Article
Biochemistry & Molecular Biology
Ebtihal H. Mustafa, Geraldine Laven-Law, Zoya Kikhtyak, Van Nguyen, Simak Ali, Alex A. Pace, Richard Iggo, Alemwork Kebede, Ben Noll, Shudong Wang, Jean M. Winter, Amy R. Dwyer, Wayne D. Tilley, Theresa E. Hickey
Summary: Targeting transcription via CDK9 could be a potential therapeutic strategy for TNBC. Preclinical studies showed that a selective CDK9 inhibitor, CDDD11-8, effectively inhibited proliferation, induced cell cycle arrest, and increased apoptosis of TNBC cells in vitro and in vivo, without apparent toxicity to normal tissues.
Article
Oncology
Somnath Pandey, Rahinatou Djibo, Anais Darracq, Gennaro Calendo, Hanghang Zhang, Ryan A. Henry, Andrew J. Andrews, Stephen B. Baylin, Jozef Madzo, Rafael Najmanovich, Jean-Pierre J. Issa, Noel J-M Raynal
Summary: By combining drug screens, transcriptomic studies, and in vitro assays, our study identified toyocamycin as a potent and selective CDK9 inhibitor. Toyocamycin exhibits specific CDK9 inhibition in cancer cells and has the potential for cancer chemotherapy.
Article
Cell Biology
Yanli Zhang, Jianbing Hou, Shaomin Shi, Juan Du, Yudong Liu, Pan Huang, Qian Li, Lichao Liu, Huanrong Hu, Yacong Ji, Leiyang Guo, Yaqiong Shi, Yaling Liu, Hongjuan Cui
Summary: The study revealed that upregulated CSN6 in melanoma stabilized CDK9 expression and mediated signaling pathways, while interacting with E3 ligase UBR5 to regulate CDK9 ubiquitination levels. Knockdown of CSN6 inhibited melanoma cell proliferation and metastasis, highlighting the potential of CSN6 as a biomarker and therapeutic target in melanoma.
CELL DEATH & DISEASE
(2021)
Article
Developmental Biology
Julia Rehnitz, Berthe Youness, Xuan Phuoc Nguyen, Jens E. Dietrich, Sabine Roesner, Birgitta Messmer, Thomas Strowitzki, Peter H. Vogt
Summary: The study reveals that FMR1 gene is highly expressed in granulosa cells of the female germline and its transcription rate is controlled by CpG methylation levels, particularly in CpG islands. The binding of E2F1 transcriptional activator to the variably methylated region 3 of FMR1 gene suggests an epigenetic mechanism regulating human folliculogenesis efficiency. Differences in CpG 94 methylation levels may impact the efficiency of folliculogenesis in primary granulosa cells of women with normal and reduced fertility.
MOLECULAR HUMAN REPRODUCTION
(2021)
Article
Multidisciplinary Sciences
Alecia-Jane Twigger, Lisa K. Engelbrecht, Karsten Bach, Isabel Schultz-Pernice, Sara Pensa, Jack Stenning, Stefania Petricca, Christina H. Scheel, Walid T. Khaled
Summary: This study characterizes cells in human milk and identifies epithelial cells similar to luminal progenitors and immune cells. The findings contribute to understanding the remodelling of human mammary tissue during pregnancy and lactation, and provide insights into the interplay between pregnancy, lactation, and breast cancer.
NATURE COMMUNICATIONS
(2022)
Article
Biochemistry & Molecular Biology
Tal Havkin-Solomon, Elad Itzhaki, Nir Joffe, Nina Reuven, Yosef Shaul, Rivka Dikstein
Summary: The study reveals that RPS3 mRNA-binding residues have multiple regulatory functions in translation and are exploited by SARS-CoV-2 to influence host and viral mRNA translation and stability.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Biochemical Research Methods
Daniel Osorio, James J. Cai
Summary: The study found that the average mtDNA% in human scRNA-seq data is significantly higher than in mouse tissues. While the 5% threshold performs well for distinguishing between healthy and low-quality cells in mouse tissues, it may not be accurate for human tissues. Omitting or using a suboptimal mtDNA% threshold in scRNA-seq data analysis could lead to erroneous biological interpretations.
