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arXiv 2023年

作者： Kobeissi, Hiba Jilberto, Javiera Karakan, M. Çağatay Gao, Xining DePalma, Samuel J. Das, Shoshana L. Quach, Lani Urquia, Jonathan Baker, Brendon M. Chen, Christopher S. Nordsletten, David Lejeune, Emma Department of Mechanical Engineering Boston University BostonMA02215 United States Center for Multiscale and Translational Mechanobiology Boston University BostonMA02215 United States Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Photonics Center Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Harvard-MIT Program in Health Sciences and Technology Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA02139 United States Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA02215 United States Department of Electrical and Computer Engineering New York Institute of Technology New YorkNY10023 United States Department of Cardiac Surgery University of Michigan MI48109 United States Department of Biomedical Engineering School of Imaging Sciences and Biomedical Engineering King’s College London King’s Health Partners LondonSE1 7EH United Kingdom

Advancing human induced pluripotent stem cell derived cardiomyocyte (hiPSC-CM) technology will lead to significant progress ranging from disease modeling, to drug discovery, to regenerative tissue engineering. Yet, alongside these potential opportunities comes a critical challenge: attaining mature hiPSC-CM tissues. At present, there are multiple techniques to promote maturity of hiPSC-CMs including physical platforms and cell culture protocols. However, when it comes to making quantitative comparisons of functional behavior, there are limited options for reliably and reproducibly computing functional metrics that are suitable for direct cross-system comparison. In addition, the current standard functional metrics obtained from time-lapse images of cardiac microbundle contraction reported in the field (i.e., post forces, average tissue stress) do not take full advantage of the available information present in these data (i.e., full-field tissue displacements and strains). Thus, we present "MicroBundleCompute, " a computational framework for automatic quantification of morphology-based mechanical metrics from movies of cardiac microbundles. Briefly, this computational framework offers tools for automatic tissue segmentation, tracking, and analysis of brightfield and phase contrast movies of beating cardiac microbundles. It is straightforward to implement, runs without user intervention, requires minimal input parameter setting selection, and is computationally inexpensive. In this paper, we describe the methods underlying this computational framework, show the results of our extensive validation studies, and demonstrate the utility of exploring heterogeneous tissue deformations and strains as functional metrics. With this manuscript, we disseminate "MicroBundleCompute" as an open-source computational tool with the aim of making automated quantitative analysis of beating cardiac microbundles more accessible to the *** Codes 92F05, 74A05 © 2023, CC BY-SA.

关键词： Cell culture

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A Reduced Features of Public Health Development Index by Using Clustering Methods 4

A Reduced Features of Public Health Development Index by Usi...

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4th International Conference on Informatics and Computing, ICIC 2019

作者： Adi Darmawan, Muhammad Prihandoko Gunadarma University Technology and Engineering Program Depok Indonesia Gunadarma University Department of Computer Science and Information Technology Depok Indonesia

ISBN: (纸本)9781728122076

Public Health Development Index (PHDI) is an index that released by Ministry of Health of The Republic of Indonesia which is used to measure the health of Indonesian people. The PHDI is divided into seven categories and measured by using 30 features. There are 497 cities that contributed to PHDI 2013. The problem emerged is that not all the cities have those 30 features required by PHDI. The objective of this paper is to reduce PHDI features with various Feature Selection For Clustering (FSFC) method. The subset of features resulted from FSFC method is then used to generate two types of clusters, i.e. k-means and DBSCAN clustering. The clusters generated from k-means and DBSCAN techniques will be evaluated by using internal and external validation method. The subset of features is then compared with the original ranking of PHDI. Experimental results show that the results of each FSFC method producing a different rank compared to the original rank of PHDI. This result concludes that all the features that are used to measure PHDI are important to be included in the process of calculation. © 2019 IEEE.

