Journal articles: 'Cellular signals'

1

Nick, Peter. "Hijacking cellular signals." Protoplasma 254, no. 6 (October 11, 2017): 2053–54. http://dx.doi.org/10.1007/s00709-017-1174-0.

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Teplitz, Linda, and Deborah A. Siwik. "Cellular signals in atherosclerosis." Journal of Cardiovascular Nursing 8, no. 3 (April 1994): 28–52. http://dx.doi.org/10.1097/00005082-199404000-00004.

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3

Warren, G. "Sorting signals and cellular membranes." BMJ 295, no. 6608 (November 14, 1987): 1259–61. http://dx.doi.org/10.1136/bmj.295.6608.1259.

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4

Edgington, Thomas. "More Cellular Signals for Atherogenesis?" Circulation 98, no. 12 (September 22, 1998): 1151–52. http://dx.doi.org/10.1161/01.cir.98.12.1151.

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5

Tursz, T., and D. Hoessli. "Chemical Signals of Cellular Interactions." International Archives of Allergy and Immunology 83, no. 1 (1987): 21–35. http://dx.doi.org/10.1159/000234388.

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6

Tarn, Woan-Yuh. "Cellular signals modulate alternative splicing." Journal of Biomedical Science 14, no. 4 (March 24, 2007): 517–22. http://dx.doi.org/10.1007/s11373-007-9161-7.

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Macrez, N., and J. Mironneau. "Local Ca2+ Signals in Cellular Signalling." Current Molecular Medicine 4, no. 3 (May 1, 2004): 263–75. http://dx.doi.org/10.2174/1566524043360762.

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Ramanathan, Harish N., and Yihong Ye. "Cellular strategies for making monoubiquitin signals." Critical Reviews in Biochemistry and Molecular Biology 47, no. 1 (October 8, 2011): 17–28. http://dx.doi.org/10.3109/10409238.2011.620943.

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9

Chiarini, L. B. "Cellular prion protein transduces neuroprotective signals." EMBO Journal 21, no. 13 (July 1, 2002): 3317–26. http://dx.doi.org/10.1093/emboj/cdf324.

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DOWNES, C. Peter, and Colin H. MACPHEE. "myo-Inositol metabolites as cellular signals." European Journal of Biochemistry 193, no. 1 (October 1990): 1–18. http://dx.doi.org/10.1111/j.1432-1033.1990.tb19297.x.

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Mazoyer, Jacques, and Véronique Terrier. "Signals in one-dimensional cellular automata." Theoretical Computer Science 217, no. 1 (March 1999): 53–80. http://dx.doi.org/10.1016/s0304-3975(98)00150-9.

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12

Lau, Chung L. "Cellular signals and responses in development." Trends in Cell Biology 6, no. 3 (March 1996): 114–16. http://dx.doi.org/10.1016/0962-8924(96)81002-6.

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13

SUZUKI, KOICHI, TAKAOMI C. SAIDO, and SHUICHI HIRAI. "Modulation of Cellular Signals by Calpain." Annals of the New York Academy of Sciences 674, no. 1 Proteases and (December 1992): 218–27. http://dx.doi.org/10.1111/j.1749-6632.1992.tb27490.x.

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Rovati, G. Enrico, and Valérie Capra. "Cysteinyl-Leukotriene Receptors and Cellular Signals." Scientific World JOURNAL 7 (2007): 1375–92. http://dx.doi.org/10.1100/tsw.2007.185.

