engineering,and acts as a promising carbon-free transportation *** design is deemed as a key to solve the restriction of energy-intensive Haber-Bosch process of ammonia *** the development of computational modeling,computation-aided catalyst design serves as one important driving force for material innovation,saving a lot of experimental efforts based on trial and *** modeling not only provides fundamental mechanistic insights into the reaction with great details regarding adsorbate geometries,electronic structures,and elementary-step energies,but also expedites the material discovery with descriptor-based catalyst design,core of which is the establishment of thermo/kinetic scaling *** this review,we present firstly the mechanistic understanding of ammonia synthesis and transition state scaling relations developed on pure transition-metal *** then summarize catalysts design strategies guided by alloy,size,and magnetic effects with the goal of breaking the limitations set by scaling relations to achieve better catalytic ***,future opportunities and challenges associated with computation design of optimal catalysts for ammonia synthesis are outlined.

关键词： catalyst design ammonia synthesis computational modeling scaling relations

来源：评论

学校读者我要写书评

暂无评论

Comparing Inducibility of Re-Entrant Arrhythmia in Patient-Specific computational Models to Clinical Atrial Fibrillation Phenotypes

引用

JACC-CLINICAL ELECTROPHYSIOLOGY 2023年第10期9卷 2149-2162页

作者： Macheret, Fima Bifulco, Savannah F. Scott, Griffin D. Kwan, Kirsten T. Chahine, Yaacoub Afroze, Tanzina Mcdonagh, Rosemary Akoum, Nazem Boyle, Patrick M. Univ Washington Div Cardiol Seattle WA USA Univ Washington Dept Bioengn Seattle WA USA Biosense Webster Irvine CA USA Univ Washington Inst Stem Cell & Regenerat Med Seattle WA USA Univ Washington Ctr Cardiovasc Biol Seattle WA USA Univ Washington Dept Bioengn 3720 15th Ave NE Seattle WA 98105 USA Univ Washington Div Cardiol 1959 NE Pacific StBox 356422 Seattle WA 98195 USA

BACKGROUND computational models of fibrosis-mediated, re-entrant left atrial (LA) arrhythmia can identify possible substrate for persistent atrial fibrillation (AF) ablation. Contemporary models use a 1-size-fits-all approach to represent electrophysiological properties, limiting agreement between simulations and patient outcomes. OBJECTIVES The goal of this study was to test the hypothesis that conduction velocity (W) modulation in persistent AF models can improve simulation agreement with clinical arrhythmias. METHODS Patients with persistent AF (n 1/4 37) underwent ablation and were followed up for $2 years to determine post-ablation outcomes: AF, atrial flutter (AFL), or no recurrence. Patient-specific LA models (n 1/4 74) were constructed using pre-ablation and $90 days' post-ablation magnetic resonance imaging data. Simulated pacing gauged in silico arrhythmia inducibility due to AF-like rotors or AFL-like macro re-entrant tachycardias. A physiologically plausible range of W values (similar to 10 or 20% vs. baseline) was tested, and model/clinical agreement was assessed. RESULTS Fifteen (41%) patients had a recurrence with AF and 6 (16%) with AFL. Arrhythmia was induced in 1,078 of 5,550 simulations. Using baseline W, model/clinical agreement was 46% (34 of 74 models), improving to 65% (48 of 74) when any possible W value was used (McNemar's test, P 1/4 0.014). W modulation improved model/clinical agreement in both pre-ablation and post-ablation models. Pre-ablation model/clinical agreement was significantly greater for patients with extensive LA fibrosis (>17.2%) and an elevated body mass index (>32.0 kg/m2). CONCLUSIONS Simulations in persistent AF models show a 41% relative improvement in model/clinical agreement when W is modulated. Patient-specific calibration of W values could improve model/clinical agreement and model usefulness, especially in patients with higher body mass index or LA fibrosis burden. This could ultimately facilitate better personalize

关键词： atrial fibrillation computational modeling conduction velocity fibrosis MRI

来源：评论

学校读者我要写书评

暂无评论

Uncertainty-Aware Deep Neural Representations for Visual Analysis of Vector Field Data

引用

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2025年第1期31卷 1343-1353页

作者： Kumar, Atul Garg, Siddharth Dutta, Soumya IIT Kanpur Dept Comp Sci & Engn Kanpur India IIT Kanpur Elect Engn Dept Kanpur India

The widespread use of Deep Neural Networks (DNNs) has recently resulted in their application to challenging scientific visualization tasks. While advanced DNNs demonstrate impressive generalization abilities, understanding factors like prediction quality, confidence, robustness, and uncertainty is crucial. These insights aid application scientists in making informed decisions. However, DNNs lack inherent mechanisms to measure prediction uncertainty, prompting the creation of distinct frameworks for constructing robust uncertainty-aware models tailored to various visualization tasks. In this work, we develop uncertainty-aware implicit neural representations to model steady-state vector fields effectively. We comprehensively evaluate the efficacy of two principled deep uncertainty estimation techniques: (1) Deep Ensemble and (2) Monte Carlo Dropout, aimed at enabling uncertainty-informed visual analysis of features within steady vector field data. Our detailed exploration using several vector data sets indicate that uncertainty-aware models generate informative visualization results of vector field features. Furthermore, incorporating prediction uncertainty improves the resilience and interpretability of our DNN model, rendering it applicable for the analysis of non-trivial vector field data sets.

