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Air Medical Blood Transfusion as a Trigger of Massive Transfusion Protocol

Air Medical Journal 2023年第5期42卷 353-357页

作者： Cornelius, Brian Thompson, Dennis Kilgore, Phillip Cvek, Urska Trutschl, Marjan Samra, Navdeep Cornelius, Angela Graduate Program in Nurse Anesthesia Texas Wesleyan University Fort Worth TX United States Department of Anesthesia John Peter Smith Hospital Fort Worth TX United States Department of Anesthesia Ochsner LSU Health Shreveport LA United States Department of Emergency Medicine Louisiana State University Health Sciences Center Shreveport LA United States Laboratory for Advanced Biomedical Informatics Department of Computer Science Louisiana State University Shreveport LA Department of Trauma and Surgical Critical Care Louisiana State University Health Sciences Center Shreveport LA United States Department of Emergency Medicine John Peter Smith Hospital Fort Worth TX United States

Air medical services can improve access to blood products at the point of injury. Studies have shown that early activation of mass transfusion protocols (MTPs) can improve the survival of trauma patients by up to 25%. There are several scoring systems to guide early activation, but the use of a single criterion has been elusive. Our study sought to determine if air medical administration of blood products was a risk factor for massive transfusion activation and utilization of prehospital vital signs for calculation of the shock index. In our retrospective study, we evaluated adult trauma patients transfused by helicopter emergency medical services (HEMS) and as a control all patients in our institution receiving the MTP. Our study found HEMS blood transfusion was not a reliable trigger for MTP, although the sample size may have limited our findings. We found that HEMS care resulted in an overall reduction in the volume of transfusion and an improvement in hemodynamic parameters upon trauma center arrival. HEMS transfusion and a higher rate of tranexamic acid administration may have contributed to these findings. Of note, the assessment of blood consumption score and shock index were nonspecific in the study populations. © 2023 Air Medical Journal Associates

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Gaussian Mixture Model Implementation for Population Stratification Estimation from Genomics Data

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Procedia computer science 2021年 179卷 202-210页

作者： Arif Budiarto Bharuno Mahesworo Alam Ahmad Hidayat Ika Nurlaila Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Genomics study, as opposed to socio-anthropology, has been demonstrated as an excellent tool to picture biological relatedness and disease risk factors. To analyze the data obtained from the study, Genome-wide Association Study (GWAS) has been more than decades known as the mainstay approach., is the most popular approach in analysing genomics data. The confounding variables selection, being that ancestry estimation or population stratification, is substantial to maintain the quality of GWAS. Researchers have developed various methods in extracting the population stratification information from high dimensional genomics data, especially Single Nucleotide Polymorphisms (SNPs) data. In the present study, we proposed an implementation of Principal Component Analysis (PCA)-complemented Gaussian Mixture Model (GMM) as an unsupervised model to estimate population stratification from samples. The results derived from this approach was further compared to that resulted from K-means and from the commonly used ancestry estimation software, fast STRUCTURE. We figured out that our recent improved approach outperformed the two later mentioned as shown by the average cluster and population scores. Furthermore, it was able to generate the probability distribution of each sample across all population, despite its limited quality. These intriguing results worth further investigations with much more comprehensive population coverage and more advanced algorithm.

关键词： Population Stratification Ancestry Estimation Gaussian Mixture Model Genomics SNPs

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Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using a Deep Learning Approach

Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using ...

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International Symposium on Applied science 2024, ISAS 2024

作者： Raza, Aquib Ching, Congo Tak Shing Liu, Peng-Ta Phan, Thien-Luan Tran, Ngoc-Luan Nguyen, Ha Anh T. Rameez, Mohammad Li, Hung-Chung Linh, Dao Nguyen Phuong Van Hieu, Nguyen Tran, Trung Nghia National Taiwan University Taipei City10673 Taiwan Research Center for Information Technology Academia Sinica Taipei City11529 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402 Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan Advanced Plant and Food Crop Biotechnology Center National Chung Hsing University Taichung402 Taiwan International Doctoral Program in Agriculture National Chung Hsing University Taichung402 Taiwan Fall Prevention Center Department of Physical Medicine & Rehabilitation Changhua Christian Hospital Changhua500 Taiwan Department of Physics and Electronic Engineering University of Science Vietnam National University - Ho Chi Minh City Ho Chi Minh City70000 Viet Nam Department of Computer Science University of York York HeslingtonYO10 5DD United Kingdom Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402 Taiwan Ho Chi Minh City72409 Viet Nam HCMUT Vietnam National University Ho Chi Minh City Thu Duc Ho Chi Minh City71308 Viet Nam

