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Proposal of a Real-Time Test Platform for Tactile Internet Systems

SENSORS 2022年第24期22卷

作者： Alves, Pedro V. A. Plentz, Patricia D. M. Fernandes, Marcelo A. C. Univ Fed Rio Grande do Norte Lab Machine Learning & Intelligent Instrumentat BR-59078970 Natal Brazil Univ Fed Santa Catarina Grad Program Comp Sci BR-88036610 Florianopolis Brazil Univ Fed Rio Grande do Norte Dept Comp Engn & Automation BR-59078970 Natal Brazil

This work aimed to develop a real-time test platform for systems associated with the tactile internet area. The proposal comprises a master device, a communication channel and a slave device. The master device is a tactile glove (wearable technology) that works as a tactile interface based on vibratory feedback. The master device can interact with virtual elements (local or remote). The Matlab/Simulink environment and a robotics toolbox form the communication channel and the slave device. The communication channel introduces a bidirectional connection of variable latency, and the slave device is defined as a robotic phantom omni manipulator emulated in Matlab/Simulink. The virtual robotic manipulator, the slave device, can generate different types of tactile sensations in the tactile glove, that is, in the master device. The platform can model tactile sensations such as coarse roughness, fine roughness, smoothness, dripping and softness. The proposed platform presented adequate results and can be used to test various algorithms and methods correlated to the tactile internet.

关键词： tactile internet real time tactile glove phantom omni

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PeakCNV: A multi-feature ranking algorithm-based tool for genome-wide copy number variation-association study

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2022年 20卷 4975-4983页

作者： Labani, Mahdieh Afrasiabi, Ali Beheshti, Amin Lovell, Nigel H. Alinejad-Rokny, Hamid UNSW Sydney Grad Sch Biomed Engn Biomed Machine Learning Lab BML Sydney NSW 2052 Australia Macquarie Univ Sch Comp Data Analyt Lab Sydney NSW 2109 Australia UNSW Sydney Grad Sch Biomed Engn GSBmE Sydney NSW 2052 Australia UNSW Sydney Tyree Inst Hlth Engn IHealthE Sydney NSW 2052 Australia Univ New South Wales UNSW Data Sci Hub Sydney NSW 2052 Australia Macquarie Univ AI Enabled Proc AIP Res Ctr Hlth Data Analyt Program Sydney 2109 Australia Macquarie Univ Sch Comp Sydney NSW Australia

Copy Number Variation (CNV) refers to a type of structural genomic alteration in which a segment of chromosome is duplicated or deleted. To date, many CNVs have been identified as causative genetic ele-ments for several diseases and phenotypes. However, performing a CNV-based genome-wide association study is challenging due to inconsistency in length and occurrence of CNVs across different individuals under investigation. One of the most efficient strategies to address this issue is building CNV regions (ge-nomic regions in which CNVs are overlapping -CNVRs). However, this approach is susceptible to a high false positive rate due to overlapping and co-occurring of confounding CNVRs with true positive CNVRs. Here, we develop PeakCNV that differentiates false-positive CNVRs from true positives by calculating a new metric, independence ranking score, (IR-score) via a feature ranking approach. We compared the performance of PeakCNV with other current existing tools by carrying out two case studies one using the CNV genotype data for individuals with prostate cancer (194 cases and 2,392 healthy individuals) and the second one for individuals with neurodevelopmental disorders (19,642 cases and 6,451 healthy individuals). Crucially, our benchmarking analyses on prostate cancer cohort indicated that PeakCNV identifies a fewer risk candidate CNVRs with shorter lengths compared to other tools. Importantly, these CNVRs cover a greater proportion of case over healthy individuals compared to other tools. The accuracy of PeakCNV in identifying relevant candidate CNVRs was reproducible in the case study on neurodevel-opmental disorders. Using data from the FANTOM5 expression atlas and the Clinical Genomic Database, we show that the candidate CNVRs identified by PeakCNV for neurodevelopmental disorders overlap with a greater number of genes with the brain-enriched expression, and a greater number of genes that are associated with neurological conditions compared to candidate CNVRs

关键词： Genetic abnormalities Copy number variations CNV association study Risk genes Artificial intelligence

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A multi-modal scanning system to digitize CBRNE emergency response scenes

A multi-modal scanning system to digitize CBRNE emergency re...

