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A Bioelectronic Sensing Platform Combining Flexible Dual-Sided Microelectrode Array and Insect Olfactory Neurons for Lung Cancer Biomarker Detection

SSRN

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SSRN 2024年

作者： Liu, Xiang Sanchez, Simon Gong, Yan Stevens, Trevor Jiang, Zebing Saha, Debajit Li, Wen Neuroscience Program Department of Physiology Michigan State University East LansingMI United States Department of Biomedical Engineering Michigan State University East LansingMI United States Department of Electrical and Computer Engineering Michigan State University East LansingMI United States East LansingMI United States

Early detection of lung cancer significantly enhances treatment outcomes, yet current screening methods are limited by accessibility, sensitivity, and cost. Detecting volatile organic compounds (VOCs) related to lung cancer offers the possibility for noninvasive, early, and accessible diagnosis of lung cancer in humans. This study introduces a bioelectronic sensing platform that integrates the highly sensitive locust olfactory system with a flexible dual-sided microelectrode array (MEA), for robust, efficient, and label-free detection of volatile lung cancer biomarkers. By leveraging a unique folding-bonding fabrication method and poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) surface functionalization, we developed and optimized MEAs with a high spatial resolution of 50-75 µm and a low impedance within 2.85 ×104 Ohm. These MEAs retain mechanical flexibility and stability, robustly penetrating the insect brain tissues without any mechanical reinforcement, and capture distinct neural activities in the locust's antennal lobe triggered by lung cancer VOCs. By combining neural responses across electrophysiological recordings and employing dimensionality reduction techniques, we obtained unique trajectories and clusters associated with distinct population responses for each lung cancer VOC biomarker. Utilizing a high dimensional population neuronal response analysis via leave-one-trial-out methodology, we obtained a high classification success rate of unknown VOCs. Combining all neurons obtained from a single electrophysiological recording showed 88% classification success of each VOC indicating the potential of this platform for one-shot and real-time lung cancer detection. This study presents a novel platform for lung cancer biomarker detection and opens new avenues for biological brain-based, non-invasive, and efficient cancer screening methods. © 2024, The Authors. All rights reserved.

关键词： Volatile organic compounds

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Scientometric Analysis of Artificial Intelligence Research in Agriculture 4

Scientometric Analysis of Artificial Intelligence Research i...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Pramono, Andi Hamim, Miftahul Prihatin, Sukeipah Yuli Arifuddin, Rahman School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia Binus Business School Bina Nusantara University Entrepreneurship Business Creation Jakarta11480 Indonesia Politeknik Kota Malang Informatics Study Program Malang Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

Artificial Intelligence Research in Agriculture has grown so common, and the potential they provide is so revolutionary that it is considered essential for competitive growth. Until we conducted this research, only 1064 academic documents on Trends of Artificial Intelligence Research in Agriculture that we found from 2012 to 2021 were obtained by searching the Scopus database. This study uses bibliometric analysis methods to map scientific publications worldwide. We use an article selection process, the year limitation, then the database is exported to RIS and CSV format files. Researchers in the United States had the most published articles and were indexed by Scopus among the most prolific authors ($\mathrm{N}=287$), followed by the United Kingdom at second ($\mathrm{N}=139$) and Italy with 66 academic publications. Data analysis reveals an increasing trend in the number of Trends of Artificial Intelligence Research in Agriculture publications worldwide, as measured by the Scopus index. © 2022 IEEE.

关键词： Agriculture

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Light-driven single-cell rotational adhesion frequency assay

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eLight 2022年第1期2卷 154-164页

作者： Yaoran Liu Hongru Ding Jingang Li Xin Lou Mingcheng Yang Yuebing Zheng Department of Electrical and Computer Engineering The University of Texas at AustinAustinTX 78712USA Walker Department of Mechanical Engineering The University of Texas at AustinAustinTX 78712USA Materials Science&Engineering Program and Texas Materials Institute The University of Texas at AustinAustinTX 78712USA School of Physical Sciences University of Chinese Academy of SciencesBeijing 100049China Beijing National Laboratory for Condensed Matter Physics and Laboratory of Soft Matter Physics Institute of PhysicsChinese Academy of SciencesBeijing 100190China Songshan Lake Materials Laboratory Dongguan 523808GuangdongChina Department of Biomedical Engineering The University of Texas at AustinAustinTX 78712USA

The interaction between cell surface receptors and extracellular ligands is highly related to many physiological processes in living *** techniques have been developed to measure the ligand-receptor binding kinetics at the single-cell ***,few techniques can measure the physiologically relevant shear binding affinity over a single cell in the clinical ***,we develop a new optical technique,termed single-cell rotational adhesion frequency assay(scRAFA),that mimics in vivo cell adhesion to achieve label-free determination of both homogeneous and heterogeneous binding kinetics of targeted cells at the subcellular ***,the scRAFA is also applicable to analyze the binding affinities on a single cell in native human *** its superior performance and general applicability,scRAFA is expected to find applications in study of the spatial organization of cell surface receptors and diagnosis of infectious diseases.