This research introduces a deep learning framework that combines convolutional neural networks with autoencoders to improve the diagnostic accuracy of knee osteoarthritis. The study utilized a publicly available dataset from the Mendeley Data repository, where knee joint images were preprocessed using Contrast Limited Adaptive Histogram Equalization to enhance contrast and ensure consistent image sizing. Initially, an autoencoder was employed in an unsupervised manner to learn compressed representations of the data. These representations were then used to train a CNN to classify knee images into 'healthy' and 'osteoarthritic' categories. The model was trained using an 80:20 split for training and testing data, incorporating early stopping and data augmentation techniques to prevent overfitting and enhance generalization. Performance metrics such as accuracy, precision, recall, and the area under the Receiver Operating Characteristic curve (ROCAUC) were used to evaluate the model, resulting in a high test accuracy of 98.95%. The high precision and recall rates further confirmed the model's robustness in diagnosing KOA. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to identify critical regions in the images that influenced the model's decisions. Additionally, t-Distributed Stochastic Neighbor Embedding (t-SNE) was used to visualize the high-dimensional encoded features in a two-dimensional space, providing deeper insights into how the model differentiates between classes. The promising results of this deep learning approach suggest its potential for broader applications in medical imaging diagnostics. By providing healthcare professionals with a tool that enhances diagnostic accuracy and operational efficiency, this framework lays the foundation for developing scalable, AI-driven diagnostic systems for various complex medical conditions. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Joints (anatomy)

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IoT Solution for Intelligent Pond Monitoring 3

IoT Solution for Intelligent Pond Monitoring

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Darmalim, Usin Darmalim, Ferdinan Darmalim, Sutristo Hidayat, Alam Ahmad Budiarto, Arif Mahesworo, Bharuno Pardamean, Bens Mega Medica Pharmaceuticals Jakarta11840 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Channa striata or the striped snakehead fish is one of snakehead fish species which inhabits all types of freshwater bodies distributed across Asian countries. Because this fish is known to have higher albumin fraction (64.61%) of protein and other economic values, domestication, and cultivation of this fish has been done in many Asian countries such as Indonesia, China, Malaysia, Thailand, Bangladesh, and India. Environmental factors such as temperature, water pH, dissolved oxygen, total dissolved solids, and turbidity are important parameters must be considered inbreeding and growing this type of fish. The aim of this paper is to propose an IoT solution to automatically monitor these environmental factors. It is designed with affordable and open-source electrical components to provide a cost-efficient solution for farmers. Five sensors are used to measure each parameter. A web application prototype is also presented as a companion application for the users to get useful information from the IoT device. It is developed using a Pythonframework. By accessing this web application, the users can immediately detect any abnormal conditions of the pond. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Fish

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Tunable Source of Quantum-Correlated Photons with Integrated Pump Rejection in a Silicon CMOS Platform

Tunable Source of Quantum-Correlated Photons with Integrated...

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Frontiers in Optics + Laser science 2021, FiO+LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Fargas Cabanillas, Josep M. Kramnik, Danielius Ramesh, Anirudh Gentry, Cale M. Stojanović, Vladimir Kumar, Prem Popović, Miloš A. Department of Electrical and Computer Engineering Photonics Center Boston University BostonMA02215 United States Department of Electrical Engineering and Computer Science UC Berkeley BerkeleyCA94720 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

ISBN: (纸本)9781557528209

A wavelength-tunable, silicon photon-pair source based on spontaneous four-wave mixing, integrated with a pump rejection filter in a single, flip-chip packaged CMOS chip, is demonstrated with a coincidence-to-accidentals ratio of 9.1 with no off-chip pump filtering. Frontiers in Optics / Laser science © Optica Publishing Group 2021.