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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： Marco Salathe Brian J. Quiter Mark S. Bandstra Xin Chen Victor Negut Micah Folsom Gunther H. Weber Christopher Greulich Mathew Swinney Nicholas Prins Daniel E. Archer Applied Nuclear Physics Program at Lawrence Berkeley National Laboratory Berkeley CA USA Machine Learning and Analytics group Computer Science Research Division Lawrence Berkeley National Laboratory Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (纸本)9781665456814

A handheld system developed to digitize a contextual understanding of the scene at a chemical, biological, radiological, nuclear and/or explosives (CBRNE) events is described. The system uses LiDAR and cameras to create a colorized 3D model of the environment, which helps domain experts that are supporting responders in the field. To generate the digitized model, a responder scans any suspicious objects and the surroundings by carrying the system through the scene. The scanning system provides a real-time user interface to inform the user about scanning progress and to indicate any areas that may have been missed either by the LiDAR sensors or the cameras. Currently, the collected data are post-processed on a different device, building a colorized triangular mesh of the encountered scene, with the intention of moving this pipeline to the scanner at a later point. The mesh is sufficiently compressed to be sent over a reduced bandwidth connection to a remote analyst. Furthermore, the system tracks fiducial markers attached to diagnostic equipment that is placed around the suspicious object. The resulting tracking information can be transmitted to remote analysts to further facilitate their supporting efforts. The paper will discuss the system's design, software components, the user interface used for scanning a scene, the necessary procedures for calibration of the sensors, and the processing steps of the resulting data. The discussion will close by evaluating the system's performance on 11 scenes.

关键词： Computer vision Three-dimensional displays Laser radar Computational modeling Pipelines User interfaces Position measurement

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Deep Reinforcement learning for RAN Optimization and Control

Deep Reinforcement Learning for RAN Optimization and Control

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Chen, Yu Chen, Jie Krishnamurthi, Ganesh Yang, Huijing Wang, Huahui Zhao, Wenjie Carnegie Mellon Univ Neurosci Inst 5000 Forbes Ave Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept 5000 Forbes Ave Pittsburgh PA 15213 USA AT&T Labs Summer Internship Program Atlanta GA USA AT&T Labs 1 AT&T Way Bedminster NJ USA

ISBN: (纸本)9781728195056

Due to the high variability of the traffic in the radio access network (RAN), fixed network configurations are not flexible enough to achieve optimal performance. Our vendors provide several settings of the eNodeB to optimize the RAN performance, such as media access control scheduler, loading balance, etc. But the detailed mechanisms of the eNodeB configurations are usually very complicated and not disclosed, not to mention the large key performance indicators (KPIs) space needed to be considered. These make constructing a simulator, offline tuning, or rule-based solutions difficult. We aim to build an intelligent controller without strong assumption or domain knowledge about the RAN and can run 24/7 without supervision. To achieve this goal, we first build a closed-loop control testbed RAN in a lab environment with one eNodeB provided by one of the largest wireless vendors and four smartphones. Next, we build a double Q network agent trained with the live feedback of the key performance indicators from the RAN. Our work proved the effectiveness of applying deep reinforcement learning to improve network performance in a real RAN network environment.

关键词： self-organizing network radio access networks deep reinforcement learning testbed

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Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis

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BIOINFORMATICS 2023年第SUPPLEMENT_1期39卷 I140-I148页

作者： Dayao, Monica T. Trevino, Alexandro Kim, Honesty Ruffalo, Matthew D'Angio, H. Blaize Preska, Ryan Duvvuri, Umamaheswar Mayer, Aaron T. Bar-Joseph, Ziv Univ Pittsburgh Joint Carnegie Mellon Univ Program Computat Biol Pittsburgh PA 15213 USA Carnegie Mellon Univ Computat Biol Dept Pittsburgh PA 15213 USA Enable Med Menlo Pk CA 94025 USA Univ Pittsburgh Dept Otolaryngol Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA

MotivationSpatial proteomics data have been used to map cell states and improve our understanding of tissue organization. More recently, these methods have been extended to study the impact of such organization on disease progression and patient survival. However, to date, the majority of supervised learning methods utilizing these data types did not take full advantage of the spatial information, impacting their performance and *** inspiration from ecology and epidemiology, we developed novel spatial feature extraction methods for use with spatial proteomics data. We used these features to learn prediction models for cancer patient survival. As we show, using the spatial features led to consistent improvement over prior methods that used the spatial proteomics data for the same task. In addition, feature importance analysis revealed new insights about the cell interactions that contribute to patient survival.

