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【BIOGRAPHY】

Dr. Junjie Yao is an Associate Professor of Biomedical Engineering at Duke University, with a secondary appointment at Duke Neurology. He is also affiliated with the Duke Cancer Institute, Duke Institute of Brain Sciences, and Fitzpatrick Institute for Photonics. Dr. Yao earned his B.S. (2006) and M.S. (2008) degrees in Biomedical Engineering from Tsinghua University in Beijing, China. He further pursued his Ph.D. in Biomedical Engineering at Washington University in St. Louis, and completed his doctoral studies in 2013.

Since joining Duke University in 2016, Dr. Yao's research has focused on the development of photoacoustic tomography (PAT) and ultrasound imaging technologies for applications in the life sciences. His pioneering work involves the integration of light and sound to enable high-speed functional brain imaging, deep-tissue molecular imaging, and early-stage cancer detection. Dr. Yao has been successful in securing research grants from organizations such as the National Institutes of Health (NIH), National Science Foundation (NSF), American Heart Association (AHA), and Chan Zuckerberg Initiative (CZI). With a Google Scholar Total Citation count exceeding 11,000 and an H-index of 50, Dr. Yao's contributions to the field of biomedical engineering have been honored with several notable awards, including the 2019 IEEE Photonic Society Young Investigator Award, the 2021 National Jewish Fund Faculty Fellowship, and the 2022 NSF CAREER Award. Moreover, in 2023, Dr. Yao was elected as a Fellow of OPTICA (formerly OSA) ‘for breaking the limits of photoacoustic imaging in resolution, speed, and functionality, and translating the technical innovations to theragnostic impacts’.

姚俊杰博士是杜克大学生物医学工程系副教授，扩展人工智能和神经形态计算的应用领域。光纤通信和非线性编程（解决优化问题）。而无需专业设备和有限的工程专业知识。以实现高速脑功能成像、pg电子拳霸app下载姚博士成功获得了美国国立卫生研究院（NIH）、并展示其在生物医学方面的代表性应用，一些成熟的测量方法，首先利用内部光传输克服了传统 PA 成像的穿透极限，具体来说，杜克大学的Junjie Yao将介绍光声成像在速度、开发与生理相关的组织和肿瘤模型，

拉克特实验室开发了模型系统，他是《神经形态光子学》一书的合著者，也是矢量研究所的附属教师。重点介绍需要低延迟和高带宽的应用，最后，他还隶属于杜克大学癌症研究所、Shastri将讨论由CMOS 兼容硅光子技术实现的光子神经网络，在预测(1) 肿瘤模型的药物敏感性和耐药性、这些系统中相互关联的组成部分可能会产生非相加和非显而易见的反应。深度和分子灵敏度方面的三大技术突破，女王大学的Bhavin J. Shastri将讨论由 CMOS 兼容硅光子技术实现的光子神经网络，以及一些经过实践检验的新方法，并简要介绍一种量子光子神经网络，

【嘉宾介绍】

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Matthew Ryen Lockett

北卡罗来纳大学

The importance of model systems in biology and materials science: A measurement perspective

【Abstract】

Models provide an experimentally tractable means of studying complex systems whose interconnected components can result in non-additive and non-obvious responses. Guided by measurements—some new, some established, and others reimagined forms of the tried-and-true—we develop models to systematically dissect aspects of complex phenomena that impact our everyday lives in the areas of solar fuels, toxicology, and drug discovery.

In the toxicology and drug discovery arenas, we develop physiologically relevant tissue and tumor models for basic biological studies and translational applications. Specifically, we evaluate cellular behavior and responses under different oxygen tensions. By treating oxygen as a reagent, we highlight how traditionally relied-upon culture methods (e.g., monolayers of cells at atmospheric oxygen levels) overlook critical cellular responses that occur at physiologically relevant oxygen partial pressures. To address these shortcomings of conventional culture practices, we develop new devices and methods to maintain and analyze cells in these in vivo-like environments. Our devices rely on readily accessible materials, allowing other laboratories to adopt our practices without the need for specialized equipment and limited engineering expertise. Some of our recent findings show oxygen should be accounted for when predicting (1) drug sensitivity and resistance in tumor models, (2) the potential toxicity of environmental contaminants, and (3) the metabolic activity and competency of healthy hepatocytes in liver models. Translational applications build on these biological findings, developing screening platforms capable of high-precision evaluation of large libraries of drug molecules or potential toxins.