关键词： diagnosis kinetics rotational

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Multi-Instance Task in Swarm Robotics: Sorting Groups of Robots or Objects into Clusters with Minimalist Controllers

Multi-Instance Task in Swarm Robotics: Sorting Groups of Rob...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Adilson Krischanski Yuri K. Lopes Andre B. Leal Ricardo F. Martins Roberto S.U. Rosso Department of Computer Science Santa Catarina State University (UDESC) Joinville Brazil Graduate Program in Applied Computing (PPGCAP) UDESC Brazil Department of Electrical Engineering Santa Catarina State University (UDESC) Joinville Brazil

Relying only on behaviors that emerge from simple responsive controllers; swarms of robots have been shown capable of autonomously aggregate themselves or objects into clusters without any form of communication. We push these controllers to the limit, requiring robots to sort themselves or objects into different clusters. Based on a responsive controller that maps the current reading of a line-of-sight sensor to a pair of speeds for the robots' differential wheels, we demonstrate how multiple tasks instances can be accomplished by a robotic swarm. Using the dividing rectangles approach and physics simulation, a training step optimizes the parameters of the controller guided by a fitness function. We conducted a series of systematic trials in physics-based simulation and evaluate the performance in terms of dispersion and the ratio of clustered robots/objects. Across 20 trials where 30 robots cluster themselves into 3 groups, an average of 99.83% of them were correctly clustered into their group after 300 s. Across 50 trials where 15 robots cluster 30 objects into 3 groups, an average of 61.20%, 82.87%, and 97.73% of objects were correctly clustered into their group after 600 s, 900 s, and 1800 s, respectively. The object cluster behavior scales well while the aggregation does not, the latter due to the requirement of control tuning based on the number of robots.

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Evolutionary Trends of Intelligent IoT Networking for Commercial and Industrial Use Cases

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中国通信（英文版） 2023年第8期20卷前插1-前插4页

作者： Shuai Han Rong Bo Nadra Guizani Sai Xu Ruoyu Su Liang Xiao Cheng Li Department of Electronics and Communication Engineering Harbin Institute of Technology Communications Research Centre Canada Engineering program and the computing Research Association School of Cybersecurity Northwestern Polytechnical UniversityXi'anChina Nanjing University of Posts and Telecommunications Department of Informatics and Communication Engineering Xiamen Univer- sityFujianChina Department of Electrical and Computer Engineering of Memorial University St.John'sCanada

The intelligent Internet of Things(IIoT)is one of the key enablers for the digital transformation of traditional industries towards Industry *** services have diversified requirements due to the nature of their commercial and industrial *** various devices in IIoT environments have their unique properties in terms of resource limita-tions,network lifetimes,and application Quali-ty-of-Service(QoS)requirements which affect the performance under such applications ***,IoT devices typically have constrained power,storage,computing,high latency,massive multiple access and communications resources which limit the performance of IIoT for commercial and industrial Use *** the other hand,devices used in IIoT were not initially designed with security in mind and they had been considered *** response to the above challenges,domestic and foreign scholars have gradually carried out research on network architecture,collaborative scheduling strategy,energy supply technology,resource man-agement,and ***,the research on IIoT is still in its infancy,and has not yet formed comprehen-sive and systematic theoretical methods and technical standards.

