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Structural basis of water-mediated cis Watson-Crick/Hoogsteen base-pair formation in non-CpG methylation

Nucleic acids research 2024年第14期52卷 8566-8579页

作者： Shan-Meng Lin Hsiang-Ti Huang Pei-Ju Fang Chi-Fon Chang Roshan Satange Chung-Ke Chang Shan-Ho Chou Stephen Neidle Ming-Hon Hou Graduate Institute of Genomics and Bioinformatics National Chung Hsing University Taichung 402 Taiwan. Genomics Research Center Academia Sinica Taipei 115 Taiwan. Taiwan Biobank Institute of Biomedical Sciences Academia Sinica Taipei 115 Taiwan. Institute of Biochemistry National Chung Hsing University Taichung 402 Taiwan. School of Pharmacy University College London London WC1N 1AX UK. Doctoral Program in Medical Biotechnology National Chung Hsing University Taichung 402 Taiwan. Graduate Institute of Biotechnology National Chung Hsing University Taichung 402 Taiwan. Department of Life Sciences National Chung Hsing University Taichung 402 Taiwan. Biotechnology Center National Chung Hsing University Taichung 402 Taiwan.

Non-CpG methylation is associated with several cellular processes, especially neuronal development and cancer, while its effect on DNA structure remains unclear. We have determined the crystal structures of DNA duplexes containing -CGCCG- regions as CCG repeat motifs that comprise a non-CpG site with or without cytosine methylation. Crystal structure analyses have revealed that the mC:G base-pair can simultaneously form two alternative conformations arising from non-CpG methylation, including a unique water-mediated cis Watson-Crick/Hoogsteen, (w)cWH, and Watson-Crick (WC) geometries, with partial occupancies of 0.1 and 0.9, respectively. NMR studies showed that an alternative conformation of methylated mC:G base-pair at non-CpG step exhibits characteristics of cWH with a syn-guanosine conformation in solution. DNA duplexes complexed with the DNA binding drug echinomycin result in increased occupancy of the (w)cWH geometry in the methylated base-pair (from 0.1 to 0.3). Our structural results demonstrated that cytosine methylation at a non-CpG step leads to an anti→syntransition of its complementary guanosine residue toward the (w)cWH geometry as a partial population of WC, in both drug-bound and naked mC:G base pairs. This particular geometry is specific to non-CpG methylated dinucleotide sites in B-form DNA. Overall, the current study provides new insights into DNA conformation during epigenetic regulation.

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A Diversity Inventory Monitoring System of Riparian Vegetation

A Diversity Inventory Monitoring System of Riparian Vegetati...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Dian Pratama Putra Nanda Satya Nugraha Teddy Suparyanto Alam Ahmad Hidayat Digdo Sudigyo Bens Pardamean Faculty of Agricultural Technology STIPER Agricultural Institute Yogyakarta Indonesia Faculty of Agricultural Technology Faculty of Forestry STIPER Agricultural Institute Yogyakarta Yogyakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

To strengthen conservation efforts for preserving biodiversity in a conservation area, forest inventory is important to understand the natural succession process in the area and to establish a monitoring strategy. Further, tree inventory aims to monitor the output yielded in the area. More specifically, the tree inventory in the watershed area plays a key role to achieve Sustainable Development Goals (SDG), especially in riparian zones which are also vital parts of green zones in forests. However, the traditional inventory approach is time-consuming and laborious therefore the development of an expert system to assist in inventory monitoring is required. In this study, we develop a monitoring system via a mobile application to collect, analyze and visualize tree inventory data. The application includes algorithms required to compute tree biodiversity, distribution, and richness for the given input of the data of all tree species in a conservation area. For the model validation stage, we compare the traditional inventory approach with our proposed application-based approach to compute diversity inventory in two riparian locations: Klaten Conservation Park and Wonosobo Conservation Park. After the three-day data collection in the areas, we obtain that the accuracy of reading data of our proposed system can achieve more than 90% in comparison with the manual approach. This demonstrates that the system can assist forestry workers to perform more efficient tree inventories in different locations.

关键词： Vegetation Manuals Forestry Soil Mobile applications Biodiversity Expert systems

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Development of label-free triboelectric nanosensors as screening platforms for anti-tumor drugs

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Nano Energy 2024年 125卷

作者： Cheng, Yu-Ying Ganguly, Anindita Cheng, Yi-Yun Ortiz, Christopher Llynard D. Pal, Arnab Shah, Pramod Kaswan, Kuldeep Yang, Lee-Wei Lin, Zong-Hong Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Institute of Biomedical Engineering National Tsing Hua University Hsinchu30013 Taiwan Internet of Senses Research Center National Tsing Hua University Hsinchu30013 Taiwan Praexisio Taiwan Inc. New Taipei221425 Taiwan Institute of Bioinformatics and Structural Biology National Tsing Hua University Hsinchu300044 Taiwan Chemical Biology and Molecular Biophysics Program Taiwan International Graduate Program Institute of Biological Chemistry Academia Sinica Taipei115 Taiwan Department of Chemistry National Tsing Hua University Hsinchu300044 Taiwan International Intercollegiate PhD Program National Tsing Hua University Hsinchu300044 Taiwan Department of Power Mechanical Engineering National Tsing Hua University Hsinchu300044 Taiwan Biomedical Artificial Intelligence PhD Program National Tsing Hua University Hsinchu300044 Taiwan Physics Division National Center for Theoretical Sciences Taipei Taiwan

