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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning

Procedia Computer science 2023年 216卷 57-66页

作者： Mira Hayati Kahlil Muchtar Roslidar Novi Maulina Irfan Syamsuddin Gregorius Natanael Elwirehardja Bens Pardamean Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh 23111 Indonesia Telematics Research Center (TRC) Universitas Syiah Kuala Banda Aceh 23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar 90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-aided diagnosis of diabetic retinopathy.

关键词： Diabetic retinopathy Deep Learning CNN Image Enhancement CLAHE

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A Large Reservoir Computing Forecasting Method Based on Randomized Fuzzy Cognitive Maps

A Large Reservoir Computing Forecasting Method Based on Rand...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Omid Orang Fabricio J. Erazo-Costa Petrônio C. L. Silva Guilherme de Alencar Barreto Frederico Gadelha Guimarães Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte Brazil Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Federal Institute of Northern Minas Gerais Januária Campus Brazil Department of Teleinformatics Engineering Universidade Federal do Ceará Fortaleza Brazil

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

This paper introduces a novel forecastings technique based on randomized fuzzy cognitive maps (FCM), called LRHFCM (or large reservoir of randomized high-order FCM) for predicting univariate time series. LR-HFCM is a hybrid method combining fuzzy time series (FTS), FCMs, and reservoir computing. It is a type of echo state network (ESN) consisting of the input layer, intermediate (or large reservoir) layer, and output layer, where LASSO regression is applied to train the output layer. The novelty of this approach is that the internal layer includes a very large reservoir, considering different combinations from the sets of concepts and order using a certain number of sub-reservoirs to capture different dynamics of input time series. It is important to highlight that the weights within each sub-reservoir are chosen randomly and remain constant throughout the training process. The validity of the LR-HFCM approach is evaluated across 15 different time series datasets. The results highlight the outperformance of the LR-HFCM technique in comparison to various baseline models.

关键词： Training Adaptation models Adaptive systems computational modeling Time series analysis Fuzzy cognitive maps Reservoirs

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Partial observation can induce mechanistic mismatches in data-constrained models of neural dynamics 38

Partial observation can induce mechanistic mismatches in dat...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qian, William Zavatone-Veth, Jacob A. Ruben, Benjamin S. Pehlevan, Cengiz Biophysics Graduate Program Harvard University CambridgeMA02138 United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Center for Brain Science Harvard University CambridgeMA02138 United States Department of Physics Harvard University CambridgeMA02138 United States

One of the central goals of neuroscience is to gain a mechanistic understanding of how the dynamics of neural circuits give rise to their observed function. A popular approach towards this end is to train recurrent neural networks (RNNs) to reproduce experimental recordings of neural activity. These trained RNNs are then treated as surrogate models of biological neural circuits, whose properties can be dissected via dynamical systems analysis. How reliable are the mechanistic insights derived from this procedure? While recent advances in population-level recording technologies have allowed simultaneous recording of up to tens of thousands of neurons, this represents only a tiny fraction of most cortical circuits. Here we show that observing only a subset of neurons in a circuit can create mechanistic mismatches between a simulated teacher network and a data-constrained student, even when the two networks have matching single-unit dynamics. In particular, partial observation of models of low-dimensional cortical dynamics based on functionally feedforward or low-rank connectivity can lead to surrogate models with spurious attractor structure. Our results illustrate the challenges inherent in accurately uncovering neural mechanisms from single-trial data, and suggest the need for new methods of validating data-constrained models for neural dynamics. © 2024 Neural information processing systems foundation. All rights reserved.

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Evaluation of the Mechanisms Acting on the Atlantic Meridional Overturning Circulation in CESM2 for the 1pctCO_(2) Experiment

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Journal of Atmospheric science Research 2024年第1期7卷 40-58页

作者： Lívia Sancho Elisa Passos Marcio Cataldi Luiz Paulo de Freitas Assad Luiz Landau Laboratory of Computational Methods in Engineering Civil Engineering ProgramAlberto Luiz Coimbra Institute for Graduate Studies and Research in EngineeringFederal University of Rio de JaneiroRio de Janeiro21941-617Brazil Civil Engineering Program Alberto Luiz Coimbra Institute for Graduate Studies and Engineering ResearchFederal University of Rio de JaneiroRio de Janeiro21941-617Brazil Physical Oceanography Laboratory Department of Physical OceanographyFaculty of OceanographyRio de Janeiro State UniversityRio de Janeiro20559-900Brazil Climate System Monitoring and Modeling Laboratory Water Resources and Environmental EngineeringFluminense Federal University24020-140Brazil Regional Atmospheric Modeling Group(MAR) Physics of the EarthDepartment of PhysicsRegional Campus of International Excellence(CEIR)“Campus Mare Nostrum”University of MurciaMurcia30100Spain Department of Meteorology Institute of GeosciencesFederal University of Rio de JaneiroRio de Janeiro21941-617Brazil

