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加利福尼亚州桑尼维尔和马萨诸塞州剑桥--(

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NTT Research, Inc.

NTT研究公司。

, a division of NTT (

，NTT的一个部门(

TYO:9432

电话号码：9432

), and the

)，以及

Harvard John A. Paulson School of Engineering and Applied Sciences

哈佛大学约翰·保尔森工程与应用科学学院

(SEAS) announced the publication of research showing an application of machine-learning directed optimization (ML-DO) that efficiently searches for high-performance design configurations in the context of biohybrid robots. Applying a machine learning approach, the researchers created mini biohybrid rays made of cardiomyocytes (heart muscle cells) and rubber with a wingspan of about 10 mm that are approximately two times more efficient at swimming than those recently developed under a conventional biomimetic approach..

（SEAS）宣布发表了一项研究，显示了机器学习导向优化（ML-DO）的应用，该优化可以在生物混合机器人的背景下有效搜索高性能设计配置。应用机器学习方法，研究人员创造了由心肌细胞（心肌细胞）和翼展约10毫米的橡胶制成的微型生物杂交射线，其游泳效率大约是最近在传统仿生方法下开发的效率的两倍。。

A team led by Harvard SEAS Postdoctoral Fellow Dr. John Zimmerman and including NTT Research Medical and Health Informatics Scientist Ryoma Ishii, Harvard SEAS Tarr Family Professor of Bioengineering and Applied Physics Kevin Kit Parker, and members of the Harvard SEAS Disease Biophysics Group led by Parker demonstrated this research in a new paper published in .

哈佛SEAS博士后约翰·齐默尔曼博士领导的团队，包括NTT研究医学和健康信息学科学家石井龙马、哈佛SEAS Tarr家族生物工程和应用物理学教授凯文·基特·帕克，以及由帕克领导的哈佛SEAS疾病生物物理小组的成员，在年发表的一篇新论文中展示了这项研究。

Science Robotics

科学机器人

titled, “

标题为“

Bioinspired Design of a Tissue Engineered Ray with Machine Learning

基于机器学习的组织工程射线生物启发设计

.”

.”

“

“

This research seeks to answer a fundamental question in the development of biohybrid robots, in this case the marine ray: How do we select fin geometries to operate under novel working environments while preserving natural scaling laws in terms of swimming speed and efficiency,” said Ishii, who also works as a visiting scientist for Harvard University.

这项研究试图回答生物混合机器人（在这种情况下是海洋射线）开发中的一个基本问题：我们如何选择鳍的几何形状在新的工作环境下运行，同时保持游泳速度和效率方面的自然缩放定律，”Ishii说，他也是哈佛大学的访问科学家。

“.

“.

Our research indicates the application of ML-DO, inspired by protein engineering, offers a more efficient and less computationally intensive path forward in automating the creation of muscular structure-function relationships.”

我们的研究表明，受蛋白质工程启发，ML-DO的应用为自动化创建肌肉结构-功能关系提供了一条更有效且计算量更小的途径。”

Limitations of the Biomimetic Approach

仿生方法的局限性

In biomimetic design, the conventional approach to biohybrids, engineers form functional devices by recreating existing biological structures. That approach, however, has limits. For biohybrid lifeforms that resemble batoid fishes (skates and rays), for example, there is a wide range of natural aspect ratios and fin morphologies.

在仿生设计中，传统的生物杂交方法是工程师通过重建现有的生物结构来形成功能装置。然而，这种方法有其局限性。例如，对于类似蝙蝠鱼（溜冰鱼和鳐鱼）的生物杂交生命形式，有各种各样的自然长宽比和鳍形态。

Which ones do you mimic? Also, biomimetics may neglect the natural biomechanical and hydrodynamic forces that govern how fast an organism can swim based on its size and body kinematics, leading to inefficient muscle mass and limited swimming speeds..

你模仿哪些？此外，仿生学可能会忽略自然的生物力学和水动力，这些力根据生物体的大小和身体运动学来控制生物体的游泳速度，从而导致肌肉质量低效和游泳速度有限。。

In that light, the motivating question in this study became: How to select fin geometries that operate under novel working environments while preserving natural scaling laws in terms of swimming speed and efficiency?

有鉴于此，这项研究的动机问题是：如何选择在新颖工作环境下运行的鳍几何形状，同时保持游泳速度和效率方面的自然缩放定律？

The Design Breakthroughs of Machine Learning

机器学习的设计突破

The multi-disciplinary and iterative nature of the problem required computationally intensive modeling, but the team believed that directed optimization by machine learning (ML-DO) would enable an efficient search for fin designs that maximized their relative swimming speeds. They based their hypothesis in part on a trial function that demonstrated an approximately 40 percent improvement of ML-DO over other leading methods in recognizing known high-rank sequences.

该问题的多学科和迭代性质需要计算密集型建模，但该团队认为，通过机器学习（ML-DO）进行定向优化将能够有效搜索最大化其相对游泳速度的鳍设计。他们的假设部分基于一个试验函数，该函数证明ML-DO在识别已知高阶序列方面比其他领先方法提高了约40%。

Testing the assumption involved three steps: 1) developing an algorithm for expressing a multitude of different fin geometries; 2) describing a generalized ML-DO approach for searching within a large discontinuous configuration space; and 3) using this methodology to identify biohybrid fin geometries for high-performance swimming with smooth and orderly flow..

测试该假设涉及三个步骤：1）开发一种表达多种不同鳍几何形状的算法；2） 描述了一种用于在大的不连续配置空间内搜索的广义ML-DO方法；3）使用这种方法来识别生物杂交鳍的几何形状，以实现平滑有序的高性能游泳。。

The ML-DO-driven results included a quantitative exploration of fin structure-function relationships and reconstruction of general trends in open-sea batoid morphology, as well as a winning design: Fins with large aspect ratios and fine tapered tips, which preserved their utility across multiple length-scales of swimming.

