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U1 snRNP regulates alternative promoter activity by inhibiting premature polyadenylation

Molecular Cell 2025年第10期85卷 1968-1981.e7页

作者： Kim, GyeungYun Carroll, Christine L. Wakefield, Zachary Peters Tuncay, Mustafa Fiszbein, Ana Biology Department Boston University Boston 02215 United States Graduate Program in Molecular Biology Cell Biology & Biochemistry Boston University Boston 02215 United States Graduate Program in Cell and Molecular Biology Boston University Boston 02215 United States Bioinformatics Program Faculty of Computing and Data Science Boston University Boston 02215 United States Center for Computing & Data Sciences Boston University Boston 02215 United States

Emerging evidence indicates that splicing factors mediate the close link between transcription and splicing. However, the mechanisms underlying this coupling remain unclear. U1 small nuclear ribonucleoprotein particle (U1 snRNP) not only initiates splicing but also plays a crucial role in preventing premature cleavage and polyadenylation, facilitating long-distance transcriptional elongation. Here, we show that U1 snRNP regulates alternative promoter activity in human cells by inhibiting premature polyadenylation. In genes carrying premature polyadenylation sites between two promoters, U1 snRNP inhibition with antisense oligonucleotides leads to a significant decrease in downstream promoter activity. Conversely, restoring U1 snRNP activity or inhibiting premature polyadenylation rescues downstream promoter activity. Mechanistically, U1 snRNP inhibition correlates with reduced chromatin accessibility, decreased RNA polymerase II serine 5 phosphorylation, and increased promoter-proximal pause at downstream promoters. Our findings support a model in which U1 snRNP favors productive elongation from upstream promoters, triggering downstream promoter activation by destabilizing nucleosomes and promoting promoter escape. © 2025 The Author(s)

关键词： alternative promoters dowsntream promoters promoter-proximal pause splicing factors splicing transcription transcription cooperativity transcription initiation transcription interfierence transcription start sites U1 snRNP

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Automatic Smart Crawling on Twitter for Weather Information in Indonesia

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Procedia Computer Science 2023年 227卷 795-804页

作者： Kartika Purwandari Reza Bayu Perdana Join W.C. Sigalingging Reza Rahutomo Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta 10720 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Although Twitter is a popular platform for social interaction analysis and text data mining, it faces challenges with geolocation automation. To address this problem, the researchers propose the utilization of a Support Vector Machine (SVM) model to develop an automated Twitter crawling system. The system aims to collect data related to weather in Indonesia by employing Twin, a Python-based Twitter scraping software. To overcome null geolocations, the study incorporates aliases created based on the common practice of Indonesian users mentioning the country's location in tweets. The results demonstrate that the SVM model, combined with automated smart crawling, achieves an 85% accuracy rate.

关键词： Social media Twitter SVM smart crawling weather

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Long Short-Term Memory-based Models for Sleep Quality Prediction from Wearable Device Time Series Data

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Procedia Computer Science 2023年 227卷 1062-1069页

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

Several studies suggest that sleep quality is associated with physical activities. Moreover, deep sleep time can be used to determine the sleep quality of an individual. In this work, we aim to find the association between physical activities and deep sleep time by modeling the time series data such as heart rate and a number of steps captured from a commercial wearable device. Our previous study demonstrates that deep learning-based time series modeling is well suited for our problem since the temporal patterns in the two physical parameters need to be captured to obtain more accurate results. We first preprocess our series data to have a time-step size of 10 minutes. To improve our previous effort in this modeling, we compare four different variants of Long Short-Term Memory (LSTM)-based models, ranging from single input to dual input models. Our result shows that the simple stacked LSTM model performs better for our data because the remaining models suffer from overfitting due to a larger number of the trained parameters.

关键词： deep learning forecasting sleep quality time series wearable devices

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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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LLM2Vec Sentence Embeddings Analysis in Sentiment Classification

LLM2Vec Sentence Embeddings Analysis in Sentiment Classifica...

