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2023 AIChE Annual Meeting

作者： Yousefi, Hanie Mahmud, Alam Chang, Dingran Das, Jagotamoy Gomis, Surath Chen, Jenise B. Wang, Hansen Been, Terek Yip, Lily Coomes, Eric Li, Zhijie Mubareka, Samira McGeer, Allison Christie, Natasha Gray-Owen, Scott Cochrane, Alan Rini, James M. Sargent, Edward H. Kelley, Shana O. Leslie Dan Faculty of Pharmacy University of Toronto TorontoONM5S 3M2 Canada Department of Chemistry Department of Biochemistry Institute of Biomaterials and Biomedical Engineering University of Toronto TorontoONM5S 3H6 Canada Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada Department of Chemistry University of Toronto TorontoONM5S 3H6 Canada Sunnybrook Research Institute TorontoONM4N 3N5 Canada Division of Infectious Disease Department of Medicine University of Toronto TorontoONM5G 2C4 Canada Department of Microbiology Sinai Health System TorontoONM5G 1X5 Canada Combined Containment Level 3 Unit University of Toronto TorontoONM5S 1A8 Canada Department of Molecular Genetics and Combined Containment Level 3 Unit University of Toronto TorontoONM5S 1A8 Canada Department of Molecular Genetics Department of Biochemistry University of Toronto TorontoONM5S 1A8 Canada

The development of new methods for direct viral detection using streamlined and ideally reagent-free assays is a timely and important, but challenging, problem. The challenge of combatting the COVID-19 pandemic has been exacerbated by the lack of rapid and effective methods to identify viral pathogens like SARS-CoV-2 on-demand. Existing gold standard nucleic acid-based approaches require enzymatic amplification to achieve clinically relevant levels of sensitivity and are not typically used outside of a laboratory setting. Here, we report reagent-free viral sensing that directly reads out the presence of viral particles in 5 minutes using only a sensor-modified electrode chip. The approach relies on a class of electrode-tethered sensors bearing an analyte-binding antibody displayed on a negatively charged DNA linker that also features a tethered redox probe. When a positive potential is applied, the sensor is transported to the electrode surface. Using chronoamperometry, the presence of viral particles and proteins can be detected as these species increase the hydrodynamic drag on the sensor. This report is the first virus-detecting assay that uses the kinetic response of a probe/virus complex to analyze the complexation state of the antibody. We demonstrate the performance of this sensing approach as a means to detect, within 5 min, the presence of the SARS-CoV-2 virus and its associated spike protein in test samples and in unprocessed patient saliva. © 2021 American Chemical Society.

关键词： Coronavirus

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Modern Views of Machine Learning for Precision Psychiatry

arXiv

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arXiv 2022年

作者： Chen, Zhe Sage Kulkarni, Prathamesh Galatzer-Levy, Isaac R. Bigio, Benedetta Nasca, Carla Zhang, Yu Department of Psychiatry New York University Grossman School of Medicine New YorkNY10016 United States Department of Neuroscience and Physiology New York University Grossman School of Medicine New YorkNY10016 United States The Neuroscience Institute New York University Grossman School of Medicine New YorkNY10016 United States Department of Biomedical Engineering New York University Tandon School of Engineering BrooklynNY11201 United States Headspace Health San FranciscoCA94102 United States Meta Reality Lab New YorkNY United States Department of Bioengineering Lehigh University PA18015 United States Department of Electrical and Computer Engineering Lehigh University PA18015 United States

The bigger picture: Machine learning (ML) and artificial intelligence (AI) have become increasingly popular in analyzing complex patterns of neural and behavioral data for medicine and psychiatry. We provide a comprehensive review of ML methodologies and applications in precision psychiatry. We argue that advances in ML-powered modern technologies have revolutionalized the current practice in diagnosis, prognosis, monitoring and treatment of various mental illnesses. We discuss conceptual and practical challenges in precision psychiatry and highlight future research in ML. © 2022, CC BY.

