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8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004: Trends in Artificial Intelligence

作者： Joung, Je-Gun Sok June, O. Zhang, Byoung-Tak Biointelligence Laboratory Graduate Program in Bioinformatics Seoul National University Seoul 151-742 Korea Republic of Seoul National University Seoul 151-742 Korea Republic of School of Computer Science and Engineering Seoul National University Seoul 151-742 Korea Republic of Department of Pharmacology College of Medicine Inje University Busan 614-735 Korea Republic of

ISBN: (纸本)3540228179

Infection by high-risk human papillomaviruses (HPVs) is associated with the development of cervical cancers. Classification of risk types is important to understand the mechanisms in infection and to develop novel instruments for medical examination such as DNA microarrays. In this paper, we classify the risk type of HPVs by using the protein sequences. Our approach is based on the hidden Markov model and the Support Vector Machines. The former searches informative subsequence positions and the latter computes efficiently to classify protein sequences. In the experiments, the proposed classifier was compared with previous methods in accuracy and F-cost, also the prediction result of four unknown types is presented. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Viruses

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Implementation of Face Patterns and Smile Recognition for Intelligent Class Attendance Systems 6

Implementation of Face Patterns and Smile Recognition for In...

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6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Miftakhurrokhmat Suparyanto, Teddy Elwirehardja, Gregorius Natanael Karimah, Shofiyati Nur Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Department of Informatics Yogyakarta Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350345162

Students 'attendance in class is one important success parameter in face-to-face learning processes. Conventional attendance systems, such as paper-based attendance sheets or identity card systems, require a long time in the manual recapitulation process. Without additional verifications, even computer vision-based methods are prone to fraudulent practices by the students instead of gaining their excitement and attention in a class. To stimulate students' attention in a class, this work designs an intelligent class attendance system, in which facial pattern and smile recognition are implemented by using the latter as an additional task-based verification to reduce the risks of fake attendance. For the face recognition module, this pilot study used FaceNet as a feature extractor combined with SVM for classification, whereas the Haar cascade algorithm is used for recognizing smiles. This face recognition pipeline was implemented as a service installed on minicomputers or Internet of Things (IoT) devices in each classroom and connected to an IP camera. Every recorded attendance will be sent as a notification to a mobile application for students that requires their active participation to confirm it with a smiling self-photo. The proposed pipeline obtained 92.86% accuracy on the test data, and 66.67% accuracy when evaluated in a real-life simulation setting through the implemented system. The lower accuracy in the simulation indicated that further improvements are indispensable, especially since the model obtained 28.57% False Negative Rate. Future studies will need to acquire more data and experiment with more efficient methods of attendance verification. © 2023 IEEE.

关键词： class attendance convolutional neural network deep learning face recognition smile

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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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Assembly independent functional annotation of short-read data using SOFA: Short-ORF functional annotation

Assembly independent functional annotation of short-read dat...

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IEEE Symposium on Computational Intelligence and bioinformatics and Computational Biology (CIBCB)

作者： Aria S. Hahn Niels W. Hanson Dongjae Kim Kishori M. Konwar Steven J. Hallam Department of Microbiology University of British Columbia Vancouver Canada Graduate Program in Bioinformatics University of British Columbia Vancouver Canada Department of Computer Science University of British Columbia Vancouver Canada

Accurate description of the microbial communities driving matter and energy transformations in complex ecosystems such as soils cannot yet be effectively accomplished using assembly-based approaches despite the rise of next generation sequencing technologies. Here we present SOFA, an open source pipeline enabling comparative functional annotation of unassembled short-read data. The pipeline attempts to merge mate pairs in fastq files, predicts open reading frames (ORFs) on merged and unmerged reads as small as 70 bps, and completes an additional step, we term `deduplication'. Deduplication prevents the double counting of ORFs predicted from unmerged paired-end reads by checking for homologous annotations that span the same ORF, allowing for quantitatively accurate predictions. The effectiveness of SOFA is validated with both simulated and bone fide soil metagenomes, and empirical results are compared to existing strategies for obtaining accurate ORF counts, and an analytical model of read duplication. SOFA enables downstream processing stages within the existing MetaPathways pipeline, and is available for download as a stand alone application at https://*** under the MIT license.

