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SSRN 2022年

作者： Lou, Esther G. Sapoval, Nicolae McCall, Camille Bauhs, Lauren Carlson-Stadler, Russell Kalvapalle, Prashant Lai, Yanlai Palmer, Kyle Penn, Ryker Rich, Whitney Wolken, Madeline Ensor, Katherine B. Hopkins, Loren Treangen, Todd J. Stadler, Lauren B. Department of Civil and Environmental Engineering Rice University 6100 Main Street MS 519 HoustonTX77005 United States Department of Computer Science Rice University 6100 Main Street HoustonTX77005 United States Systems Synthetic and Physical Biology Graduate Program Rice University 6100 Main Street HoustonTX77005 United States Houston Health Department 8000 N. Stadium Dr. HoustonTX77054 United States Department of Statistics Rice University 6100 Main Street MS 138 HoustonTX77005 United States

Over the course of the COVID-19 pandemic, variants of SARS-CoV-2 have emerged that are more contagious and more likely to cause breakthrough infections. Targeted amplicon sequencing approach is a gold standard for identification and analysis of variants. However, when applied to environmental samples such as wastewater, it remains unclear how sensitive this method is for detecting variant-associated mutations in environmental samples. Here we directly compare a targeted amplicon sequencing approach (using ARTIC v3;hereafter referred to as sequencing) with RT-ddPCR quantification for the detection of five mutations that are characteristic of variants of concern (VoCs) in wastewater samples. In total, 547 wastewater samples were analyzed using both methods in parallel. When we observed positive mutation detections by RT-ddPCR, 42.6% of the detection events were missed by sequencing, due to negative detection or the limited read coverage at the mutation position. Further, when sequencing reported negative or depth-limited mutation detections, 26.7% of those events were instead positive detections by RT-ddPCR, highlighting the relatively poor sensitivity of sequencing. No or weak associations were observed between quantitative measurements of target mutations determined by RT-ddPCR and sequencing. These findings caution the use of quantitative measurements of SARS-CoV-2 variants in wastewater samples determined solely based on sequencing. © 2022, The Authors. All rights reserved.

关键词： SARS

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A Machine Learning Method for Vehicle Classification by Inductive Waveform Analysis ⁎

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IFAC-PapersOnLine 2020年第2期53卷 13928-13932页

作者： Bruno R. Vasconcellos Marcelo Rudek Marcelo de Souza Pontifícia Universidade Católica do Paraná - PUCPR Control and Automation Egineering Curitiba PR 80215-901 Pontifícia Universidade Católica do Paraná - PUCPR Industrial and Systems Engineering Graduate Program Curitiba PR 80215-901 Fiscaltech Curitiba PR 81290-270

The classification of vehicles is a matter of great importance for traffic control and management, helping with traffic surveillance as well as in statistical data collection. Among the several vehicular classification techniques, the most popular uses inductive loop sensors, because they achieve high accuracy rate at low cost. This paper proposes 5 different vehicle classification models by inductive waveform analysis: KNN, SVC, Decision Tree, Random Forest, and Voting Classifier. A brief introduction to the mathematical basis of these models and the main forms of vehicle detection are also presented. The obtained results reached an accuracy of 94% and showed how inductive waveform analysis is still a valid option for vehicle classification.

关键词： Machine learning Intelligent transportation systems Information processing Decision support

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Multimodal Whole Slide Foundation Model for Pathology

arXiv

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arXiv 2024年

作者： Ding, Tong Wagner, Sophia J. Song, Andrew H. Chen, Richard J. Lu, Ming Y. Zhang, Andrew Vaidya, Anurag J. Jaume, Guillaume Shaban, Muhammad Kim, Ahrong Williamson, Drew F.K. Chen, Bowen Almagro-Perez, Cristina Doucet, Paul Sahai, Sharifa Chen, Chengkuan Komura, Daisuke Kawabe, Akihiro Ishikawa, Shumpei Gerber, Georg Peng, Tingying Le, Long Phi Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Harvard Data Science Initiative Harvard University CambridgeMA United States Department of Systems Biology Harvard Medical School BostonMA United States Department of Preventive Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan Division of Pathology National Cancer Center Exploratory Oncology Research & Clinical Trial Center Chiba Japan

The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose TITAN, a multimodal whole slide foundation model pretrained using 335,645 WSIs via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any finetuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that TITAN outperforms both ROI and slide foundation models across machine learning settings such as linear probing, few-shot and zero-shot classification, rare cancer retrieval and cross-modal retrieval, and pathology report generation. The model is publicly accessible at https://***/mahmoodlab/TITAN © 2024, CC BY-NC-ND.

