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Durante F, Raparelli T, Beomonte Zobel P. Resistance Feedback of a Ni-Ti Alloy Actuator at Room Temperature in Still Air. MICROMACHINES 2024; 15:545. [PMID: 38675356 PMCID: PMC11051945 DOI: 10.3390/mi15040545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024]
Abstract
This paper illustrates an experimental activity for the closed-loop position control of an actuator made using shape memory alloy (SMA) wire. A solution with the self-sensing effect was implemented to miniaturize the systems, i.e., without external sensors. A proportional control algorithm was initially used, demonstrating the idea's feasibility; the wire can behave simultaneously as an actuator and sensor. An experimental investigation was subsequently conducted for the optimization of the developed actuator. As for the material, a Flexinol wire, Ni-Ti alloy, with a diameter of 0.150 mm and a length of 200 mm, was used. Preliminarily, characterization of the SMA wire at constant and variable loads was carried out; the characteristics detected were elongation vs. electric current and elongation vs. electrical resistance. The control system is PC based with a data acquisition card (DAQ). A drive board was designed and built to read the wire's electrical resistance and power it by pulse width modulation (PWM). A notable result is that the actuator works with good precision and in dynamic conditions, even when it is called to support a load up to 65% different from that for which the electrical resistance-length correlation has previously been experimentally obtained, on which the control is based. This opens up the possibility of using the actuator in a counteracting configuration with a spring, which makes hardware implementation and control management simple.
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Affiliation(s)
- Francesco Durante
- Department of Industrial and Information Engineering and Economy (DIIIE), University of L’Aquila, P.le Pontieri 1, Località Monteluco, 67100 L’Aquila, Italy;
| | - Terenziano Raparelli
- Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy;
| | - Pierluigi Beomonte Zobel
- Department of Industrial and Information Engineering and Economy (DIIIE), University of L’Aquila, P.le Pontieri 1, Località Monteluco, 67100 L’Aquila, Italy;
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Fan YL, Hsu FR, Wang Y, Liao LD. Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization. Med Biol Eng Comput 2023; 61:2797-2814. [PMID: 37558927 DOI: 10.1007/s11517-023-02903-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 08/01/2023] [Indexed: 08/11/2023]
Abstract
Zebrafish have become a widely accepted model organism for biomedical research due to their strong cortisol stress response, behavioral strain differences, and sensitivity to both drug treatments and predators. However, experimental zebrafish studies generate substantial data that must be analyzed through objective, accurate, and repeatable analysis methods. Recently, advancements in artificial intelligence (AI) have enabled automated tracking, image recognition, and data analysis, leading to more efficient and insightful investigations. In this review, we examine key AI applications in zebrafish research, including behavior analysis, genomics, and neuroscience. With the development of deep learning technology, AI algorithms have been used to precisely analyze and identify images of zebrafish, enabling automated testing and analysis. By applying AI algorithms in genomics research, researchers have elucidated the relationship between genes and biology, providing a better basis for the development of disease treatments and gene therapies. Additionally, the development of more effective neuroscience tools could help researchers better understand the complex neural networks in the zebrafish brain. In the future, further advancements in AI technology are expected to enable more extensive and in-depth medical research applications in zebrafish, improving our understanding of this important animal model. This review highlights the potential of AI technology in achieving the full potential of zebrafish research by enabling researchers to efficiently track, process, and visualize the outcomes of their experiments.
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Affiliation(s)
- Yi-Ling Fan
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, 35, Keyan Road, Zhunan Town, Miaoli County, 35053, Taiwan
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung, 407, Taiwan
| | - Fang-Rong Hsu
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung, 407, Taiwan
| | - Yuhling Wang
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, 35, Keyan Road, Zhunan Town, Miaoli County, 35053, Taiwan
- Department of Electrical Engineering, National United University, 2, Lien-Da, Nan-Shih Li, Miaoli, 360302, Taiwan
| | - Lun-De Liao
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, 35, Keyan Road, Zhunan Town, Miaoli County, 35053, Taiwan.
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Loganathan D, Wu SH, Chen CY. Behavioural responses of zebrafish with sound stimuli in microfluidics. LAB ON A CHIP 2022; 23:106-114. [PMID: 36453125 DOI: 10.1039/d2lc00758d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Neuronal activities of the human brain responsible for cognitive features have been theorized through several animal models that exhibited various complementary spatial learning modes by generating a flexible repertoire of behavioral strategies. However, for such studies associated with a neurodegenerative disease, which can be further manipulated to provide therapeutic strategies, the animal models employed in their developmental stages have been preferred over the adult ones. This pilot work was incepted to underscore the spatial memory capabilities that strengthened the intricate mechanism of memory acquisition potential in one of the low-order evolutionarily conserved species, such as zebrafish larvae. Initially, a reliable and more easily reproducible microfluidic platform integrating simple and intricate paths was designed to learn and test the spatial information in zebrafish larvae of 4-6 d.p.f. under non-invasive acoustic stimuli. Further, to acquire spatial information as the representation of spatial memory formation in zebrafish larvae, the acoustic startle responses were evaluated by quantifying various dynamic behaviors under distinct operating parameters. After significant conditioning sessions, the spatial memory was tested by employing variable 'freezing'. By the end of the 30 min-long test session, 6 d.p.f. larvae were found to exhibit the highest value of freezing of approximately 43% and 20% in the short and long paths, respectively. Even though a substantial rate of memory loss was observed, it can be envisaged to serve several behavioral strategies that process the dynamic cognitive memory among distinct spatiotemporal environments. Further, the proposed behavioral paradigm had the advantage of being more adaptable and reliably replicable by other researchers. As a consequence, different hypotheses can be readily tested to generate more reproducible findings towards distinct neurobehavioral characteristics. Therefore, the proposed paradigm for the consolidation of spatial memory based on the non-invasive spatial avoidance strategies could provide an enduring framework of reference for behavioral studies using zebrafish larvae.
