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Clinical characteristics of hospitalized children with community-acquired pneumonia and respiratory infections: Using machine learning approaches to support pathogen prediction at admission

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Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi 2023年第4期56卷 772-781页

作者： Tu-Hsuan Chang Yun-Chung Liu Siang-Rong Lin Pei-Hsin Chiu Chia-Ching Chou Luan-Yin Chang Fei-Pei Lai Department of Pediatrics Chi Mei Medical Center Tainan City Taiwan. Department of Pediatrics National Taiwan University Hospital College of Medicine National Taiwan University Taipei City Taiwan. Institute of Applied Mechanics National Taiwan University Taipei City Taiwan. Institute of Applied Mechanics National Taiwan University Taipei City Taiwan. Electronic address: ccchou@iam.ntu.edu.tw. Department of Pediatrics National Taiwan University Hospital College of Medicine National Taiwan University Taipei City Taiwan. Electronic address: lychang@ntu.edu.tw. Graduate Institute of Biomedical Electronics and Bioinformatics Taipei City National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei City Taiwan Department of Electrical Engineering National Taiwan University Taipei City Taiwan.

BACKGROUND:Acute respiratory infections (ARIs) are common in children. We developed machine learning models to predict pediatric ARI pathogens at admission. METHODS:We included hospitalized children with respiratory infections between 2010 and 2018. Clinical features were collected within 24 h of admission to construct models. The outcome of interest was the prediction of 6 common respiratory pathogens, including adenovirus, influenza virus types A and B, parainfluenza virus (PIV), respiratory syncytial virus (RSV), and Mycoplasma pneumoniae (MP). Model performance was estimated using area under the receiver operating characteristic curve (AUROC). Feature importance was measured using Shapley Additive exPlanation (SHAP) values. RESULTS:A total of 12,694 admissions were included. Models trained with 9 features (age, event pattern, fever, C-reactive protein, white blood cell count, platelet count, lymphocyte ratio, peak temperature, peak heart rate) achieved the best performance (AUROC: MP 0.87, 95% CI 0.83-0.90; RSV 0.84, 95% CI 0.82-0.86; adenovirus 0.81, 95% CI 0.77-0.84; influenza A 0.77, 95% CI 0.73-0.80; influenza B 0.70, 95% CI 0.65-0.75; PIV 0.73, 95% CI 0.69-0.77). Age was the most important feature to predict MP, RSV and PIV infections. Event patterns were useful for influenza virus prediction, and C-reactive protein had the highest SHAP value for adenovirus infections. CONCLUSION:We demonstrate how artificial intelligence can assist clinicians identify potential pathogens associated with pediatric ARIs upon admission. Our models provide explainable results that could help optimize the use of diagnostic testing. Integrating our models into clinical workflows may lead to improved patient outcomes and reduce unnecessary medical costs.

关键词： Children Community-acquired pneumonia Machine learning Pathogens prediction Respiratory infections

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Magneto-optical Spectroscopy with RAMBO: A Table-Top 30 T Magnet

arXiv

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arXiv 2022年

作者： Tay, Fuyang Baydin, Andrey Katsutani, Fumiya Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

Optically probing materials in high magnetic fields can provide enlightening insight into field-modified electronic states and phases, while optically driving materials in high magnetic fields can induce novel nonequilibrium many-body dynamics of spin and charge carriers. While there are high-field magnets compatible with standard optical spectroscopy methods, they are generally bulky and have limited optical access, which prohibit performing state-of-the-art ultrafast and/or nonlinear optical experiments. The Rice Advanced Magnet with Broadband Optics (RAMBO), a unique 30-T pulsed mini-coil magnet system with direct optical access, has enabled previously challenging experiments using femtosecond optical pulses, including time-domain terahertz spectroscopy, in cutting-edge materials placed in strong magnetic fields. Here, we review recent experimental advances made possible by the first-generation RAMBO setup. After summarizing technological aspects of combining optical spectroscopic techniques with the mini-coil magnet, we describe results of magneto-optical studies of a wide variety of materials, providing new insight into the states and dynamics of four types of quasiparticles in solids – excitons, plasmons, magnons, and phonons – in high magnetic fields. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnets

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A micromirror array with annular partitioning for high-speed random-access axial focusing

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Light(Science & Applications) 2020年第1期9卷 241-255页

作者： Nathan Tessema Ersumo Cem Yalcin Nick Antipa Nicolas Pegard Laura Waller Daniel Lopez Rikky Muller The University of California Berkeley and University of CaliforniaSan Francisco Graduate Program in BioengineeringBerkeleyCA 94720USA Department of Electrical Engineering&Computer Sciences University of CaliforniaBerkeleyCA 94720USA Department of Applied Physical Sciences University of North Carolina at Chapel HillChapel HillNC 27514USA Chan Zuckerberg Biohub San FranciscoCA 94158USA Physical Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD 20899USA