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Cysteinyl-leukotrienes (cysteinyl-LTs) exert a range of proinflammatory effects, such as constriction of airways and vascular smooth muscle, increase of endothelial cell permeability leading to plasma exudation and edema, and enhanced mucus secretion. They have proved to be important mediators in asthma, allergic rhinitis, and other inflammatory conditions, including cardiovascular diseases, cancer, atopic dermatitis, and urticaria. The classification into subtypes of the cysteinyl-LT receptors (CysLTRs) was based initially on binding and functional data, obtained using the natural agonists and a wide range of antagonists. CysLTRs have proved remarkably resistant to cloning. However, in 1999 and 2000, the CysLT1R and CysLT2R were successfully cloned and both shown to be members of the G-protein coupled receptors (GPCRs) superfamily. Molecular cloning has confirmed most of the previous pharmacological characterization and identified distinct expression patterns only partially overlapping. Recombinant CysLTRs couple to the Gq/11pathway that modulates inositol phospholipids hydrolysis and calcium mobilization, whereas in native systems, they often activate a pertussis toxin-insensitive Gi/o-protein, or are coupled promiscuously to both G-proteins. Interestingly, recent data provide evidence for the existence of an additional receptor subtype that seems to respond to both cysteinyl-LTs and uracil nucleosides, and of an intracellular pool of CysLTRs that may have roles different from those of plasma membrane receptors. Finally, a cross-talk between the cysteinyl-LT and the purine systems is being delineated. This review will summarize recent data derived from studies on the molecular and cellular pharmacology of CysLTRs.

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Kolesnick, Richard N. "Sphingomyelin and derivatives as cellular signals." Progress in Lipid Research 30, no. 1 (January 1991): 1–38. http://dx.doi.org/10.1016/0163-7827(91)90005-p.

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16

Nick, Peter. "Moonlighting organelles—signals and cellular architecture." Protoplasma 250, no. 1 (January 12, 2013): 1–2. http://dx.doi.org/10.1007/s00709-012-0477-4.

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Piperno, Anna, Angela Scala, Antonino Mazzaglia, Giulia Neri, Rosamaria Pennisi, Maria Sciortino, and Giovanni Grassi. "Cellular Signaling Pathways Activated by Functional Graphene Nanomaterials." International Journal of Molecular Sciences 19, no. 11 (October 27, 2018): 3365. http://dx.doi.org/10.3390/ijms19113365.

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The paper reviews the network of cellular signaling pathways activated by Functional Graphene Nanomaterials (FGN) designed as a platform for multi-targeted therapy or scaffold in tissue engineering. Cells communicate with each other through a molecular device called signalosome. It is a transient co-cluster of signal transducers and transmembrane receptors activated following the binding of transmembrane receptors to extracellular signals. Signalosomes are thus efficient and sensitive signal-responding devices that amplify incoming signals and convert them into robust responses that can be relayed from the plasma membrane to the nucleus or other target sites within the cell. The review describes the state-of-the-art biomedical applications of FGN focusing the attention on the cell/FGN interactions and signalosome activation.

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18

Kang, Jeong-Hun, Riki Toita, Tetsuro Tomiyama, Jun Oishi, Daisuke Asai, Takeshi Mori, Takuro Niidome, and Yoshiki Katayama. "Cellular signal-specific peptide substrate is essential for the gene delivery system responding to cellular signals." Bioorganic & Medicinal Chemistry Letters 19, no. 21 (November 2009): 6082–86. http://dx.doi.org/10.1016/j.bmcl.2009.09.034.

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19

Teng, Guanlong, Yue Xu, Feng Hong, Jianbo Qi, Ruobing Jiang, Chao Liu, and Zhongwen Guo. "Recognizing and Counting Freehand Exercises Using Ubiquitous Cellular Signals." Sensors 21, no. 13 (July 4, 2021): 4581. http://dx.doi.org/10.3390/s21134581.