This study introduces a general label-free drug screening platform with potential to support high-throughput drug screening for any protein target. Its innovation lies in quantifying the affinity of molecule-molecule interactions via voltage output variation, which achieves unprecedented selectivity and sensitivity. It combines computational analysis complemented by experimental substantiation with a solid-liquid triboelectric nanosensor. Owing to its high binding affinity, FKBP-rapamycin is used as a model system to demonstrate the platform's sensitivity. Subsequently, the research is extended to study drug interactions with an oncogenic protein ATG4B. The findings unveil the binding of S130 and Tioconazole to ATG4B, a critical cysteine protease involved in autophagy, while revealing that Dexamethasone does not bind ATG4B. This binding exerts a specific inhibitory effect on autophagic flux and triggers cancer cell apoptosis. The results support the previously verified inhibitory effects of these drugs and the effectiveness of a newly developed self-powered drug screening platform, leveraging the principles of solid-liquid contact electrification. This approach adeptly confronts enduring challenges in traditional drug development methods where biochemical assays need to be designed for each individual protein target or only time-consuming and concentration-demanding molecular interaction measurements were made available. © 2024 Elsevier Ltd

关键词： Nanosensors

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New candidate genes potentially involved in Zika virus teratogenesis

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Computers in Biology and Medicine 2024年 173卷 108259-108259页

作者： Ferrão Maciel-Fiuza, Miriãn Rengel, Bruna Duarte Wachholz, Gabriela Elis do Amaral Gomes, Julia de Oliveira, Maikel Rosa Kowalski, Thayne Woycinck Roehe, Paulo Michel Luiz Vianna, Fernanda Sales Schüler-Faccini, Lavínia Mayer, Fabiana Quoos Varela, Ana Paula Muterle Fraga, Lucas Rosa Graduate Program in Genetics and Molecular Biology Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Instituto Nacional de Genética Médica Populacional Porto Alegre Brazil Genomics Medicine Laboratory Center of Experimental Research Hospital de Clínicas de Porto Alegre Porto Alegre Brazil Department of Morphological Sciences Institute of Health Sciences Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Graduate Program in Medicine: Medical Sciences Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Teratogen Information System Medical Genetics Service Hospital de Clínicas de Porto Alegre Porto Alegre Brazil Bioinformatics Core Hospital de Clínicas de Porto Alegre Porto Alegre Brazil Centro Universitário CESUCA Cachoeirinha Brazil Department of Microbiology Immunology and Parasitology Institute of Health Sciences Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Graduate Program in Molecular and Cellular Biology Biotechnology Center Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Graduate Program in Biosciences Universidade Federal de Ciências da Saúde de Porto Alegre Porto Alegre Brazil

Despite efforts to elucidate Zika virus (ZIKV) teratogenesis, still several issues remain unresolved, particularly on the molecular mechanisms behind the pathogenesis of Congenital Zika Syndrome (CZS). To answer this question, we used bioinformatics tools, animal experiments and human gene expression analysis to investigate genes related to brain development potentially involved in CZS. Searches in databases for genes related to brain development and CZS were performed, and a protein interaction network was created. The expression of these genes was analyzed in a CZS animal model and secondary gene expression analysis (DGE) was performed in human cells exposed to ZIKV. A total of 2610 genes were identified in the databases, of which 1013 were connected. By applying centrality statistics of the global network, 36 candidate genes were identified, which, after selection resulted in nine genes. Gene expression analysis revealed distinctive expression patterns for PRKDC, PCNA, ATM, SMC3 as well as for FGF8 and SHH in the CZS model. Furthermore, DGE analysis altered expression of ATM, PRKDC, PCNA. In conclusion, systems biology are helpful tools to identify candidate genes to be validated in vitro and in vivo. PRKDC, PCNA, ATM, SMC3, FGF8 and SHH have altered expression in ZIKV-induced brain malformations. © Elsevier Ltd

关键词： Viruses

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1 Slow expanders invade by forming dented fronts in microbial 2 colonies

arXiv

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arXiv 2021年

作者： Lee, Hyunseok Gore, Jeff Korolev, Kirill S. Physics of Living Systems Group Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Graduate Program in Bioinformatics Biological Design Center Boston University BostonMA02215 United States

Most organisms grow in space, whether they are viruses spreading within a host tissue or invasive species colonizing a new continent. Evolution typically selects for higher expansion rates during spatial growth, but it has been suggested that slower expanders can take over under certain conditions. Here, we report an experimental observation of such population dynamics. We demonstrate that the slower mutants win not only when the two types are intermixed at the front but also when they are spatially segregated into sectors. The latter was thought to be impossible because previous studies focused exclusively on the global competitions mediated by expansion velocities but overlooked the local competitions at sector boundaries. We developed a theory of sector geometry that accounts for both local and global competitions and describes all possible sector shapes. In particular, the theory predicted that a slower, but more competitive, mutant forms a dented V-shaped sector as it takes over the expansion front. Such sectors were indeed observed experimentally and their shapes matched up quantitatively with the theory. In simulations, we further explored several mechanism that could provide slow expanders with a local competitive advantage and showed that they are all well-described by our theory. Taken together, our results shed light on previously unexplored outcomes of spatial competition and establish a universal framework to understand evolutionary and ecological dynamics in expanding populations. © 2021, CC BY-NC-ND.