关键词： Feature Selection

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Lattice Transformation from 2-D to Quasi 1-D and Phonon Properties of Exfoliated ZrS2 and ZrSe2

arXiv

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arXiv 2022年

作者： Alsulami, Awsaf Alharbi, Majed Alsaffar, Fadhel Alolaiyan, Olaiyan Aljalham, Ghadeer Albawardi, Shahad Alsaggaf, Sarah Alamri, Faisal Tabbakh, Thamer A. Amer, Moh R. Center of Excellence for Green Nanotechnologies Joint Centers of Excellence Program King Abdulaziz City for Science and Technology Riyadh Saudi Arabia Department of Mechanical and Aerospace Engineering University of California Los Angeles Los AngelesCA90095 United States National Center for Nanotechnology Materials Science Institute King Abdulaziz City for Science and Technology Riyadh Saudi Arabia Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA90095 United States

Recent reports on thermal and thermoelectric properties of emerging 2-Dimensional (2D) materials have shown promising results. Yet, most of these emerging materials are underrepresented or lack a consistent and thorough investigation. Among these materials are Zirconium based chalcogenides such as Zirconium Disulfide (ZrS2) and Zirconium Dieselenide (ZrSe2). Here, we investigate the thermal properties of these Zirconium based materials using confocal Raman spectroscopy. We observed 2 different and distinctive Raman signatures for exfoliated ZrX2 (where X = S or Se). These Raman modes generally depend on the shape of the exfoliated nanosheets, regardless of the incident laser polarization. These 2 shapes are divided into 2D- ZrX2 and quasi 1D- ZrX2. For 2D- ZrX2, Raman modes are in alignment with those reported in literature. However, for quasi 1D-ZrX2, we show that Raman modes are identical to exfoliated ZrX3 nanosheets, indicating a major lattice transformation from 2D to quasi-1D. We also measure thermal properties of each resonant Raman mode for each ZrX2 shape. Based on our measurements, most Raman modes exhibit a linear downshift dependence with temperature. However, for ZrS2, we see an upshift (blueshift) with temperature for A1g mode, which is attributed to non-harmonic effects caused by dipolar coupling with IR-active modes. Moreover, the observed temperature dependence coefficient for some phonon modes of quasi 1D-ZrX2 differ dramatically, which can be caused by the quasi 1D lattice. Finally, we measure phonon dynamics under optical heating for each of 2D-ZrX2 and quasi 1D-ZrX2 and show phonon confinement in quasi 1D-ZrX2 nanosheets. We extract the thermal conductivity and the interfacial thermal conductance for each of 2D-ZrX2 and quasi 1D-ZrX2 nanosheets. Our calculations indicate lower interfacial thermal conductance for quasi 1D-ZrX2 compared to 2D-ZrX2, which can be attributed to the phonon confinement in 1D. Based on our model, we show low thermal con

关键词： Thermal conductivity

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Design and Implementation of Enterprise Recruitment Mini program

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Journal of Cyber Security 2021年第3期3卷 125-132页

作者： Yu Xue Nan Wei Junyang Han Chishe Wang Moayad Aloqaily School of Computer and Software Nanjing University of Information Science and TechnologyNanjingChina Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjingChina Faculty of Engineering Al Ain UniversityAl AinUnited Arab Emirates NUIST Students’Platform for Innovation and Entrepreneurship Training Program NanjingChina

Since the 21st century,the Internet has been updated and developed at an alarming *** the same time,WeChat applets are constantly improving and introducing new *** an enterprise recruitment system based on WeChat applets for the majority of job seekers and recruiter users,provide job seekers with easy-to-reach employment opportunities,and provide a convenient and clear screening environment for job *** front-end part of the applet is developed using WeChat developer tools,and the back-end system is developed using *** Spring Boot+Spring MVC framework,implemented in Java *** is managed using MySql *** function of this company’s recruitment applet is similar to the ordinary traditional native recruitment *** achieves basic functions such as job search,job search,collection of jobs,delivery of resumes,viewing of the job search process,recruitment of job information,screening of job resumes,notification of interviews,etc.