关键词： CMOS integrated circuits

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Unsupervised News Topic Modelling with Doc2Vec and Spherical Clustering

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Procedia computer science 2021年 179卷 40-46页

作者： Arif Budiarto Reza Rahutomo Hendra Novyantara Putra Tjeng Wawan Cenggoro Muhamad Fitra Kacamarga Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Information System Department School of Information System Bina Nusantara University Jakarta Indonesia 11480 Eureka AI Singapore Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

In the digital and Internet era, companies are racing to profile their target users based on their online activities. One of the reliable sources is the news articles they read that can represent their interests. However, extracting latent information from the news articles is not an easy task for a human. In this paper, we introduced a practical model to automatically extract latent information from news articles with pre-determined topics. Our proposed model used unsupervised learning, thus alleviating the need for humans to label news items manually. Doc2vec was used to generate word vectors for each article. Afterward, a spectral clustering algorithm was applied to group the data based on the similarity. A supervised Long Short Term Memory (LSTM) model was built to compare the clustering performance. The best 1, best 3, and best 5 scores were used to evaluate our model. The result showed that our model could not outperformed LSTM model for the best 1 score. However, the best 5 score result indicated that our model was sufficiently robust to cluster the articles based on topic similarity. Additionally, the proposed unsupervised model was implemented in both an on-premise server, and a cloud server. Surprisingly, our proposed method could run faster in the cloud server despite its less number of CPU cores.

关键词： Topic Modelling Latent Information News Article Document Embedding Spherical Clustering

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Menarche - a journey into womanhood: age at menarche and health-related outcomes in East Asians

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Human Reproduction 2024年第6期39卷 1336-1350页

作者： Chiou, Jian-Shiun Lin, Ying-Ju Chang, Cherry Yin-Yi Liang, Wen-Miin Liu, Ting-Yuan Yang, Jai-Sing Chou, Chen-Hsing Lu, Hsing-Fang Chiu, Mu-Lin Lin, Ting-Hsu su Liao, Chiu-Chu Huang, Shao-Mei Chou, I-Ching Li, Te-Mao Huang, Peng-Yan Chien, Tzu-Shun Chen, Hou-Ren Tsai, Fuu-Jen PhD Program for Health Science and Industry College of Health Care China Medical University Taichung Taiwan Department of Health Services Administration China Medical University Taichung Taiwan Genetic Center Department of Medical Research China Medical University Hospital Taichung Taiwan School of Chinese Medicine College of Chinese Medicine China Medical University Taichung Taiwan Division of Minimal Invasive Endoscopy Surgery Department of Obstetrics and Gynecology China Medical University Hospital Taichung Taiwan School of Medicine College of Medicine China Medical University Taichung Taiwan Million-Person Precision Medicine Initiative Department of Medical Research China Medical University Hospital Taichung Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Laboratory for Statistical and Translational Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan Department of Pediatrics China Medical University Children's Hospital Taichung Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan Division of Medical Genetics China Medical University Children's Hospital Taichung Taiwan Department of Medical Laboratory Science and Biotechnology Asia University Taichung Taiwan

STUDY QUESTION: Are there associations of age at menarche (AAM) with health-related outcomes in East Asians? SUMMARY ANSWER: AAM is associated with osteoporosis, Type 2 diabetes (T2D), glaucoma, and uterine fibroids, as demonstrated through observational studies, polygenic risk scores, genetic correlations, and Mendelian randomization (MR), with additional findings indicating a causal effect of BMI and T2D on earlier AAM. WHAT IS KNOWN ALREADY: Puberty timing is linked to adult disease risk, but research predominantly focuses on European populations, with limited studies in other groups. STUDY DESIGN, SIZE, DURATION: We performed an AAM genome-wide association study (GWAS) with 57 890 Han Taiwanese females and examined the association between AAM and 154 disease outcomes using the Taiwanese database. Additionally, we examined genetic correlations between AAM and 113 diseases and 67 phenotypes using Japanese GWAS summary statistics. PARTICIPANTS/MATERIALS, SETTING, METHODS: We performed AAM GWAS and gene-based GWAS studies to obtain summary statistics and identify potential AAM-related genes. We applied phenotype, polygenic risk scores, and genetic correlation analyses of AAM to explore health-related outcomes, using multivariate regression and linkage disequilibrium score regression analyses. We also explored potential bidirectional causal relationships between AAM and related outcomes through univariable and multivariable MR analyses. MAIN RESULTS AND THE ROLE OF CHANCE: Fifteen lead single-nucleotide polymorphisms and 24 distinct genes were associated with AAM in Taiwan. AAM was genetically associated with later menarche and menopause, greater height, increased osteoporosis risk, but lower BMI, and reduced risks of T2D, glaucoma, and uterine fibroids in East Asians. Bidirectional MR analyses indicated that higher BMI/T2D causally leads to earlier AAM. LIMITATIONS, REASONS FOR CAUTION: Our findings were specific to Han Taiwanese individuals, with genetic correlatio

