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A novel label-free electrochemical immunosensor for the detection of heat shock protein 70 of lung adenocarcinoma cell line following paclitaxel treatment using l-cysteine-functionalized Au@MnO2/MoO3 nanocomposites

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RSC Advances 2023年第43期13卷 29847-29861页

作者： Chen, Yi-An Shie, Ming-You Ho, Chia-Che Ye, Sheng-Wen Chen, I.-Wen Peter Shih, Yu-Yin Shen, Yu-Fang Chen, Yi-Wen x-Dimension Center for Medical Research and Translation China Medical University Hospital Taichung City404332 Taiwan The Master Program for Biomedical Engineering China Medical University Taichung City406040 Taiwan Department of Biomedical Engineering China Medical University Taichung City40447 Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung City41354 Taiwan Department of Chemistry National Cheng Kung University Tainan70101 Taiwan Graduate Institute of Biomedical Sciences China Medical University Taichung City40447 Taiwan

The future trend in achieving precision medicine involves the development of non-invasive cancer biomarker sensors that offer high accuracy, low cost, and time-saving benefits for risk clarification, early detection, disease detection, and therapeutic monitoring. A facile approach for the synthesis of MoO3 nanosheets was developed by thermally oxidizing MoS2 nanosheets in air followed by thermal annealing. Subsequently, Au@MnO2 nanocomposites were prepared using a combined hydrothermal process and in situ chemical synthesis. In this study, we present a novel immunosensor design strategy involving the immobilization of antiHSP70 antibodies on Au@MnO2/MoO3 nanocomposites modified on a screen-printed electrode (SPE) using EDC/NHS chemistry. This study establishes HSP70 as a potential biomarker for monitoring therapeutic response during anticancer therapy. Impedance measurements of HSP70 on the Au@MnO2/MoO3/SPE immunosensor using EIS showed an increase in impedance with an increase in HSP70 concentration. The electrochemical immunosensor demonstrated a good linear response in the range of 0.001 to 1000 ng mL−1 with a detection limit of 0.17 pg mL−1 under optimal conditions. Moreover, the immunosensor was effective in detecting HSP70 at low concentrations in a lung adenocarcinoma cell line following Paclitaxel treatment, indicating its potential for early detection of the HSP70 biomarker in organ-on-a-chip and clinical applications. © 2023 The Royal Society of Chemistry.

关键词： Nanosheets

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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Single-cell RNA-sequencing and subcellular spatial transcriptomics facilitate the translation of liver microphysiological systems for regulatory application

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Journal of Pharmaceutical Analysis 2023年第7期13卷 691-693页

作者： Dan Li Zhou Fang Qiang Shi Nicholas Zhang Binsheng Gong Weida Tong Ahmet F.Coskun Joshua Xu Division of Bioinformatics and Biostatistics National Center for Toxicological ResearchU.S.Food and Drug AdministrationJeffersonAR72079USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory UniversityAtlantaGA30332USA Machine Learning Graduate Program Georgia Institute of TechnologyAtlantaGA30332USA Division of Systems Biology National Center for Toxicological ResearchU.S.Food and Drug AdministrationJeffersonAR72079USA Interdisciplinary Bioengineering Graduate Program Georgia Institute of TechnologyAtlantaGA30332USA Parker H.Petit Institute for Bioengineering and Bioscience Georgia Institute of TechnologyAtlantaGA30332USA

Reducing the use of animal models in drug development and safety assessment has long been supported by the *** and Drug Administration(FDA).The report by Royal Society for the Prevention of Cruelty to Animals indicates that in 2020,experiments involved the use of over 100 million animals,with the United States leading the list by utilizing 20 million *** ethical considerations associated with animal testing and the costs in terms of time and money,animal models are not always effective in predicting human reactions to drug *** animal testing has been the traditional method for assessing the safety and efficacy of drugs.

关键词： money utilizing testing

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ConvNeXt Model for Breast Cancer Image Classification

ConvNeXt Model for Breast Cancer Image Classification

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Devin Setiawan Andrea Stevens Karnyoto Indo Intan Bens Pardamean Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83.

