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Successive responses of three coral holobiont components (coral hosts, symbiotic algae, and bacteria) to daily temperature fluctuations

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Ecological Indicators 2024年 158卷

作者： Hsieh, Yunli Eric Lu, Chih-Ying Liu, Po-Yu Kao, Jia-Min Yang, Sung-Yin Wu, Chien-Yi Wong, Jing-Wen Michelle Shikina, Shinya Fan, Tung-Yung Yang, Shan-Hua Systems Biology and Mathematical Modelling group Max Planck Institute of Molecular Plant Physiology Potsdam Germany Institute of Biochemistry and Biology Bioinformatics Department University of Potsdam Potsdam Germany School of BioSciences The University of Melbourne ParkvilleVIC Australia Molecular and Biological Agricultural Sciences Program Taiwan International Graduate Program National Chung Hsing University and Academia Sinica Taipei Taiwan Graduate Institute of Biotechnology National Chung Hsing University Taichung Taiwan Biodiversity Research Center Academia Sinica Taipei Taiwan School of Medicine College of Medicine National Sun Yat-sen University Kaohsiung Taiwan Institute of Fisheries Science National Taiwan University Taipei Taiwan Department of Aquatic Bioscience National Chiayi University Chiayi Taiwan Institute of Marine Environment and Ecology National Taiwan Ocean University Keelung Taiwan National Museum of Marine Biology and Aquarium Pingtung Taiwan

Corals and their associated microbiota form a "holobiont," which includes symbiotic algae and other associated microbiota dominated by bacteria. The composition of holobiont is influenced by various environmental factors, such as increasing sea water temperatures. Previous studies of the effects of temperature changes on coral physiology and associated bacterial communities have been conducted based on stable water temperatures set by mean temperatures, or by slowly increasing/decreasing temperatures. However, the daily temperature fluctuations that corals experience in nature are not stable. The current understanding of the effects of large daily temperature fluctuations on coral and associated bacterial community dynamics is limited. Hence, we conducted a four-week tank experiment using different large daily temperature fluctuations (±5 °C and ±7 °C) accompanied by continuous warming conditions (26 °C to 29 °C) to investigate the effects on two common reef-building corals, Stylophora pistillata and Pocillopora acuta. During the experiment, the activity of coral host catalase and superoxide dismutase were measured, the photosynthetic ability of symbiotic algae was recorded, and the variation in bacterial communities was analyzed using the V6-V8 region of 16S rDNA. According to the results, different parts of the holobionts of two coral species exhibited varying response rates to the continuous warming conditions and diurnal temperature fluctuations. Additionally, it was found that diurnal temperature fluctuations may mitigate the heat stress on the host and reduce the changes in bacterial response to warming. Therefore, it is suggested that the holobionts of different coral species may adopt different adaptation and survival strategies in response to diurnal temperature fluctuations and warming. Finally, based on the response of these two coral species under the conditions of diurnal temperature fluctuations and continuous warming, Acinetobacter and Rhodobacteracea

关键词： Temperature distribution

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Impact of SARS-CoV-2 lockdown on expansion of HIV transmission clusters among key populations: A retrospective phylogenetic analysis

The Lancet Regional Health - Americas

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The Lancet Regional Health - Americas 2022年 16卷 100369-100369页

作者： Miller, Rachel L. McLaughlin, Angela Montoya, Vincent Toy, Junine Stone, Sarah Harding, John Liang, Richard H. Wong, Jason Barrios, Rolando Montaner, Julio S.G. Joy, Jeffrey B. British Columbia Centre for Excellence in HIV/AIDS Vancouver BC Canada Bioinformatics Program University of British Columbia Vancouver BC Canada Vancouver Coastal Health Vancouver BC Canada British Columbia Centre for Disease Control Vancouver BC Canada Department of Medicine University of British Columbia Vancouver BC Canada

Background: Public health measures designed to reduce SARS-CoV-2 transmission led to reduced access to care and prevention services for people living with or at risk of acquiring HIV, particularly during the initial introduction of extensive restrictions. This reduction in access may have contributed to increases in HIV transmission not outweighed by decreases in transmission occurring as a result of reduced contact rates promoted by the same public health measures. Methods: We synthesize available province-wide HIV data in British Columbia, Canada, together with public mobility data to phylogenetically investigate the early impacts of SARS-CoV-2 on HIV transmission. Cluster growth, coalescent branching events and lineage-level diversification rates were assessed in “pre-lockdown” (January 22–March 21, 2020), “lockdown” (March 22–May 20, 2020) and “post-lockdown” (May 21–July 19, 2020) to facilitate comparison of transmission trends across key populations. Findings: Results reveal increased HIV transmission in a limited number of clusters in association with reduced access to health services during the initial introduction of SARS-CoV-2-related restrictions. In particular, clusters associated with people who inject drugs (PWID) show rapid growth, extensive branching events in phylogenetic trees during and following the lockdown period, and elevated median change in individuals’ viral diversification rates during lockdown compared to clusters associated with men who have sex with men (MSM), consistent with increased transmission rates between PWID. Interpretation: Increased vigilance and innovative targeted solutions are critical to offset potential negative impacts of SARS-CoV-2 or future pandemic-related restrictions on HIV epidemic dynamics. Funding: Funding sources include Genome Canada and Genome BC, the Public Health Agency of Canada, the BC Centre for Excellence in HIV/AIDS, and the Canadian Institutes of Health Research Coronavirus Rapid Response programme. S

