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arXiv 2025年

作者： Wang, Kai Gilmer, Gabrielle Araña, Matheus Cândia Iijima, Hirotaka Bergmann, Juliana Woollard, Antonio Mesits, Boris McGraw, Meghan Zoltowski, Brian Cappellaro, Paola Ungar, Alex Pekker, David Waldeck, David H. Saxena, Sunil Lloyd, Seth Ambrosio, Fabrisia Discovery Center for Musculoskeletal Recovery Schoen Adams Research Institute at Spaulding BostonMA United States Department of Physical Medicine & Rehabilitation Spaulding Rehabilitation Hospital BostonMA United States Department of Physical Medicine & Rehabilitation Harvard Medical School BostonMA United States Cellular and Molecular Pathology Graduate Program University of Pittsburgh PittsburghPA United States Medical Scientist Training Program School of Medicine University of Pittsburgh PittsburghPA United States École Polytechnique Institut Polytechnique de Paris Palaiseau France Department of Physics & Astronomy University of Pittsburgh PittsburghPA United States Department of Chemistry Southern Methodist University DallasTX United States Department of Nuclear Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Chemistry University of Pittsburgh PittsburghPA United States Department of Mechanical Engineering Massachusetts Institute of Technology BostonMA United States

Diverse organisms exploit the geomagnetic field (GMF) for migration. Migrating birds employ an intrinsically quantum mechanical mechanism for detecting the geomagnetic field: absorption of a blue photon generates a radical pair whose two electrons precess at different rates in the magnetic field, thereby sensitizing cells to the direction of the GMF. In this work, using an in vitro injury model, we discovered a quantum-based mechanism of cellular migration. Specifically, we show that migrating cells detect the GMF via an optically activated, electron spin-based mechanism. Cell injury provokes acute emission of blue photons, and these photons sensitize muscle progenitor cells to the magnetic field. We show that the magnetosensitivity of muscle progenitor cells is (a) activated by blue light, but not by green or red light, and (b) disrupted by the application of an oscillatory field at the frequency corresponding to the energy of the electron-spin/magnetic field interaction. A comprehensive analysis of protein expression reveals that the ability of blue photons to promote cell motility is mediated by activation of calmodulin calcium sensors. Collectively, these data suggest that cells possess a light-dependent magnetic compass driven by electron spin dynamics. © 2025, CC BY.

关键词： Spin dynamics

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A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds

Artificial Intelligence Chemistry

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Artificial Intelligence Chemistry 2024年第2期2卷

作者： Muhamad Akrom Supriadi Rustad Hermawan Kresno Dipojono Ryo Maezono Study Program in Informatics Engineering Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung 40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa 923-1292 Japan

In this investigation, a quantitative structure-property relationship (QSPR) model coupled with a quantum neural network (QNN) was used to explore the corrosion inhibition efficiency (CIE) of quinoxaline compounds. Integrating quantum chemical properties (QCP) features reduced computational burden by strategically reducing the features from 11 to 4 while maintaining prediction accuracy. QNN models outperform traditional methods like artificial neural networks (ANN) and multilayer perceptron neural networks (MLPNN), with a coefficient of determination (R 2 ) value of 0.987, coupled with diminished root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.97, 0.92, and 1.10, respectively. Predictions for six newly synthesized quinoxaline derivatives: quinoxaline-6-carboxylic acid (Q1) , methyl quinoxaline-6-carboxylate (Q2) , (2 E ,3 E )-2,3-dihydrazono-1,2,3,4-tetrahydroquinoxaline (Q3) , (2 E ,3 E ) 2,3-dihydrazono-6-methyl-1,2,3,4-tetrahydroquinoxaline (Q4) , ( E )-3-(4-methoxyethyl)-7-methylquinoxalin-2(1 H)-one (Q5) , and 2-(4-methoxyphenyl)-7-methylthieno[3,2- b ] quinoxaline (Q6) , show remarkable CIE values of 95.12, 96.72, 91.02, 92.43, 89.58, and 93.63 %, respectively. This breakthrough technique simplifies testing and production procedures for new anti-corrosion materials.