Article
Cell Biology
Elisa Mazzoni, Chiara Mazziotta, Maria Rosa Iaquinta, Carmen Lanzillotti, Francesca Fortini, Antonio D'Agostino, Lorenzo Trevisiol, Riccardo Nocini, Giovanni Barbanti-Brodano, Andrea Mescola, Andrea Alessandrini, Mauro Tognon, Fernanda Martini
Summary: Human bone marrow-derived mesenchymal stem cells (hBMSCs) and their derivative enhanced green fluorescent protein (eGFP)-hBMSCs were used to evaluate an innovative hybrid scaffold composed of granular hydroxylapatite and collagen hemostat. The scaffold supported cell growth, metabolism, and osteogenic differentiation, indicating its potential for bone repair and tissue engineering applications.
FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY
(2021)
Article
Endocrinology & Metabolism
Enrichetta Mileti, Kelvin H. M. Kwok, Daniel P. Andersson, Anthony Mathelier, Amitha Raman, Jesper Backdahl, Jutta Jalkanen, Lucas Massier, Anders Thorell, Hui Gao, Peter Arner, Niklas Mejhert, Carsten O. Daub, Mikael Ryden
Summary: Selective hepatic insulin resistance is a feature of obesity and type 2 diabetes. This study showed that white adipose tissue in obese individuals also displays a selective insulin response, which is normalized by weight loss in most genes.
Article
Multidisciplinary Sciences
Jason A. Carter, Leonie Stroemich, Matthew Peacey, Sarah R. Chapin, Lars Velten, Lars M. Steinmetz, Benedikt Brors, Sheena Pinto, Hannah Meyer
Summary: This study reveals the transcriptomic diversity of mTECs in the thymus and establishes a web portal for querying their transcriptome, which may aid in identifying the drivers of autoimmune diseases.
NATURE COMMUNICATIONS
(2022)
Article
Multidisciplinary Sciences
Xianchun Lan, Song Ding, Tianzhe Zhang, Ying Yi, Conghui Li, Wenwen Jin, Jian Chen, Kaiwei Liang, Hengbin Wang, Wei Jiang
Summary: This study reveals that PCGF6 plays a crucial role in determining the lineage specification of human pluripotent stem cells by promoting neuroectoderm differentiation and repressing mesendoderm differentiation. The activation of the SOX2 gene and the repression of the WNT/beta-catenin signaling pathway are key mechanisms involved in this process.
NATURE COMMUNICATIONS
(2022)
Article
Multidisciplinary Sciences
Zlata Vershinin, Michal Feldman, Thilo Werner, Lital Estrella Wei, Margarita Kublanovsky, Elina Abaev-Schneiderman, Menachem Sklarz, Enid Y. N. Lam, Khawla Alasad, Sarah Picaud, Barak Rotblat, Ruth A. McAdam, Vered Chalifa-Caspi, Marcus Bantscheff, Trevor Chapman, Huw D. Lewis, Panagis Filippakopoulos, Mark A. Dawson, Paola Grandi, Rab K. Prinjha, Dan Levy
Summary: Studies have found that BRD4 is methylated on chromatin at lysine-99 by the protein lysine methyltransferase SETD6, which negatively regulates the expression of genes involved in translation and inhibits total mRNA translation in cells. This methylation does not directly associate with acetylated histone H4, but specifically determines the recruitment of the transcription factor E2F1 to selected target genes involved in mRNA translation.
Article
Biochemical Research Methods
Yash D. Patel, Adam J. Brown, Jie Zhu, Guglielmo Rosignoli, Suzanne J. Gibson, Diane Hatton, David C. James
Summary: This study found that synthetic promoters can indeed be used in vectors to mediate the expression of multiple genes at predictable relative stoichiometries, but deviations from expected promoter-mediated transcriptional activity were observed. Positional effects and gene copy levels were found to impact the coexpression of genes within a multigene vector.
