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Machine intelligence-accelerated discovery of all-natural plastic substitutes

Nature Nanotechnology 2024年第6期19卷 782-791页

作者： Chen, Tianle Pang, Zhenqian He, Shuaiming Li, Yang Shrestha, Snehi Little, Joshua M. Yang, Haochen Chung, Tsai-Chun Sun, Jiayue Whitley, Hayden Christopher Lee, I-Chi Woehl, Taylor J. Li, Teng Hu, Liangbing Chen, Po-Yen Department of Chemical and Biomolecular Engineering University of Maryland College ParkMD United States Department of Mechanical Engineering University of Maryland College ParkMD United States Department of Chemistry and Biochemistry University of Maryland College ParkMD United States Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Materials Science and Engineering University of Maryland College ParkMD United States Maryland Robotics Center College ParkMD United States

One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model’s prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database. © The Author(s) 2024.

关键词： Neural networks

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FluidFM for single-cell biophysics

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Nano Research 2022年第2期15卷 773-786页

作者： Mi Li Lianqing Liu Tomaso Zambelli Laboratory of Biosensors and Bioelectronics Institute for Biomedical EngineeringETH ZurichZurich 8092Switzerland State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China

Fluidic force microscopy(FluidFM),which combines atomic force microscopy(AFM)with microchanneled cantilevers connected to a pressure controller,Is a technique allowing the realization of force-sensitive nanopipette under aqueous *** has unique advantages in simultaneous three-dimensional manipulations and mechanical measurements of biological specimens at the micro-/*** the past decade,FluidFM has shown its potential in biophysical assays particularly in the investigations at single-cell level,offering novel possibilities for discovering the underlying mechanisms guiding life ***,we review the utilization of FluidFM to address biomechanical and biophysical issues in the life ***,the fundamentals of FluidFM are ***,the applications of FluidFM for biophysics at single-cell level are surveyed from several facets,including single-cell manipulations,single-cell force spectroscopy,and single-cell ***,the challenges and perspectives for future progressions are provided.

关键词： atomic force microscopy fluidic force microscopy single-cell manipulation single-cell force spectroscopy single-cell electrophysiology

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Optimal control of electric automated buses in intercity lines: scenario-based analysis of the Pareto-optimal solutions

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IFAC-PapersOnLine 2024年第10期58卷 37-42页

作者： C. Pasquale S. Sacone S. Siri A. Ferrara Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

This paper deals with the optimal control of electric and automated buses that have to follow an intercity line, where some stops are equipped with a flash-charging infrastructure to charge the batteries, while others are not. In order to control these buses, it is necessary to account for the traffic conditions along the roads of the bus line, and to minimize two objectives related to the deviations from the bus timetable and the lack of energy, at the end of the bus trip, with respect to a desired final energy level. The resulting optimal control problem is a multi-objective linear quadratic problem with both continuous and integer variables. In this paper, we propose and apply two methods based on lexicographic ordering aimed at determining the Pareto-optimal solutions of the control problem in twelve scenarios inspired by a real case.

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Au-MPN@Ag Nanocomposite-Assisted Mass Spectrometry for Metabolite Detection

Au-MPN@Ag Nanocomposite-Assisted Mass Spectrometry for Metab...

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2024 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale, 3M-NANO 2024

作者： Sang, Qi Li, Yanyan Liu, Wanshan Wang, Yuning Qian, Kun Shanghai Jiao Tong University State Key Labtoratory of Systems Medicine for Cancer School of Biomedical Engineering Institute of Medical Robotics and Shanghai Academy of Experiment Medicine Shanghai China

ISBN: (纸本)9798350362107

Sensitive detection of metabolites in biofluids plays an important role in disease diagnosis. Herein, we synthesized a novel noble metal based nanocomposite using metal-polyphenol network (MPN) as coordination and redox reaction. The formed Au-MPN@Ag nanocomposite exhibited excellent metabolite detection performance in laser desorption ionization mass spectrometry (LDI-MS). Notably, Au-MPN@Ag nanocomposite based LDI-MS platform achieved direct detection of metabolites in biofluids without any sample pretreatment. Our work provides a new advanced platform for metabolic analysis towards in vitro diagnostics. © 2024 IEEE.

关键词： Redox reactions

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Modeling of Thermal Processes in a Microcontroller System with the Use of Hybrid, Fractional Order Transfer Functions 21st

Modeling of Thermal Processes in a Microcontroller System wi...

