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Quantitative Point of Care Tests for Timely Diagnosis of Early-Onset Preeclampsia with High Sensitivity and Specificity

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Angewandte Chemie 2023年第26期135卷

作者： Sahar Masoumeh Ghorbanpour Dr. Shihui Wen Prof. Tu'uhevaha J Kaitu'u-Lino Prof. Natalie J. Hannan Prof. Dayong Jin Assoc. Prof. Lana McClements School of Life Sciences & Institute for Biomedical Materials and Devices Faculty of Science University of Technology Sydney Sydney NSW 2007 Australia Contribution: Conceptualization (equal) Data curation (lead) Formal analysis (lead) Methodology (lead) Validation (lead) Visualization (lead) Writing - original draft (lead) Institute for Biomedical Materials and Devices School of Mathematical and Physical Sciences Faculty of Science University of Technology Sydney Sydney NSW 2007 Australia ARC Research Hub for Integrated Device for End-user Analysis at Low-levels (IDEAL) Faculty of Science University of Technology Sydney Sydney NSW 2007 Australia Department of Obstetrics & Gynaecology Mercy Hospital for Women The University of Melbourne Heidelberg Australia Mercy Perinatal Mercy Hospital for Women Heidelberg Australia Contribution: Data curation (supporting) Formal analysis (supporting) Resources (supporting) Writing - review & editing (supporting) Contribution: Conceptualization (lead) Funding acquisition (lead) Supervision (lead) Visualization (supporting) Writing - review & editing (supporting)

Preeclampsia is a heterogeneous and multiorgan cardiovascular disorder of pregnancy. Here, we report the development of a novel strip-based lateral flow assay (LFA) using lanthanide-doped upconversion nanoparticles conjugated to antibodies targeting two different biomarkers for detection of preeclampsia. We first measured circulating plasma FKBPL and CD44 protein concentrations from individuals with early-onset preeclampsia (EOPE), using ELISA. We confirmed that the CD44/FKBPL ratio is reduced in EOPE with a good diagnostic potential. Using our rapid LFA prototypes, we achieved an improved lower limit of detection: 10 pg ml −1 for FKBPL and 15 pg ml −1 for CD44, which is more than one order lower than the standard ELISA method. Using clinical samples, a cut-off value of 1.24 for CD44/FKBPL ratio provided positive predictive value of 100 % and the negative predictive value of 91 %. Our LFA shows promise as a rapid and highly sensitive point-of-care test for preeclampsia.

关键词： CD44 FKBPL Point of Care Testing Preeclampsia Upconversion Nanoparticles

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Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII

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database 2023年第2023期2023卷 baad005-baad005页

作者： Leaman, Robert Islamaj, Rezarta Adams, Virginia Alliheedi, Mohammed A. Almeida, João Rafael Antunes, Rui Bevan, Robert Chang, Yung-Chun Erdengasileng, Arslan Hodgskiss, Matthew Ida, Ryuki Kim, Hyunjae Li, Keqiao Mercer, Robert E. Mertová, Lukrécia Mobasher, Ghadeer Shin, Hoo-Chang Sung, Mujeen Tsujimura, Tomoki Yeh, Wen-Chao Lu, Zhiyong National Center for Biotechnology Information (NCBI) National Library of Medicine National Institutes of Health 8600 Rockville Pike Bethesda 20894 MD United States Nvidia 2788 San Tomas Expressway Santa Clara 95051 CA United States Department of Computer Science Al Baha University 4781 King Fahd Rd Al Aqiq 65779 Saudi Arabia Department of Electronics Telecommunications and Informatics (DETI) Institute of Electronics and Informatics Engineering of Aveiro (IEETA) University of Aveiro Campus Universitário de Santiago Aveiro 3810-193 Portugal Department of Information and Communications Technologies University of A Coruña Camiño Do Lagar de Castro A Coruña 15008 Spain Informatics Department Medicines Discovery Catapult Alderley Park Block 35 Mereside Macclesfield SK10 4ZF United Kingdom Graduate Institute of Data Science Taipei Medical University No. 172-1 Section 2 Keelung Rd Da’an District Taipei City 106 Taiwan Department of Statistics Florida State University 117 N. Woodward Ave Tallahassee 32306 FL United States Computational Intelligence Laboratory Toyota Technological Institute 2-12-1 Hisakata Tempaku-ku Nagoya Aichi 468-8511 Japan Department of Computer Science and Engineering Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 South Korea Department of Computer Science The University of Western Ontario Middlesex College Room 355 London N6A 5B7 ON Canada Scientific Databases and Visualization Group Heidelberg Institute for Theoretical Studies (HITS GGmbH) Schloss-Wolfsbrunnenweg 35 Heidelberg 69118 Germany Institute of Computer Science Heidelberg University Im Neuenheimer Feld 205 Heidelberg 69120 Germany Institute of Information Systems and Applications National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu 30013 Taiwan