关键词： COVID-19 HIV MSM Phylogenetics Public health PWID SARS-CoV-2 Transmission clusters

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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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Microglial SIRT2 deficiency aggravates cognitive decline and amyloid pathology in Alzheimer’s disease

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Brain, Behavior, and Immunity 2025年 129卷 223-243页

作者： Noemi Sola-Sevilla Maider Garmendia-Berges Mikel Aleixo MCarmen Mera-Delgado Maite Solas Rosa M. Tordera Lucía García-Carracedo Sara Expósito Elena Anaya-Cubero Joaquín Fernández-Irigoyen Elisabeth Guruceaga Enrique Santamaria Eduardo D. Martin Elena Puerta Department of Pharmaceutical Sciences Division of Pharmacology University of Navarra 31008 Pamplona Spain IdISNA Navarra Institute for Health Research 31008 Pamplona Spain Department of Neurology Research Center for Immunotherapy (FZI) and Focus Program Translational Neuroscience (FTN) Rhine Main Neuroscience Network (rmn2) University Medical Centre of the Johannes Gutenberg University Mainz 55131 Mainz Germany Laboratory of Neurophysiology and Synaptic Plasticity Instituto Cajal Consejo Superior de Investigaciones Científicas Madrid Spain Clinical Neuroproteomics Unit Navarrabiomed Hospital Universitario de Navarra (HUN) Universidad Pública de Navarra (UPNA) IdiSNA 31008 Pamplona Spain Bioinformatics Platform CIMA University of Navarra Pamplona Spain

Sirtuin 2 (SIRT2), a NAD+-dependent deacetylase, has been implicated in aging and neurodegenerative diseases such as Alzheimer’s disease (AD). While global SIRT2 inhibition has shown promise in reducing amyloid-beta pathology and cognitive deficits in different mouse models of AD, peripheral SIRT2 inhibition has been associated with adverse effects, such as increased inflammation. This suggests that targeted inhibition of specific cellular populations within the brain may represent a more precise and effective approach for the treatment of AD. To explore this hypothesis, we generated a conditional microglial SIRT2 knockout mouse model in the context of AD. Our results reveal that microglial SIRT2 reduction does not confer protective effects in the APP/PS1 model; rather, it aggravates cognitive decline, accelerates amyloid plaque deposition, and increases levels of pro-inflammatory cytokines at early stages of AD pathology. Transcriptomic analysis further indicates that SIRT2-deficient microglia exhibit altered expression of genes associated with aging and synaptic dysfunction. This phenotype was accompanied by increased phagocytosis of PSD95 and impaired long-term potentiation. These findings suggest that while SIRT2 inhibition in some contexts may be beneficial, targeted inhibition within microglia could accelerate AD progression, underscoring the need for cell-specific approaches when considering SIRT2 as a therapeutic target.

关键词： Aging Alzheimer’s disease Microglia Neurodegenerative diseases Neuroinflammation Sirtuin 2

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U1 snRNP regulates alternative promoter activity by inhibiting premature polyadenylation

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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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Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundational Models

arXiv

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arXiv 2024年

作者： Sankar, Baradwaj Simha Gilliland, Destiny Rincon, Jack Hermjakob, Henning Yan, Yu Adam, Irsyad Lemaster, Gwyneth Wang, Dean Watson, Karol Bui, Alex Wang, Wei Ping, Peipei Department of Physiology University of California Los AngelesCA United States NIH BRIDGE2AI Center UCLA NHLBI Integrated Cardiovascular Data Science Training Program at UCLA Los AngelesCA United States Cambridge United Kingdom Bioinformatics IDP University of California Los AngelesCA United States University of California Los AngelesCA United States Medical Informatics Home Area University of California Los AngelesCA United States Department of Computer Science University of California Los AngelesCA United States