Dynamic axial focusing functionality has recently experienced widespread incorporation in microscopy,augmented/virtual reality(AR/VR),adaptive optics and material ***,the limitations of existing varifocal tools continue to beset the performance capabilities and operating overhead of the optical systems that mobilize such *** varifocal tools that are the least burdensome to operate(*** crystal,elastomeric or optofluidic lenses)suffer from low(≈100 Hz)refresh ***,the fastest devices sacrifice either critical capabilities such as their dwelling capacity(*** gradient lenses or monolithic micromechanical mirrors)or low operating overhead(*** mirrors).Here,we present a general-purpose random-access axial focusing device that bridges these previously conflicting features of high speed,dwelling capacity and lightweight drive by employing lowrigidity micromirrors that exploit the robustness of defocusing phase ***,the device consists of an 8.2mm diameter array of piston-motion and 48-μm-pitch micromirror pixels that provide 2πphase shifting for wavelengths shorter than 1100 nm with 10-90% settling in 64.8μs(i.e.,15.44 kHz refresh rate).The pixels are electrically partitioned into 32 rings for a driving scheme that enables phase-wrapped operation with circular symmetry and requires<30 V per *** experiments demonstrated the array’s wide focusing range with a measured ability to target 29 distinct resolvable depth ***,the features of the proposed array offer the potential for compact,straightforward methods of tackling bottlenecked applications,including high-throughput single-cell targeting in neurobiology and the delivery of dense 3D visual information in AR/VR.

关键词： mirror axial random

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Graphene calorimetric single-photon detector

arXiv

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arXiv 2024年

作者： Huang, Bevin Arnault, Ethan G. Jung, Woochan Fried, Caleb Russell, B. Jordan Watanabe, Kenji Taniguchi, Takashi Henriksen, Erik A. Englund, Dirk Lee, Gil-Ho Fong, Kin Chung Intelligence Community Postdoctoral Research Fellowship Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Pohang University of Science and Technology Pohang790-784 Korea Republic of Department of Physics Washington University in St. Louis St. LouisMO United States Research Center for Electronic and Optical Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan RTX BBN Technologies Quantum Engineering and Computing Group CambridgeMA02138 United States

Single photon detectors (SPDs) [1, 2] are essential technology in quantum science, quantum network, biology, and advanced imaging [3-5]. To detect the small quantum of energy carried in a photon, conventional SPDs rely on energy excitation across either a semiconductor bandgap or superconducting gap. While the energy gap suppresses the false-positive error, it also sets an energy scale that can limit the detection efficiency of lower energy photons and spectral bandwidth of the SPD [6-8]. Here, we demonstrate an orthogonal approach to detect single near-infrared photons using graphene calorimeters [9-13]. By exploiting the extremely low heat capacity of the pseudo-relativistic electrons in graphene near its charge neutrality point [14], we observe an electron temperature rise up to ∼2 K using a hybrid Josephson junction. In this proof-of-principle experiment, we achieve an intrinsic quantum efficiency of 87% (75%) with dark count © 2024, CC BY.

关键词： Particle beams

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Design Considerations for 3D Position Sensitive Scintillation Detectors that Achieve 100 ps Coincidence Time Resolution

Design Considerations for 3D Position Sensitive Scintillatio...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Shirin Pourashraf Zhixiang Zhao Andrea Gonzalez - Montoro Joshua W. Cates Jun Yeon Won Jae Sung Lee Craig S. Levin Department of Radiology Stanford University Stanford CA USA School of Biomedical Engineering Shanghai Jiao Tong University Shanghai People’s Republic of China Applied Nuclear Physics Program Lawrence Berkeley National Laboratory Berkeley CA USA Department of Nuclear Medicine and Biomedical Sciences Seoul National University Seoul South Korea Departments of Radiology Bioengineering Physics and Electrical Engineering Molecular Imaging Program Stanford University Stanford CA USA

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

We have studied design considerations for three dimension positioning sensitive (3DPS) scalable TOF- PET detectors that achieve 100 ps coincidence time resolution (CTR). These scintillation detectors are based on 3×3×10 mm³ “LGSO” crystals side- coupled to arrays of 3×3 mm 2 SiPMs. A CPLD is used to provide 3 mm positioning resolution along the length of each crystal element. Revised mixed-signal front-end electronics decreased the power dissipation by 200% and increased the detector packing fraction while preserving the CTR performance. A low timing jitter FPGA-based TDC was used for data acquisition of the timing channels. Measured CTR was 102.2 ± 1.3 ps FWHM using a 22Na point source.