The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and - as highlighted during the coronavirus disease 2019 pandemic - their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We, therefore, organized the BioCreative NLM-Chem track as a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: (i) chemical identification and (ii) chemical indexing. The chemical identification task required predicting all chemicals mentioned in recently published full-text articles, both span [i.e. named entity recognition (NER)] and normalization (i.e. entity linking), using Medical Subject Headings (MeSH). The chemical indexing task required identifying which chemicals reflect topics for each article and should therefore appear in the listing of MeSH terms for the document in the MEDLINE article indexing. This manuscript summarizes the BioCreative NLM-Chem track and post-challenge experiments. We received a total of 85 submissions from 17 teams worldwide. The highest performance achieved for the chemical identification task was 0.8672 F-score (0.8759 precision and 0.8587 recall) for strict NER performance and 0.8136 F-score (0.8621 precision and 0.7702 recall) for strict normalization performance. The highest performance achieved for the chemical indexing task was 0.6073 F-score (0.7417 precision and 0.5141 recall). This community challenge demonstrated that (i) the current substantial achievements in deep learning technologies can be utilized to improve automated prediction accuracy further and (ii) the chemical indexing task is substanti

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Universal Sub-Nanoreactor Strategy for Synthesis of Yolk-Shell MoS2Supported Single Atom Electrocatalysts toward Robust Hydrogen Evolution Reaction

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Angewandte Chemie 2023年第40期135卷

作者： Feilong Gong Yuheng Liu Yang Zhao Prof. Wei Liu Prof. Guang Zeng Prof. Guoqing Wang Prof. Yonghui Zhang Lihua Gong Prof. Jian Liu Key Laboratory of Surface and Interface Science and Technology of Henan Province College of Material and Chemical Engineering Zhengzhou University of Light Industry Zhengzhou Henan 450001 P. R. China Contribution: Conceptualization (equal) Formal analysis (equal) Funding acquisition (lead) Investigation (lead) Visualization (equal) Writing - original draft (lead) Contribution: Data curation (lead) Formal analysis (equal) Investigation (equal) Validation (equal) Visualization (equal) State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian Liaoning 116023 P. R. China Contribution: Data curation (equal) Formal analysis (equal) Resources (equal) Validation (equal) Visualization (equal) Contribution: Data curation (equal) Formal analysis (equal) Investigation (equal) Contribution: Data curation (equal) Methodology (equal) Software (equal) Validation (equal) Contribution: Formal analysis (equal) Software (equal) Writing - review & editing (equal) Contribution: Conceptualization (equal) Data curation (equal) Formal analysis (lead) Software (lead) Writing - review & editing (equal) DICP-Surrey Joint Centre for Future Materials Department of Chemical and Process Engineering and Advanced Technology Institute of University of Surrey Guildford Surrey GU2 7XH UK College of Chemistry and Chemical Engineering Inner Mongolia University Hohhot Inner Mongolia 010021 P. R. China Contribution: Conceptualization (lead) Formal analysis (equal) Funding acquisition (equal) Project administration (lead) Supervision (lead) Writing - review & editing (lead)

The coordination structure determines the electrocatalytic performances of single atom catalysts (SACs), while it remains a challenge to precisely regulate their spatial location and coordination environment. Herein, we report a universal sub-nanoreactor strategy for synthesis of yolk-shell MoS 2 supported single atom electrocatalysts with dual-anchored microenvironment of vacancy-enriched MoS 2 and intercalation carbon toward robust hydrogen-evolution reaction. Theoretical calculations reveal that the “E-Lock” and “E-Channel” are conducive to stabilize and activate metal single atoms. A group of SACs is subsequently produced with the assistance of sulfur vacancy and intercalation carbon in the yolk-shell sub-nanoreactor. The optimized C−Co−MoS 2 yields the lowest overpotential (η 10 =17 mV) compared with previously reported MoS 2 -based electrocatalysts to date, and also affords a 5–9 fold improvement in activity even comparing with those as-prepared single-anchored analogues. Theoretical results and in situ characterizations unveil its active center and durability. This work provides a universal pathway to design efficient catalysts for electro-refinery.

