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Research integrity and peer review 2024年第1期9卷 14页

作者： Paul E van der Vet Harm Nijveen Human Media Interaction Group Department of Computer Science University of Twente Drienerlolaan 5 7522 NB Enschede the Netherlands. ZGT Academy Ziekenhuisgroep Twente Zilvermeeuw 1 7609 PP Almelo the Netherlands. Bioinformatics Laboratory Wageningen University Droevendaalsesteeg 1 6708 PB Wageningen the Netherlands. harm.nijveen@wur.nl. Wageningen Seed Lab Laboratory of Plant Physiology Wageningen University Droevendaalsesteeg 1 6708 PB Wageningen the Netherlands. harm.nijveen@wur.nl.

EPICAST 2.0: A LARGE-SCALE, DEMOGRAPHICALLY DETAILED, AGENT-BASED MODEL FOR SIMULATING RESPIRATORY PATHOGEN SPREAD IN THE UNITED STATES

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arXiv 2025年

作者： Alexander, Prescott C. Harris, Thomas J. Kitson, Joy Tuccillo, Joseph V. Del Valle, Sara Y. Germann, Timothy C. Los Alamos National Laboratory Los AlamosNM United States Los Alamos National Laboratory Los AlamosNM United States School of Computing and Information Systems University of Melbourne ParkvilleVIC Australia Department of Computer Science University of Maryland College ParkMD United States Geospatial Science and Human Security Division Oak Ridge National Laboratory Oak RidgeTN United States Los Alamos National Laboratory Los AlamosNM United States

The recent history of respiratory pathogen epidemics, including those caused by influenza and SARS-CoV-2, has highlighted the urgent need for advanced modeling approaches that can accurately capture heterogeneous disease dynamics and outcomes at the national scale, thereby enhancing the effectiveness of resource allocation and decision-making. In this paper, we describe Epicast 2.0, an agent-based model that utilizes a highly detailed, synthetic population and high-performance computing techniques to simulate respiratory pathogen transmission across the entire United States. This model replicates the contact patterns of over 320 million agents as they engage in daily activities at school, work, and within their communities. Epicast 2.0 supports vaccination and an array of non-pharmaceutical interventions that can be promoted or relaxed via highly granular, user specified policies. We illustrate the model’s capabilities using a wide range of outbreak scenarios, highlighting the model’s varied dynamics as well as its extensive support for policy exploration. This model provides a robust platform for conducting what if scenario analysis and providing insights into potential strategies for mitigating the impacts of infectious diseases. Copyright © 2025, The Authors. All rights reserved.

关键词： Lung cancer

Adaptive optics two-photon microscopy for in vivo imaging of mouse retina 6

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Adaptive optics two-photon microscopy for in vivo imaging of...

Adaptive Optics and Wavefront Control for Biological Systems VI 2020

作者： Qin, Zhongya He, Sicong Chen, Congping Yang, Chao Yung, Jasmine Wu, Wanjie Leung, Christopher K. Liu, Kai Qu, Jianan Y. Biophotonics Research Laboratory Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Center of Systems Biology and Human Health School of Science and Institute for Advanced Study Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Division of Life Science Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Ophthalmology and Visual Sciences Chinese University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9781510632592

Non-invasive retinal imaging has greatly facilitated the research of eye disease and neurodegenerative disorders in the central nervous system (CNS). Two-photon microscopy is a powerful tool for in vivo imaging of mouse retina because it provides intrinsic optical sectioning capability and the infrared laser is less likely to excite the photoreceptors. However, the dilated mouse eye has large optical aberrations, which must be corrected to achieve high-resolution or even diffraction-limited imaging. Here, we developed an adaptive optics (AO) two-photon microscope for in vivo imaging of retinal neurons through the eyeball of living mouse. We used the two-photon excited fluorescence signal of retina as the guide star to measure and correct the aberration of mouse eye. After AO correction, the fluorescence signal was increased by at least fivefold and the fine structures such as axons of retinal ganglion cells (RGC) were clearly resolved. To take advantage of the non-invasive high-resolution imaging, we demonstrated functional calcium imaging of RGC responding to the light stimulations. © 2020 SPIE.

关键词： Adaptive optics

SketchGAN: Joint Sketch Completion and Recognition with Generative Adversarial Network

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SketchGAN: Joint Sketch Completion and Recognition with Gene...

IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Fang Liu Xiaoming Deng Yu-Kun Lai Yong-Jin Liu Cuixia Ma Hongan Wang State Key Laboratory of Computer Science and Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Cardiff University Tsinghua University

ISBN: (纸本)9781728132945

Hand-drawn sketch recognition is a fundamental problem in computer vision, widely used in sketch-based image and video retrieval, editing, and reorganization. Previous methods often assume that a complete sketch is used as input;however, hand-drawn sketches in common application scenarios are often incomplete, which makes sketch recognition a challenging problem. In this paper, we propose SketchGAN, a new generative adversarial network (GAN) based approach that jointly completes and recognizes a sketch, boosting the performance of both tasks. Specifically, we use a cascade Encode-Decoder network to complete the input sketch in an iterative manner, and employ an auxiliary sketch recognition task to recognize the completed sketch. Experiments on the Sketchy database benchmark demonstrate that our joint learning approach achieves competitive sketch completion and recognition performance compared with the state-of-the-art methods. Further experiments using several sketch-based applications also validate the performance of our method.

关键词： Sketches images computer vision Recognition (Psychology) hand-drawn

Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach

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arXiv 2021年

作者： Li, Guoliang Wang, Shuai Li, Jie Wang, Rui Liu, Fan Peng, Xiaohui Han, Tony Xiao Xu, Chengzhong Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Huawei technologies Co. Ltd. Shenzhen China Department of Computer Science University of Macau China

Characterizing the sensing and communication performance tradeoff in integrated sensing and communication (ISAC) systems is challenging in the applications of learning-based human motion recognition. This is because of the large experimental datasets and the black-box nature of deep neural networks. This paper presents SDP3, a Simulation-Driven Performance Predictor and oPtimizer, which consists of SDP3 data simulator, SDP3 performance predictor and SDP3 performance optimizer. Specifically, the SDP3 data simulator generates vivid wireless sensing datasets in a virtual environment, the SDP3 performance predictor predicts the sensing performance based on the function regression method, and the SDP3 performance optimizer investigates the sensing and communication performance tradeoff analytically. It is shown that the simulated sensing dataset matches the experimental dataset very well in the motion recognition accuracy. By leveraging SDP3, it is found that the achievable region of recognition accuracy and communication throughput consists of a communication saturation zone, a sensing saturation zone, and a communication-sensing adversarial zone, of which the desired balanced performance for ISAC systems lies in the third one. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

Generating Synthetic Medical Images by Using GAN to Improve CNN Performance in Skin Cancer Classification

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Generating Synthetic Medical Images by Using GAN to Improve ...

RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Pooyan Sedigh Rasoul Sadeghian Mehdi Tale Masouleh Science and Research Branch Islamic Azad University Tehran Iran Human and Robot Interaction Laboratory Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9781728166049

ISBN: (纸本)9781728166056

One of the main reasons of slow progress in using deep learning methods for cancer detection is the lack of data, especially the annotated data which is usually used for supervised learning algorithms. This paper presents a Convolutional Neural Network (CNN) to detect skin cancer. The primary database which is used to train the designed CNN algorithm has 97 members (50 benign and 47 malignant), which are collected from the International Skin Imaging Collaboration (ISIC). In order to compensate the lack of data for training the proposed CNN algorithm, a Generative Adversarial Network (GAN) is designed to produce synthetic skin cancer images. The classification performance of the designed trained CNN without the obtained synthetic images is near 53%, but by adding the synthetic images to the primary database the performance of the model is increased to 71%.

关键词： Gallium nitride Generative adversarial networks Databases Skin cancer Medical diagnostic imaging

BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck

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arXiv 2022年

作者： Zheng, Kaizhong Yu, Shujian Li, Baojuan Jenssen, Robert Chen, Badong National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China The Department of Computer Science Vrije Universiteit Amsterdam Amsterdam and the Machine Learning Group UiT - Arctic University of Norway Tromsø Norway The Machine Learning Group UiT - Arctic University of Norway Tromsø Norway The School of Biomedical Engineering Fourth Military Medical University Xi’an China