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International Conference on Data and Software Engineering (ICODSE)

作者： Matthew Martianus Henry Nur Adhianti Hervanto Mahmud Isnan Dian Kurnianingrum Chyntia Ika Ratnapuri Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506407

ISBN: (纸本)9798331506414

Sentiment analysis is crucial method in business intelligence to extract insights, which typically begin with sentiment classification. One of the latest frameworks for generating sentence embeddings for sentiment classification is LLM2Vec, which allows Transformer decoder-based models to generate sentence embeddings for text representation. Its capability is deemed language-agnostic, which, in this study, the framework is leveraged for Tokopedia tweet sentiment analysis to prove the claim. The base decoder models used in the LLM2Vec framework were Llama 3 8B, Llama 2 7B, Sheared Llama 1.3B, and Mistral 7B. Two BERT-based models, which are the Indonesian SBERT model and IndoBERT trained with the SimCSE approach, were employed as a comparison. The generated embeddings were classified using logistic regression, SVM, and MLP Classifier. Classifiers using embedding generated by LLM2Vec with Llama 3 8B and Mistral 7B achieves on-par performance with classifiers that utilize IndoBERT SimCSE embeddings, while classifiers using embeddings generated by LLM2Vec with Llama 2 7B and Sheared Llama 1.3B achieves much lower performance. Classifiers with Indonesian SBERT embeddings achieve the highest F1 score performance. Despite slightly lower performance, this study has proven the language-agnostic capability of LLM2Vec, especially with Llama 3 8B and Mistral 7B in colloquial Bahasa Indonesia sentiment analysis, since none of the base decoders were ever trained using the Bahasa Indonesia corpus.

关键词： Support vector machines Sentiment analysis Logistic regression Social networking (online) Blogs Transformers Encoding Decoding Business intelligence Software engineering

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Twitter-based text classification using svm for weather information system 6

Twitter-based text classification using svm for weather info...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Purwandari, Kartika Rahutomo, Reza Sigalingging, Join W. C. Ajy Kusuma, Mahisa Prasetyo, Aji Pardamean, Bens Research Center Bina Nusantara University Bioinformatics and Data Science Jakarta11480 Indonesia BMKG Meteorological Climatological and Geophysical Agency Database Center Division Jakarta10720 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

The popularity of social media amplified the amount of text data that is used to enrich text classification research with machine learning approach. The modernization in weather information system is needed to produce real-time weather information using the involvement of Twitter users. GetOldTweets was used to cover data acquisition from Twitter. Support Vector Machine (SVM) was deployed as a machine learning model for text classification. The result was delivered in Esri Maps features. It facilitated a social sensing for Indonesia weather information system by using Twitter posts from Indonesian users with 93% accuracy. © 2021 IEEE.

关键词： Data acquisition

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis

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Procedia Computer Science 2025年 253卷 84-93页

作者： Anak Agung Ayu Putri Ardyanti Zhiqiang Xie Andrea Stevens Karnyoto Bens Pardamean Ni Made Estiyanti School of Computer Science and Technology Harbin University of Science and Technology Harbin 150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Doctoral School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer Science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions.

关键词： integrated scheduling assembly job shop scheduling heuristic algorithm genetic algorithm bibliometric analysis

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Missing Value Recovery for Single Cell RNA Sequencing Data

Missing Value Recovery for Single Cell RNA Sequencing Data

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Wenjuan Zhang William Yang John Talburt Sherman Weissman Mary Qu Yang MidSouth Bioinformatics Center and Bioinformatics Graduate Program University of Arkansas Little Rock and Univ. of Arkansas for Medical Sciences Little Rock USA Computer Science Department Princeton University Princeton USA Information Science Department University of Arkansas Little Rock Little Rock USA Department of Genetics Yale University New Haven USA

ISBN: (纸本)9781665458429

The emergence of single-cell sequencing technologies has enabled the production of high-resolution data at the individual cell level, providing unprecedented opportunities to capture cell population diversity and dissect the cellular heterogeneity of complex diseases. At the same time, relatively high biological and technical noise poses new challenges for single-cell data analysis. The single-cell RNA sequencing (scRNA-seq) data often contains substantial missing values due to gene dropout events. Here, we developed a convolutional neural network based model to recover missing values for scRNA-seq data. We first calculated the probability of dropout employing gamma-normal expectation maximum algorithm. Unlike most existing approaches, our model only recovered the expression values that have a dropout probability larger than a threshold. The mean square error and Pearson correlation coefficient were used to assess the accuracy of predicted expression values. The purity and entropy were computed to measure the homogeneity of cell clusters using imputed gene expression profiles. Across various scRNAseq datasets, our model demonstrated robust performance and achieved comparable or better results compared to the other imputation methods.