关键词： bioinformatics Pipelines Genomics Databases Soil Proteins Assembly

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Comparative Proteomic Analysis Reveals Altered Ciliary Proteins in Sickle Cell Disease

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Journal of Proteome Research 2025年 2025 May 15页

作者： Mohieldin, Ashraf M. Spencer, Madison Bernal, Carter Fadol, Wala B. Gupta, Ankan Thirugnanam, Karthikeyan Delahunty, Claire Nunez, Francisco Pan, Amy Y. Brandow, Amanda M. Palecek, Sean P. Rarick, Kevin R. Ramchandran, Ramani Zennadi, Rahima Yates, John Nauli, Surya M. College of Graduate Studies Master Program of Pharmaceutical Science California Northstate University Elk Grove 95757 CA United States Department of Basic Science College of Medicine California Northstate University Elk Grove 95757 CA United States Department of Clinical Science College of Medicine California Northstate University Elk Grove 95757 CA United States Department of Pediatrics Developmental Vascular Biology Program Division of Neonatology Children’s Research Institute (CRI) Medical College of Wisconsin Milwaukee 53226 WI United States Department of Molecular Medicine The Scripps Research Institute La Jolla 92037 CA United States Department of Pharmaceutical Sciences School of Pharmacy Chapman University Irvine 92618 CA United States Department of Pediatrics Division of Bioinformatics and Quantitative Child Health CRI Medical College of Wisconsin Milwaukee 53226 WI United States Department of Pediatrics Division of Hematology/Oncology/Bone Marrow Transplantation Medical College of Wisconsin Milwaukee 53226 WI United States Department of Chemical and Biological Engineering College of Engineering University of Wisconsin−Madison Madison 53706 WI United States Department of Pediatrics Division of Critical Care CRI Medical College of Wisconsin Milwaukee 53226 WI United States Department of Physiology College of Medicine The University of Tennessee Health Science Memphis 38163 TN United States Department of Medicine University of California Irvine Irvine 92697 CA United States

Sickle cell disease (SCD) is an inherited hemoglobinopathy characterized by sickle-shaped red blood cells (RBCs). Primary cilia are mechanosensory organelles and are projected in the lumen of blood vessels to detect blood flow. We previously reported that interaction between microvasculature endothelial cells and sickled RBCs resulted in altered blood flow that can elevate reactive oxygen species, leading to increased deciliation in SCD patients. However, the impact of deciliation mediated by sickled RBCs in the context of the ciliary protein profiles remains unclear. Here, we investigated cell-cilia stability under different physiological shear-stress magnitudes and examined cilia protein profiles in SCD, utilizing mouse models and human participants. Our results demonstrate that subjecting endothelial cilia to sickled RBCs at 5.0 dyn/cm² led to significant deciliation events. The proteomic and bioinformatic analyses showed different ciliary protein profiles, distinct signaling pathways, and unique post-translational modification processes in the SCD mouse model. Consistent with the SCD mouse model results, our translational studies validated the enrichment of specific proteins, including Transferrin Receptor-1 (TfR1), Glyceraldehyde-3-Phosphate-Dehydrogenase (GAPDH), and ADP Ribosylation Factor Like GTPase-13B (ARL13B) in SCD patients. These findings underscore the clinical relevance of cilia in SCD and suggest that ciliary proteins are potential biomarkers for assessing vascular damage. © 2025 The Authors. Published by American Chemical Society.

关键词： diagnostic biomarker extracellular vesicles primary cilia proteomics red blood cells sickle cell disease sickle cell trait vascular damage

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S100A4 released from highly bone-metastatic breast cancer cells plays a critical role in osteolysis

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Bone Research 2019年第4期7卷 424-436页

作者： Haemin Kim Bongjun Kim Sang Il Kim Hyung Joon Kim Brian YRyu Junho Chung Zang Hee Lee Hong-Hee Kim Department of Cell and Developmental Biology BK21 Program and DRISeoul National UniversitySeoulKorea Department of Biochemistry and Molecular Biology Seoul National University College of MedicineSeoulKorea Cancer Research Institute Seoul National University College of MedicineSeoulKorea Department of Cancer Biology Seoul National University College of MedicineSeoulKorea Department of Oral Physiology BK21 PLUS Projectand Dental and Life Science InstituteSchool of DentistryPusan National UniversityYangsanKorea Interdisciplinary Program in Bioinformatics Seoul National University College of Natural SciencesSeoulKorea Department of Biomedical Sciences Seoul National University Graduate SchoolSeoulKorea

Bone destruction induced by breast cancer metastasis causes severe complications,including death,in breast cancer *** between cancer cells and skeletal cells in metastatic bone microenvironments is a principal element that drives tumor progression and ***-derived factors play fundamental roles in this form of *** identify soluble factors released from cancer cells in bone metastasis,we established a highly bone-metastatic subline of MDA-MB-231 breast cancer *** subline(mtMDA)showed a markedly elevated ability to secrete S100A4 protein,which directly stimulated osteoclast formation via surface receptor *** S100A4 stimulated osteoclastogenesis in vitro and bone loss in *** medium from mtMDA cells in which S100A4 was knocked down had a reduced ability to stimulate ***,the S100A4 knockdown cells elicited less bone destruction in mice than the control knockdown *** addition,administration of an anti-S100A4 monoclonal antibody(mAb)that we developed attenuated the stimulation of osteoclastogenesis and bone loss by mtMDA in *** together,our results suggest that S100A4 released from breast cancer cells is an important player in the osteolysis caused by breast cancer bone metastasis.

关键词： S100A4 breast metastatic

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Comparative analysis of deep learning models for detecting face mask 7

Comparative analysis of deep learning models for detecting f...