关键词： Support vector machines Accuracy Head Computational modeling Transfer learning Breast cancer Magnetic heads Data models Reliability Image classification

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Automatic question generation for bahasa indonesia examination using copynet

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Procedia Computer Science 2024年 245卷 953-962页

作者： Matthew Martianus Henry Gregorius Natanael Elwirehardja Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

In educational institutions, an educator is responsible for assessing the student's knowledge grasp through examination. Creating exam questions, even the low-level factoid questions, is time-consuming, especially for inexperienced educators. Therefore, this study aims to create a sequence-to-sequence model using CopyNet by exploiting its copying mechanism advantage to automatically generate Bahasa Indonesia factoid questions to ease the educator's burden. Indonesian records in the TyDi QA dataset are used as the model input. GRU and Bi-GRU are employed as the CopyNet encoder, while LSTM is used as the CopyNet decoder. The model that utilizes GRU as the encoder achieves BLEU1, BLEU2, BLEU3, BLEU4, and ROUGE-L scores of 0.28, 0.19, 0.14, 0.1, and 0.32, respectively. Bi-GRU utilization as the model encoder achieves BLEU1, BLEU2, BLEU3, BLEU4, and ROUGE-L scores of 0.26, 0.17, 0.12, 0.09, and 0.30, respectively. Models using either encoder still achieve low scores. However, compared with the previous work, the result is still on par regarding the BLEU score. Further examination found that the generated questions do not adhere to semantic and syntactical correctness. Adding more records to the dataset and utilizing a more advanced architecture like CopyBERT are encouraged to improve the model performance in future work. Despite the result, this study has shown that CopyNet, primarily designed for text summarization or single-turn dialogue, can be tailored for factoid question generation.

关键词： Automatic question generation Bahasa Indonesia Copying mechanism Natural language generation Sequence-to-sequence

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Competition on the edge of an expanding population

arXiv

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arXiv 2023年

作者： Swartz, Daniel W. Lee, Hyunseok Kardar, Mehran Korolev, Kirill S. Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Graduate Program in Bioinformatics and Biological Design Center Boston University BostonMA02215 United States

In growing populations, the fate of mutations depends on their competitive ability against the ancestor and their ability to colonize new territory. Here we present a theory that integrates both aspects of mutant fitness by coupling the classic description of one-dimensional competition (Fisher equation) to the minimal model of front shape (KPZ equation). We solved these equations and found three regimes, which are controlled solely by the expansion rates, solely by the competitive abilities, or by both. Collectively, our results provide a simple framework to study spatial competition. Copyright © 2023, The Authors. All rights reserved.

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Single-Cell RNA-Seq Data Clustering: Highlighting Computational Challenges and Considerations

Single-Cell RNA-Seq Data Clustering: Highlighting Computatio...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Mohamad Nossier Sherin M. Moussa Nagwa Badr Program of Bioinformatics Department of Information Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Program of Bioinformatics Department of Information Systems Faculty of Computer and Information Sciences Ain Shams University Laboratoire Interdisciplinaire de l'Université Française d'Égypte (UFEID Lab) Université Francaise d'Égypte Cairo Egypt Department of Information Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

Recent advancements in single-cell transcriptomics have revolutionized data generation, resulting in the production of vast amounts of single-cell RNA sequencing (scRNA-seq) data. The intricate biology of tissues and organs may be better understood through the analysis of such data, which aims to not only identify known cell types but also uncover novel ones. In this paper, we outline the standard single-cell RNA-seq data analytic process with particular focus on the critical role of unsupervised clustering, since it has a significant influence over subsequent analyses and the resulting biological insights. However, navigating the computational landscape for clustering single-cell transcriptomic data poses significant challenges. Thus, in this article, we provide a simplified exploration of key computational challenges and considerations within the realm of scRNA-seq data clustering, emphasizing the application and relevance of selected unsupervised methods in current research, while acknowledging the potential for further investigation of the biological aspects in this field. Furthermore, we present recommendations on how such challenges may be addressed. This work not only sheds light on the complexities of scRNA-seq data cluster analysis, but also provides insights into how to effectively address and surmount these challenges.

