Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (4)

Search Parameters:
Keywords = transfer ADEV

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
8 pages, 1057 KB  
Article
Time Domain Statistics for Evaluating Residual Noise, Including Instabilities in Time/Frequency Transfer Systems
by Thomas Parker
Time Space 2025, 1(1), 3; https://doi.org/10.3390/timespace1010003 - 8 Jun 2025
Viewed by 408
Abstract
The instabilities in time and frequency transfer systems, a form of residual noise, can contribute significantly to the total uncertainty in time or frequency comparisons. Understanding the characteristics of transfer instabilities is increasingly important with the new high-stability optical frequency standards being developed. [...] Read more.
The instabilities in time and frequency transfer systems, a form of residual noise, can contribute significantly to the total uncertainty in time or frequency comparisons. Understanding the characteristics of transfer instabilities is increasingly important with the new high-stability optical frequency standards being developed. First-difference statistics such as the rms Time Interval Error (TIErms), the Frequency Transfer Uncertainty (FTU), and ADEVS (a novel use of the Allan deviation equation) provide a more direct and accurate measure of residual noise than second-difference statistics such as the Allan Deviation (ADEV), the Modified Allan Deviation (MDEV), and the Time Deviation (TDEV). A unifying discussion on the use of existing first-difference statistics with residual noise, introduced individually in two previous publications, is presented here. Simulated noise data is then analyzed to illustrate the differences in the various statistics. Their strengths and weaknesses are discussed. The impact of pre-averaging phase (time) data is also shown. Full article
Show Figures

Figure 1

21 pages, 731 KB  
Review
Obesity and Adipose-Derived Extracellular Vesicles: Implications for Metabolic Regulation and Disease
by Michele Malaguarnera, Omar Cauli and Andrea Cabrera-Pastor
Biomolecules 2025, 15(2), 231; https://doi.org/10.3390/biom15020231 - 5 Feb 2025
Cited by 2 | Viewed by 3331
Abstract
Obesity, a global epidemic, is a major risk factor for chronic diseases such as type 2 diabetes, cardiovascular disorders, and metabolic syndrome. Adipose tissue, once viewed as a passive fat storage site, is now recognized as an active endocrine organ involved in metabolic [...] Read more.
Obesity, a global epidemic, is a major risk factor for chronic diseases such as type 2 diabetes, cardiovascular disorders, and metabolic syndrome. Adipose tissue, once viewed as a passive fat storage site, is now recognized as an active endocrine organ involved in metabolic regulation and inflammation. In obesity, adipose tissue dysfunction disrupts metabolic balance, leading to insulin resistance and increased production of adipose-derived extracellular vesicles (AdEVs). These vesicles play a key role in intercellular communication and contribute to metabolic dysregulation, affecting organs such as the heart, liver, and brain. AdEVs carry bioactive molecules, including microRNAs, which influence inflammation, insulin sensitivity, and tissue remodeling. In the cardiovascular system, AdEVs can promote atherosclerosis and vascular dysfunction, while those derived from brown adipose tissue offer cardioprotective effects. In type 2 diabetes, AdEVs exacerbate insulin resistance and contribute to complications such as diabetic cardiomyopathy and cognitive decline. Additionally, AdEVs are implicated in metabolic liver diseases, including fatty liver disease, by transferring inflammatory molecules and lipotoxic microRNAs to hepatocytes. These findings highlight the role of AdEVs in obesity-related metabolic disorders and their promise as therapeutic targets for related diseases. Full article
(This article belongs to the Collection Feature Papers in Section 'Molecular Medicine')
Show Figures

Figure 1

18 pages, 4969 KB  
Article
Astrocytes-Derived Small Extracellular Vesicles Hinder Glioma Growth
by Carmela Serpe, Antonio Michelucci, Lucia Monaco, Arianna Rinaldi, Mariassunta De Luca, Pietro Familiari, Michela Relucenti, Erika Di Pietro, Maria Amalia Di Castro, Igea D’Agnano, Luigi Catacuzzeno, Cristina Limatola and Myriam Catalano
Biomedicines 2022, 10(11), 2952; https://doi.org/10.3390/biomedicines10112952 - 17 Nov 2022
Cited by 9 | Viewed by 2720
Abstract
All cells are capable of secreting extracellular vesicles (EVs), which are not a means to eliminate unneeded cellular compounds but represent a process to exchange material (nucleic acids, lipids and proteins) between different cells. This also happens in the brain, where EVs permit [...] Read more.
All cells are capable of secreting extracellular vesicles (EVs), which are not a means to eliminate unneeded cellular compounds but represent a process to exchange material (nucleic acids, lipids and proteins) between different cells. This also happens in the brain, where EVs permit the crosstalk between neuronal and non-neuronal cells, functional to homeostatic processes or cellular responses to pathological stimuli. In brain tumors, EVs are responsible for the bidirectional crosstalk between glioblastoma cells and healthy cells, and among them, astrocytes, that assume a pro-tumoral or antitumoral role depending on the stage of the tumor progression. In this work, we show that astrocyte-derived small EVs (sEVs) exert a defensive mechanism against tumor cell growth and invasion. The effect is mediated by astrocyte-derived EVs (ADEVs) through the transfer to tumor cells of factors that hinder glioma growth. We identified one of these factors, enriched in ADEVs, that is miR124. It reduced both the expression and function of the volume-regulated anion channel (VRAC), that, in turn, decreased the cell migration and invasion of murine glioma GL261 cells. Full article
Show Figures

Graphical abstract

12 pages, 6826 KB  
Article
High-Precision Time-Frequency Signal Simultaneous Transfer System via a WDM-Based Fiber Link
by Qi Zang, Honglei Quan, Kan Zhao, Xiang Zhang, Xue Deng, Wenxiang Xue, Faxi Chen, Tao Liu, Ruifang Dong and Shougang Zhang
Photonics 2021, 8(8), 325; https://doi.org/10.3390/photonics8080325 - 10 Aug 2021
Cited by 20 | Viewed by 3829
Abstract
In this paper, we demonstrate a wavelength division multiplexing (WDM)-based system for simultaneously delivering ultra-stable optical frequency reference, 10 GHz microwave frequency reference, and a one pulse per second (1 PPS) time signal via a 50 km fiber network. For each signal, a [...] Read more.
In this paper, we demonstrate a wavelength division multiplexing (WDM)-based system for simultaneously delivering ultra-stable optical frequency reference, 10 GHz microwave frequency reference, and a one pulse per second (1 PPS) time signal via a 50 km fiber network. For each signal, a unique noise cancellation technique is used to maintain their precision. After being compensated, the transfer frequency instability in terms of the overlapping Allan deviation (OADEV) for the optical frequency achieves 2 × 1017/s and scales down to 2 × 1020/10,000 s, which for the 10 GHz microwave reference, approaches 4 × 1015/s and decreases to 1.4 × 1017/10,000 s, and the time uncertainty of the 1 PPS time signal along the system is 2.08 ps. In this scheme, specific channels of WDM are, respectively, occupied for different signals to avoid the possible crosstalk interference effect between the transmitted reference signals. To estimate the performance of the above scheme, which is also demonstrated in this 50 km link independent of these signals, the results are similar to that in the case of simultaneous delivery. This work shows that the WDM-based system is a promising method for building a nationwide time and frequency fiber transfer system with a communication optical network. Full article
(This article belongs to the Special Issue Optical Network and Access Technologies)
Show Figures

Figure 1

Back to TopTop