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21st Polish Control Conference, PCC 2023

作者： Oprzedkiewicz, Krzysztof Rosol, Maciej Mitkowski, Wojciech Department of Automatic Control and Robotics Faculty of Electrical Engineering Automatic Control Informatics and Biomedical Engineering AGH University of Science and Technology al. A Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031351693

In the paper the problem of modeling of thermal processes in microcontroller system is addressed. The proposed models allow to describe thermal processes during work of an evaluation system. The temperature in critical places of the system is measured using thermal camera. The proposed models have the form of hybrid transfer functions, containing both Integer Order (IO) and Fractional Order (FO) parts. The step responses of the proposed transfer functions are computed analytically. Results of experiments show that the proposed models are more accurate in the sense of Mean Square Error (MSE) cost function than typical transfer function models with delay. The proposed models can be applied to predict of overheating of microcotroller systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Microcontrollers

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Numerical Estimation of the Internal Positivity of the Fractional Order Model of a Two-Dimensional Heat Transfer Process 27th

Numerical Estimation of the Internal Positivity of the F...

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27th International Conference on Automation, AUTOMATION 2023

作者： Oprzędkiewicz, Krzysztof Department of Automatic Control and Robotics Faculty of Electrical Engineering Automatic Control Informatics and Biomedical Engineering AGH University of Science and Technology al. A Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031258435

In the paper the numerical estimation of the internal positivity of the model of a two dimensional heat transfer process is addressed. The considered thermal process is described by the fractional order state equation, derived from parabolic heat equation with homogenous Neumann boundary conditions and distributed control and observation. Two numerical algorithms of the positivity testing are proposed and compared. The first one consists in the full examination of the whole area. The next one uses Monte Carlo method. From tests it can be concluded that the considered model can be internally positive only for its lower orders and heating and measurements located closely to the corner of the tested area. The use of the Monte Carlo method allows to significantly shorten the duration of computations. The presented results can be useful in measurements using a thermal camera. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fractional order system Monte Carlo method Positivity Thermal camera

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Robotic Anchoring System with Two Locomotion Modes for Preoperative Localization of Colorectal Cancer

Robotic Anchoring System with Two Locomotion Modes for Preop...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Wang, Jiale Ren, Hao Yang, Liu Zhao, Haoxiang Zhang, Ruijia Shen, Yajing Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Kowloon Hong Kong City University of Hong Kong Department of Biomedical Engineering Kowloon Hong Kong Institute of Robotics and Automatic Information System Nankai University China

ISBN: (纸本)9781665481090

At present, colorectal cancer is one of the most prevalent cancers in the world. Colectomy serves as an effective treatment option for this disease. Preoperative localization plays a vital role in the colectomy procedure. However, traditional preoperative localization techniques have a greater risk of infection and lower accuracy. Moreover, it is difficult for the previous anchoring robots to adapt to the unstructured environment of the GI tract because of their limited movement abilities. To address these challenges, this work develops a robotic anchoring system with two locomotion modes which can anchor in the designated position. Driven by the external magnetic field, the robot can achieve slipping and flipping in the stomach. When the gradients of the field increase, the robot is triggered to deploy the anchoring mechanism in the intestine. Two in vitro experiments are conducted in the stomach and intestine phantoms. The first experiment demonstrates that the robot system can achieve both slipping and flipping in the stomach and its average position error is 2.96mm. The second experiment shows that the robot system can anchor in the designated position near the tumor and can resist a peristalsis force of 0.82N from the intestine wall. © 2024 IEEE.

关键词： Diseases

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Improving Intersession Reproducibility for Forearm Ultrasound based Hand Gesture Classification through an Incremental Learning Approach

arXiv

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arXiv 2024年

作者： Bimbraw, Keshav Rothenberg, Jack Zhang, Haichong K. Robotics Engineering Worcester Polytechnic Institute Worcester United States Robotics and Biomedical Engineering Worcester Polytechnic Institute Worcester United States

Ultrasound images of the forearm can be used to classify hand gestures towards developing human machine interfaces. In our previous work, we have demonstrated gesture classification using ultrasound on a single subject without removing the probe before evaluation. This has limitations in usage as once the probe is removed and replaced, the accuracy declines since the classifier performance is sensitive to the probe location on the arm. In this paper, we propose training a model on multiple data collection sessions to create a generalized model, utilizing incremental learning through fine tuning. Ultrasound data was acquired for 5 hand gestures within a session (without removing and putting the probe back on) and across sessions. A convolutional neural network (CNN) with 5 cascaded convolution layers was used for this study. A pre-trained CNN was fine tuned with the convolution blocks acting as a feature extractor, and the parameters of the remaining layers updated in an incremental fashion. Fine tuning was done using different session splits within a session and between multiple sessions. We found that incremental fine tuning can help enhance classification accuracy with more fine tuning sessions. After 2 fine tuning sessions for each experiment, we found an approximate 10% increase in classification accuracy. This work demonstrates that incremental learning through fine tuning on ultrasound based hand gesture classification can be used improves accuracy while saving storage, processing power, and time. It can be expanded to generalize between multiple subjects and towards developing personalized wearable devices. © 2024, CC BY.