关键词：

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Semi-supervised Classification of Chest Radiographs 3rd

Semi-supervised Classification of Chest Radiographs

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3rd Int Workshop on Interpretabil of machine Intelligence in Med Image Comp / 2nd Int Workshop on Med Image learning with Less Labels and Imperfect Data / 5th Int Workshop on Largescale Annotat of Biomed Data and Expert Label Synthesis

作者： Pooch, Eduardo H. P. Ballester, Pedro Barros, Rodrigo C. Pontificia Univ Catolica Rio Grande do Sul Sch Technol Machine Learning Theory & Applicat Res Grp MALTA Porto Alegre RS Brazil McMaster Univ Neurosci Grad Program Hamilton ON Canada

ISBN: (纸本)9783030611651;9783030611668

To train deep learning models in a supervised fashion, we need a significant amount of training data, but in most medical imaging scenarios, there is a lack of annotated data available. In this paper, we compare state-of-the-art semi-supervised classification methods in a medical imaging scenario. We evaluate the performance of different approaches in a chest radiograph classification task using the ChestX-ray14 dataset. We adapted methods based on pseudo-labeling and consistency regularization to perform multi-label classification and to use a state-of-the-art model architecture in chest radiograph classification. Our proposed approaches resulted in average AUCs up to 0.6691 with only 25 labeled samples per class, and an average AUC of 0.7182 when using only 2% of the labeled data, achieving results superior to previous approaches on semi-supervised chest radiograph classification.

关键词： Medical imaging Semi-supervised learning Deep learning

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Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer

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BRIEFINGS IN BIOINFORMATICS 2024年第2期25卷 bbae071页

作者： Lu, Yitao Chu, Qian Li, Zhen Wang, Mengdi Gatenby, Robert Zhang, Qingpeng City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China Huazhong Univ Sci & Technol Med Oncol & Radiat Oncol Tongji Hosp Tongji Med Coll Wuhan Peoples R China Huazhong Univ Sci & Technol Dept Radiol Tongji Hosp Tongji Med Coll Wuhan Peoples R China Princeton Univ Dept Elect & Comp Engn Princeton NJ USA Princeton Univ Ctr Stat & Machine Learning Princeton NJ USA H Lee Moffitt Canc Ctr & Res Inst Dept Chair Radiol Tampa FL USA H Lee Moffitt Canc Ctr & Res Inst Canc Biol & Evolut Program Tampa FL USA Univ Hong Kong Musketeers Fdn Inst Data Sci LKS Fac Med Hong Kong Peoples R China Univ Hong Kong Dept Pharmacol & Pharm LKS Fac Med Hong Kong Peoples R China

The evolution of drug resistance leads to treatment failure and tumor progression. Intermittent androgen deprivation therapy (IADT) helps responsive cancer cells compete with resistant cancer cells in intratumoral competition. However, conventional IADT is population-based, ignoring the heterogeneity of patients and cancer. Additionally, existing IADT relies on pre-determined thresholds of prostate-specific antigen to pause and resume treatment, which is not optimized for individual patients. To address these challenges, we framed a data-driven method in two steps. First, we developed a time-varied, mixed-effect and generative Lotka-Volterra (tM-GLV) model to account for the heterogeneity of the evolution mechanism and the pharmacokinetics of two ADT drugs Cyproterone acetate and Leuprolide acetate for individual patients. Then, we proposed a reinforcement-learning-enabled individualized IADT framework, namely, IADT, to learn the patient-specific tumor dynamics and derive the optimal drug administration policy. Experiments with clinical trial data demonstrated that the proposed IADT can significantly prolong the time to progression of prostate cancer patients with reduced cumulative drug dosage. We further validated the efficacy of the proposed methods with a recent pilot clinical trial data. Moreover, the adaptability of IADT makes it a promising tool for other cancers with the availability of clinical data, where treatment regimens might need to be individualized based on patient characteristics and disease dynamics. Our research elucidates the application of deep reinforcement learning to identify personalized adaptive cancer therapy.

关键词： Adaptive therapy Prostate cancer Personalized medicine Reinforcement learning

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CRISPR-GPT: An LLM Agent for Automated Design of Gene-Editing Experiments

arXiv

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arXiv 2024年

作者： Huang, Kaixuan Qu, Yuanhao Cousins, Henry Johnson, William A. Yin, Di Shah, Mihir Zhou, Denny Altman, Russ Wang, Mengdi Cong, Le Department of Electrical and Computer Engineering Princeton University United States Department of Pathology Department of Genetics Stanford University School of Medicine United States Cancer Biology Program Stanford University School of Medicine United States Department of Medicine Stanford University School of Medicine United States Medical Scientist Training Program Stanford University School of Medicine United States Google DeepMind United Kingdom Department of Bioengineering Department of Genetics Stanford University United States Center for Statistics and Machine Learning Princeton University United States