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Freehand exercises help improve physical fitness without any requirements for devices or places. Existing fitness assistant systems are typically restricted to wearable devices or exercising at specific positions, compromising the ubiquitous availability of freehand exercises. In this paper, we develop MobiFit, a contactless freehand exercise assistant using just one cellular signal receiver placed on the ground. MobiFit passively monitors the ubiquitous cellular signals sent by the base station, which frees users from the space constraints and deployment overheads and provides accurate repetition counting, exercise type recognition and workout quality assessment without any attachments to the human body. The design of MobiFit faces new challenges of the uncertainties not only on cellular signal payloads but also on signal propagations because the sender (base station) is beyond the control of MobiFit and located far away. To tackle these challenges, we conducted experimental studies to observe the received cellular signal sequence during freehand exercises. Based on the observations, we constructed the analytic model of the received signals. Guided by the insights derived from the analytic model, MobiFit segments out every repetition and rest interval from one exercise session through spectrogram analysis and extracts low-frequency features from each repetition for type recognition. Extensive experiments were conducted in both indoor and outdoor environments, which collected 22,960 exercise repetitions performed by ten volunteers over six months. The results confirm that MobiFit achieves high counting accuracy of 98.6%, high recognition accuracy of 94.1% and low repetition duration estimation error within 0.3 s. Besides, the experiments show that MobiFit works both indoors and outdoors and supports multiple users exercising together.

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HOLZHÜTTER, HERMANN-GEORG, and JÖRN QUEDENAU. "MATHEMATICAL MODELLING OF CELLULAR RESPONSES TO EXTERNAL SIGNALS." Journal of Biological Systems 03, no. 01 (March 1995): 127–38. http://dx.doi.org/10.1142/s0218339095000125.

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An empirical mathematical model is proposed to describe the response (growth rate, metabolic activity etc.) of a cell population to various intensities of an external signal (hormone, antibody, pharmacon etc.). The model is based on the assumption that the signal causes the target system to pass consecutively through i=1, …, N distinct population states having response coefficients Ri. Describing the interaction of the system with the signal according to the rules of chemical kinetics by two phenomenological parameters (k - sensitivity, n - cooperativity index) one arrives at a series expansion for Ri which is linear in the Ri’s but nonlinear with respect to k and n (“R-decomposition”). The pattern of expansion coefficients Ri is characteristic of a given signal and can be used to reveal similarities in the responses of the cell population to various signals. A user-friendly microcomputer program has been developed to fit the model equation to experimental data by means of constraint nonlinear regression analysis and to determine all characteristic curve parameters (number and location of extremal values, inflection points etc.). The robustness and benefit of the model is demonstrated by applications to various types of “exotic” dose-reponse-curves obtained from a neutral-red assay of fibroblasts. Similarities between response curves are studied.

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21

GORGADZE, SVETLANA F., and ANASTASIA V. ERMAKOVA. "EFFICIENCY OF MULTIPLE ACCESS OPTIONS FOR 5G AND 6G CELLULAR NETWORKS." H&ES Research 14, no. 2 (2022): 19–26. http://dx.doi.org/10.36724/2409-5419-2022-14-2-19-26.

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Introduction: IMT2020 (5G) networks can significantly improve the performance of previous generation mobile communication systems in terms of improving broadband multiple access (eMBB – enhanced Mobile Broadband) and providing ultra-reliable low-latency communication (ULLRC – Ultra Low Latency Rellable Communication). The purpose of the work is to review and comparative analysis of multiple access technologies for promising mobile communication networks, which are based on OFDM. Result: The general principles of signal generation and processing when using variants of signals with OFDM using various methods of digital filtering of subcarrier frequencies are considered. For those OFDM options where a cyclic prefix may not be used (FBMC, UFMC), the possibility of increasing the information transfer rate is controversial, since the impulse responses of filters, for example, with FBMC, significantly exceed the duration of information signals, which increases intersymbol interference. Practical significance: The analysis of the general principles for constructing devices for generating and processing physical layer signals for OFDM variants with various methods of additional digital filtering of subcarrier frequencies shows that in all cases there is a significant complication of digital algorithms for generating and processing signals with a practical absence or in some cases a small gain (not more than (0.5&0.8) dB in relation to signal/noise) by the magnitude of the error per information bit. Based on the results of computer simulation, it is shown that OFDM technology and its variants do not allow obtaining breakthrough solutions in the field of physical layer signal characteristics for 5G and 6G networks, and alternative options for building group signal structures should be considered.

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22

Ruskoaho, Heikki, Päivi Kinnunen, Pentti Mäntymaa, Paavo Uusimaa, Tarja Taskinen, Olli Vuolteenaho, and Juhani Leppäluoto. "Cellular signals regulating the release of ANF." Canadian Journal of Physiology and Pharmacology 69, no. 10 (October 1, 1991): 1514–24. http://dx.doi.org/10.1139/y91-227.