关键词： Quantum neural network QSPR Corrosion inhibition Quinoxaline

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BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting 23rd

BCData: A Large-Scale Dataset and Benchmark for Cell Detecti...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Huang, Zhongyi Ding, Yao Song, Guoli Wang, Lin Geng, Ruizhe He, Hongliang Du, Shan Liu, Xia Tian, Yonghong Liang, Yongsheng Zhou, S. Kevin Chen, Jie School of Electronic and Computer Engineering Peking University Shenzhen China University of Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China Shenzhen Hospital University of Chinese Academy of Sciences Shenzhen China The Second Peoples Hospital of Shenzhen Shenzhen China School of Electronics Engineering and Computer Science Peking University Beijing China Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030597214

Breast cancer is a main malignant tumor for women and the incidence is trending to ascend. Detecting positive and negative tumor cells in the immunohistochemically stained sections of breast tissue to compute the Ki-67 index is an essential means to determine the degree of malignancy of breast cancer. However, there are scarcely public datasets about cell detection of Ki-67 stained images. In this paper, we introduce a large-scale Breast tumor Cell Dataset (BCData) for cell detection and counting, which contains 1,338 images with 181,074 annotated cells belonging to two categories, i.e., positive and negative tumor cells. (We state that our dataset can only be used for non-commercial research.) Our dataset varies widely in both the distributing density of tumor cells and the Ki-67 index. We conduct several cell detection and counting methods on this dataset to set the first benchmark. We believe that our dataset will facilitate further research in cell detection and counting fields in clustering, overlapping, and variational stained conditions. Our dataset is available at https://***/view/bcdataset © 2020, Springer Nature Switzerland AG.

关键词： Cells

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Robust profit maximization with double sandwich algorithms in social networks 39

Robust profit maximization with double sandwich algorithms i...

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39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019

作者： Gao, Chuangen Gu, Shuyang Yang, Ruiqi Du, Hongwei Ghosh, Smita Wang, Hua School of Computer Science and Technology Shandong University Jinan250101 China Department of Computer Science University of Texas at Dallas TX75080 United States Department of Information and Operations Research Beijing University of Technology Beijing100124 China Department of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China

ISBN: (纸本)9781728125190

Social networks are becoming important dissemination platforms, and a large body of works have been performed on viral marketing, but most are to maximize the benefits associated with the number of active nodes. In this paper, we study the benefits related to interactions among activated nodes. Furthermore, due to the uncertainty in edge probability estimates in social networks, we propose the robust profit maximization problem to have the best solution in the worst case of probability settings. We design a double sandwich algorithm to this problem and further improve the algorithm with sampling method such that it increases robustness of the output. Through real data sets, we verify the effectiveness of our proposed algorithm. © 2019 IEEE.

关键词： Uncertainty analysis

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State Predictive Control with Multiple Modification Terms and Robust Stability Analysis Based on Complementary Sensitivity Functions

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IFAC-PapersOnLine 2020年第2期53卷 4838-4843页

作者： Shotaro Yanase Yoichiro Masui Kentaro Hirata Tomomichi Hagiwara Department of Electrical Engineering Kyoto University Kyoto Japan Department of Computer Science and Electronic Engineering National Institute of Technology Tokuyama College Yamaguchi Japan Graduate School of Natural Science and Technology Okayama University Okayama Japan

As an effective control method for systems with time delay in the input, state predictive control is well known. An idea of adding a single modification term to its control law was presented recently, and it was suggested that an appropriate modification term could contribute to improving robust stability of the control system to some parametric uncertainties. This extended control method is called modified state predictive control. Motivated by the preceding study, this paper considers introducing multiple modification terms into the control law of state predictive control, aiming at improving robust stability for non-parametric uncertainties. We first derive the characteristic equation of the modified state predictive control systems with multiple modification terms, and give a necessary and sufficient condition for stability. We then derive an explicit representation of the complementary sensitivity function associated with the robust stability analysis problem for multiplicative uncertainties. Finally, we demonstrate through numerical examples that state predictive control with appropriate multiple modification terms could be useful in improving robust stability compared with that with a single modification term or no such a term.