ACS SYNTHETIC BIOLOGY
(2021)
Article
Multidisciplinary Sciences
Jian Lu, Raheel Ahmad, Thomas Nguyen, Jeffrey Cifello, Humza Hemani, Jiangyuan Li, Jinguo Chen, Siyi Li, Jing Wang, Achouak Achour, Joseph Chen, Meagan Colie, Ana Lustig, Christopher Dunn, Linda Zukley, Chee W. Chia, Irina Burd, Jun Zhu, Luigi Ferrucci, Nan-Ping Weng
Summary: The decline of CD8(+) T cell functions with aging is a significant health issue, and this study uses single-cell RNA sequencing and machine learning to understand the mechanisms and predict the age of individual cells. This research has important implications for predicting functional outcomes in vaccination or infection.
NATURE COMMUNICATIONS
(2022)
Article
Oncology
Jedrzej Borowczak, Krzysztof Szczerbowski, Mateusz Maniewski, Marek Zdrenka, Piotr Slupski, Paulina Antosik, Sylwia Kolodziejska, Marta Sekielska-Domanowska, Mariusz Dubiel, Magdalena Bodnar, Lukasz Szylberg
Summary: This article investigates the prognostic role of cyclin-dependent kinase 9 expression in urothelial carcinoma. The results show that high CDK9 expression is associated with a better prognosis, tumor grade, stage, and invasiveness, contradicting previous findings. CDK9 may play a crucial role in the early stages of urothelial carcinoma.
Article
Computer Science, Interdisciplinary Applications
Fang Zhou, Avrum Gillespie, Djordje Gligorijevic, Jelena Gligorijevic, Zoran Obradovic
JOURNAL OF BIOMEDICAL INFORMATICS
(2020)
Article
Computer Science, Information Systems
Branimir Ljubic, Ameen Abdel Hai, Marija Stanojevic, Wilson Diaz, Daniel Polimac, Martin Pavlovski, Zoran Obradovic
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2020)
Article
Computer Science, Artificial Intelligence
Daniel Saranovic, Martin Pavlovski, William Power, Ivan Stojkovic, Zoran Obradovic
Summary: With the increasing prevalence of drones, understanding and preparing for possible adversarial uses of drones and drone swarms is crucial. Developing defensive mechanisms utilizing swarms to protect against adversarial UAVs is a problem that needs attention. This work proposes a novel interception method that utilizes swarm's onboard PID controllers for setting drones' states during interception, showing promising results in virtual environment simulations.
INTEGRATED COMPUTER-AIDED ENGINEERING
(2021)
Article
Neurosciences
Nima Asadi, Ingrid R. Olson, Zoran Obradovic
Summary: In this study, an optimization-based null model is proposed to infer significant ties from dynamic functional connectivity network that cannot be reduced to local strengths and properties of nodes. Various aspects of this approach were evaluated, showing its adaptability to most temporal segmentation methods. The approach has advantages such as considering the global information of the network, and was compared with commonly applied null models both empirically and theoretically.
NETWORK NEUROSCIENCE
(2021)
Article
Biochemistry & Molecular Biology
Hongbo M. Xie, Kathrin M. Bernt
Summary: Angiosarcoma is a rare and deadly malignancy, with 33% of patients showing recurrent amplifications of HOXA-cluster genes. The amplifications typically affect multiple pro-angiogenic HOXA genes and commonly co-occur with CD36 and KDR amplifications, with a low overall mutation rate.
Article
Engineering, Electrical & Electronic
Mohammad Alqudah, Martin Pavlovski, Tatjana Dokic, Mladen Kezunovic, Yi Hu, Zoran Obradovic
Summary: This paper proposes an end-to-end supervised learning method for fault detection in the electric grid using Big Data from multiple Phasor Measurement Units (PMUs). The approach includes preprocessing steps to reduce data noise and dimensionality, and utilizes six classification models for fault detection. The experiments show that the CNN-based models outperform traditional methods in outage detection over the entire grid.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Computer Science, Information Systems
Rashid Baembitov, Mladen Kezunovic, Keith A. Brewster, Zoran Obradovic
Summary: This paper expands the approach of predicting weather outages in the distribution grid by incorporating wind modeling. It describes the process of extracting features for the Machine Learning (ML) algorithm and tests the new solution utilizing actual grid performance data. The results reveal that the proposed method improves model performance.