关键词： Neuroimaging

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Miniaturized Microarray-Format Digital ELISA Enabled by Lithographic Protein Patterning

SSRN

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SSRN 2023年

作者： Stephens, Andrew D. Song, Yujing McClellan, Brandon L. Su, Shiuan-Haur Xu, Sonnet Chen, Kevin Castro, Maria G. Singer, Benjamin H. Kurabayashi, Katsuo Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States Department of Neurosurgery University of Michigan Medical School Ann ArborMI48109 United States Department of Cell and Developmental Biology University of Michigan Medical School Ann ArborMI48109 United States Graduate Program in Immunology University of Michigan Medical School Ann ArborMI48109 United States Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Rogel Cancer Center University of Michigan Ann ArborMI48109 United States Department of Internal Medicine Division of Pulmonary and Critical Care Medicine University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI48109 United States

The search for reliable protein biomarker candidates is critical for early disease detection and treatment. However, current immunoassay technologies are failing to meet increasing demands for sensitivity and multiplexing. Here, the authors have created a highly sensitive protein microarray using the principle of single-molecule counting for signal amplification, capable of simultaneously detecting a panel of cancer biomarkers at sub-pg/mL levels. To enable this amplification strategy, the authors introduce a novel method of protein patterning using photolithography to subdivide addressable arrays of capture antibody spots into hundreds of thousands of individual microwells. This allows for the total sensor area to be miniaturized, increasing the total possible multiplex capacity. With the immunoassay realized on a standard 75x25 mm form factor glass substrate, sample volume consumption is minimized to © 2023, The Authors. All rights reserved.

关键词： Biomarkers

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Analytical phase optical transfer function for Gaussian illumination and the optimized profiles

arXiv

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arXiv 2020年

作者： Huang, Jianhui Bao, Yijun Gaylord, Thomas K. School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Optics Laboratory School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332-0250 United States Department of Biomedical Engineering Duke University DurhamNC27708 United States

The imaging performance of tomographic deconvolution phase microscopy can be described in terms of the phase optical transfer function (POTF) which, in turn, depends on the illumination profile. To facilitate the optimization of the illumination profile, an analytical calculation method based on polynomial fitting is developed to describe the POTF for general non-uniform axially-symmetric illumination. This is then applied to Gaussian and related profiles. Compared to numerical integration methods that integrate over a series of annuli, the present analytical method is much faster and is equally accurate. Further, a "balanced distribution" criterion for the POTF and a least-squares minimization are presented to optimize the uniformity of the POTF. An optimum general profile is found analytically by "relaxed optimal search" and an optimum Gaussian profile is found through a tree search. Numerical simulations confirm the performance of these optimum profiles and support the "balanced distribution" criterion introduced. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian distribution

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Bond-Selective Intensity Diffraction Tomography

arXiv

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arXiv 2022年

作者： Zhao, Jian Matlock, Alex Zhu, Hongbo Song, Ziqi Zhu, Jiabei Wang, Biao Chen, Fukai Zhan, Yuewei Chen, Zhicong Xu, Yihong Lin, Xingchen Tian, Lei Cheng, Ji-Xin Department of Electrical and Computer Engineering Boston University BostonMA02215 United States State Key Laboratory of Luminescence and Applications Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Department of Biology Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Department of Physics Boston University BostonMA02215 United States The Picower Institute for Learning and Memory Massachusetts Institute of Technology CambridgeMA02142 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02142 United States

Recovering molecular information remains a grand challenge in the widely used holographic and computational imaging technologies. To address this challenge, we developed a computational mid-infrared photothermal microscope, termed Bond-selective Intensity Diffraction Tomography (BS-IDT). Based on a low-cost brightfield microscope with an add-on pulsed light source, BS-IDT recovers both infrared spectra and bond-selective 3D refractive index maps from intensity-only measurements. High-fidelity infrared fingerprint spectra extraction is validated. Volumetric chemical imaging of biological cells is demonstrated at a speed of ~20 seconds per volume, with a lateral and axial resolution of ~350 nm and ~1.1 µm, respectively. BS-IDT’s application potential is investigated by chemically quantifying lipids stored in cancer cells and volumetric chemical imaging on Caenorhabditis elegans with a large field of view (~100 µm x 100 µm). Copyright © 2022, The Authors. All rights reserved.