关键词： Mini-program recruitment WeChat developer tools MyEclipse

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Analysis and Design of University Teaching Equipment Management Information System

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Journal of Cyber Security 2021年第3期3卷 177-185页

作者： Yu Xue Xiang Jiao Chishe Wang Huanlun Chen Moayad Aloqaily School of Computer and Software Nanjing University of Information Science and TechnologyNanjingChina Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjingChina Faculty of Engineering Al Ain UniversityAl AinUnited Arab Emirates NUIST Students’Platform for Innovation and Entrepreneurship Training Program NanjingChina

Teaching equipment management is an important factor for colleges and universities to improve their teaching level,and its management level directly affects the service life and efficiency of teaching *** in recent years,our university recruitment of students scale is increasing year by year,the size of the corresponding teaching equipment is also growing,therefore to develop a teaching equipment management information system is necessary,not only can help universities to effective use of the existing teaching resources,also can update scrap equipment,related equipment maintenance,and build a good learning environment to students and to the improvement of the teaching quality of colleges and universities play a reliable safeguard *** paper first introduces some common development tools,and then analyzes the user functional requirements and data requirements of the system,and analyzes the feasibility of the system development from many aspects,finally based on B/S mode,using Java language,JSP technology and MySQL database design and implementation of a teaching equipment management information *** main functional modules of the system include equipment basic information management,equipment loan and return information management,equipment maintenance information management,equipment scrap information management,the interface of each functional module is shown in the paper.

关键词： Information systems teaching equipment management system analysis

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Supervised Contrastive Learning Enhances MHC-II Peptide Binding Affinity Prediction

SSRN

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SSRN 2024年

作者： Shen, Long-Chen Liu, Yan Liu, Zi Zhang, Yumeng Wang, Zhikang Guo, Yuming Rossjohn, Jamie Song, Jiangning Yu, Dong-Jun School of Computer Science and Engineering Nanjing University of Science and Technology 200 Xiaolingwei Nanjing210094 China Department of Computer Science Yangzhou University Yangzhou225100 China School of Information Enginnering Jingdezhen Ceramic University Jingdezhen333403 China Monash Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVIC3800 Australia Monash Data Futures Institute Monash University MelbourneVIC3800 Australia School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai200240 China Department of Epidemiology and Preventive Medicine Xiangya Hospital China Infection and Immunity Program Netherlands

Accurate prediction of major histocompatibility complex (MHC)-peptide binding affinity can improve our understanding of cellular immune responses and guide personalized immunotherapies. Nevertheless, the existing deep learning-based approaches for predicting MHC-II peptide interactions fall short of satisfactory performance and offer restricted model interpretability. In this study, we propose a novel deep neural network, termed ConBoTNet, to address the above issues by introducing the designed supervised contrastive learning and bottleneck transformer extractors. Specifically, the supervised contrastive learning pre-training enhances the model’s representative and generalizable capabilities on MHC-II peptides by pulling positive pairs closer and pushing negative pairs further in the feature space, while the bottleneck transformer module focuses on MHC-II peptide interactions to precisely identify binding cores and anchor positions in an unsupervised manner. Extensive experiments on benchmark datasets under 5-fold cross-validation, leave-one-molecule-out validation, independent testing, and binding core prediction settings highlighted the superiority of our proposed ConBoTNet over current state-of-the-art methods. Data distribution analysis in the latent feature space demonstrated that supervised contrastive learning can aggregate MHC-II-peptide samples with similar affinity labels and learn common features of similar affinity. Additionally, we interpreted the trained neural network by associating the attention weights with peptides and innovatively find both well-established and potential peptide motifs. This work not only introduces an innovative tool for accurately predicting MHC-II peptide affinity, but also provides new insights into a new paradigm for modeling essential biological interactions, advancing data-driven discovery in biomedicine. © 2024, The Authors. All rights reserved.