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Atrial natriuretic factor (ANF), a peptide hormone that regulates salt and water balance and blood pressure, is synthesized, stored, and secreted from mammalian myocytes. Stretching of atrial myocytes stimulates ANF secretion, but the cellular processes involved in linking mechanical distension to ANF release are unknown. We reported that phorbol esters, which mimic the action of diacylglycerol by acting directly on protein kinase C and the Ca2+ ionophore A23187, which introduces free Ca2+ into the cell, both increase basal ANF secretion in the isolated perfused rat heart. Phorbol ester also increased responsiveness to Ca2+ channel agonists, such as Bay k8644, and to agents that increase cAMP, such as forskolin and membrane-permeable cAMP analogs. In neonatal cultured rat atrial myocytes, protein kinase C activation by 12-O-tetradecanoylphorbol 13-acetate stimulated ANF secretion, whereas the release was unresponsive to changes in intracellular Ca2+. Endothelin, which stimulates phospholipase C mediated hydrolysis of phosphoinositides and activates protein kinase C, increased both basal and atrial stretch-induced ANF secretion from isolated perfused rat hearts. Similarly, phorbol ester enhanced atrial stretch-stimulated ANF secretion, while the increase in intracellular Ca2+ appeared to be negatively coupled to the stretch-induced ANF release. Finally, phorbol ester stimulated ANF release from the severely hypertrophied ventricles of hypertensive animals but not from normal rat myocardium. These results suggest that the protein kinase C activity may play an important role in the regulation of basal ANF secretion both from atria and ventricular cells, and that stretch of atrial myocytes appears to be positively modulated by phorbol esters.Key words: hormone secretion, atrial stretch, protein kinase C, phosphoinositide hydrolysis, cellular calcium.

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23

MATSUHASHI, MICHIO, AKINORI SHINDO, HIDEYUKI OHSHIMA, MIKIO TOBI, SHIGEO ENDO, HIROSHI WATANABE, KATSURA ENDOH, and ALLA N. PANKRUSHINA. "Cellular Signals Regulating Antibiotic Sensitivities of Bacteria." Microbial Drug Resistance 2, no. 1 (January 1996): 91–93. http://dx.doi.org/10.1089/mdr.1996.2.91.

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24

Poghossian, A., S. Ingebrandt, A. Offenhäusser, and M. J. Schöning. "Field-effect devices for detecting cellular signals." Seminars in Cell & Developmental Biology 20, no. 1 (February 2009): 41–48. http://dx.doi.org/10.1016/j.semcdb.2009.01.014.

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25

Bradbury, Jane. "Melanoma spread involves signals in cellular environment." Lancet 358, no. 9284 (September 2001): 817. http://dx.doi.org/10.1016/s0140-6736(01)06015-9.

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26

Kültz, D. "Signals and genes involved in cellular osmoprotection." Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 124 (August 1999): S4. http://dx.doi.org/10.1016/s1095-6433(99)90015-6.

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27

Robertson, A. D. J., and James F. Grutsch. "Biphasic responses, quantal signals and cellular behaviour." Journal of Theoretical Biology 125, no. 1 (March 1987): 41–60. http://dx.doi.org/10.1016/s0022-5193(87)80178-9.

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28

Schmucker, Dietmar. "ABL to Integrate Opposing Cellular Growth Signals." Developmental Cell 42, no. 2 (July 2017): 108–9. http://dx.doi.org/10.1016/j.devcel.2017.07.006.

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29

Herschman, Harvey R. "Extracellular signals, transcriptional responses and cellular specificity." Trends in Biochemical Sciences 14, no. 11 (November 1989): 455–58. http://dx.doi.org/10.1016/0968-0004(89)90105-9.