Developing a new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus. Recently, machine learning-based classifiers using functional connectivity (FC) for psychiatric disorders and healthy controls are developed to identify brain markers. However, existing machine learning-based diagnostic models are prone to over-fitting (due to insufficient training samples) and perform poorly in new test environment. Furthermore, it is difficult to obtain explainable and reliable brain biomarkers elucidating the underlying diagnostic decisions. These issues hinder their possible clinical applications. In this work, we propose BrainIB, a new graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI), by leveraging the famed Information Bottleneck (IB) principle. BrainIB is able to identify the most informative edges in the brain (i.e., subgraph) and generalizes well to unseen data. We evaluate the performance of BrainIB against 3 baselines and 7 state-of-the-art brain network classification methods on three psychiatric datasets and observe that our BrainIB always achieves the highest diagnosis accuracy. It also discovers the subgraph biomarkers which are consistent to clinical and neuroimaging findings. The source code and implementation details of BrainIB are freely available at GitHub repository (https://***/SJYuCNEL/brain-and-Information-Bottleneck/). Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

Generalizability of PRS313 for breast cancer risk amongst non-Europeans in a Los Angeles biobank

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arXiv 2023年

作者： Shang, Helen Ding, Yi Venkateswaran, Vidhya Boulier, Kristin Kathuria-Prakash, Nikhita Malidarreh, Parisa Boodaghi Luber, Jacob M. Pasaniuc, Bogdan Division of Internal Medicine Ronald Reagan UCLA Medical Center Los AngelesCA United States Department of Computer Science and Engineering University of Texas at Arlington ArlingtonTX United States Los AngelesCA United States Department of Oral Biology UCLA School of Dentistry Los AngelesCA United States Division of Cardiology Department of Medicine Ronald Reagan UCLA Medical Center Los AngelesCA United States Division of Hematology-Oncology Department of Medicine Ronald Reagan UCLA Medical Center Los AngelesCA United States Department of Computer Science and Engineering University of Texas at Arlington ArlingtonTX United States Multi-Interprofessional Center for Health Informatics University of Texas at Arlington ArlingtonTX United States Department of Bioengineering University of Texas at Arlington ArlingtonTX United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Institute of Precision Health University of California Los Angeles Los AngelesCA United States

Polygenic risk scores (PRS) summarize the combined effect of common risk variants and are associated with breast cancer risk in patients without identifiable monogenic risk factors. One of the most well-validated PRSs in breast cancer to date is PRS313, which was developed from a Northern European biobank but has shown attenuated performance in non-European ancestries. We further investigate the generalizability of the PRS313 for American women of European (EA), African (AFR), Asian (EAA), and Latinx (HL) ancestry within one institution with a singular EHR system, genotyping platform, and quality control process. We found that the PRS313 achieved overlapping Areas under the ROC Curve (AUCs) in females of Lantix (AUC, 0.68;95 CI, 0.65-0.71) and European ancestry (AUC, 0.70;95 CI, 0.69-0.71) but lower AUCs for the AFR and EAA populations (AFR: AUC, 0.61;95 CI, 0.56-0.65;EAA: AUC, 0.64;95 CI, 0.60-0.680). While PRS313 is associated with Hormone Positive (HR+) disease in European Americans (OR, 1.42;95 CI, 1.16-1.64), for Latinx females, it may be instead associated with human Epidermal Growth Factor Receptor 2 (HER2+) disease (OR, 2.52;95 CI, 1.35-4.70) although due to small numbers, additional studies are needed. In summary, we found that PRS313 was significantly associated with breast cancer but with attenuated accuracy in women of African and Asian descent within a singular health system in Los Angeles. Our work further highlights the need for additional validation in diverse cohorts prior to clinical implementation of polygenic risk scores. © 2023, CC BY.

关键词： Diseases

A standardised open science framework for sharing and re-analysing neural data acquired to continuous stimuli

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arXiv 2023年

作者： Di Liberto, Giovanni M. Nidiffer, Aaron Crosse, Michael J. Zuk, Nathaniel J. Haro, Stephanie Cantisani, Giorgia Winchester, Martin M. Igoe, Aoife McCrann, Ross Chandra, Satwik Lalor, Edmund C. Baruzzo, Giacomo School of Computer Science and Statistics University of Dublin Trinity College Ireland ADAPT Centre Trinity College Institute of Neuroscience Ireland Dept Biomedical Engineering Dept Neuroscience Del Monte Institute for Neuroscience Center for Visual Science University of Rochester NY United States Segotia Galway Ireland Department of Mechanical Manufacturing and Biomedical Engineering TCBE Trinity College Dublin Ireland Department of Psychology Nottingham Trent University Nottingham United Kingdom Human Health and Performance Systems MIT Lincoln Laboratory LexingtonMA United States Speech and Hearing Bioscience and Technology Harvard Medical School BostonMA United States Laboratoire des systémes perceptifs Département d’études cognitives ENS PSL University CNRS Paris75005 France Dept Biomedical Engineering Center for Visual Science University of Rochester NY United States Department of Information Engineering University of Padova Padova Italy