关键词： Self-supervised learning

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Multi-label classification for multi-temporal, multi-spatial coral reef condition monitoring using vision foundation model with adapter learning

arXiv

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arXiv 2025年

作者： Shao, Xinlei Chen, Hongruixuan Zhao, Fan Magson, Kirsty Chen, Jundong Li, Peiran Wang, Jiaqi Sasaki, Jun Department of Socio-Cultural Environmental Studies Graduate School of Frontier Sciences The University of Tokyo Chiba Kashiwa277-8563 Japan Department of Complexity Science and Engineering Graduate School of Frontier Sciences The University of Tokyo Chiba Kashiwa277-8563 Japan Department of Environment Systems Graduate School of Frontier Sciences The University of Tokyo Chiba Kashiwa277-8563 Japan New Heaven Reef Conservation Program Koh Tao Surat Thani84360 Thailand Data Science and AI Innovation Research Promotion Center Shiga University Shiga Hikone522-8522 Japan Interfaculty Initiative in Information Studies The University of Tokyo Tokyo113-0033 Japan

Coral reef ecosystems provide essential ecosystem services, but face significant threats from climate change and human activities. Although advances in deep learning have enabled automatic classification of coral reef conditions, conventional deep models struggle to achieve high performance when processing complex underwater ecological images. Vision foundation models, known for their high accuracy and cross-domain generalizability, offer promising solutions. However, fine-tuning these models requires substantial computational resources and results in high carbon emissions. To address these challenges, adapter learning methods such as Low-Rank Adaptation (LoRA) have emerged as a solution. This study introduces an approach integrating the DINOv2 vision foundation model with the LoRA fine-tuning method. The approach leverages multi-temporal field images collected through underwater surveys at 15 dive sites at Koh Tao, Thailand, with all images labeled according to universal standards used in citizen science-based conservation programs. The experimental results demonstrate that the DINOv2-LoRA model achieved superior accuracy, with a match ratio of 64.77%, compared to 60.34% achieved by the best conventional model. Furthermore, incorporating LoRA reduced the trainable parameters from 1,100M to 5.91M. Transfer learning experiments conducted under different temporal and spatial settings highlight the exceptional generalizability of DINOv2-LoRA across different seasons and sites. This study is the first to explore the efficient adaptation of foundation models for multi-label classification of coral reef conditions under multi-temporal and multi-spatial settings. The proposed method advances the classification of coral reef conditions and provides a tool for monitoring, conserving, and managing coral reef ecosystems. © 2025, CC BY-NC-SA.

关键词： Biotic

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Generation of high aspect-ratio pdms microfibers for 3D myelination culture of schwann cells 23

Generation of high aspect-ratio pdms microfibers for 3D myel...

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23rd International Conference on Miniaturized systems for Chemistry and Life Sciences, MicroTAS 2019

作者： Lin, Hui-Ying Chiu, Ing-Ming Wang, Horng-Dar Hsu, Chia-Hsien Graduate Program of Biotechnology in Medicine NTHU NHRI Department of Life Sciences National Tsing Hua University Hsinchu Taiwan Institute of Biomedical Engineering and Nanomedicine NHRI Miaoli Taiwan Institute of Cellular and Systems Medicine National Health Research Institutes Miaoli Taiwan

ISBN: (纸本)9781733419000

We report a new approach to generate synthetic axons for in-vitro myelination culture of Schwann cells using high aspect-ratio (HAR) PDMS microfibers. In our approach, a microdevice containing ordered no-crossing suspended PDMS micropillars was first fabricated by using softlithography technique. Next, by applying a mechanical force to the device, the PDMS micropillars can be elongated to become microfibers > 100 um long with small diameters ( © 2019 CBMS-0001.

关键词： Aspect ratio

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Alternation of Magnetic Anisotropy Accompanied by Metal-Insulator Transition in Strained Ultrathin Manganite Heterostructures

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Physical Review Applied 2021年第6期15卷 064019-064019页

作者： Masaki Kobayashi Le Duc Anh Masahiro Suzuki Shingo Kaneta-Takada Yukiharu Takeda Shin-ichi Fujimori Goro Shibata Arata Tanaka Masaaki Tanaka Shinobu Ohya Atsushi Fujimori Department of Electrical Engineering and Information Systems The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Center for Spintronics Research Network The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Institute of Engineering Innovation Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan PRESTO JST 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Materials Sciences Research Center Japan Atomic Energy Agency Sayo-gun Hyogo 679-5148 Japan Department of Applied Physics Tokyo University of Science Katsushika Tokyo 125-8585 Japan Quantum Matter Program Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima 739-8530 Japan Department of Applied Physics Waseda University Okubo Shinjuku Tokyo 169-8555 Japan

A fundamental understanding of the interfacial magnetic properties in ferromagnetic heterostructures is essential for utilizing ferromagnetic materials for spintronic device applications. Here, we investigate the interfacial magnetic and electronic structures of epitaxial single-crystalline LaAlO3(LAO)/La0.6Sr0.4MnO3(LSMO)/Nb:SrTiO3(Nb:STO) heterostructures with varying LSMO layer thicknesses, in which the magnetic anisotropy strongly changes with the LSMO thickness due to the delicate balance between strains originating from both the Nb:STO and LAO layers, using x-ray magnetic circular dichroism and photoemission spectroscopy. We successfully detect the clear change of the magnetic behavior of the Mn ions concomitant with the thickness-dependent metal-insulator transition. Our results suggest that the double-exchange interaction induces ferromagnetism in the metallic LSMO film under tensile strain caused by the STO substrate, while the superexchange interaction determines the magnetic behavior in the insulating LSMO film under compressive strain originating from the top LAO layer. The change in strain, depending on LSMO layer thickness, is confirmed by scanning transmission electron microscopy. Based on those findings, the formation of a magnetic dead layer near the LAO/LSMO interface is attributed to competition between the superexchange interaction via Mn 3d3z2−r2 orbitals under compressive strain and the double-exchange interaction via the 3dx2−y2 orbitals. These findings provide key aspects of ferromagnetic oxide heterostructures for the development of spintronic device applications.