Article
Computer Science, Information Systems
Mohammad Alqudah, Mladen Kezunovic, Zoran Obradovic
Summary: Electric power system operators monitor large multi-modal data streams to operate. The data setups are becoming more complex due to the addition of smart-grid sensors collecting more data from power system substations and other locations. Our proposed methodology utilizes multi-modal data for automated power system fault prediction and precursor discovery, incorporating data from both utility measurements and other databases such as weather observation systems. By automatically preprocessing and learning a joint latent representation from various data sources, we can predict events and discover precursors simultaneously. Applying this methodology to early fault predictions in the U.S. Western Interconnection achieves an AU-ROC of 0.94 using season-specific models, providing valuable insights into the interpretation of precursors for predicted events.
Article
Computer Science, Information Systems
Mohammad Alqudah, Zoran Obradovic
Summary: This research proposes a novel model that jointly predicts power grid outages and discovers precursors using multi-level data. The model utilizes multi-task learning and multi-instance learning to predict outages and learn event precursors, improving event detection and precursor discovery with the introduction of distance-aware self-attention. Experiments conducted on data from the U.S. Pacific Northwest show that the proposed methodology achieves a high accuracy, allowing grid operators to deploy outage mitigation plans and improve grid reliability.
Proceedings Paper
Computer Science, Artificial Intelligence
Jumanah Alshehri, Marija Stanojevic, Parisa Khan, Benjamin Rapp, Eduard Dragut, Zoran Obradovic
Summary: This study introduces MultiLayerET, a method to unify the representation of entities and topics in articles and comments. By parsing the content of articles and associated comments into a multilayer graph, this method analyzes the relationship between articles and comments and provides richer information about emerging events. MultiLayerET can be applied to various tasks, such as detecting media bias and misinformation.
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II
(2023)
Article
Computer Science, Information Systems
Jumanah Alshehri, Martin Pavlovski, Eduard Dragut, Zoran Obradovic
Summary: Disagreement among annotators and data imbalance are challenges in supervised learning for natural language processing. We propose a framework for aligning imbalanced news data with varying degrees of annotator agreement. We introduce a Semi-Automatic Labeling solution based on Human-AI collaboration, which outperforms traditional techniques by 17% in article-comment alignment.
Article
Engineering, Electrical & Electronic
Martin Pavlovski, Mohammad Alqudah, Tatjana Dokic, Ameen Abdel Hai, Mladen Kezunovic, Zoran Obradovic
Summary: Event classification is a central component of automated disturbance analysis based on PMU measurements. A sensitivity study showed that hierarchical convolutional neural networks outperform traditional classification models regardless of the quality of available event labels. It was found that similar performance to domain-driven labeling can be achieved as long as expert mislabeling does not exceed around 5% of the event data captured by PMU measurements.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Computer Science, Information Systems
Ameen Abdel Hai, Tatjana Dokic, Martin Pavlovski, Taif Mohamed, Daniel Saranovic, Mohammad Alqudah, Mladen Kezunovic, Zoran Obradovic
Summary: Event detection in electrical grids is a challenging problem for machine learning methods due to nonstationary systems and the inability to automate event labeling in high-volume data. To improve automated event detection, researchers utilized a transfer learning model to reduce the need for additional data labeling by leveraging labeled data instances from a small number of event detection tasks.
Article
Urology & Nephrology
Avrum Gillespie, Edward L. Fink, Heather M. Gardiner, Crystal A. Gadegbeku, Peter P. Reese, Zoran Obradovic
Summary: The seating arrangement in in-center hemodialysis affects relationship formation among patients, with those requesting living donors more likely to form relationships. Patients discussing transplantation in the center tend to form relationships with others discussing transplantation, but living-donation requests are not influenced by social contagion.
Article
Biochemistry & Molecular Biology
Bi Zhao, Akila Katuwawala, Christopher J. Oldfield, A. Keith Dunker, Eshel Faraggi, Jorg Gsponer, Andrzej Kloczkowski, Nawar Malhis, Milot Mirdita, Zoran Obradovic, Johannes Soding, Martin Steinegger, Yaoqi Zhou, Lukasz Kurgan
Summary: DescribePROT is a database of predicted amino acid-level descriptors of protein structure and function, offering a comprehensive collection of descriptors predicted using accurate algorithms for key model organisms. Users can search and download pre-computed results for various research purposes. Future releases will expand the coverage of DescribePROT.
NUCLEIC ACIDS RESEARCH
(2021)