The Atlantic Meridional Overturning Circulation(AMOC)is a crucial component of the Earth’s climate system due to its fundamental role in heat distribution,carbon and oxygen transport,and the *** climate components,such as the atmosphere and sea ice,influence the *** the physical mechanisms of those interactions is paramount to increasing knowledge about AMOC’s *** this study,the authors used outputs from the Community Earth System Model version 2 and observational data to investigate changes in theAMOC and the associated physical *** DECK experiments were evaluated:piControl and 1pctCO_(2),with an annual increase of 1%of atmospheric CO_(2).The analysis revealed a significant decrease in the AMOC,associated with changes in mixed layer depth and buoyancy in high latitudes of the North Atlantic,resulting in the shutdown of deep convection and potentially affecting the formation of North Atlantic Deep Water and Antarctic Bottom Water.A vital aspect observed in this study is the association between increased runoff and reduced water evaporation,giving rise to a positive feedback ***,the rates of freshwater spreading have intensified during this period,which could lead to an accelerated disruption of the AMOC beyond the projections of existing models.

关键词： AMOC Meridional cell Climate change Deep circulation CESM2 results CMIP6

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A Bioinformatics Analysis and Homology 3D Modeling of Dihydroxy-orotate-dehydrogenase of Plasmodium ovale

A Bioinformatics Analysis and Homology 3D Modeling of Dihydr...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Rudi Nirwantono Khoirunnisa Assidqi Alam Ahmad Hidayat Joko Pebrianto Trinugroho Bens Pardamean Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Food Biotechnology Research Center Bina Nusantara University Jakarta Indonesia Mathematics Departement School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the liver. Thus, the research to study the drug to treat Plasmodium vivax is essential, where the enzyme dihydroorotate dehydrogenase (DHODH) has recently become a new drug target. However, the drug-enzyme interaction study has only recently been conducted in Plasmodium falciparum (pfDHODH) due to the lack of the 3D structure of enzyme DHODH from Plasmodium vivax that is crucial for the study. Therefore, this study aimed to perform the modelling study of Plasmodium vivax DHODH (PvDHODH) to create a 3D structure as a basis for drug-protein interaction study in upcoming studies. Sequence pvDHODH (Accession: SCO68359.1) was used in homology modelling using Modeller 10.2 with the crystal structure of DHODH from DHODH (Accession: 7KZY) as a template. Overall, the model generated from the homology modelling was considered a good model, and the 3D structure was close to the native state according to several parameters, including DOPE score, GA341 score, QMEAN4 value, 3D-1D score, ERRAT2 score, and ProSA score. Ramachandran plot also revealed that almost all amino acids were distributed in the desirable and allowed area (99.1%), and only 0.9% were in generously allowed regions. Superimposition of the model with the template also indicated that the model has an almost similar structure and amino acids positioning, including in the binding pocket and active site. Therefore, the model can be used for downstream analysis in drug-protein interaction studies. Nevertheless, some improvements can still be performed to upgrade the quality of the model by deleting unaligned residues on the C-terminal and realigning the residues at the doubting site.

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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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Substitutional oxygen activated photoluminescence enhancement in monolayer transition metal dichalcogenides

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science China Materials 2022年第4期65卷 1034-1041页

作者： Shilong Zhao Junyang Tan Chengxuan Ke Simin Feng Yongjue Lai Baofu Ding Guangfu Luo Junhao Lin Bilu Liu Shenzhen Geim Graphene Center Tsinghua-Berkeley Shenzhen Institute and Institute of Materials ResearchTsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen518055China Department of Physics Southern University of Science and TechnologyShenzhen518055China Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen518055China Guangdong Provincial Key Laboratory of Computational Science and Material Design Southern University of Science and TechnologyShenzhen518055China Shenzhen Key Laboratory of Advanced Quantum Functional Materials and Devices Southern University of Science and TechnologyShenzhen518055China

Atomically thin transition metal dichalcogenides(TMDCs)are intriguing semiconductors for photonics and optoelectronics,and therefore enhancing their photoluminescence(PL)efficiency is crucial for these *** efforts have been contributed to enhancing the PL performance of monolayer TMDCs,yet the complexity between the microstructure and the PL efficiency has hindered the manipulation of their PL *** we demonstrate that the PL intensity of the monolayer TMDC can be enhanced by nearly one order of magnitude with a~20%narrower spectral linewidth after a pre-activation plateau using laser irradiation in ambient *** experimental and theoretical studies reveal that low-power laser irradiation generates many sulfur vacancy clusters,which are subsequently filled up by oxygen,and the lattice substitutional oxygen clusters induce the dramatic PL enhancement of monolayer WS;.Such PL enhancement phenomenon is found to be universal for other monolayer TMDCs,and thus would benefit their versatile optical applications.