关键词： Special issues and sections Internet of Things Intelligent systems Commercialization Requirements engineering Industrial engineering

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What Are Large Language Models Mapping to in the Brain? A Case Against Over-Reliance on Brain Scores

arXiv

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arXiv 2024年

作者： Feghhi, Ebrahim Hadidi, Nima Song, Bryan Blank, Idan A. Kao, Jonathan C. Department of Electrical and Computer Engineering The University of California Los Angeles United States Neuroscience Interdepartmental Program The University of California Los Angeles United States Department of Psychology The University of California Los Angeles United States Department of Linguistics The University of California Los Angeles United States

Given the remarkable capabilities of large language models (LLMs), there has been a growing interest in evaluating their similarity to the human brain. One approach towards quantifying this similarity is by measuring how well a model predicts neural signals, also called "brain score". Internal representations from LLMs achieve state-of-the-art brain scores, leading to speculation that they share computational principles with human language processing. This inference is only valid if the subset of neural activity predicted by LLMs reflects core elements of language processing. Here, we question this assumption by analyzing three neural datasets used in an impactful study on LLM-to-brain mappings, with a particular focus on an fMRI dataset where participants read short passages. We first find that when using shuffled train-test splits, as done in previous studies with these datasets, a trivial feature that encodes temporal autocorrelation not only outperforms LLMs but also accounts for the majority of neural variance that LLMs explain. We therefore caution against shuffled train-test splits, and use contiguous test splits moving forward. Second, we explain the surprising result that untrained LLMs have higher-than-expected brain scores by showing they do not account for additional neural variance beyond two simple features: sentence length and sentence position. This undermines evidence used to claim that the transformer architecture biases computations to be more brain-like. Third, we find that brain scores of trained LLMs on this dataset can largely be explained by sentence length, sentence position, and pronoun-dereferenced static word embeddings;a small, additional amount is explained by sense embeddings and contextual representations of sentence structure. We conclude that over-reliance on brain scores can lead to over-interpretations of similarity between LLMs and brains, and emphasize the importance of deconstructing what LLMs are mapping to in neural signals.

关键词： Brain

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Evaluation of Benchmarking Pre-Trained CNN Model for Autonomous Vehicles Object Detection in Mixed Traffic 9

Evaluation of Benchmarking Pre-Trained CNN Model for Autonom...

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9th International Conference on ICT for Smart Society, ICISS 2022

作者： Afdhal, Afdhal Nasaruddin, Nasaruddin Fuadi, Zahrul Sugiarto, Sugiarto Riza, Hammam Saddami, Khairun Doctoral Program School of Engineering Universitas Syiah Kuala Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Mechanical and Industrial Engineering Banda Aceh Indonesia Universitas Syiah Kuala Department of Civil Engineering Banda Aceh Indonesia Jakarta Indonesia Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

In the next few years, the new generation of Autonomous Vehicles (AVs) promises an advanced level of self-driving experiences. One of the most challenging topics in AVs development is the readiness of object detection models in complex urban environments. Mixed traffic is a complex urban environment that contains much uncertainty and is composed of heterogeneous objects. Therefore, this paper evaluates benchmarking the pre-trained CNN model for object detection in a mixed traffic environment. The evaluation is conducted for five modern algorithms and architecture of neural networks, including Faster RCNN, SSD, YOLOv3, YOLOv4, and EfficientDet. Then, we provide a new dataset in the mixed traffic environment under night conditions for more accurate object detection. Moreover, we conduct the simulation by considering the performance parameters that are recall, precision, and F measure. The performance of our dataset is also compared to the MS-COCO dataset. The result shows that the average precision value of Faster RCNN, SSD, YOLOv3, YOLOv4, and EfficientDet is 16.70%, 8.90%, 19.67%, 43.90%, and 55.56% respectively. It shows that YOLOv4 and EfficientDet provide better object detection accuracy than other CNN models. © 2022 IEEE.

关键词： Object detection

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BRAIN-TO-TEXT BENCHMARK’24: LESSONS LEARNED

arXiv

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arXiv 2024年

作者： Willett, Francis R. Li, Jingyuan Le, Trung Fan, Chaofei Chen, Mingfei Shlizerman, Eli Chen, Yue Zheng, Xin Okubo, Tatsuo S. Benster, Tyler Lee, Hyun Dong Kounga, Maxwell Buchanan, E. Kelly Zoltowski, David Linderman, Scott W. Henderson, Jaimie M. Department of Neurosurgery Stanford University United States Department of Electrical & Computer Engineering University of Washington United States Department of Computer Science Stanford University United States Department of Applied Mathematics University of Washington United States China Neurosciences Phd Program Stanford University United States Wu Tsai Neurosciences Institute Department of Statistics Stanford University United States

Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with paralysis who have lost the ability to speak intelligibly. The Brain-to-Text Benchmark’24 and associated competition was created to foster the advancement of decoding algorithms that convert neural activity to text. Here, we summarize the lessons learned from the competition ending on June 1, 2024 (the top 4 entrants also presented their experiences in a recorded webinar). The largest improvements in accuracy were achieved using an ensembling approach, where the output of multiple independent decoders was merged using a fine-tuned large language model (an approach used by all 3 top entrants). Performance gains were also found by improving how the baseline recurrent neural network (RNN) model was trained, including by optimizing learning rate scheduling and by using a diphone training objective. Improving upon the model architecture itself proved more difficult, however, with attempts to use deep state space models or transformers not yet appearing to offer a benefit over the RNN baseline. The benchmark will remain open indefinitely to support further work towards increasing the accuracy of brain-to-text algorithms. © 2024, CC BY.