关键词： Convolutional neural networks

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Hand Gesture Classification Based on Forearm Ultrasound Video Snippets Using 3D Convolutional Neural Networks

arXiv

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arXiv 2024年

作者： Bimbraw, Keshav Talele, Ankit Zhang, Haichong K. Robotics Engineering Worcester Polytechnic Institute Worcester United States Robotics and Biomedical Engineering Worcester Polytechnic Institute Worcester United States

Ultrasound based hand movement estimation is a crucial area of research with applications in human-machine interaction. Forearm ultrasound offers detailed information about muscle morphology changes during hand movement which can be used to estimate hand gestures. Previous work has focused on analyzing 2-Dimensional (2D) ultrasound image frames using techniques such as convolutional neural networks (CNNs). However, such 2D techniques do not capture temporal features from segments of ultrasound data corresponding to continuous hand movements. This study uses 3D CNN based techniques to capture spatio-temporal patterns within ultrasound video segments for gesture recognition. We compared the performance of a 2D convolution-based network with (2+1)D convolution-based, 3D convolution-based, and our proposed network. Our methodology enhanced the gesture classification accuracy to 98.8 ± 0.9%, from 96.5 ± 2.3% compared to a network trained with 2D convolution layers. These results demonstrate the advantages of using ultrasound video snippets for improving hand gesture classification performance. © 2024, CC BY.

关键词： Convolutional neural networks

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Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces 38

Exploring the trade-off between deep-learning and explainabl...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Cubillos, Luis H. Revach, Guy Mender, Matthew J. Costello, Joseph T. Temmar, Hisham Hite, Aren Zutshi, Diksha Wallace, Dylan M. Ni, Xiaoyong Kelberman, Madison M. Willsey, Matthew S. van Sloun, Ruud J.G. Shlezinger, Nir Patil, Parag Draelos, Anne Chestek, Cynthia A. Departments of Electrical & Computer Engineering Biomedical Engineering Robotics Computational Medicine & Bioinformatics and Neurosurgery University of Michigan United States Biointerfaces Institute Neuroscience Institute University of Michigan United States Department of Information Technology and Electrical Engineering ETH Zürich Switzerland Department of Electrical Engineering Eindhoven University of Technology Netherlands School of Electrical and Computer Engineering Ben-Gurion University Israel

People with brain or spinal cord-related paralysis often need to rely on others for basic tasks, limiting their independence. A potential solution is brain-machine interfaces (BMIs), which could allow them to voluntarily control external devices (e.g., robotic arm) by decoding brain activity to movement commands. In the past decade, deep-learning decoders have achieved state-of-the-art results in most BMI applications, ranging from speech production to finger control. However, the'black-box' nature of deep-learning decoders could lead to unexpected behaviors, resulting in major safety concerns in real-world physical control scenarios. In these applications, explainable but lower-performing decoders, such as the Kalman filter (KF), remain the norm. In this study, we designed a BMI decoder based on KalmanNet, an extension of the KF that augments its operation with recurrent neural networks to compute the Kalman gain. This results in a varying "trust" that shifts between inputs and dynamics. We used this algorithm to predict finger movements from the brain activity of two monkeys. We compared KalmanNet results offline (pre-recorded data, n = 13 days) and online (real-time predictions, n = 5 days) with a simple KF and two recent deep-learning algorithms: tcFNN (non-ReFIT version) and LSTM. KalmanNet achieved comparable or better results than other deep learning models in offline and online modes, relying on the dynamical model for stopping while depending more on neural inputs for initiating movements. We further validated this mechanism by implementing a heteroscedastic KF that used the same strategy, and it also approached state-of-the-art performance while remaining in the explainable domain of standard KFs. However, we also see two downsides to KalmanNet. KalmanNet shares the limited generalization ability of existing deep-learning decoders, and its usage of the KF as an inductive bias limits its performance in the presence of unseen noise distributions. Despite thi

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