Interstitial telomeric sequences (ITSs) consist of tandem repeats of the canonical telomeric repeat and are common in mammals. They are localized at intrachromosomal sites, including those repeats located close to the centromeres and those found at interstitial sites, i.e., between the centromeres and the telomeres. ITSs might originate from ancestral intrachromosomal rearrangements (inversions and fusions), from differential crossing-over or from the repair of double-strand break during evolution. Three classes of ITSs have been described in the human genome, namely, short ITSs, long subtelomeric ITSs and fusion ITSs. The fourth class of ITSs, pericentromeric ITSs, has been found in other species. The function of ITSs can be inferred from the association of heritable diseases with ITS polymorphic variants, both in copy number and sequence. This is one of the most attractive aspects of ITS studies because it leads to new and useful markers for genetic linkage studies, forensic applications, and detection of genetic instability in tumors. Some ITSs also might be hotspots of chromosome breakage, rearrangement and amplification sites, based on the type of clastogens and the nature of ITSs. This study will contribute new knowledge with respect to ITSs' biology and mechanism, prevalence of diseases, risk evaluation and prevention of related diseases, thus facilitates the design of early detection markers for diseases caused by genomic instability.
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http://dx.doi.org/10.1016/j.mrrev.2007.08.006 | DOI Listing |
Sensors (Basel)
January 2025
Department of Computer Science, King AbdulAziz University, Jeddah 21589, Saudi Arabia.
Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed.
View Article and Find Full Text PDFToxics
December 2024
Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli 35053, Taiwan.
Non-animal assessment of skin sensitization is a global trend. Recently, scientific efforts have been focused on the integration of multiple evidence for decision making with the publication of OECD Guideline No. 497 for defined approaches to skin sensitization.
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November 2024
Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314019, China.
Intelligent transportation systems (ITSs) present new opportunities for enhanced traffic management by leveraging advanced driving behavior sensors and real-time information exchange via vehicle-based and cloud-vehicle communication technologies. Specifically, onboard sensors can effectively detect whether human-driven vehicles are adhering to traffic management directives. However, the formulation and validation of effective strategies for vehicle implementation rely on accurate driving behavior models and reliable model-based testing; in this paper, we focus on large roundabouts as the research scenario.
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November 2024
University of Electronic Science and Technology of China, Chengdu 611731, China.
Intelligent Transportation Systems (ITSs) leverage Internet of Things (IoT) technology to facilitate smart interconnectivity among vehicles, infrastructure, and users, thereby optimizing traffic flow. This paper constructs an optimization model for the fresh food supply chain distribution route of fresh products, considering factors such as carbon emissions, time windows, and cooling costs. By calculating carbon emission costs through carbon taxes, the model aims to minimize distribution costs.
View Article and Find Full Text PDFPeerJ Comput Sci
October 2024
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
Traffic data imputation is crucial for the reliability and efficiency of intelligent transportation systems (ITSs), forming the foundation for downstream tasks like traffic prediction and management. However, existing deep learning-based imputation methods struggle with two significant challenges: poor performance under high missing data rates and the limited incorporation of external traffic-related factors. To address these challenges, we propose a novel knowledge graph-enhanced generative adversarial network (KG-GAN) for traffic data imputation.
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