关键词： Timing jitter Three-dimensional displays Image resolution Data acquisition Detectors Crystals Timing

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A robust CNN framework with dual feedback feature accumulation for detecting pneumonia opacity from chest x-ray images

arXiv

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arXiv 2020年

作者： Alam, Md. Jahin Ali, Shams Nafisa Hasan, Md. Zubair Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Biomedical Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

Pneumonia is one of the most acute respiratory diseases having remarkably high prevalence and mortality rate. Chest X-ray (CXR) has been widely utilized for the diagnosis of this disease owing to its availability, diagnostic speed and accuracy. However, even for an expert radiologist, it is quite challenging to readily determine pneumonia opacity by examining CXRs. Therefore, this study has been structured to automate the pneumonia detection process by introducing a robust deep learning framework. The proposed network comprises of Process Convolution (Pro_Conv) blocks for feature accumulation inside Dual Feedback (DF) blocks to propagate the feature maps towards a viable detection. Experimental analysis showcase: (1) the proposed network proficiently distinguishes between normal and pneumonia opacity containing CXRs with the mean accuracy, sensitivity and specificity of 97.78%, 98.84% and 95.04%, respectively;(2) the network is constructed with significantly low parameters than the traditional ImageNets to reduce memory consumption for deployment in memory constrained mobile platforms;(3) the trade-off between accuracy and number of parameters of the model outperforms the considered classical networks by a remarkable margin;and (4) the false-negatives are lower than the false-positives (both of which are low in count) which prove the model's low-fatality prediction. Hence, the proposed network can be deployed for a rapid screening of pneumonia and can act as a great assistive tool for the radiologists in the diagnosis process. Copyright © 2020, The Authors. All rights reserved.

关键词： computer aided diagnosis

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Low-frequency electronic noise spectroscopy of quasi-2D van der Waals antiferromagnetic semiconductors

arXiv

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arXiv 2021年

作者： Ghosh, Subhajit Kargar, Fariborz Mohammadzadeh, Amirmahdi Rumyantsev, Sergey Balandin, Alexander A. Department of Electrical and Computer Engineering Bourns College of Engineering University of California RiversideCA92521 United States Centera Laboratories Institute of High-Pressure Physics Polish Academy of Sciences Warsaw01-142 Poland Center Materials Science and Engineering Program University of California RiversideCA92521 United States

We investigated low-frequency current fluctuations, i.e. noise, in the quasi-two-dimensional (2D) van der Waals antiferromagnetic semiconductor FePS3 with the electronic bandgap of 1.5 eV. The electrical and noise characteristics of the p-type, highly resistive, thin films of FePS3 were measured at different temperatures. The noise spectral density was of the 1/f-type over most of the examined temperature range but revealed well-defined Lorentzian bulges, and increased strongly near the Néel temperature TN=118 K (f is the frequency). Intriguingly, the noise spectral density attained its minimum at temperature T~200 K, which was attributed to an interplay of two opposite trends in noise scaling - one for semiconductors and another for materials with the phase transitions. The Lorentzian corner frequencies revealed unusual dependence on temperature and bias voltage, suggesting that their origin is different from the generation - recombination noise in conventional semiconductors. The obtained results are important for proposed applications of antiferromagnetic semiconductors in spintronic devices. They also attest to the power of the noise spectroscopy for monitoring various phase transitions. Copyright © 2021, The Authors. All rights reserved.

关键词： Spectral density

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SpectralGPT: Spectral Remote Sensing Foundation Model

arXiv

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arXiv 2023年

作者： Hong, Danfeng Zhang, Bing Li, Xuyang Li, Yuxuan Li, Chenyu Yao, Jing Yokoya, Naoto Li, Hao Ghamisi, Pedram Jia, Xiuping Plaza, Antonio Gamba, Paolo Benediktsson, Jon Atli Chanussot, Jocelyn Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China School of Mathematics Southeast University Nanjing210096 China Graduate School of Frontier Sciences the University of Tokyo Chiba277–8561 Japan Geospatial Data Management Technical University of Munich Munich85521 Germany Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology Freiberg09599 Germany Institute of Advanced Research in Artificial Intelligence Vienna1030 Austria School of Engineering and Information Technology University of New South Wales CanberraACT2612 Australia Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politécnica University of Extremadura Cáceres10003 Spain Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia27100 Italy Faculty of Electrical and Computer Engineering University of Iceland Reykjavik102 Iceland Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK Grenoble38000 France