关键词： age at menarche genetic correlation genome-wide association study Mendelian randomization polygenic risk score

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Parameter Optimization of Support Vector Regression Using Harris Hawks Optimization

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Procedia computer science 2021年 179卷 17-24页

作者： I Nyoman Setiawan Robert Kurniawan Budi Yuniarto Rezzy Eko Caraka Bens Pardamean Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Department of Information Management College of Informatics Chaoyang University of Technology Taiwan (ROC) Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia 11530 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia. 11530.

Support Vector Regression (SVR) is often used in forecasting. Adjustment of parameters in the SVR affects the results of forecasting. This study aims to analyze the SVR method that is optimized using Harris Hawks Optimization (HHO), hereinafter referred to as HHO-SVR. The HHO-SVR was evaluated using five benchmark datasets to determine the performance of this method. The HHO process is also compared based on the type of kernel and other metaheuristic algorithms. The results showed that the HHO-SVR has almost the same performance as other methods but is less efficient in terms of time. In addition, the type of kernel also affects the process and results.

关键词： SVR harris hawks optimization parameter optimization kernel forecasting

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Deep Learning with Greedy Layer-Wise Compound Scaling for Temperature and Humidity Prediction in Solar Dryer Dome

SSRN

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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Cryo-EM structure of the plant 26S proteasome

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Plant Communications 2022年第3期3卷 116-126页

作者： Susanne Kandolf Irina Grishkovskaya Katarina Belacic Derek L.Bolhuis Sascha Amann Brent Foster Richard Imre Karl Mechtler Alexander Schleiffer Hemant D.Tagare Ellen D.Zhong Anton Meinhart Nicholas G.Brown David Haselbach Research Institute of Molecular Pathology(IMP) Vienna BioCenter(VBC)Campus-Vienna-BioCenter 11030 ViennaAustria Department of Radiology and Biomedical Imaging Yale UniversityNew HavenCT 06510USA Department of Pharmacology and Lineberger Comprehensive Cancer Center University of North Carolina School of MedicineChapel HillNC 27599USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of TechnologyCambridgeMA 02139USA Vienna BioCenter PhD Program Doctoral School of the University at Vienna and Medical University of ViennaVienna BioCenter(VBC)ViennaAustria Institute of Physical Chemistry University of FreiburgAlbertstraße 21Freiburg 79104Germany CIBSS Centre for Integrative Biological Signalling Studies University of FreiburgFreiburgGermany

Targeted proteolysis is a hallmark of *** is especially important in long-lived cells that can be found in higher eukaryotes,like *** task is mainly fulfilled by the ubiquitin–proteasome ***,proteolysis by the 26S proteasome is vital to development,immunity,and cell *** the yeast and animal proteasomes are well characterized,there is only limited information on the plant *** determined the first plant 26S proteasome structure from Spinacia oleracea by single-particle electron cryogenic microscopy at an overall resolution of 3.3 A°.We found an almost identical overall architecture of the spinach proteasome compared with the known structures from mammals and ***,we noticed a structural difference in the proteolytic active b1 ***,we uncovered an unseen compression state by characterizing the proteasome’s conformational *** suspect that this new conformation of the 20S core protease,in correlation with a partial opening of the unoccupied gate,may contribute to peptide release after *** data provide a structural basis for the plant proteasome,which is crucial for further studies.

关键词： 26S proteasome spinach UPS cryo-EM conformational landscape

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