关键词： Cancer cells

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Multi-Frequency SAR Images for Investigations of the Cryosphere: Preliminary Results of Criosar Project

Multi-Frequency SAR Images for Investigations of the Cryosph...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： S. Pettinato E. Santi S. Paloscia F. Baroni S. Pilia L. Santurri E. Palchetti F. Bovenga A. Belmonte A. Refice I. Argentiero R. Colombo G. Bramati B. Di Mauro C. Marin G. Cuozzo L. De Gregorio M. Callegari M. S. Heredia V. Premier C. Notarnicola M. Pasian M. Lodigiani L. Silvestri E. Cremonese A. Montuori IFAC-CNR Institute of Applied Physics – National Research Council Firenze Italy IREA-CNR Institute for Electromagnetic Sensing of the Environment – National Research Council Bari Italy Dept. of Earth and Environmental Sciences University of Milano-Bicocca Italy ISP – CNR Institute of Polar Sciences Venezia Italy EURAC Research -Institute for Applied Remote Sensing Bolzano Italy Dept. of Electrical Computer and Biomedical Engineering University of Pavia Italy ARPA VDA Environmental Protection Agency of Aosta Valley Aosta Italy P.O. COSMO-SkyMed ASI - Agenzia Spaziale Italiana Rome Italy

This research aims to exploit the potentialities of multi-mission SAR data at X-, C- and L-band for the monitoring of snowpack and alpine soils. The snow parameters as snow water equivalent, snow liquid water content and snow metamorphism have been monitored and different methods are proposed for their retrieval. In order to gather consistent datasets, experimental activities have been conducted in two selected sites in Northern Italy, which are covered by alpine snow during winter and spring periods and are in some cases characterized by the presence of permafrost. Microwave responses of snow and soil have been then simulated by using electromagnetic (i.e., AIEM, Oh, SFT and DMRT-QCA), and physical models (SNOWPACK). Finally, machine learning approaches, as Artificial Neural Networks and Random Forest, were implemented for retrieving snow parameters; whereas interferometric techniques were used in case of snow and soil displacement as rock glaciers. Preliminary and consistent results have been obtained in terms of estimate of snow parameters and soil displacement. This multi-frequency/multi-mission approach enhances the ability of SAR sensors to monitor and analyze snow dynamics, contributing to improved decision-making in various domains.

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Erratum — A 3D semantic segmentation network for accurate neuronal soma segmentation

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Journal of Innovative Optical Health Sciences 2025年第1期18卷

作者： Li Ma Qi Zhong Yezi Wang Xiaoquan Yang Qian Du School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan 430074 P. R. China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology Wuhan 430074 P. R. China HUST-Suzhou Institute for Brainsmatics JITRI Suzhou 215123 P. R. China Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS 39762 USA

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Nanoplasmonic imaging of extracellular exosomes on undercut arrayed gold nanodisks

Nanoplasmonic imaging of extracellular exosomes on undercut ...

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Optical Sensors, Sensors 2020 - Part of Optical Sensors and Sensing Congress 2020

作者： Kafle, Bijesh Misbah, Ibrahim Gunawardhana, Lkadds Lin, Steven H. Shih, Wei-Chuan Department of Electrical and Computer Engineering University of Houston 4800 Calhoun Rd HoustonTX77204 United States Program of Materials Science and Engineering University of Houston 4800 Calhoun Rd HoustonTX77204 United States Department of chemistry University of Houston 4800 Calhoun Rd HoustonTX77204 United States Department of Biomedical Engineering University of Houston 4800 Calhoun Rd HoustonTX77204 United States Department of Radiation Oncology M.D. Anderson Cancer Center 1515 Holcombe Blvd. HoustonTX77030 United States

We present nanoplasmonic imaging of exosome using antibody-functionalized undercut arrayed gold nanodisks. We show a nanoplasmonic shift map that allows the detection and quantitation of exosome binding. © OSA 2020.