关键词： Electrocatalyst Nanostructures Sub-Nanoreactor Universal Strategy Water Splitting

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An overview on deep learning-based approximation methods for partial differential equations

arXiv

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arXiv 2020年

作者： Beck, Christian Hutzenthaler, Martin Jentzen, Arnulf Kuckuck, Benno Department of Mathematics ETH Zurich Zurich Switzerland Applied Mathematics: Institute for Analysis and Numerics Faculty of Mathematics and Computer Science University of Münster Münster Germany Faculty of Mathematics University of Duisburg-Essen Essen Germany School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

It is one of the most challenging problems in applied mathematics to approximatively solve high-dimensional partial differential equations (PDEs). Recently, several deep learning-based approximation algorithms for attacking this problem have been proposed and tested numerically on a number of examples of high-dimensional PDEs. This has given rise to a lively field of research in which deep learning-based methods and related Monte Carlo methods are applied to the approximation of high-dimensional PDEs. In this article we offer an introduction to this field of research by revisiting selected mathematical results related to deep learning approximation methods for PDEs and reviewing the main ideas of their proofs. We also provide a short overview of the recent literature in this area of *** Codes 65M99 (Primary), 35-02, 65-02, 68T07 (Secondary) Copyright © 2020, The Authors. All rights reserved.

关键词： Partial differential equations

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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How to develop rapid reviews of diagnostic tests according to experts: A qualitative exploration of researcher views

Cochrane Evidence Synthesis and Methods

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Cochrane Evidence Synthesis and Methods 2023年第2期1卷

作者： Ingrid Arevalo-Rodriguez Susan Baxter Karen R. Steingart Andrea C. Tricco Barbara Nussbaumer-Streit David Kaunelis Pablo Alonso-Coello Patrick M. Bossuyt Javier Zamora Clinical Biostatistics Unit Hospital Universitario Ramón y Cajal Madrid Spain CIBER of Epidemiology and Public Health (CIBERESP) Madrid Spain Contribution: Conceptualization Data curation Formal analysis Investigation Methodology Project administration Visualization Writing - original draft Writing - review & editing School of Health and Related Research (ScHARR) University of Sheffield Sheffield UK Contribution: Conceptualization Data curation Formal analysis Investigation Methodology Resources Software Writing - original draft Writing - review & editing Honorary Research Fellow Department of Clinical Sciences Liverpool School of Tropical Medicine Liverpool UK Contribution: Conceptualization Data curation Investigation Methodology Supervision Validation Visualization Writing - original draft Writing - review & editing Li Ka Shing Knowledge Institute St. Michael's Hospital Unity Health Toronto Toronto Ontario Canada Epidemiology Division and Institute for Health Policy Management and Evaluation Dalla Lana School of Public Health University of Toronto Toronto Canada Contribution: Conceptualization Data curation Funding acquisition Investigation Methodology Resources Supervision Validation Visualization Writing - original draft Writing - review & editing Cochrane Austria Department for Evidence-based Medicine and Evaluation University of Krems Krems Austria Contribution: Conceptualization Data curation Formal analysis Investigation Methodology Resources Supervision Validation Visualization Writing - original draft Writing - review & editing Canadian Agency for Drugs and Technologies in Health (CADTH) Ottawa Ontario Canada Contribution: Conceptualization Investigation Methodology Supervision Validation Visualization Writing - original draft Writing - review & editing Iberoamerican Cochrane Center-Servicio de Epidemiología Clínica y Salud Pública Biomedical Research Institute (IIB-Sant Pau) Barcelona Spain CIBER of Epidemiology