关键词： Competition

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review

Trends, Opportunities, and Challenges in Detecting Depressiv...

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Computer System, Information Technology, and Electrical Engineering (COSITE), International Conference on

作者： Gregorius Natanael Elwirehardja Mahmud Isnan Anzaludin Samsinga Perbangsa Kahlil Muchtar Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Computer Science Department BINUS Graduate Program -Master of Computer Science Bina Nusantara University Jakarta Indonesia

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches.

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A Convolutional Neural Network-based Ancient Sundanese Character Classifier with Data Augmentation

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Procedia Computer Science 2021年 179卷 195-201页

作者： Alam Ahmad Hidayat Kartika Purwandari Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

With an increasing interest in the digitization effort of ancient manuscripts, ancient character recognition becomes one of the most important areas in the automated document image analysis. In this regard, we propose a Convolutional Neural Network (CNN)-based classifier to recognize the ancient Sundanese characters obtained from a digital collection of Southeast Asian palm leaf manuscripts. In this work, we utilize two different preprocessing techniques for the dataset. The first technique involves the use of geometric transformations, noise background addition, and brightness adjustment to augment the imbalanced samples to be fed into the classifier. The second technique makes use of the Otsu’s threshold method to binarize the characters and only uses the usual geometric transformations for the data augmentation. The proposed network with different data augmentation processes is trained on the training set and tested on the testing set. Image binarization from the second technique can outperform the performance of the CNN-based classifier upon the first technique by achieving a testing accuracy of 97.74%.

关键词： ancient sundanese characters convolutional neural network data augmentation document image analysis Otsu’s threshold method

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Convolutional Neural Networks for Scops Owl Sound Classification

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Procedia Computer Science 2021年 179卷 81-87页

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

Adopting a deep learning model into bird sound classification tasks becomes a common practice in order to construct a robust automated bird sound detection system. In this paper, we employ a four-layer Convolutional Neural Network (CNN) formulated to classify different species of Indonesia scops owls based on their vocal sounds. Two widely used representations of an acoustic signal: log-scaled mel-spectrogram and Mel Frequency Cepstral Coefficient (MFCC) are extracted from each sound file and fed into the network separately to compare the model performance with different inputs. A more complex CNN that can simultaneously process the two extracted acoustic representations is proposed to provide a direct comparison with the baseline model. The dual-input network is the well-performing model in our experiment that achieves 97.55% Mean Average Precision (MAP). Meanwhile, the baseline model achieves a MAP score of 94.36% for the mel-spectrogram input and 96.08% for the MFCC input.

关键词： acoustic features bird sound classification convolutional neural network mean average precision scops owl

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Systematic Evaluation of Cross Population Polygenic Risk Score on Colorectal Cancer

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Procedia Computer Science 2021年 179卷 344-351页

作者： Bharuno Mahesworo Arif Budiarto Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The number of findings in cancer genomics research has grown rapidly in the last decade due to the decline in the cost of human sequencing and genotyping. However, the majority of the reported significant marker associated with cancer traits are based on European and East Asian population. Large population such as South Asian and South-East Asian population are under-represented in genomics research. In this study, we explored the possibility of computing a Polygenic Risk Score (PRS) of colorectal cancer on our test sample based on reported significant Single Nucleotide Polymorphism (SNP). The SNPs used to compute the risk score were collected from GWAS Central and GWAS Catalog. Significant SNPs from IC3 study were used as a benchmark. The result shows that calculating colorectal cancer risk score using reported significant marker from different population group is possible. The p-value of our statistic model shows significant differences between case and control group risk score.

关键词： Polygenic Risk Score Cross Population Colorectal Cancer

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Multi-class Weather Forecasting from Twitter Using Machine Learning Aprroaches

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Procedia Computer Science 2021年 179卷 47-54页

作者： Kartika Purwandari Join W.C. Sigalingging Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta Indonesia 10720 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Now in the era of big data, many are applying information methods accurately especially by social media. The aims of this study to classify the weather based on Twitter automatically using text mining by using Support Vector Machine (SVM), MultinomialNaive Bayes (MNB), and Logistic Regression (LR) method. The experimental results show that SVM substantially outperforms various other machine learning algorithms for the task of text classification with an accuracy value of 93%. This result proves that SVM is very suitable for text categorization. We use clustering technique to read the pattern in customers’ opinion about the restaurant based on some measurement variables.

关键词： weather classification twitter machine learning support vector machine multinomial naive bayes logistic regression

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