We recently observed that insertion of unloaded rest between each load cycle substantially enhanced bone formation induced by mild loading regimens. To begin to explore this result, we have developed an agent based model for real-time signaling induced when osteocytic networks are challenged by mechanical stimuli. In the model, activity induced in individual osteocytes were governed by the following cellular functions: (1) threshold levels of tissue strain magnitudes were required to initiate and maximally activate cells, (2) cell activity beyond thresholds were propagated within localized neighborhoods and influenced recipient cell activity, (3) cellular activity was modulated by 'molecular' stores and the rates at which stores were replenished when cells were quiescent. Using this model, the real-time response of osteocyte networks was determined as the average of individual cell activity. While not explicitly embedded within the model, interactions between cellular functions served as positive, negative, and end-point feedback mechanisms and resulted in unique real-time network responses to distinct mechanical stimuli. Specifically, the real-time network response to cyclic stimuli consisted of a large magnitude transient followed by low-level steady state fluctuations, while rest-inserted stimuli induced multiple secondary transients. Analysis of interaction patterns suggested that rest-inserted stimuli induced this enhanced and sustained signaling within osteocytic networks by enabling cell recovery of expended molecular stores and by efficiently utilizing properties inherent to cell-cell communication in bone. Importantly, this emergence based approach suggested mechanisms potentially underlying the benefit of rest-inserted stimuli and provides a unique framework for a broader exploration of mechanotransduction function within bone.
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http://dx.doi.org/10.1016/j.jbiomech.2005.08.023 | DOI Listing |
Sci Rep
December 2024
School of Mechanical Engineering, Liaoning Engineering Vocational College, Tieling, 112008, Liaoning, People's Republic of China.
The paper proposes a multi-rigid-body system state identification method based on self-healing model in order to improve the accuracy and reliability of CNC machine tools. Firstly, considering the influence of the joint surface, the Lagrange method is used to establish the mechanical model of the multi-rigid-body system. We input acceleration information and use the second-order modulation function to complete the online real-time identification of the joint surface parameters, thereby establishing the self-healing mechanical model of the multi-rigid-body system.
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December 2024
Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, China.
The intelligent identification of wear particles in ferrography is a critical bottleneck that hampers the development and widespread adoption of ferrography technology. To address challenges such as false detection, missed detection of small wear particles, difficulty in distinguishing overlapping and similar abrasions, and handling complex image backgrounds, this paper proposes an algorithm called TCBGY-Net for detecting wear particles in ferrography images. The proposed TCBGY-Net uses YOLOv5s as the backbone network, which is enhanced with several advanced modules to improve detection performance.
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December 2024
School of Electrical Engineering, Vellore Institute of Technology, Chennai, 600127, India.
Spherical tanks have been predominantly used in process industries due to their large storage capability. The fundamental challenges in process industries require a very efficient controller to control the various process parameters owing to their nonlinear behavior. The current research work in this paper aims to propose the Approximate Generalized Time Moments (AGTM) optimization technique for designing Fractional-Order PI (FOPI) and Fractional-Order PID (FOPID) controllers for the nonlinear Single Spherical Tank Liquid Level System (SSTLLS).
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December 2024
Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing, China.
While circular RNAs (circRNAs) exhibit lower abundance compared to corresponding linear RNAs, they demonstrate potent biological functions. Nevertheless, challenges arise from the low concentration and distinctive structural features of circRNAs, rendering existing methods operationally intricate and less sensitive. Here, we engineer an intelligent tetrahedral DNA framework (TDF) possessing precise spatial pattern-recognition properties with exceptional sensing speed and sensitivity for circRNAs.
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December 2024
Department of Computing and Information Systems, Sunway University, 47500, Petaling Jaya, Selangor Darul Ehsan, Malaysia.
Urban mobility prediction is crucial for optimizing resource allocation, managing transportation systems, and planning urban development. We propose a novel framework, GeoTemporal LSTM (GT-LSTM), designed to address the intricate spatiotemporal dynamics of urban environments. GT-LSTM integrates temporal dependencies with geographic information through a multi-modal approach that combines attention mechanisms and Recurrent Neural Networks (RNNs).
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