ISBN: (纸本)9781557528209

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Evaluation of Frequency-Dependent Effects of Deep Brain Stimulation in a Cortex-Basal Ganglia-Thalamus Network Model of Parkinson’s Disease*

Evaluation of Frequency-Dependent Effects of Deep Brain Stim...

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Marcelo R. Romano Renan C. Moioli Leonardo A. Elias Neural Engineering Research Laboratory School of Electrical and Computer Engineering University of Campinas Campinas SP Brazil Graduate Program in Bioinformatics Digital Metropolis Institute Federal University of Rio Grande do Norte Natal Brazil Center for Biomedical Engineering University of Campinas Campinas SP Brazil

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Parkinson's disease (PD) is a chronic neurodegenerative disease whose motor symptoms are accompanied by an exaggerated power in the alpha-beta (7-35Hz) band and an increased synchronization of neurons encompassing the cortex-basal ganglia-thalamus network. Currently, deep brain stimulation (DBS) is used as an effective therapy for reducing the excessive power and synchrony observed in brain circuits, thereby ameliorating the PD symptoms. In the present study, we used a biologically plausible computational model of cortex-basal ganglia-thalamus network, which represents both healthy and PD conditions, to systematically investigate the effects of DBS frequency on the model outputs. DBS was applied to the subthalamic nucleus (STN) at different stimulation frequencies (40Hz to 300Hz). Spike train variability and spectral power in the 7-35Hz band were measured from the several nuclei represented in the model. In addition, the magnitude squared coherence between the nuclei was assessed. An increased DBS frequency tended to produce interspike intervals (ISIs) with higher variability as compared to PD condition. Also, DBS significantly reduced the alpha-beta power for almost all brain nuclei. The median of the magnitude-squared coherence matrix (which is a metric of global network synchronization) decreased significantly with the increase of DBS frequency.

关键词： Satellite broadcasting Brain modeling Neurons Computational modeling Coherence Partial discharges Synchronization

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Semiconductor Nanowires for Polarization Control in Integrated Waveguides

Semiconductor Nanowires for Polarization Control in Integrat...

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IEEE Nanotechnology Materials and Devices Conference (NMDC)

作者： V. Vitali A. Fontana V. Demontis D. Prete A. Salamon V. Bellani I. Cristiani F. Rossella C. Lacava Istituto Nazionale Fisica Nucleare (INFN) Sezione di Pavia Pavia Italy Department of Electrical Computer and Biomedical Engineering Photonics Research Group University of Pavia Pavia Italy Optoelectronics Research Centre University of Southampton Southampton UK NEST Scuola Normale Superiore Istituto Nanoscienze-CNR Pisa Italy Dipartimento di Scienze Fisiche Informatiche e Matematiche Università di Modena e Reggio Emilia Modena Italy Istituto Nazionale di Fisica Nucleare (INFN) Sezione di Roma Tor Vergata Roma Italy Department of Physics University of Pavia Pavia Italy

Polarization control is an important aspect of silicon photonics, as polarization-dependent loss can significantly affect device performance. Various methods have been proposed and implemented to control polarization in integrated devices, including waveguide design, birefringent materials, and polarization rotators. One approach involves incorporating a polarization diversity circuit, which splits the input light into two orthogonal polarization states and then recombines them to eliminate any polarization-dependent loss. Another method is to use polarization rotators, which rotate the polarization of the light to match the polarization of the waveguide. Overall, these techniques enable improved performance and stability of silicon photonics devices in practical applications. [1].

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