ML-DO驱动的结果包括对鳍结构-功能关系的定量探索和公海蝙蝠形态总体趋势的重建，以及一个成功的设计：具有大纵横比和精细锥形尖端的鳍，它在多个长度尺度上保持了它们的效用游泳。

On that basis, the team built biohybrid mini-rays out of engineered cardiac muscle tissue, which were capable of self-propelled swimming at the millimeter length scale and demonstrated improved swimming efficiencies approximately two times greater than observed in previous biomimetic designs..

在此基础上，该团队用工程化的心肌组织构建了生物杂交迷你射线，这种迷你射线能够在毫米长的范围内自行游泳，并且显示出比以前的仿生设计中观察到的游泳效率提高了大约两倍。。

Looking Ahead

展望未来

While promising, researchers note that additional work is needed to completely match natural scaling laws. While the devices presented in this study demonstrated greater efficiency than other recent biomimetic designs, they were still slightly less efficient on average than naturally occurring marine lifeforms..

虽然前景看好，但研究人员指出，需要进一步的工作才能完全符合自然标度定律。虽然这项研究中提出的设备比其他最近的仿生设计显示出更高的效率，但它们的平均效率仍然略低于天然存在的海洋生物。。

In the future, researchers expect to continue the development of biohybrid robotics for use cases including remote sensors, probes for dangerous working environments and as therapeutic delivery vehicles. Researchers believe that the ML-DO-informed approach better mimics the selective pressures of evolution, enabling them to better understand how biological tissues are shaped—both in a healthy physiology as well as the maladaptive pathophysiology of disease.

未来，研究人员预计将继续开发生物混合机器人，用于包括遥感器、危险工作环境探针和治疗运载工具在内的用例。研究人员认为，ML-DO知情方法更好地模拟了进化的选择性压力，使他们能够更好地了解生物组织在健康生理学和疾病适应不良的病理生理学中是如何形成的。

Additionally, this research advances scientific understanding of 3D organ biofabrication, such as a biohybrid heart..

此外，这项研究提高了对3D器官生物制造的科学理解，例如生物杂交心脏。。

“

“

The Harvard Biophysics Group entered into a joint research agreement with NTT Research two years ago aiming to fundamentally interrogate our understanding of cardiac physiology, with an eye towards advancing the development of biohybrid devices, including biohybrid robotics and biohybrid human heart,” Parker said.

两年前，哈佛生物物理集团（Harvard Biophysics Group）与NTT research签订了一项联合研究协议，旨在从根本上审问我们对心脏生理学的理解，着眼于推进生物混合设备的开发，包括生物混合机器人和生物混合人类心脏，”Parker说。

“.

“.

This paper indicates the positive progress our shared research has achieved so far, and I am excited to see what the future of our collaboration has in store.”

这篇论文表明了迄今为止我们共同研究取得的积极进展，我很高兴看到我们合作的未来。”

In 2022, NTT Research and Harvard SEAS

2022年，NTT Research和Harvard SEAS

announced

已宣布

a three-year joint research agreement to engineer a model of the human heart, study fundamental laws of muscular pumps and apply joint findings to the development of a cardiovascular

一项为期三年的联合研究协议，旨在设计人类心脏模型，研究肌肉泵的基本定律，并将联合研究结果应用于心血管疾病的发展

bio digital twin

生物数字双胞胎

model.

型号。

About NTT Research

关于NTT Research

NTT Research opened its offices in July 2019 as a new Silicon Valley startup to conduct basic research and advance technologies that promote positive change for humankind. Currently, three labs are housed at NTT Research facilities in Sunnyvale: the Physics and Informatics (PHI) Lab, the Cryptography and Information Security (CIS) Lab, and the Medical and Health Informatics (MEI) Lab.

NTT Research于2019年7月成立了办公室，这是一家新成立的硅谷初创公司，从事基础研究和先进技术，促进人类的积极变革。目前，位于桑尼维尔的NTT研究机构设有三个实验室：物理与信息学（PHI）实验室、密码与信息安全（CIS）实验室和医疗与健康信息学（MEI）实验室。

The organization aims to advance science in three areas: 1) quantum information, neuroscience and photonics; 2) cryptographic and information security; and 3) medical and health informatics. NTT Research is part of NTT, a global technology and business solutions provider with an annual R&D budget of $3.6 billion..

该组织旨在推进三个领域的科学：1）量子信息、神经科学和光子学；2） 密码和信息安全；3）医学和健康信息学。NTT Research是全球技术和商业解决方案提供商NTT的一部分，年研发预算为36亿美元。。

About the Harvard John A. Paulson School of Engineering and Applied Sciences

关于哈佛大学约翰·保尔森工程与应用科学学院

The Harvard John A. Paulson School of Engineering and Applied Sciences serves as the connector and integrator of Harvard's teaching and research efforts in engineering, applied sciences, and technology. Through collaboration with researchers from all parts of Harvard, other universities, and corporate and foundational partners, we bring discovery and innovation directly to bear on improving human life and society.

哈佛大学约翰·保尔森工程与应用科学学院（HarvardJohnA.Paulson School of Engineering and Applied Sciences）是哈佛大学在工程、应用科学和技术领域的教学和研究工作的连接器和集成商。通过与哈佛大学、其他大学、企业和基础合作伙伴的研究人员合作，我们将发现和创新直接应用于改善人类生活和社会。

For more information, visit: .

有关更多信息，请访问：。

http://seas.harvard.edu

http://seas.harvard.edu

.

.

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