The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which offers valuable information for scene understanding, especially in remote sensing (RS) applications. To fill this gap, we created for the first time a universal RS foundation model, named SpectralGPT, which is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT). Compared to existing foundation models, SpectralGPT 1) accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS big data;2) leverages 3D token generation for spatial-spectral coupling;3) captures spectrally sequential patterns via multi-target reconstruction;4) trains on one million spectral RS images, yielding models with over 600 million parameters. Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS big data applications within the field of geoscience across four downstream tasks: single/multi-label scene classification, semantic segmentation, and change detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Polarization-Tunable Colorimetric Metasurfaces for All-Optical and Non-Destructive Structural Characterization of Polymeric Nanofibers

arXiv

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arXiv 2025年

作者： Kirya, Paula Hochberg, Justin D. Kim, Han Sol Haddadin, Zaid Bordy, Samantha Byun, Jiuk Pokorski, Jonathan K. Poulikakos, Lisa V. Department of Mechanical and Aerospace Engineering UC San Diego La Jolla CA United States Aiiso Yufeng Li Family Department of Chemical and Nano Engineering UC San Diego La Jolla CA United States Department of Electrical and Computer Engineering UC San Diego La Jolla CA United States Program in Materials Science and Engineering UC San Diego La Jolla CA United States

Metasurfaces have pioneered significant improvements in sensing technology by tailoring strong optical responses to weak signals. When designed with anisotropic subwavelength geometries, metasurfaces can tune responses to varying polarization states of light. Leveraging this to quantify structural alignment in fibrous materials unveils an alternative to destructive characterization methods. This work introduces metasurface-enhanced polarized light microscopy (Meta-PoL), which employs polarization-tunable, guided-mode-resonant colorimetric metasurfaces to characterize molecular and bulk alignment of poly(Ε-caprolactone) (PCL) nanofibers in a far-field configuration. PCL nanofibers drawn at 0%, 400%, and 900% ratios were interfaced with the studied metasurfaces. Metasurface resonances coinciding with the intrinsic drawn nanofiber resonances – confirmed by Stokes Polarimetry – produced the strongest colorimetric enhancement, resultant from alignment-specific nanofiber reflectivity. The enhancement degree corresponded with molecular and bulk alignments for each draw ratio, as measured through differential scanning calorimetry and scanning electron microscopy, respectively. Thus, Meta-PoL presents an all-optical, non-destructive, and quantitative measurement of nanofiber alignment. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential scanning calorimetry

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Modelling Hybrid Acoustofluidic Devices for Enhancing Nano- and Mirco-Particle Manipulation in Microfluidics

SSRN

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SSRN 2022年

作者： Wang, Hanlin Yuan, Fan Xie, Zhihua Sun, Chao Wu, Fangda Mikhaylov, Roman Shen, Minghong Yang, Jian Zhou, You Liang, Dongfang Sun, Xianfang Wu, Zhenlin Yang, Zhiyong Yang, Xin Department of Electrical and Electronic Engineering School of Engineering Cardiff University CF24 3AA United Kingdom Department of Biomedical Engineering School of Engineering Duke University NC27708-0281 United States Department of Civil Engineering School of Engineering Cardiff University CF24 3AA United Kingdom School of Life Sciences Northwestern Polytechnical University 710072 China Division of Cancer and Genetics School of Medicine Cardiff University CF14 4XN United Kingdom Systems Immunity Research Institute School of Medicine Cardiff University CF14 4XN United Kingdom Department of Engineering University of Cambridge CB2 1PZ United Kingdom School of Computer Science Cardiff University CF24 3AA United Kingdom School of Optoelectronic Engineering and Instrumentation Science Dalian University of Technology 116023 China School of Mechanical Engineering Tianjin University 300072 China

Acoustofluidic techniques are increasingly used to manipulate nano- and micro-particles in microfluidics. A wide range of acoustofluidic devices integrating microchannel and acoustic sources have been developed for applications in biochemistry and biomedicine. In this work, we developed two novel acoustofluidic configurations: a microchannel was sandwiched between two identical surface acoustic wave (SAW) transducers (SAW-SAW), or between a piezoelectric transducer (PZT) and a SAW transducer (PZT-SAW). We numerically simulated the distributions of the acoustic pressure, time-averaged streaming velocity, and particle trajectories in these devices, and compared the simulation results among the different acoustofluidic configurations. The results revealed that both the SAW-SAW and PZT-SAW configurations produced significantly higher acoustic pressure and particle velocity in the microchannel © 2022, The Authors. All rights reserved.

关键词： Microfluidics

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