关键词： Uncertainty Data visualization Vectors Predictive models Estimation Data models computational modeling Implicit Neural Network Monte Carlo Dropout Deep Ensemble Vector Field Visualization Deep Learning

来源：评论

学校读者我要写书评

暂无评论

Toward Mitigating Architecture Overfitting on Distilled Datasets

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年 PP卷 PP页

作者： Zhong, Xuyang Liu, Chen City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Dataset distillation (DD) methods have demonstrated remarkable performance for neural networks trained with very limited training data. However, a significant challenge arises in the form of architecture overfitting: the distilled training dataset synthesized by a specific network architecture (i.e., training network) generates poor performance when trained by other network architectures (i.e., test networks), especially when the test networks have a larger capacity than the training network. This article introduces a series of approaches to mitigate this issue. Among them, DropPath renders the large model to be an implicit ensemble of its subnetworks, and knowledge distillation (KD) ensures each subnetwork acts similar to the small but well-performing teacher network. These methods, characterized by their smoothing effects, significantly mitigate architecture overfitting. We conduct extensive experiments to demonstrate the effectiveness and generality of our methods. Particularly, across various scenarios involving different tasks and different sizes of distilled data, our approaches significantly mitigate architecture overfitting. Furthermore, our approaches achieve comparable or even superior performance when the test network is larger than the training network. Codes are available at https://***/CityU-MLO/mitigate_architecture_overfitting.

关键词： Training Computer architecture computational modeling Optimization Neural networks Network architecture Training data Smoothing methods Metalearning Dataset distillation (DD) efficient learning neural network architecture overfitting overfitting

来源：评论

学校读者我要写书评

暂无评论

Dynamical System-Based computational Models for Solving Combinatorial Optimization on Hypergraphs

引用

IEEE JOURNAL ON EXPLORATORY SOLID-STATE computational DEVICES AND CIRCUITS 2023年第1期9卷 21-28页

作者： Bashar, Mohammad Khairul Mallick, Antik Ghosh, Avik W. Shukla, Nikhil Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22904 USA

The intrinsic energy minimization in dynamical systems offers a valuable tool for minimizing the objective functions of computationally challenging problems in combinatorial optimization. However, most prior works have focused on mapping such dynamics to combinatorial optimization problems whose objective functions have quadratic degree [e.g., maximum cut (MaxCut)];such problems can be represented and analyzed using graphs. However, the work on developing such models for problems that need objective functions with degree greater than two, and subsequently, entail the use of hypergraph data structures, is relatively sparse. In this work, we develop dynamical system-inspired computational models for several such problems. Specifically, we define the "energy function" for hypergraph-based combinatorial problems ranging from Boolean Satisfiability (SAT) and its variants to integer factorization, and subsequently, define the resulting system dynamics. We also show that the design approach is applicable to optimization problems with quadratic degree, and use it to develop a new dynamical system formulation for minimizing the Ising Hamiltonian. Our work not only expands on the scope of problems that can be directly mapped to, and solved using physics-inspired models, but also creates new opportunities to design high-performance accelerators for solving combinatorial optimization.

关键词： computational modeling Optimization Dynamical systems Linear programming Biological system modeling Oscillators Minimization Combinatorial optimization hypergraph integer factorization maximum cut (MaxCut) satisfiability (SAT) set splitting

来源：评论

学校读者我要写书评

暂无评论

A computational model for learning from repeated traumatic experiences under uncertainty

引用

COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2023年第3期23卷 894-904页

作者： Kaye, Alfred P. Rao, Manasa G. Kwan, Alex C. Ressler, Kerry J. Krystal, John H. Yale Univ Dept Psychiat New Haven CT 06520 USA VA Natl Ctr PTSD Clin Neurosci Div West Haven CT 06516 USA Mt Sinai Icahn Sch Med New York NY USA Cornell Univ Meinig Sch Biomed Engn Ithaca NY USA McLean Hosp Div Depress & Anxiety Disorder Belmont MA USA Harvard Med Sch Dept Psychiat Boston MA USA

Traumatic events can lead to lifelong, inflexible adaptations in threat perception and behavior, which characterize posttraumatic stress disorder (PTSD). This process involves associations between sensory cues and internal states of threat and then generalization of the threat responses to previously neutral cues. However, most formulations neglect adaptations to threat that are not specific to those associations. To incorporate nonassociative responses to threat, we propose a computational theory of PTSD based on adaptation to the frequency of traumatic events by using a reinforcement learning momentum model. Recent threat prediction errors generate momentum that influences subsequent threat perception in novel contexts. This model fits primary data acquired from a mouse model of PTSD, in which unpredictable footshocks in one context accelerate threat learning in a novel context. The theory is consistent with epidemiological data that show that PTSD incidence increases with the number of traumatic events, as well as the disproportionate impact of early life trauma. Because the theory proposes that PTSD relates to the average of recent threat prediction errors rather than the strength of a specific association, it makes novel predictions for the treatment of PTSD.

关键词： Stress sensitization PTSD Trauma Uncertainty computational modeling Bayesian Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：