The introduction of genome engineering technology has transformed biomedical research, making it possible to make precise changes to genetic information. However, creating an efficient gene-editing system requires a deep understanding of CRISPR technology, and the complex experimental systems under investigation. While Large Language Models (LLMs) have shown promise in various tasks, they often lack specific knowledge and struggle to accurately solve biological design problems. In this work, we introduce CRISPR-GPT, an LLM agent augmented with domain knowledge and external tools to automate and enhance the design process of CRISPR-based gene-editing experiments. CRISPR-GPT leverages the reasoning ability of LLMs to facilitate the process of selecting CRISPR systems, designing guide RNAs, recommending cellular delivery methods, drafting protocols, and designing validation experiments to confirm editing outcomes. We showcase the potential of CRISPR-GPT for assisting non-expert researchers with gene-editing experiments from scratch and validate the agent’s effectiveness in a real-world use case. Furthermore, we explore the ethical and regulatory considerations associated with automated gene-editing design, highlighting the need for responsible and transparent use of these tools. Our work aims to bridge the gap between beginner biological researchers and CRISPR genome engineering techniques, and demonstrate the potential of LLM agents in facilitating complex biological discovery tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Genes

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DEEP ENSEMBLE FOR ROTORCRAFT ATTITUDE PREDICTION

arXiv

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arXiv 2023年

作者： Khan, Hikmat Bouaynaya, Nidhal C. Rasool, Ghulam Travis, Tyler Thompson, Lacey Johnson, Charles C. Dept. of Electrical and Computer Engineering Rowan University GlassboroNJ United States Dept. of Machine Learning Moffitt Cancer Center TampaFL United States Federal Aviation Administration Atlantic CityNJ United States Research Engineer Flight Test Engineer and Program Lead Federal Aviation Administration Egg Harbor Township United Kingdom

Historically, the rotorcraft community has experienced a higher fatal accident rate than other aviation segments, including commercial and general aviation. To date, traditional methods applied to reduce incident rates have not proven hugely successful for the rotorcraft community. Recent advancements in artificial intelligence (AI) and the application of these technologies in different areas of our lives are both intriguing and encouraging. When developed appropriately for the aviation domain, AI techniques may provide an opportunity to help design systems that can address rotorcraft safety challenges. Our recent work demonstrated that AI algorithms could use video data from onboard cameras and correctly identify different flight parameters from cockpit gauges, e.g., indicated airspeed. These AI-based techniques provide a potentially cost-effective solution, especially for small helicopter operators, to record the flight state information and perform post-flight analyses. We also showed that carefully designed and trained AI systems can accurately predict rotorcraft attitude (i.e., pitch and yaw) from outside scenes (images or video data). Ordinary off-the-shelf video cameras were installed inside the rotorcraft cockpit to record the outside scene, including the horizon. The AI algorithm was able to correctly identify rotorcraft attitude at an accuracy in the range of 80%. In this work, we combined five different onboard camera viewpoints to improve attitude prediction accuracy to 94%. Our current approach, which is referred to as ensembled prediction, significantly increased the reliability in the predicted attitude (i.e., pitch and yaw). For example, in some camera views, the horizon may be obstructed or not visible. The proposed ensemble method can combine visual details recorded from other cameras and predict the attitude with high reliability. In our setup, the five onboard camera views included pilot windshield, co-pilot windshield, pilot Electronic Flight In

关键词： Instrument displays

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Inferring population structure in biobank-scale genomic data

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AMERICAN JOURNAL OF HUMAN GENETICS 2022年第4期109卷 727-737页

作者： Chiu, Alec M. Molloy, Erin K. Tan, Zilong Talwalkar, Ameet Sankararaman, Sriram Univ Calif Los Angeles Bioinformat Interdept Program Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA Univ Maryland Inst Adv Comp Studies College Pk MD 20742 USA Facebook Inc Menlo Pk CA 94025 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Univ Calif Los Angeles Dept Human Genet Los Angeles CA 90095 USA Univ Calif Los Angeles David Geffen Sch Med Dept Computat Med Los Angeles CA 90095 USA

Inferring the structure of human populations from genetic variation data is a key task in population and medical genomic studies. Although a number of methods for population structure inference have been proposed, current methods are impractical to run on biobank-scale genomic datasets containing millions of individuals and genetic variants. We introduce SCOPE, a method for population structure inference that is orders of magnitude faster than existing methods while achieving comparable accuracy. SCOPE infers population structure in about a day on a dataset containing one million individuals and variants as well as on the UK Biobank dataset containing 488,363 individuals and 569,346 variants. Furthermore, SCOPE can leverage allele frequencies from previous studies to improve the interpretability of population structure estimates.

关键词： population structure admixture genetic structure biobank scalability ancestry

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