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Systematic Literature Review On Machine Learning Predictive Models For Indoor Climate In Smart Solar Dryer Dome

Systematic Literature Review On Machine Learning Predictive ...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Karli Eka Setiawan Gregorius Natanael Elwirehardja Bens Pardamean Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

Precise predictions of indoor climate conditions are required in the implementation of Smart Solar Dryer Dome (SDD). Trend development of prediction models is discussed in this review from 15 selected research papers (2018–2022) on indoor climate prediction which was obtained from research paper databases The output shows that the most used model for predicting indoor climate is Artificial Neural Network (ANN), especially Recurrent Neural Network (RNN) such as LSTM and GRU. However, there are some potential methods such as Transformer, Combined Support Vector Machines (SVM)-Deep Learning, and sequence-to-sequence which could outperform other commonly used models. Based on findings various opportunities exist to improve the precision of indoor climate prediction, which can bring power consumption efficiency and others benefit to Smart SDD users. Such studies may further be explored to produce more accurate machine learning models.

关键词： Support vector machines Systematics Recurrent neural networks Power demand Bibliographies Artificial neural networks Predictive models

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Transductive and inductive outlier detection with robust autoencoders 24

Transductive and inductive outlier detection with robust aut...

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Ofir Lindenbaum Yariv Aizenbud Yuval Kluger Faculty of Engineering Bar-Ilan University Ramat Gan Israel School of Mathematical Sciences Tel Aviv University Tel Aviv Israel Program in Applied Mathematics Yale University New Haven CT and Department of Pathology Yale University School of Medicine New Haven CT and Computational Biology & Bioinformatics Program Yale University New Haven CT

Accurate detection of outliers is crucial for the success of numerous data analysis tasks. In this context, we propose the Probabilistic Robust AutoEncoder (PRAE) that can simultaneously remove outliers during training (transductive) and learn a mapping that can be used to detect outliers in new data (inductive). We first present the Robust AutoEncoder (RAE) objective that excludes outliers while including a subset of samples (inliers) that can be effectively reconstructed using an AutoEncoder (AE). RAE minimizes the autoencoder's reconstruction error while incorporating as many samples as possible. This could be formulated via regularization by subtracting an ℓ0 norm, counting the number of selected samples from the reconstruction term. As this leads to an intractable combinatorial problem, we propose two probabilistic relaxations of RAE, which are differentiable and alleviate the need for a combinatorial search. We prove that the solution to the PRAE problem is equivalent to the solution of RAE. We then use synthetic data to demonstrate that PRAE can accurately remove outliers in various contamination levels. Finally, we show that using PRAE for outlier detection leads to state-of-the-art results for inductive and transductive outlier detection.

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Time-series Analysis of Correlation between Climatic Parameters and Dengue Fever in Indonesia

Time-series Analysis of Correlation between Climatic Paramet...

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

作者： Rudi Nirwantono Joko Pebrianto Trinugroho Digdo Sudigyo Alam Ahmad Hidayat Bens Pardamean 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

ISBN: (纸本)9781665473286

Dengue fever is mainly caused by female mosquitoes from the genus Aedes. It predominantly occurs in equatorial to sub-tropical regions. The weather factors play a significant role in the mosquito life cycle, so a study on the impact of climate parameters on dengue incidence is required. This study aimed to understand the statistical association between the annual fluctuation of dengue cases with climate factors in Indonesia. Climatic data, annual rainfall, and annual mean ambient temperature of Indonesia were obtained from *** from 2010 to 2020, while the data on the annual incidence of dengue fever in Indonesia were collected from the Indonesian Health Profile by the Ministry of Health of Indonesia from 2010 to 2020. The association between climatic factors with dengue cases was calculated with Spearman statistical test after the data was indicated as not normally distributed. In addition, we performed Poisson regression analysis to examine how the changes in climatic parameters affect the dengue cases. The rain precipitation was insignificantly correlated with the dengue incidence in Indonesia during the study time. In contrast, the ambient temperature statistically showed a strong association with the dengue cases. The association between climatic factors and the number of dengue cases suggests that the model would likely be prospectively applied to the dengue early warning system as a nationwide intervention to reduce the epidemic impact in Indonesia.

关键词： Epidemics Temperature distribution Rain Meteorological factors Multimedia systems Time series analysis Weather forecasting

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