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7th International Conference on Computer Science and Computational Intelligence, CSCI 2022

作者： Ramadhan, M. Vickya Muchtar, Kahlil Nurdin, Yudha Oktiana, Maulisa Fitria, Maya Maulina, Novi Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Microbiology Department School of Medicine Universitas Syiah Kuala Banda Aceh23111 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

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-The-Art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-Trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification. © 2022 Elsevier B.V.. All rights reserved.

关键词： Face recognition

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Determining Variables Associated with Annual Oil Palm Yield: An Explainable Gradient Boosting Approach 8

Determining Variables Associated with Annual Oil Palm Yield:...

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

作者： Elwirehardja, Gregorius Natanael Suparyanto, Teddy Miftakhurrokhmat Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta55282 Indonesia Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta55584 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous studies showed that meteorological variables, including rainfalls and wind speed, are associated with oil palm yields. In this research, the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive exPlanations (SHAP) were deployed for analyzing the importance of 15 agrometeorological variables in predicting oil palm yield. The best model attained 1.911 RMSE and 0.855 RRʹ. By analyzing the weights and gains of the XGBoost model along with the SHAP values, it was found that the yield in the previous year, the age and number of plants, the area of peat lands, and meteorological parameters such as rainfall rates and the number of rainy days in the previous three years were considered important. The previous year's yield in particular possesses the greatest weight and gain values according to the model, and the highest SHAP value among all input variables. However, the meteorological data used in this research are only limited to rainfall rates and the number of rainy days. In the future, more diverse variables can be analyzed. © 2023 The Authors. Published by Elsevier B.V.

关键词： Machine Learning Oil Palm Yield SHAP XAI XGBoost

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P202: Post-COVID syndrome presented with psychomotor change and suicidal ideations: a geriatric case report

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International Psychogeriatrics 2023年第S1期35卷 229-230页

作者： Lin, Ying Wang, Ying-Chi Chiang, Dai-Lun Lai, Feipei Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei City Taiwan MacKay Memorial Hospital Department of Psychiatry Taipei Taiwan Financial Technology Applications Program Ming Chuan University Taoyuan City Taiwan

Background:COVID-19 is notorious for its neuropsychiatric sequelae. Some patients present with anosmia and cognitive and attention deficits, also known as “brain-fog”. In COVID-19 survivors, psychiatric manifestations such as depression, fatigue, anxiety, and neurocognitive impairments, also known as the “post-acute COVID-19 syndrome” or “long COVID”, were reported in 35% of the patients after the infection. New-onset anxiety, depression, psychosis, seizures, and even suicidal behaviors are also reported. Inflammation was proposed to be the *** Report:The 68 year-old married female who lived with her husband did not have psychiatric history before this year. She had covid-19 infection 3 months prior to her psychiatric hospitalization. Although she had recovered, she started to have depressed mood, disrupted sleep and anhedonia since then. She became easily anxious and could hardly feed her cats as usual. She was not able to make decisions. She no longer did house chores that she had been doing. Poor appetite led to a loss of body weight from 46 to 40kgs. She had blunted emotional response, and could not cry after her cat died. She went to clinic for help, BZD and antidepressants were given, but the condition persisted. One week prior to hospitalization, her speech became weak, murmuring and could not finish a long sentence. The physiological change can be observed using wearable device. After 3 weeks of treatment, her heat rate, activity and sleep improved. The progress was also recorded via wearable devices and clinical ***:In our patient, the depression was associated with circadian rhythm disruption, suicidal ideations and psychomotor slowing. This may reveal some special features of post-COVID depression. In previous studies, antidepressants have proved to be effective in treating post-COVID-19 depression. Sigma receptors are one of the possible mechanisms. Besides, agomelatine, with its melatonergic activity of regulating circadia

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Count-Data Mixed Models of Topical Tweets: A Case of Indonesia Flood Events

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Procedia Computer Science 2023年 227卷 805-812页

作者： Alam Ahmad Hidayat Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Examining topic-level variability in modeling Twitter data can potentially yield more comprehensive insights into public perception during critical periods, thereby enhancing natural disaster mitigation and surveillance efforts. In this study, we utilized generalized linear mixed models (GLMMs) to illustrate the variability in tweet counts related to specific topics in Indonesia during the flood events that occurred in February 2021. The glmmTMB library in R was employed for this purpose. The data were assumed to follow two distinct exponential distributions: Poisson and Negative Binomial. To incorporate random effects, random intercepts and random slopes were introduced, allowing them to vary randomly across topics in the initial two models. Additionally, the final model addressed issues related to dispersion and zero-inflation. By evaluating the Akaike Information Criteria scores, we determined that a model based on the Negative Binomial distribution with random zero-inflation intercepts best fit the data. The chosen model formulation and the estimated parameters have the potential to forecast topic-specific trends in Indonesian flood-related Twitter data.

关键词： count data disaster surveillance floods mixed models Twitter

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