关键词： two-dimensional materials transition metal dichalcogenides photoluminescence enhancement laser oxygen

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Improved camptothecin encapsulation and efficacy by environmentally sensitive block copolymer/chitosan/fucoidan nanoparticles for targeting lung cancer cells

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International Journal of Biological Macromolecules 2024年第Part2期277卷 133901页

作者： Don, Trong-Ming Hong, Yu-Ting Jheng, Pei-Ru Chuang, Er-Yuan Huang, Yi-Cheng Department of Chemical and Materials Engineering Tamkang University New Taipei City Taiwan College of Life Sciences Department of Food Science National Taiwan Ocean University Keelung Taiwan Graduate Institute of Biomedical Materials and Tissue Engineering International PhD Program in Biomedical Engineering Taipei Medical University Taipei Taiwan Cell Physiology and Molecular Image Research Center Taipei Medical University–Wan Fang Hospital Taipei Taiwan

In this study, thermo-sensitive poly(N-isopropyl acrylamide) (PNP) was polymerized with pH-sensitive poly(acrylic acid) (PAA) to prepare a PAA-b-PNP block copolymer. Above its cloud point, the block copolymer self-assembled into nanoparticles (NPs), encapsulating the anticancer drug camptothecin (CPT) in situ. Chitosan (CS) and fucoidan (Fu) further modified these NPs, forming Fu-CPT-NPs to enhance biocompatibility, drug encapsulation efficiency (EE), and loading content (LC), crucially facilitating P-selectin targeting of lung cancer cells through a drug delivery system. The EE and LC reached 82 % and 3.5 %, respectively. According to transmission electron microscope observation, these Fu-CPT-NPs had uniform spherical shapes with an average diameter of ca. 250 nm. They could maintain their stability in a pH range of 5.0–6.8. In vitro experimental results revealed that the Fu-CPT-NPs exhibited good biocompatibility and had anticancer activity after encapsulating CPT. It could deliver CPT to cancer cells by targeting P-selectin, effectively increasing cell uptake and inducing cell apoptosis. Animal study results showed that the Fu-CPT-NPs inhibited lung tumor growth by increasing tumor cell apoptosis without causing significant tissue damage related to generating reactive oxygen species in lung cancer cells. This system can effectively improve drug-delivery efficiency and treatment effects and has great potential for treating lung cancer. © 2024 Elsevier B.V.

关键词： Nanoparticles

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LE-DTA: Local Extrema Convolution for Drug Target Affinity Prediction

LE-DTA: Local Extrema Convolution for Drug Target Affinity P...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tanoj Langore Te-Cheng Hsu Yi-Hsien Hsieh Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Computational and Systems Biology and Center for Biotechnology NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

One of the essential parts of drug discovery and design is the prediction of drug-target affinity (DTA). Researchers have proposed computational approaches for predicting DTA to circumvent the more expensive in vivo and in vitro tests. More recent approaches employed deep network architectures to obtain the features from the drug molecules and protein sequences. The drug compounds are represented as graphs and the target protein as a sequence to extract this information. In this work, we develop a new graph-based prediction model, termed LE-DTA, that utilizes local extrema convolutions for effective feature extraction. It focuses on the local and global extrema of graphs for node embedding. We investigated the performances of both the proposed models on three different benchmark datasets. Our proposed model showed improvement in CI by 1.12% and 0.35% and a reduction in MSE by 7.7% and 3.33% on the KIBA and BindingDB datasets, respectively. we also showed that despite using various pooling operations on our proposed model, we achieved an average reduction in MSE by 7% on the KIBA dataset and 3% improvement on the BindingDB dataset.

关键词： Drugs In vivo Neural networks Predictive models Network architecture Feature extraction Protein sequence

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Blockchain Applied In Decentralization of Ground Stations To Educational Nanosatellites

Blockchain Applied In Decentralization of Ground Stations To...

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Latin American Test Workshop, LATW

作者： Edilson Filho Jarbas Silveira César Marcon Department of Teleinformatics Engineering (DETI) Federal University of Ceara (UFC) Fortaleza Brazil Graduate Program in Computer Science (PPGCC) Pontifical Catholic University of Rio Grande do Sul (PUCRS) Porto Alegre Brazil

Space has increasingly attracted the attention of governments, large industries, and universities. One of the most popular strategies in recent years has been the adoption of nanosatellites to fulfill different missions, which can work alone or in constellations. Universities stand out among the agents launching nanosatellites, with more than 600 launches until 2022. Given the growth of entities that control space missions, it is necessary to implement new methods for communication between control and satellite to accelerate data transmission and provide a high-security degree. Our work proposes a consortium archi-tecture between Ground Stations (GSs) so that a GS as a Service (GSaaS) works with low cost, reliability, and resource sharing. We simulated a nanosatellite mission in Low Earth Orbit (LEO) with MATLAB to obtain the parameters of average communication time, propagation loss, and at which angles the communication would be most affected by atmospheric phenomena. Then, we implement business rules for communication between GS and satellites using smart contract concepts. We set up a blockchain to provide the decentralization infrastructure and created a web service to provide a communication API between nanosatellite and blockchain. We simulated the firmware update process, showing that the nanosatellite took around 20 minutes to request all 32-byte fragments of 301 Kb firmware. Considering the time interval that the communication window between GS and nanosatellite remains active, the entire firmware transmission takes two to three communication slots. However, the transmission time is drastically reduced in a scenario with two or more GSs. Furthermore, the GSaaS decentralized infrastructure allows the consortium of GSs to communicate agnostically with the satellites, preserving firmware privacy due to the cryptography used in blockchain transactions

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