Gene expression profiling of early eosinophil development shows increased transcript levels of proinflammatory cytokines, chemokines, transcription factors, and a novel gene, EGO (eosinophil granule ontogeny). EGO is nested within an intron of the inositol triphosphate receptor type 1 (ITPR1) gene and is conserved at the nucleotide level; however, the largest open reading frame (ORF) is 86 amino acids. Sucrose density gradients show that EGO is not associated with ribosomes and therefore is a noncoding RNA (ncRNA). EGO transcript levels rapidly increase following interleukin-5 (IL-5) stimulation of CD34(+) hematopoietic progenitors. EGO RNA also is highly expressed in human bone marrow and in mature eosinophils. RNA silencing of EGO results in decreased major basic protein (MBP) and eosinophil derived neurotoxin (EDN) mRNA expression in developing CD34(+) hematopoietic progenitors in vitro and in a CD34(+) cell line model. Therefore, EGO is a novel ncRNA gene expressed during eosinophil development and is necessary for normal MBP and EDN transcript expression.
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http://dx.doi.org/10.1182/blood-2006-06-027987 | DOI Listing |
Sensors (Basel)
December 2024
Western Science City Intelligent and Connected Vehicle Innovation Center (Chongqing) Co., Ltd., Chongqing 400015, China.
With the rise in the intelligence levels of automated vehicles, increasing numbers of modules of automated driving systems are being combined to achieve better performance and adaptability by reducing information loss. In this study, an integrated decision and motion planning system is designed for multi-object highways. A two-layer structure is presented to decouple the influence of the traffic environment and the dynamic control of ego vehicles using the cognitive safety area, the size of which is determined by naturalistic driving behavior.
View Article and Find Full Text PDFBehav Sci (Basel)
November 2024
School of Psychology, Northeast Normal University, Changchun 130024, China.
Fairness-related decision-making often involves a conflict between egoistic and prosocial motives. Previous research based on Terror Management Theory (TMT) indicates that mortality salience can promote both selfish and prosocial behaviors, leaving its effect on fairness-related decision-making uncertain. This study integrates TMT with the strength model of self-control to investigate the effects of mortality salience on fairness-related decision-making and to examine the moderating role of dispositional self-control.
View Article and Find Full Text PDFNeural Netw
December 2024
CAS Key Laboratory of GIPAS, University of Science and Technology of China, Hefei, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China. Electronic address:
In MARL (Multi-Agent Reinforcement Learning), the trial-and-error learning paradigm based on multiple agents requires massive interactions to produce training samples, significantly increasing both the training cost and difficulty. Therefore, enhancing data efficiency is a core issue in MARL. However, in the context of MARL, agent partially observed information leads to a lack of consideration for agent interactions and coordination from an ego perspective under the world model, which becomes the main obstacle to improving the data efficiency of current proposed MARL methods.
View Article and Find Full Text PDFHealth Sci Rep
January 2025
Department of Microbiology Dr D. Y. Patil Medical College, Hospital and Research Centre, Dr D. Y. Patil Vidyapeeth (Deemed-to-be-University) Pune Maharashtra India.
Background And Aims: Artificial Intelligence (AI) beginning to integrate in healthcare, is ushering in a transformative era, impacting diagnostics, altering personalized treatment, and significantly improving operational efficiency. The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote monitoring. The goal of this review is to present a thorough analysis of AI's effects on healthcare while providing stakeholders with a road map for navigating this changing environment.
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