Foundational Models (FMs) are gaining increasing attention in the biomedical AI ecosystem due to their ability to represent and contextualize multimodal biomedical data. These capabilities make FMs a valuable tool for a variety of tasks, including biomedical reasoning, hypothesis generation, and interpreting complex imaging data. In this review paper, we address the unique challenges associated with establishing an ethical and trustworthy biomedical AI ecosystem, with a particular focus on the development of FMs and their downstream applications. We explore strategies that can be implemented throughout the biomedical AI pipeline to effectively tackle these challenges, ensuring that these FMs are translated responsibly into clinical and translational settings. Additionally, we emphasize the importance of key stewardship and co-design principles that not only ensure robust regulation but also guarantee that the interests of all stakeholders-especially those involved in or affected by these clinical and translational applications-are adequately represented. We aim to empower the biomedical AI community to harness these models responsibly and effectively. As we navigate this exciting frontier, our collective commitment to ethical stewardship, co-design, and responsible translation will be instrumental in ensuring that the evolution of FMs truly enhances patient care and medical decision-making, ultimately leading to a more equitable and trustworthy biomedical AI ecosystem. © 2024, CC BY.

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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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Investigation of Aframomum melegueta compounds as ERK5 inhibitor related to breast cancer via molecular docking and dynamic simulation

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In silico pharmacology 2025年第1期13卷 18页

作者： Paul Olamide Ottu Olorunfemi Oyewole Babalola Cecilia Oluwamodupe Ayodeji Folasade Oluwatobiloba Idayat Oyinkansola Kehinde Olufemi Adebisi Akinola Sulyman Olalekan Ibrahim Olusola Olalekan Elekofehinti Teady Bioscience Research Laboratory Ilara Mokin Ondo State Nigeria. Phytomedicine Molecular Biology and Bioinformatics lab Department of Chemical Science (Biochemistry program) Olusegun Agagu University of Science and Technology Okitipupa Ondo State Nigeria. Bioinformatics and Molecular Biology Lab Department of Biochemistry Federal University of Technology Akure Akure Ondo State Nigeria. Department of Industrial Chemistry University of Ilorin Ilorin Kwara State Nigeria.

Breast cancer remains a global health challenge, with rising cases predicted in the coming decades. The complexity of breast cancer treatment arises from its complex nature, often involving multiple therapeutic strategies. One promising approach is targeting the ERK5 pathway, a key regulator in cancer cell proliferation and survival. In this study, we explored the anticancer potential of bioactive compounds from Aframomum melegueta, a plant traditionally used in West African medicine. The 3D structure of ERK5 (PDB ID: 4B99) was prepared and optimized using the Schrödinger Protein Preparation Wizard. Six phytochemicals from Aframomum melegueta were screened for their binding affinities to ERK5 using GlideXP docking. Dihydrogingerenone A,1-(3,4-dihydroxy-5-methoxyphenyl)-7-(3,4-dihydroxyphenyl) heptane-3,5-diyldiacetate and Dihydrogingerenone C emerged as the lead compound, demonstrating a high docking score of − 9.659 kcal/mol, − 9.383 kcal/mol, and − 8.264 kcal/mol compared to standard anticancer drugs like Docetaxel (− 4.175 kcal/mol) and Temozolomide (− 5.443 kcal/mol). Post-docking analyses using MM-GBSA free energy calculations confirmed the compound’s high binding stability, with van der Waals interactions and hydrogen bonding at critical residues such as Met140 playing a significant role. Pharmacokinetic profiling using ADME analysis showed that our compounds exhibited favorable drug-likeness properties, adhering to Lipinski’s Rule of Five without violations. QSAR modeling and molecular dynamics (MD) simulations further validated its pharmacological potential. These findings suggest that Aframomum melegueta contains bioactive compounds with strong potential as ERK5 inhibitors, offering a novel approach to breast cancer treatment.

关键词： Aframomum melegueta Breast cancer Dihydrogingerenone A ERK5 Molecular dynamic simulation

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ζ-QVAE: A Quantum Variational Autoencoder utilizing Regularized Mixed-state Latent Representations

arXiv

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arXiv 2024年

作者： Wang, Gaoyuan Warrell, Jonathan Emani, Prashant S. Gerstein, Mark Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Department of Computer Science Yale University New HavenCT06520 United States Department of Statistics & Data Science Yale University New HavenCT06520 United States Department of Biomedical Informatics & Data Science Yale University New HavenCT06520 United States NEC Laboratories America PrincetonNJ08540 United States

A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves compressing the original data to manageable dimensions while still representing essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To bridge this gap, we present a fully quantum framework, ζ-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied for both classical and quantum data compression. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full-data density matrix and allow for a"global"training objective. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of ζ-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results consistently indicate that ζ-QVAE exhibits similar or better performance compared to matched classical models. © 2024, CC BY-NC-ND.

关键词： Large datasets

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