关键词： Electronic structure Ferromagnetism Magnetic anisotropy Metal-insulator transition Spintronics Transition metal oxides Photoemission spectroscopy X-ray magnetic circular dichroism

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Application of factor analysis method to support the users acceptance model of ERP systems implementation

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Journal of Physics: Conference Series 2021年第1期1836卷

作者： W Sardjono J Sudirwan W Priatna G R Putra Information Systems Management Department BINUS Graduate Program -Master of Information Systems Management Bina Nusantara University Jakarta Indonesia – 11480 School of Information Systems Department BINUS Undergraduate Program Bina Nusantara University Jakarta Indonesia 11480 Informatics Engineering Engineering Bhayangkara University Jakarta Raya Indonesia Computer Science Study Program Faculty of Mathematics & Natural Sciences Pakuan University Bogor Indonesia

An e-Commerce company has been using an Enterprise Resource Planning (ERP) system for several years, but is still constrained in its implementation, this is reflected in the number of issue/change request tickets submitted by users who have not been able to reach ideal conditions. To overcome this problem, we need an appropriate reference model, the ERP system user acceptance model in companies. The study conducted factors that influence the level of acceptance of SAP ERP users in the XYZ group to then determine a model that is able to explain practically the level of ERP system user acceptance, which in turn can produce concrete proposals to increase ERP system user acceptance. Research is quantitative descriptive using factor analysis and regression analysis methods. The research method uses a questionnaire with a total of 107 valid respondents coming from 8 company names under the XYZ group in 6 Southeast Asian countries. The conclusion of the study is that there are 24 indicators and 4 factors that influence user acceptance of the SAP ERP system. These four factors are the low contribution of the ERP system, the quality of ERP systems and data, the development of ERP systems, and also the low quality of ERP system supporters. It is hoped that by optimizing the value of the 4 factors, the XYZ group can increase user acceptance of the SAP ERP system so that the system can provide benefits expected by the company to be competitive advantage.

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Tissue clearing and its applications in neuroscience (vol 21, pg 61, 2020)

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NATURE REVIEWS NEUROSCIENCE 2020年第5期21卷 298-298页

作者： Ueda, Hiroki R. Ertuerk, Ali Chung, Kwanghun Gradinaru, Viviana Chedotal, Alain Tomancak, Pavel Keller, Philipp J. Department of Systems Pharmacology University of Tokyo Tokyo Japan Laboratory for Synthetic Biology RIKEN BDR Suita Japan Institute for Stroke and Dementia Research Klinikum der Universität München Ludwig-Maximilian University of Munich Munich Germany Institute of Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Munich Cluster for Systems Neurology (SyNergy) Munich Germany Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA USA Picower Institute for Learning and Memory Massachusetts Institute of Technology Cambridge MA USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA USA Eli & Edythe Broad Institute of MIT and Harvard Cambridge MA USA Center for NanoMedicine Institute for Basic Science Seoul Republic of Korea Graduate Program of Nano Biomedical Engineering Yonsei-IBS Institute Yonsei University Seoul Republic of Korea Division of Biology and Biological Engineering California Institute of Technology Pasadena CA USA Institut de la Vision Sorbonne Université INSERM CNRS Paris France Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany IT4Innovations Technical University of Ostrava Ostrava Czech Republic Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： Immunological techniques Light-sheet microscopy Neuroscience

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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A Study on Indoor 3D Modeling by SOKUIKI Sensor and Omnidirectional Camera

A Study on Indoor 3D Modeling by SOKUIKI Sensor and Omnidire...

引用

IEEE Global Conference on Consumer Electronics (GCCE)

作者： Shoma Inoue Masahiro Fujii Graduate Program in Information Electrical and Electronics Systems Engineering Utsunomiya University 7-1-2 Yoto Tochigi Utsunomiya Japan

ISBN: (数字)9781728135755

ISBN: (纸本)9781728135762

In this paper, we propose a new 3D space modeling method for a SLAM (Simultaneous Localization and Mapping) by using distance measurement information on the horizontal plane by a SOKUIKI sensor and a 360-degree panoramic image by an omnidirectional camera. The proposed method can suppress the measurement time and the amount of the measurement data in comparison with the conventional SLAM. We evaluate the proposed method by some measurement experiments. We show that it is possible to measure the size of simultaneous objects with a high degree of accuracy.

关键词： Cameras Three-dimensional displays Simultaneous localization and mapping Estimation Time measurement Distance measurement Solid modeling

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