Neurophysiology research has demonstrated that it is possible and valuable to investigate sensory processing in scenarios involving continuous sensory streams, such as speech and music. Over the past 10 years or so, novel analytic frameworks combined with the growing participation in data sharing has led to a surge of publicly available datasets involving continuous sensory experiments. However, open science efforts in this domain of research remain scattered, lacking a cohesive set of guidelines. This paper presents an end-to-end open science framework for the storage, analysis, sharing, and re-analysis of neural data recorded during continuous sensory experiments. We propose a data structure that builds on existing custom structures (Continuous-event Neural Data or CND), providing precise naming conventions and data types, as well as a workflow for storing and loading data in the general-purpose BIDS structure. The framework has been designed to interface with existing EEG/MEG analysis toolboxes, such as Eelbrain, NAPLib, MNE, and mTRF-Toolbox. We present guidelines by taking both the user view (rapidly re-analyse existing data) and the experimenter view (store, analyse, and share), making the process straightforward and accessible. Additionally, we introduce a web-based data browser that enables the effortless replication of published results and data re-analysis. © 2023, CC BY-NC-ND.

关键词： Data assimilation

Development of an Artificial Intelligence Model for Individualized FSH Daily Dosage Guidance During Ovulation Stimulation

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SSRN

SSRN 2023年

作者： Duan, Liyan Xia, Yu Mei, Jie Zhang, Hui Zhu, YingChun Huang, ChenYnag Yan, Guijun Kong, Na Li, Wujun Sun, Haixiang Center for Reproductive Medicine and Obstetrics and Gynecology Drum Tower Clinic Medical College Nanjing Medical University Nanjing210008 China Center for Molecular Reproductive Medicine Nanjing University Nanjing210008 China National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210008 China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China Center for Medical Big Data Nanjing Drum Tower Hospital Affifiliated Drum Tower Hospital Medical School of Nanjing University Jiangsu Nanjing210008 China State Key Laboratory of Reproductive Medicine China International Joint Research Center on Environment and Human Health Center for Global Health School of Public Health Nanjing Medical University Nanjing211166 China Department of Reproductive Medicine Center Nanjing Drum Tower Hospital State Key Laboratory of Pharmaceutical Biotechnology Nanjing University Nanjing210023 China

Background: We aim to develop a real-time, interpretable artificial intelligence (AI) system, using an algorithm for estimating follicle stimulating hormone (FSH) daily dose in individualized controlled ovarian stimulation (COS).Methods: Between January 2018 and December 2020, a total of 13,788 cycles of GnRHa long-protocol ovulation stimulation cycles were included from the Reproductive Medicine Centre of Nanjing Drum Tower Hospital. We used a method of cross-temporal and cross-feature joint encoding to learn the time series information X=X1, X2,..., XN of the N days to get its hidden layer representation H. Then, we applied K-nearest neighbors (KNN) with the hidden layer representation H to obtain the ***: Our proposed CTFE model obtained Accuracy and Weighted-F1 metrics of 0.737 and 0.733, to predict the daily FSH dose respectively, which are the highest among other popular models for processing time series data. On days 1 and 5, Lasso regression predicted the FSH daily dosage with an accuracy of 0.696 and 0.522, respectively, whereas our technique predicted it with 0.834 and 0.819. The introduction of a sliding window mechanism after 13 days helps to maintain good performance, although accuracy drops due to insufficient ***: Our study demonstrated that the CTFE model can be used to calculate the daily FSH dose with high accuracy. This model may be easily applied in daily clinical practice by ***: This work was supported by the National Natural science Foundation of China (NO.82030040), Scientific research project of Jiangsu Province Association of Maternal and Child Health (No. FYX202203).Declaration of Interest: HX S, WJ L,GJ Y, N K, J M, LY D and Y X are listed as inventors of patents relating to this work. The authors affirm that they have no known financial or interpersonal conflicts that would have seemed to have an impact on the research presented in this *** Approval: The study had ethical approval fro

关键词： Hospitals