关键词： continuous-time systems time delay state predictive control robust stability complementary sensitivity function

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Handling Problems of Credit Data for Imbalanced Classes using SMOTEXGBoost

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Journal of Physics: Conference Series 2021年第1期1830卷

作者： Heru Mardiansyah Rahmat Widia Sembiring Syahril Efendi Graduate Program of Computer Science Department of Information Technology Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medam Indonesia

Some researchers find data with imbalanced class conditions, where there are data with a number of minorities and a majority. SMOTE is a data approach for an imbalanced classes and XGBoost is one algorithm for an imbalanced data problems. This research uses SMOTE and XGBoost or abbreviated as SMOTEXGBoost for handling data with an imbalanced classes. The results showed almost the same accuracy value between SMOTE and SMOTEXGBoost at 99%. While the value of AUC SMOTEXBoost has a more stable value than SMOTE that is equal to 99.89% for training and 98.51% for testing.

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Generating Lymphoma Variability Through Adversarial Generation Models: A Limited Resource Approach for Synthetic Histopathology Image Generation and Classification

SSRN

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SSRN 2024年

作者： Tiwari, Ashutosh Saudarshan, Harsh Krisnawati, Dyah Ika Khafid, Muhamad Wardani, Erika Martining Mutalik, Chinmaya Chen, Chi-Long Kuo, Tsung-Rong International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Computer Science and Engineering Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495509 India Department of Nursing Faculty of Nursing and Midwifery Universitas Nahdlatul Ulama Surabaya East Java Surabaya60237 Indonesia Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Pathology School of Medicine College of Medicine and Taipei Medical University Hospital Taipei Medical University Taipei11031 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Precision Medicine and Translational Cancer Research Center Taipei Medical University Hospital Taipei11031 Taiwan Tulane University United States Graduate Institute of Clinical Medicine Taiwan

Lymphomas, a diverse group of blood cancers originating from the lymphatic system, present significant challenges for diagnosis and treatment due to their morphological and genetic differences. These variability obscures the development of personalized therapies and impacts disease outcomes. The scarcity of high-quality, diverse images of lymphoma cells is further hindered by patient privacy concerns, the rarity of certain types, and logistical challenges of collecting and processing biomedical images. Additionally, the costs and technical difficulties of microscopic imaging limit the scope of research. This paper addresses these challenges by utilizing generative adversarial networks (GANs) to generate synthetic yet realistic images of lymphoma histopathology and classify them in a low resource setting. Focusing specifically on chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL), we employed GANs to enhance the understanding of lymphoma variability and support advancements in digital cancer biology and pathology. This dual approach of image generation and classification aims to improve disease diagnosis and prognosis, ultimately contributing to better patient outcomes. © 2024, The Authors. All rights reserved.

关键词： Lung cancer

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Merger and Postmerger of Binary Neutron Stars with a Quark-Hadron Crossover Equation of State

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Physical Review Letters 2022年第18期129卷 181101-181101页

作者： Yong-Jia Huang Luca Baiotti Toru Kojo Kentaro Takami Hajime Sotani Hajime Togashi Tetsuo Hatsuda Shigehiro Nagataki Yi-Zhong Fan Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Science Nanjing 210023 China School of Astronomy and Space Sciences University of Science and Technology of China Hefei Anhui 230026 China RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) RIKEN Wako 351-0198 Japan International College and Graduate School of Science Osaka University 1-2 Machikaneyama-cho Toyonaka Osaka 560-0043 Japan Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics Central China Normal University Wuhan 430079 China Department of Physics Tohoku University Sendai 980-8578 Japan Kobe City College of Technology 651-2194 Kobe Japan RIKEN Astrophysical Big Bang Laboratory (ABBL) Cluster for Pioneering Research Wako Saitama 351-0198 Japan

Fully general-relativistic binary-neutron-star (BNS) merger simulations with quark-hadron crossover (QHC) equations of state (EOS) are studied for the first time. In contrast to EOS with purely hadronic matter or with a first-order quark-hadron phase transition (1PT), in the transition region QHC EOS show a peak in sound speed and thus a stiffening. We study the effects of such stiffening in the merger and postmerger gravitational (GW) signals. Through simulations in the binary-mass range 2.5

关键词： Gravitational waves Nuclear matter in neutron stars Binary stars Neutron stars & pulsars Numerical relativity Numerical simulations in gravitation & astrophysics

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In vitro biocompatibility of polycaprolactone/hydroxyapatite nanocomposite membranes modified by oleic acid for bone regeneration

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Colloids and Surfaces A: Physicochemical and Engineering Aspects 2024年 688卷