关键词： Peptides

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Comparative analysis of machine learning-based approaches for prediction of colon cancer

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AIP Conference Proceedings 2024年第1期2919卷

作者： Shubhangi Bhargava Adarsh Kumar Moolchand Sharma Indu Bharadwaj Victor Hugo C. de Albuquerque Department of Computer Science & Engineering Maharaja Agrasen Institute of Technology Rohini Delhi India Department of Electronics and Communication Engineering Galgotias University Greater Noida Uttar Pradesh India Telecommunication Engineering Program-PPGET Federal Institute of Education Science and Technology of Ceará (IFCE) Fortaleza Ceará Brazil Department of Teleinformatics Engineering Federal University of Ceará Fortaleza/CE Brazil

Colon cancer, along with lung, prostate, and breast cancer, is one of the most common types of cancer globally and is regarded as one of the world’s leading causes of death. In therapy for this common illness, substantial progress has been achieved recently. Adjuvant chemotherapy is effective, specifically in stage III patients, and surgery was tailored for the most outstanding results with the least amount of morbidity. Several novel target-oriented medications are being evaluated, and some of them (cetuximab and bevacizumab, for example) have already shown high activity/efficacy, primarily when used in conjunction with chemotherapy. To get the best results, it is necessary to provide current recommendations for treating these patients in medical trials and daily practices. This research seeks to use a person’s gene expression to determine whether or not they have colon cancer. This information is used by the Logistic Regression, Naive–Bayes Algorithm and Catboost Algorithm to predict human colon cancer. The learning classification dataset included 7457 gene expression levels from 36 patients, 18 of whom were healthy and 18 of whom were malignant.

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Blue micro-LED with a red/green blended polymer film for 3.5-Gbps visible light communication employing adaptive SNR-Flattening Algorithm

Next Nanotechnology

引用

Next Nanotechnology 2024年 5卷

作者： Ho, Pin-Wei Cheng, Chih-Hsien Liao, Yu-Sheng Chi, Yu-Chieh Sadhu, Annada Sankar Matsumoto, Atsushi Akahane, Kouichi Chen, Li-Yin Kuo, Hao-Chung Lin, Gong-Ru Graduate Institute of Photonics and Optoelectronics and Department of Electrical Engineering National Taiwan University Taipei 106319 Taiwan National Institute of Information and Communications Technology Koganei Tokyo 184-8795 Japan SANWAY OPTOELECTRONICS TECH. CORP New Taipei City 235601 Taiwan Department of Photonics Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan International Ph.D. Program in Photonics (UST) College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei 11492 Taiwan NTU-Tektronix Joint Research Center Tektronix Inc. and National Taiwan University Taipei 106319 Taiwan

By the semipolar blue single GaN μ-LED and blended Ir(piq)2(acac) + CC-MP5 polymer thin film color converter, a high-speed white-light μ-LED-based system is built up for the applications of short-distance VLC. The characteristics and properties of both devices are analyzed to understand the requirements for transmission and illumination. By selecting the growing orient, the influence of QCSE is reduced in this GaN μ-LED. Meanwhile, possessing the low reflection characteristic, it is beneficial for signal modulation. For the polymer thin film color converter with a lifetime of 7.8 ns, low surface reflection and high conversion efficiency are thought of good properties. Color-converted cool white light has a CCT of around 7000 K and high color accuracy with a CRI of about 90. The APD-combined frequency responses of the GaN μ-LED and GaN μ-LED + polymer are measured as 750 MHz and 600 MHz, respectively. After the optimization by utilizing the adaptive SNR-flattening pre-emphasis algorithm, the transmission performance of the white-light μ-LED is significantly promoted. For NRZ-OOK encoding, a 1.4 Gbps 0.15-m free-space transmission is achieved with a rising time of 656.33 ps, a falling time of 493.32 ps, and a Q-factor of 4.75. Besides, in more advanced data formats, the performance of this white-light μ-LED can be better highlighted. For the same 0.15-m free-space VLC, a high-speed 3 Gbps broadband 8-QAM-OFDM transmission is fulfilled with an EVM of 23.9%, an average SNR of 12.5, and a BER below 3.8 × 10^-3;while to the best of our knowledge, a record 3.5 Gbps BL-DMT transmission is implemented as well. This white-light μ-LED can also be integrated into large-scale arrays for multi-functional VLC applications. © 2024 The Authors

关键词： Adaptive SNR-Flattening algorithm Blue micro-LED Red/Green blended polymer Visible light communication

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Comparison of the Effectiveness of Machine Learning Techniques for the Number of Road Traffic Injuries

Comparison of the Effectiveness of Machine Learning Techniqu...