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Affiliation(s)
- Dineshkumar Loganathan
- Department of Mechanical Engineering, National Cheng Kung University, Tainan 701, Taiwan.
| | - Shu-Heng Wu
- Department of Mechanical Engineering, National Cheng Kung University, Tainan 701, Taiwan.
| | - Chia-Yuan Chen
- Department of Mechanical Engineering, National Cheng Kung University, Tainan 701, Taiwan.
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Frey N, Sönmez UM, Minden J, LeDuc P. Microfluidics for understanding model organisms. Nat Commun 2022; 13:3195. [PMID: 35680898 PMCID: PMC9184607 DOI: 10.1038/s41467-022-30814-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 05/20/2022] [Indexed: 11/29/2022] Open
Abstract
New microfluidic systems for whole organism analysis and experimentation are catalyzing biological breakthroughs across many fields, from human health to fundamental biology principles. This perspective discusses recent microfluidic tools to study intact model organisms to demonstrate the tremendous potential for these integrated approaches now and into the future. We describe these microsystems' technical features and highlight the unique advantages for precise manipulation in areas including immobilization, automated alignment, sorting, sensory, mechanical and chemical stimulation, and genetic and thermal perturbation. Our aim is to familiarize technologically focused researchers with microfluidics applications in biology research, while providing biologists an entrée to advanced microengineering techniques for model organisms.
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Affiliation(s)
- Nolan Frey
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Utku M Sönmez
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Jonathan Minden
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Philip LeDuc
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Computation Biology, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
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Jia X, Yang X, Luo G, Liang Q. Recent progress of microfluidic technology for pharmaceutical analysis. J Pharm Biomed Anal 2021; 209:114534. [PMID: 34929566 DOI: 10.1016/j.jpba.2021.114534] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 12/06/2021] [Accepted: 12/08/2021] [Indexed: 12/13/2022]
Abstract
In recent years, the progress of microfluidic technology has provided new tools for pharmaceutical analysis and the proposal of pharm-lab-on-a-chip is appealing for its great potential to integrate pharmaceutical test and pharmacological test in a single chip system. Here, we summarize and highlight recent advances of chip-based principles, techniques and devices for pharmaceutical test and pharmacological/toxicological test focusing on the separation and analysis of drug molecules on a chip and the construction of pharmacological models on a chip as well as their demonstrative applications in quality control, drug screening and precision medicine. The trend and challenge of microfluidic technology for pharmaceutical analysis are also discussed and prospected. We hope this review would update the insight and development of pharm-lab-on-a-chip.
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Affiliation(s)
- Xiaomeng Jia
- Center for Synthetic and Systems Biology, MOE Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Xiaoping Yang
- Center for Synthetic and Systems Biology, MOE Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Department of Chemistry, Tsinghua University, Beijing 100084, PR China
| | - Guoan Luo
- Center for Synthetic and Systems Biology, MOE Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Department of Chemistry, Tsinghua University, Beijing 100084, PR China.
| | - Qionglin Liang
- Center for Synthetic and Systems Biology, MOE Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Department of Chemistry, Tsinghua University, Beijing 100084, PR China.
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Subendran S, Wang YC, Lu YH, Chen CY. The evaluation of zebrafish cardiovascular and behavioral functions through microfluidics. Sci Rep 2021; 11:13801. [PMID: 34226579 PMCID: PMC8257654 DOI: 10.1038/s41598-021-93078-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 06/11/2021] [Indexed: 02/06/2023] Open
Abstract
This study proposed a new experimental approach for the vascular and phenotype evaluation of the non-anesthetized zebrafish with representative imaging orientations for heart, pectoral fin beating, and vasculature views by means of the designed microfluidic device through inducing the optomotor response and hydrodynamic pressure control. In order to provide the visual cues for better positioning of zebrafish, computer-animated moving grids were generated by an in-house control interface which was powered by the larval optomotor response, in conjunction with the pressure suction control. The presented platform provided a comprehensive evaluation of internal circulation and the linked external behaviors of zebrafish in response to the cardiovascular parameter changes. The insights from these imaging sections was extended to identify the linkage between the cardiac parameters and behavioral endpoints. In addition, selected chemicals such as ethanol and caffeine were employed for the treatment of zebrafish. The obtained findings can be applicable for future investigation in behavioral drug screening serving as the forefront in psychopharmacological and cognition research.
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Affiliation(s)
- Satishkumar Subendran
- Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Road, Tainan, 701, Taiwan
| | - Yi-Chieh Wang
- Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Road, Tainan, 701, Taiwan
| | - Yueh-Hsun Lu
- Department of Radiology, Shuang-Ho Hospital, Taipei Medical University, New Taipei City, 235, Taiwan
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
- Department of Radiology, National Yang-Ming University School of Medicine, Taipei, 112, Taiwan
| | - Chia-Yuan Chen
- Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Road, Tainan, 701, Taiwan.
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