Background Rapid reviews (RRs) have been used to provide timely evidence for policymakers, health providers, and the public in several healthcare scenarios, most recently during the coronavirus disease 2019 pandemic. Despite the essential role of diagnosis in clinical management, data about how to perform RRs of diagnostic tests are scarce. We aimed to explore the views and perceptions of experts in evidence synthesis and diagnostic evidence about the value of methods used to accelerate the review process. Methods We performed semistructured interviews with a purposive sample of experts in evidence synthesis and diagnostic evidence. We carried out the interviews in English between July and December 2021. Initial reading and coding of the transcripts were performed using NVIVO qualitative data analysis software. Results Of a total of 23 invited experts, 16 (70%) responded. We interviewed all 16 participants representing key roles in evidence synthesis. We identified 14 recurring themes including the review question, characteristics of the review team, and use of automation, as the topics with the highest number of quotes. Some participants considered several methodological “shortcuts” to be ineffective or risky, such as automating quality appraisal, using only one reviewer for diagnostic data extraction and only performing descriptive analysis. The introduction of limits might depend on whether the test being assessed is a new test, the availability of alternative tests, the needs of providers and patients, and the availability of high-quality systematic reviews. Conclusions Our findings suggest that organizational strategies (e.g., defining the review question, availability of a highly experienced team) may have a role in conducting RRs of diagnostic tests. Several methodological shortcuts were considered inadequate for accelerating the review process, though they need to be assessed in well-designed studies. Improved reporting of RRs would support evidence-based de

关键词： accelerated methods diagnostic tests evidence synthesis rapid reviews systematic reviews

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Glucose-Responsive Charge-Switchable Lipid Nanoparticles for Insulin Delivery

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Angewandte Chemie 2023年第20期135卷

作者： Yun Liu Yanfang Wang Yuejun Yao Juan Zhang Wei Liu Kangfan Ji Xinwei Wei Yuanwu Wang Prof. Xiangsheng Liu Prof. Shiming Zhang Prof. Jinqiang Wang Prof. Zhen Gu Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province College of Pharmaceutical Sciences Zhejiang University 310058 Hangzhou China Jinhua Institute of Zhejiang University 321299 Jinhua China Contribution: Data curation (lead) Formal analysis (lead) Investigation (lead) Methodology (lead) Software (lead) Validation (lead) Visualization (lead) Writing - original draft (lead) Writing - review & editing (lead) Contribution: Data curation (supporting) Investigation (supporting) Visualization (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) Investigation (supporting) Contribution: Formal analysis (supporting) Investigation (supporting) Methodology (supporting) Contribution: Formal analysis (supporting) Investigation (supporting) Contribution: Writing - review & editing (supporting) Contribution: Investigation (supporting) Zhejiang Cancer Hospital Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences 310022 Hangzhou China Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong SAR China Second Affiliated Hospital Zhejiang University School of Medicine 310009 Hangzhou China Contribution: Conceptualization (lead) Data curation (equal) Formal analysis (equal) Funding acquisition (lead) Investigation (lead) Methodology (equal) Project administration (lead) Resources (lead) Software (lead) Supervision (lead) Validation (supporting) Visualization (supporting) Writing - review & editing (lead) Department of General Surgery Sir Run Run Shaw Hospital School of Medicine Zhejiang University 310009 Hangzhou China Zhejiang Laboratory of Systems & Precision Medicine Zhejiang University Medical Center 310009 Hangzhou China MOE Key Laboratory of Macromolecular Synthesis and Functionalization Department of Polymer Science and Engineering Zhejiang University 310009 Hangzhou China Contribution: Funding acquisition (lead) Project administration (lead) Resources (lead) Softwar

Lipid nanoparticle-based drug delivery systems have a profound clinical impact on nucleic acid-based therapy and vaccination. Recombinant human insulin, a negatively-charged biomolecule like mRNA, may also be delivered by rationally-designed positively-charged lipid nanoparticles with glucose-sensing elements and be released in a glucose-responsive manner. Herein, we have designed phenylboronic acid-based quaternary amine-type cationic lipids that can self-assemble into spherical lipid nanoparticles in an aqueous solution. Upon mixing insulin and the lipid nanoparticles, a heterostructured insulin complex is formed immediately arising from the electrostatic attraction. In a hyperglycemia-relevant glucose solution, lipid nanoparticles become less positively charged over time, leading to reduced attraction and subsequent insulin release. Compared with native insulin, this lipid nanoparticle-based glucose-responsive insulin shows prolonged blood glucose regulation ability and blood glucose-triggered insulin release in a type 1 diabetic mouse model.

关键词： Diabetes Drug Delivery Glucose-Responsive Insulin Delivery Lipid Nanoparticles

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NTIRE 2023 Challenge on Light Field Image Super-Resolution: dataset, Methods and Results

arXiv

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arXiv 2023年

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany ETH Zürich Switzerland The Shenzhen Campus of Sun Yat-Sen University Sun Yat-Sen University China Bigo Technology Pte. Ltd Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023, CC BY-SA.

