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Nucleosomal organization at gene promoters is critical for transcription, with a nucleosome-depleted region (NDR) at transcription start sites (TSSs) being required for transcription initiation. How NDRs and the precise positioning of the +1 nucleosomes are maintained on active genes remains unclear. Here, we report that the nonspecific lethal (NSL) complex is necessary to maintain this stereotypical nucleosomal organization at promoters. Upon NSL1 depletion, nucleosomes invade the NDRs at TSSs of NSL-bound genes. NSL complex member NSL3 binds to TATA-less promoters in a sequence-dependent manner. The NSL complex interacts with the NURF chromatin remodeling complex and is necessary and sufficient to recruit NURF to target promoters. Not only is the NSL complex essential for transcription, but it is required for accurate TSS selection for genes with multiple TSSs. Furthermore, loss of the NSL complex leads to an increase in transcriptional noise. Thus, the NSL complex establishes a canonical nucleosomal organization that enables transcription and determines TSS fidelity.
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http://dx.doi.org/10.1101/gad.321489.118 | DOI Listing |
Int J Dent
August 2025
Department of Orthodontics, University Hospital of Regensburg, University of Regensburg, Regensburg 93047, Germany.
The correct classification of orthodontic patients is essential in individualized diagnostics and treatment planning. However, due to the complexity of the craniofacial skeleton and differences related to gender, age, and ethnicity, cephalometric analysis can be prone to errors. This multicenter, cross-sectional study aimed to compare cephalometric measurements between skeletal class I and II in German orthodontic patients and analyze the effect of gender/age subgroups.
View Article and Find Full Text PDFSensors (Basel)
July 2025
School of Ocean Informattion Engineering, Jimei University, Xiamen 361000, China.
Resource-constrained Internet of Things (IoT) devices demand efficient and robust intrusion detection systems (IDSs) to counter evolving cyber threats. The traditional IDS models, however, struggle with high computational complexity and inadequate feature extraction, limiting their accuracy and generalizability in IoT environments. To address this, we propose FFT-RDNet, a lightweight IDS framework leveraging depthwise separable convolution and frequency-domain feature fusion.
View Article and Find Full Text PDFSci Rep
July 2025
School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450045, China.
Federated learning is a distributed machine learning framework that allows multiple parties to collaboratively train models without sharing raw data. While it enhances data privacy, it is vulnerable to malicious attacks, especially data poisoning attacks like label flipping. Traditional defense mechanisms perform poorly against these complex and diverse attacks, particularly multi-label flipping attacks.
View Article and Find Full Text PDFAnn Anat
August 2025
Selcuk University Faculty of Medicine, Anatomy Department, Konya, Türkiye.
Background: Cleft lip and palate (CLP) is a common congenital anomaly affecting the maxillofacial region, influenced by genetic and environmental factors. This study aims to investigate facial development in adult CLP patients and compare it with healthy controls.
Methods: A total of 67 adult CLP patients and 67 healthy controls were included in this study.
Sci Rep
July 2025
Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.
Growing volumes and sensitivities of information in the growing IoT require strong cybersecurity measures to adequately counter increasingly sophisticated cyberattacks. Machine learning-based anomaly detection has the potential to be a viable solution through abnormal network traffic behavior identification that foretells intrusions. Existing approaches, however, are usually hampered by the inability to effectively counter the sophisticated and evolving nature of such threats, especially in preprocessing optimization and hyperparameter tuning, which typically adopt conventional machine learning and deep learning models.
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