作者： Lin, Ruei-Hong Hung, Huey-Shan Tang, Cheng-Ming Tsou, Hsi-Kai Chen, Po-Hsiang Yueh, Chun-Yu Wang, Hui-Min David Functional Neurosurgery Division Neurological Institute Taichung Veterans General Hospital Taichung40293 Taiwan Doctoral Program in Tissue Engineering and Regenerative Medicine National Chung Hsing University Taichung40227 Taiwan Translational Medicine Research China Medical University Hospital Taichung404327 Taiwan Graduate Institute of Biomedical Science China Medical University Taichung404333 Taiwan Institute of Oral Sciences Chung Shan Medical University Taichung40201 Taiwan Department of Dentistry Chung Shan Medical University Hospital Taichung40201 Taiwan Functional Neurosurgery Division Neurological Institute Taichung Veterans General Hospital Taichung40293 Taiwan Department of Rehabilitation Jen-Teh Junior College of Medicine Nursing and Management Miaoli County35664 Taiwan College of Health National Taichung University of Science and Technology Taichung403027 Taiwan School of Medicine China Medical University Taichung404333 Taiwan Graduate Institute of Medicine College of Medicine Kaohsiung Medical University Kaohsiung80708 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung40227 Taiwan Department of Medical Laboratory Science and Biotechnology China Medical University Taichung40402 Taiwan

The bone defect is common in the orthopedic clinic, possibly due to trauma, tumor, or congenital diseases. Also, some patients with alveolar bone defects in the dental clinic result from the different proliferation speeds between soft and hard tissue. Biomaterials made of polymers and bioceramics have been developed to deal with these problems but mixing them directly can result in a low-mechanical strength complex. To solve this, we used oleic acid (C18) as a surfactant to improve the dispersity of hydroxyapatite (HA) in the polycaprolactone (PCL) matrix, and then immerse the material in a simulated biomineralization process. The results showed that the low crystallinity PCL formed an evenly distributed mineralized layer that had phosphate ions signal and a hydroxyapatite peak. The biocompatibility of the material was assessed using MC3T3 cells, and found that the PCL-HA membrane and PCL-C18-HA membranes had significant cell filopodia and lamellipodia. Additionally, the polycaprolactone/oleic acid/hydroxyapatite (PCL-C18-HA) membrane had the highest cell viability among all groups. The addition of C18 also decreased the platelets activation degree but had a significant difference in the reactive oxygen species (ROS) amount compared to the control, which requires further investigation. Overall, this study successfully fabricated a safe and promising polycaprolactone/hydroxyapatite/oleic acid nanocomposite membrane as a potential solution for bone biomaterials. © 2024 Elsevier B.V.

关键词： Hydroxyapatite

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Classification of Mammography based on Semi-supervised Learning

Classification of Mammography based on Semi-supervised Learn...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Lilei Sun Jie Wen Junqian Wang Yong Zhao Yong Xu College of Computer Science and Technology Guizhou University Guiyang China Harbin Institute of Technology Shenzhen Shenzhen China School of Electronic and Computer Engineering Shenzhen Graduate School of Peking University Shenzhen China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

In recent years, deep learning, especially the convo-lutional neural network (CNN), has been widely used in the field of molybdenum target image classification. Generally speaking, the CNN based methods train the network with a huge number of samples in a supervised way. However, it is too expensive to collect such a large-scale labeled mammogram dataset for training. In this paper, we propose a semi-supervised mammography classification approach to address this issue. Compared with the existing method, our method can simultaneously use the labeled and unlabeled samples to train the mammogram classification network. Specifically, for the breast mass images represented by Mediolateral Oblique (MLO) and Craniocaudal (CC) views, a new classification network is designed to extract features from both views simultaneously. The compact bilinear pooling (CBP) is introduced to extract the fine-grained features of breast mass images. Moreover, considering that the samples near the decision boundary are easy to be misclassified and those misclassified samples are harmful to the model training, we develop a K-Means based strategy, which first clusters the unlabeled samples and then removes those samples far away from the clustering center during model training. The method proposed in this paper is compared with the mainstream deep learning classification methods and semi-supervised methods on the Digital Database for Screening Mammography (DDSM) public mammogram database. The experimental results show that the method proposed is superior to most of the compared methods in terms of processing time and classification performance.

关键词： Training Deep learning Breast Feature extraction Delta-sigma modulation Mammography Image classification

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