关键词： Recurrent neural networks

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Analysis of the Brazilian Climate Data Influence on Soybean Pricing by Using Long Short-Term Memory Models

SSRN

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SSRN 2024年

作者： Oliva, Jefferson Tales Martelli, Otávio Pigozzo de Castro Barbosa, Marco Antônio Dal Molin, Viviane Casanova, Dalcimar Teixeira, Marcelo Graduate Program in Electrical and Computer Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Department of Academic Informatics Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Graduate Program in Production and Systems Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil

This work uses machine learning to verify whether the Brazilian climate influences international soybean price prediction. For this, climatic data, dollar quotes, inflation, and annual soybean production were collected from different data sources. Then, the collected data were pre-processed and grouped by Brazilian state. Afterward, data from the five most productive Brazilian states are chosen, such as Mato Grosso (MT), Mato Grosso do Sul (MS), Goiás (GO), Paraná (PR), and Rio Grande do Sul (RS). Afterward, predictive models are built using the long short-term memory (LSTM) method, which is applied in each chosen state dataset considering two different settings: with and without climatic data. Thus, two models were built for each Brazilian state, totaling ten predictors. In the experimental evaluation, when trained with climatic data, the PR model achieved the lowest values of root mean squared error (RMSE) and mean absolute error (MAE). Regarding predictors trained without climate data, the RS and PR obtained the lowest values of RMSE and MAE, respectively. After conducting statistical hypothesis tests, no significant difference was found among models, stating, with 95% certainty, that, in this study, the Brazilian climatic data did not influence the model performance for predicting soybean prices. On the other hand, one of the main contributions of work is conducting an unprecedented study about soybean price prediction using Brazilian climatic data. © 2024, The Authors. All rights reserved.

关键词： Long short-term memory

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Physics Constrained Multi-Objective Bayesian Optimization to Accelerate 3d Printing of Thermoplastics

SSRN

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SSRN 2024年

作者： Lin, Jian Sattari, Kianoosh Wu, Yuchao Chen, Zhenru Mahjoubnia, Alireza Su, Changhua Department of Mechanical and Aerospace Engineering University of Missouri ColumbiaMO65211 United States Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO65211 United States Food Science Program Division of Food Nutrition & Exercise Sciences University of Missouri ColumbiaMO65211 United States

Vat photopolymerization (VPP) of thermoplastics faces an issue of unpredictable printability due to the involved competing photopolymerization and dissolution reactions. The printing outcome largely depends on physicochemical properties of monomers and their compositions in resins, which also greatly determine the material properties, e.g., strength/toughness and phase transition temperature (Tg). Thus, a methodology for optimizing the resin formulation is of paramount importance in realizing highly printable thermoplastics with balanced strength/toughness and target Tg while remaining largely underexplored. Herein, we introduce a multi-objective Bayesian optimization (MOBO) algorithm with two physics informed constraints (printability and Tg) to optimize two conflicting properties: tensile strength (σT) and toughness (UT). The two constraints are formulated as two machine learning (ML) models, which are trained with weight ratios of the six input monomers and physics informed (PI) descriptors derived from their physiochemical parameters. Dimensional reduction analysis reveals that the algorithm avoids recommendation of the monomer ratios that do not pass the two constraints. The printing failure rate is reduced from 16% in the background experiments to 3% in the recommended experiments. Within only 36 iterations (72 samples), the MOBO algorithm successfully identifies five sets of ratios leading to Pareto optimal of σT and UT. Due to the constraint in Tg they show appropriate Tg for shape memory application. The partial dependence analysis indicates that σT and UT depend on both the ratios and physiochemical features of the monomers. These results underscore capability of such a smart decision-making algorithm—with constraints that are not fully understood but can be informed by prior knowledge—in planning the experiments from the vast design space, thus holding a great promise for broader applications in materials and manufacturing. © 2024, The Authors. All rights

关键词： Resins

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