关键词： Sequential analysis Scientific computing Computational modeling RNA Sociology Production Mean square error methods

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Enhanced diabetic wound healing using platelet-derived extracellular vesicles and reduced graphene oxide in polymer-coordinated hydrogels

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Journal of Nanobiotechnology 2023年第1期21卷 1-17页

作者： Hao, Ping-Chien Burnouf, Thierry Chiang, Chih-Wei Jheng, Pei-Ru Szunerits, Sabine Yang, Jen-Chang Chuang, Er-Yuan Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei10617 Taiwan Department of Orthopedics Taipei Medical University Hospital Taipei11031 Taiwan Univ. Lille CNRS Centrale Lille Univ. Polytechnique Hauts-de-France UMR 8520 IEMN LilleF- 59000 France Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei110-52 Taiwan Cell Physiology and Molecular Image Research Center Taipei Medical University-Wan Fang Hospital Taipei11696 Taiwan Precision Medicine and Translational Cancer Research Center Taipei Medical University Hospital Taipei11031 Taiwan

Impaired wound healing is a significant complication of diabetes. Platelet-derived extracellular vesicles (pEVs), rich in growth factors and cytokines, show promise as a powerful biotherapy to modulate cellular proliferation, angiogenesis, immunomodulation, and inflammation. For practical home-based wound therapy, however, pEVs should be incorporated into wound bandages with careful attention to delivery strategies. In this work, a gelatin-alginate hydrogel (GelAlg) loaded with reduced graphene oxide (rGO) was fabricated, and its potential as a diabetic wound dressing was investigated. The GelAlg@rGO-pEV gel exhibited excellent mechanical stability and biocompatibility in vitro, with promising macrophage polarization and reactive oxygen species (ROS)-scavenging capability. In vitro cell migration experiments were complemented by in vivo investigations using a streptozotocin-induced diabetic rat wound model. When exposed to near-infrared light at 2 W cm− 2, the GelAlg@rGO-pEV hydrogel effectively decreased the expression of inflammatory biomarkers, regulated immune response, promoted angiogenesis, and enhanced diabetic wound healing. Interestingly, the GelAlg@rGO-pEV hydrogel also increased the expression of heat shock proteins involved in cellular protective pathways. These findings suggest that the engineered GelAlg@rGO-pEV hydrogel has the potential to serve as a wound dressing that can modulate immune responses, inflammation, angiogenesis, and follicle regeneration in diabetic wounds, potentially leading to accelerated healing of chronic wounds. © 2023, BioMed Central Ltd., part of Springer Nature.

关键词： Platelets

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Lateral transfer of mRNA and protein by migrasomes modifies the recipient cells

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细胞研究（英文版） 2021年第2期31卷 237-240页

作者： Mingli Zhu Qin Zou Rongyao Huang Ying Li Xudong Xing Jianhuo Fang Liang Ma Lifei Li Xuerui Yang Li Yu State Key Laboratory of Membrane Biology Tsinghua University-Peking University Joint Center for Life Sciences Beijing Frontier Research Center for Biological Structure School of Life ScienceTsinghua University Beijing 100084 China Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science Tsinghua University Beijing 100084 China Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science Tsinghua University Beijing 100084 China MOE Key Laboratory of Bioinformatics Tsinghua University Center for Synthetic & Systems Biology Tsinghua University School of Life Science Beijing 100084China State Key Laboratory of Membrane Biology Tsinghua University-Peking University Joint Center for Life Sciences Beijing Frontier Research Center for Biological Structure School of Life ScienceTsinghua University Beijing 100084 China MOE Key Laboratory of Bioinformatics Tsinghua University Center for Synthetic & Systems Biology Tsinghua University School of Life Science Beijing 100084China

Dear Editor,Migrasomes are recently discovered vesicular organelles which form on the retraction fibers(RFs)of migrating cells.1 Once detached from cells,migrasomes can rupture and release their luminal contents1 in a process named ***,migrasomes have been shown to play an important role in zebrafish organ morphogenesis by releasing chemokine signals to defined regions of the embryo.2 Thus,migracytosis is considered as a major mechanism for migrasomes to carry out their *** has been frequently observed that intact migrasomes can be engulfed by surrounding cells,1 and this has been proposed as a potential mechanism for lateral transfer of cellular contents between ***,it is not clear whether the lateral transfer of cellular contents by migrasomes has functional consequences for the recipient cells.

关键词： Extracellular signalling molecules Organelles

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