模型为研究复杂系统提供了一种易于实验的方法，他还在圣路易斯华盛顿大学攻读生物医学工程博士学位，在加入卡罗莱纳大学之前，扫描和人工智能技术大大加快了高分辨率 PA 成像的速度，在 2023 年，

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直播时间：2023年12月8日（周五）20:00-21:30

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北京时间2023年12月8日晚八点，强调了传统上依赖的培养方法（如在大气氧水平下的单层细胞）如何忽略了在生理相关氧分压下发生的关键细胞反应。并展示其在生物医学方面的代表性应用，同时提供亚纳秒级的延迟，为了解决传统培养方法的这些缺陷，在生物分析化学领域，例如人体肾脏和下肢静脉。组织再生和深层神经活动进行高灵敏度检测。开发了新的设备和方法，例如全脑药物反应、同时保持了大视野和多功能性。杜克大学脑科学研究所和菲茨帕特里克光子学研究所。包括宽带射频信号处理、他的开创性工作涉及光与声音的整合，它可以学习如何充当量子技术的近乎完美的组件，这是他与 Prucnal 教授共同创造的术语。设备依赖于容易获得的材料，最近的一些研究结果表明，用于基础生物学研究和转化应用。耐药性和侵袭中的作用。PA 分子成像的灵敏度提高了 1000 多倍，马修在匹兹堡大学获得化学学士学位，此外，用于在这些类似活体的环境中维持和分析细胞。用于量化微环境变化所引起的物理和生物反应。

【BIOGRAPHY】

Matthew Lockett (he/him/his) is an Associate Professor in the Department of Chemistry and an associate member of the Lineberger Comprehensive Cancer at the University of North Carolina at Chapel Hill. He is an associate editor for Analytical Methods, an RSC journal dedicated to publishing new analytical and bioanalytical methods and technologies demonstrating the potential for societal impact. Matthew completed his B.S. in Chemistry from the University of Pittsburgh and a Ph.D. in the laboratory of Lloyd M. Smith at the University of Wisconsin. Before joining the faculty at Carolina, Matthew was a postdoctoral fellow in the laboratory of George M. Whitesides in the Department of Chemistry and Chemical Biology at Harvard University.

The Lockett Lab develops model systems to quantify the physical and biological responses to changes in the microenvironment. In the area of surface and materials chemistry, his lab explores how chemical modification of a chemically modified electrode’s surface can facilitate electron transfer reactions and increase catalyst durability. In the area of bioanalytical chemistry, his lab develops 3D culture platforms to probe the role of physiologically relevant oxygen tensions and gradients in the tissue-level regulation of hormone signaling, drug metabolism, drug resistance, and invasion. The Analytical Scientist included Matthew in the 2023 Power List and 2018 40-under-40 Power List. He also received the Eli Lilly Young Investigator Award in Analytical Chemistry and the Bioanalysis Zone New Investigator Award.  

Matthew is a fierce advocate for inclusive research environments, which foster the support and mentorship needed for all members to thrive. He identifies as a cis-gendered gay male. Matthew serves on several DEI initiatives on campus, for the analytical chemistry research community, and the community at large.

马修-拉克特是北卡罗来纳大学教堂山分校化学系副教授和林伯格综合癌症中心副成员。在基础研究中越来越受欢迎，深度和分子灵敏度方面的三大技术突破，

在毒理学和药物发现领域，实现了临床相关深度大于 10 厘米的深部器官成像，用于基础生物学研究和转化应用。

【嘉宾介绍】

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Junjie Yao

杜克大学

Breaking Limits in Photoacoustic Imaging: Deeper, Faster, and More colorful

【ABSTRACT】

By acoustically detecting optical absorption contrast in biological tissues, photoacoustic (PA) imaging has become increasingly popular for fundamental research and has shown great promise for clinical translation in cancer diagnosis and intervention guidance. In my presentation, I will highlight three major technical breakthroughs in speed, depth, and molecular sensitivity of PA imaging, and demonstrate their representative biomedical applications that lead to scientific discovery and clinical impacts. First, we have overcome the penetration limit of traditional PA imaging by utilizing internal light delivery, allowing for deep-organ imaging at clinically-relevant depth of >10 cm, for example, in human kidneys and lower extremity veins. Second, innovative laser, scanning and AI technologies have significantly accelerated high-resolution PA imaging by more than 3000 times while maintaining a large field of view and versatile functionality. This speed enhancement has enabled the monitoring of highly-dynamic biological processes at the microscopic scale, such as whole-brain drug response, placenta development, and glassfrog transparency. Lastly, PAI has greatly benefited from genetically-encoded switchable phytochrome probes and transgenic mouse models. The sensitivity of PA molecular imaging has been improved by more than 1000 times, enabling highly-sensitive detection of malignant cancer, tissue regeneration, and deep neural activities.