引用

International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Wirawan Puttamat Wikanda Phaphan Anuchit Jitpattanakul Chainarong Pianpailoon Sakorn Mekruksavanich Department of Mathematics Faculty of Education Chaiyaphum Rajabhat University Chaiyaphum Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Research Group in Statistical Learning and Inference Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Curriculum and Teaching Program (Mathematics) Faculty of Education Sakon Nakhon Rajabhat University Sakon Nakhon Thailand Department of Computer Engineering School of Information and Communication University of Phayao Phayao Thailand

This article aims to compare forecasting techniques with machine learning techniques for predicting the number of people injured in road accidents using data from the Injury Information Collaboration Center, Department of Disease Control, Ministry of Public Health Outpatient Files (OPD) spanning the years 2018 to 2022. The four machine-learning techniques examined in this study include Decision Tree Regression, Random Forest Regression, Support Vector Regression, and Multiple Linear Regression. The data analysis was performed using the R programming language. The results revealed that the Decision Tree Regression technique yielded the most accurate predictions for the number of road traffic injuries, as evidenced by its lowest values of MAE, MSE, and RMSE.

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AI-driven 3D Spatial Transcriptomics

arXiv

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arXiv 2025年

作者： Almagro-Pérez, Cristina Song, Andrew H. Weishaupt, Luca Kim, Ahrong Jaume, Guillaume Williamson, Drew F.K. Hemker, Konstantin Lu, Ming Y. Singh, Kritika Chen, Bowen Le, Long Phi Baras, Alexander S. Jiang, Sizun Bashashati, Ali Liu, Jonathan T.C. Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Department of Computer Science & Technology University of Cambridge Cambridge United Kingdom Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Rutgers Robert Wood Johnson Medical School New BrunswickNJ United States Department of Pathology Johns Hopkins Hospital BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins Hospital BaltimoreMD United States Broad Institute of Harvard and MIT CambridgeMA United States Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States Department of Pathology Dana Farber Cancer Institute BostonMA United States Department of Pathology and Laboratory Medicine University of British Columbia VancouverBC Canada School of Biomedical Engineering University of British Columbia VancouverBC Canada Department of Mechanical Engineering Bioengineering Laboratory Medicine & Pathology University of Washington SeattleWA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications1. However, most spatial transcriptomics (ST) approaches remain limited to two-dimensional (2D) sections of tissue2–4. Although current 3D ST methods hold promise, they typically require extensive tissue sectioning, are complex, are not compatible with non-destructive 3D tissue imaging technologies, and often lack scalability5–8. Here, we present VOlumetrically Resolved Transcriptomics EXpression (VORTEX), an AI framework that leverages 3D tissue morphology and minimal 2D ST to predict volumetric 3D ST. By pretraining on diverse 3D morphology–transcriptomic pairs from heterogeneous tissue samples and then fine-tuning on minimal 2D ST data from a specific volume of interest, VORTEX learns both generic tissue-related and sample-specific morphological correlates of gene expression. This approach enables dense, high-throughput, and fast 3D ST, scaling seamlessly to large tissue volumes far beyond the reach of existing 3D ST techniques. By offering a cost-effective and minimally destructive route to obtaining volumetric molecular insights, we anticipate that VORTEX will accelerate biomarker discovery and our understanding of morphomolecular associations and cell states in complex tissues. Interactive 3D ST volumes can be viewed at https://***/. © 2025, CC BY-NC-ND.

关键词： Vortex flow

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