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30

Rué, P., N. Domedel-Puig, J. Garcia-Ojalvo, and A. J. Pons. "Integration of cellular signals in chattering environments." Progress in Biophysics and Molecular Biology 110, no. 1 (September 2012): 106–12. http://dx.doi.org/10.1016/j.pbiomolbio.2012.05.003.

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31

Sandhya, K., and Mohan C. Vemuri. "Regulation of cellular signals by G-proteins." Journal of Biosciences 22, no. 3 (June 1997): 375–97. http://dx.doi.org/10.1007/bf02703240.

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32

Ben-Porath, Ittai, and Robert A. Weinberg. "The signals and pathways activating cellular senescence." International Journal of Biochemistry & Cell Biology 37, no. 5 (May 2005): 961–76. http://dx.doi.org/10.1016/j.biocel.2004.10.013.

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33

Johnson, David G., and Sharon Y. R. Dent. "Chromatin: Receiver and Quarterback for Cellular Signals." Cell 152, no. 4 (February 2013): 685–89. http://dx.doi.org/10.1016/j.cell.2013.01.017.

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34

Kreutzer, Ulrike, and Thomas Jue. "Role of myoglobin as a scavenger of cellular NO in myocardium." American Journal of Physiology-Heart and Circulatory Physiology 286, no. 3 (March 2004): H985—H991. http://dx.doi.org/10.1152/ajpheart.00115.2003.

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Recent studies have detected a 1H nuclear magnetic resonance (NMR) reporter signal of metmyoglobin (metMb) during bradykinin stimulation of an isolated mouse heart. The observation has led to the hypothesis that Mb reacts with cellular nitric oxide (NO). However, the hypothesis depends on an unequivocal detection of metMb signals in vivo. In solution, nitrite oxidization of Mb produces a characteristic set of paramagnetically shifted 1H NMR signals. In the upfield spectral region, MbO2 and MbCO exhibit the γCH3 Val E11 signals at –2.8 and –2.4 ppm, respectively. In the same spectral region, nitrite oxidation of Mb produces a set of signals at –3.7 and –4.7 ppm at 35°C. Previous studies have confirmed the visibility of metMb signals in perfused rat myocardium. With bradykinin infusion, perfusion pressure and rate-pressure product decrease, consistent with endogenous NO formation. However, neither myocardial O2 consumption nor high-energy phosphate levels, as reflected in the 31P NMR signals, show any significant change. Bradykinin still triggers a similar physiological response even in the presence of CO that is sufficient to inhibit 86% Mb. In all cases, the 1H NMR spectra from perfused rat myocardium reveal no metMb signals. The results suggest that bradykinin-induced NO does not interact significantly with cellular Mb to produce an NMR-detectable quantity of metMb in the perfused rat myocardium. As a consequence, the experiments cannot confirm the intriguing proposal that Mb acts as a cellular NO scavenger.

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35

Košuda, Marek. "Signals of Opportunity: Using Signal Defined Radio to Identify Potential Candidate." Repüléstudományi Közlemények 31, no. 2 (May 29, 2019): 67–76. http://dx.doi.org/10.32560/rk.2019.2.5.

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This paper deals with the prospect of using cellular network signals as one of the candidates from the signals of opportunity as a supplementary or alternative source for navigation and positioning via onboard software defined radio. A low-cost system for modelling GSM coverage over a particular area is proposed.

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36

Johnson, James D., and John P. Chang. "Function- and agonist-specific Ca2+signalling: The requirement for and mechanism of spatial and temporal complexity in Ca2+signals." Biochemistry and Cell Biology 78, no. 3 (April 2, 2000): 217–40. http://dx.doi.org/10.1139/o00-012.