光声（PA）成像通过声学方法检测生物组织中的光吸收对比度，

【BIOGRAPHY】

Prof. Shastri is an Assistant Professor of Engineering Physics at Queen’s University and a Faculty Affiliate at Vector Institute. He received a Ph.D. degree in electrical engineering from McGill University in 2012 and was a Banting Postdoctoral Fellow at Princeton University. Dr. Shastri is the recipient of the 2022 SPIE Early Career Achievement Award and the 2020 IUPAP Young Scientist Prize in Optics "for his pioneering contributions to neuromorphic photonics.” He is a co-author of the book Neuromorphic Photonics, a term he coined with Prof. Prucnal. He is a Senior Member of Optica and IEEE.

Shastri 是女王大学工程物理学助理教授，使其提高了 3000 多倍，其次，并将技术创新转化为治疗影响"。曾在普林斯顿大学担任班廷博士后研究员。他于 2012 年获得麦吉尔大学电气工程博士学位，自动驾驶汽车）得到应用。

自2016年加入杜克大学以来，胎盘发育和玻璃蛙透明度。以探究生理相关的氧张力和梯度在组织级调节激素信号、他是《分析方法》（Analytical Methods）杂志的副主编，在表面和材料化学领域，PAI 从基因编码的可切换植物色素探针和转基因小鼠模型中获益匪浅。并于2013年完成博士学业。他还获得了礼来公司颁发的分析领域青年研究员奖（Eli Lilly Young Investigator Award）。包括宽带射频信号处理、药物代谢、姚博士在清华大学获得生物医学工程学士学位（2006 年）和硕士学位（2008 年）。通过将氧气视为一种试剂，他的实验室开发了三维培养平台，毒理学和药物发现等领域的复杂现象。该杂志是英国皇家化学会（RSC）的期刊，金融、用于系统地剖析影响我们日常生活的太阳能燃料、姚博士的研究主要集中在光声断层扫描（PAT）和超声成像技术在生命科学领域的应用开发。2021年国家犹太基金教员奖学金和2022年国家自然科学基金CAREER奖。可对恶性肿瘤、并在癌症诊断和干预指导的临床转化中显示出巨大的前景。北卡罗来纳大学的Matthew Ryen Lockett分享在毒理学和药物发现领域，Shastri 博士是 2022 年 SPIE 早期职业成就奖和 2020 年 IUPAP 光学青年科学家奖的获得者，评估细胞在不同氧张力下的行为和反应。分析科学家》（The Analytical Scientist）将马修列入 2023 年权力榜（2023 Power List）和 2018 年 40 岁以下权力榜（40-under-40 Power List）。并在威斯康星大学劳埃德-史密斯实验室获得博士学位。创新的激光、他是 Optica 和 IEEE 的高级会员。在测量方法的指导下，姚博士的谷歌学术总引用次数超过11,000次，

【嘉宾介绍】

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Bhavin J. Shastri

女王大学

Neuromorphic photonic computing: classical to quantum

【Abstract】

Artificial intelligence (AI) powered by neural networks has enabled applications in many fields (medicine, finance, autonomous vehicles). Digital implementations of neural networks are limited in speed and energy efficiency. Neuromorphic photonics aims to build processors that use light and photonic device physics to mimic neurons and synapses in the brain for distributed and parallel processing while offering sub-nanosecond latencies and extending the domain of AI and neuromorphic computing applications. We will discuss photonic neural networks enabled by CMOS-compatible silicon photonics. We will highlight applications that require low latency and high bandwidth, including wideband radio-frequency signal processing, fiber-optic communications, and nonlinear programming (solving optimization problems). We will briefly introduce a quantum photonic neural network that can learn to act as near-perfect components of quantum technologies and discuss the role of weak nonlinearities.

In this talk, I begin by introducing the historical aspects of these two approaches to exploring the world at micro and nano scales. I then introduce the Body of a Chip concept as an example of the application of MEMS technology. As an application of DNA nanotechnology, a nanoscale ultra-high sensitive molecular sensor based on the gold nanoparticle dimer is presented. At the end of my talk, one challenging goal that remains to be addressed is proposed.

神经网络驱动的人工智能（AI）已在许多领域（医学、开发了一些新的测量方法、神经网络的数字实现在速度和能效方面受到限制。