关键词： Image enhancement

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Gene signatures associated with genomic aberrations predict prognosis in neuroblastoma

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Cancer Communications 2020年第2期40卷 105-118页

作者： Xiaoyan He Chao Qin Yanding Zhao Lin Zou Hui Zhao Chao Cheng Center for Clinical Molecular Medicine Ministry of Education Key Laboratory of Child Development and DisordersNational Clinical Research Center for Child Health and DisordersChina International Science and Technology Cooperation Base of Child Development and Critical DisordersChongqing Key Laboratory of PediatricsChildren’s Hospital of Chongqing Medical UniversityChongqing 400014P.R.China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijing 100044P.R.China Department of Biomedical Data Science Geisel School of Medicine at DartmouthLebanonNH 03766USA School of Biomedical Sciences Faculty of MedicineThe Chinese University of Hong KongHong Kong 999077P.R.China Department of Medicine Baylor College of MedicineHoustonTX 77030USA Institute for Clinical and Translational Research Baylor College of MedicineHoustonTX 77030USA

Background:Neuroblastoma(NB)is a heterogeneous disease with respect to genomic abnormalities and clinical *** recent advances in our understanding of the association between the genetic aberrations and clinical features,it remains one of the major challenges to predict prognosis and stratify patients for determining personalized therapy in this *** aim of this study was to develop an effective prognosis prediction model for NB ***:We integrated diverse computational analyses to define gene signatures that reflect MYCN activity and chromosomal aberrations including deletion of chromosome 1p(Chr1p_del)and chromosome 11q(Chr11q_del)as well as chromosome 11q whole loss(Chr11q_wls).We evaluated the prognostic and predictive values of these signatures in seven NB gene expression datasets(the number of samples ranges from 94 to 498,with a total of 2120)generated from both RNA sequencing and microarray ***:MYCN signature was a more effective prognostic marker than MYCN amplification status and MYCN ***,the Chr1p_del score was more prognostic than Chr1p *** activity scores of MYCN,Chr1p_del and Chr11q_del were associated with poor prognosis,while the Chr11q_wls score was linked to good *** integrated the activity scores of MYCN,Chr1p_del,Chr11q_del,and Chr11q_wls and clinical variables into an integrative prognostic model,which displayed significant performance over the clinical variables or each genomic aberration ***:Our integrative gene signature model shows a significantly improved forecast performance with prognostic and predictive information,and thereby can be served as a biomarker to stratify NB patients for prognosis evaluation and surveillance programs.

关键词： chromosome aberration gene expression profile MYCN gene neuroblastoma prognosis survival analysis

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GIscience in the Era of Artificial Intelligence: A Research Agenda Towards Autonomous GIS

arXiv

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arXiv 2025年

作者： Li, Zhenlong Ning, Huan Gao, Song Janowicz, Krzysztof Li, Wenwen Arundel, Samantha T. Yang, Chaowei Bhaduri, Budhendra Wang, Shaowen Zhu, A. Xing Gahegan, Mark Shekhar, Shashi Ye, Xinyue McKenzie, Grant Cervone, Guido Hodgson, Michael E. Geoinformation and Big Data Research Lab Department of Geography The Pennsylvania State University University ParkPA United States Department of Geography University of Wisconsin – Madison WI United States STKO Lab Department of Geography and Regional Research University of Vienna Vienna Austria Spatial Analysis Research Center School of Geographical Sciences and Urban Planning Arizona State University AZ United States Center of Excellence for Geospatial Information Science U.S. Geological Survey VA United States NSF Spatiotemporal Innovation Center Department of Geography & Geoinformation Science George Mason University VA United States TN United States CyberGIS Center for Advanced Digital and Spatial Studies Department of Geography and Geographic Information Science University of Illinois Urbana-Champaign IL United States School of Computer Science University of Auckland New Zealand Department of Computer Science & Engineering University of Minnesota MN United States Department of Landscape Architecture & Urban Planning Center for Geospatial Sciences Applications & Technology Texas A&M University TX United States Platial Analysis Lab Department of Geography McGill University Quebec Canada Institute for Computational and Data Sciences Department of Geography The Pennsylvania State University University ParkPA United States Department of Geography University of South Carolina SC United States

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five autonomous levels, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modeling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIscience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future. © 2025, CC BY.

关键词： Image retrieval

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