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Calcium signals have been implicated in the regulation of many diverse cellular processes. The problem of how information from extracellular signals is delivered with specificity and fidelity using fluctuations in cytosolic Ca2+concentration remains unresolved. The capacity of cells to generate Ca2+signals of sufficient spatial and temporal complexity is the primary constraint on their ability to effectively encode information through Ca2+. Over the past decade, a large body of literature has dealt with some basic features of Ca2+-handling in cells, as well as the multiplicity and functional diversity of intracellular Ca2+stores and extracellular Ca2+influx pathways. In principle, physiologists now have the necessary information to attack the problem of function- and agonist-specificity in Ca2+signal transduction. This review explores the data indicating that Ca2+release from diverse sources, including many types of intracellular stores, generates Ca2+signals with sufficient complexity to regulate the vast number of cellular functions that have been reported as Ca2+-dependent. Some examples where such complexity may relate to neuroendocrine regulation of hormone secretion/synthesis are discussed. We show that the functional and spatial heterogeneity of Ca2+stores generates Ca2+signals with sufficient spatiotemporal complexity to simultaneously control multiple Ca2+-dependent cellular functions in neuroendocrine systems.Key words: signal coding, IP3receptor, ryanodine receptor, endoplasmic reticulum, Golgi, secretory granules, mitochondria, exocytosis.

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37

Ristovski, Miliça, Danny Farhat, Shelly Ellaine M. Bancud, and Jyh-Yeuan Lee. "Lipid Transporters Beam Signals from Cell Membranes." Membranes 11, no. 8 (July 26, 2021): 562. http://dx.doi.org/10.3390/membranes11080562.

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Lipid composition in cellular membranes plays an important role in maintaining the structural integrity of cells and in regulating cellular signaling that controls functions of both membrane-anchored and cytoplasmic proteins. ATP-dependent ABC and P4-ATPase lipid transporters, two integral membrane proteins, are known to contribute to lipid translocation across the lipid bilayers on the cellular membranes. In this review, we will highlight current knowledge about the role of cholesterol and phospholipids of cellular membranes in regulating cell signaling and how lipid transporters participate this process.

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38

Carafoli, Ernesto, Luigia Santella, Donata Branca, and Marisa Brini. "Generation, Control, and Processing of Cellular Calcium Signals." Critical Reviews in Biochemistry and Molecular Biology 36, no. 2 (January 2001): 107–260. http://dx.doi.org/10.1080/20014091074183.

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39

Nussinov, Ruth. "How do dynamic cellular signals travel long distances?" Mol. BioSyst. 8, no. 1 (2012): 22–26. http://dx.doi.org/10.1039/c1mb05205e.

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Irmler, Martin, Margot Thome, Michael Hahne, Pascal Schneider, Kay Hofmann, Véronique Steiner, Jean-Luc Bodmer, et al. "Inhibition of death receptor signals by cellular FLIP." Nature 388, no. 6638 (July 1997): 190–95. http://dx.doi.org/10.1038/40657.

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41

Pandey, Sona, S. B. Tiwari, K. C. Upadhyaya, and Sudhir K. Sopory. "Calcium Signaling: Linking Environmental Signals to Cellular Functions." Critical Reviews in Plant Sciences 19, no. 4 (July 2000): 291–318. http://dx.doi.org/10.1080/07352680091139240.

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Carafoli, Ernesto. "Calcium-mediated cellular signals: a story of failures." Trends in Biochemical Sciences 29, no. 7 (July 2004): 371–79. http://dx.doi.org/10.1016/j.tibs.2004.05.006.

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43

Li, Ping, Mingxue Gu, and Haoxing Xu. "Lysosomal Ion Channels as Decoders of Cellular Signals." Trends in Biochemical Sciences 44, no. 2 (February 2019): 110–24. http://dx.doi.org/10.1016/j.tibs.2018.10.006.

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44

Shears, Stephen B. "The versatility of inositol phosphates as cellular signals." Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids 1436, no. 1-2 (December 1998): 49–67. http://dx.doi.org/10.1016/s0005-2760(98)00131-3.

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Moore, M. N. "Environmental distress signals: Cellular reactions to marine pollution." Progress in Histochemistry and Cytochemistry 23, no. 1-4 (January 1991): 1–19. http://dx.doi.org/10.1016/s0079-6336(11)80164-6.

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46

Behar, Marcelo, and Alexander Hoffmann. "Understanding the temporal codes of intra-cellular signals." Current Opinion in Genetics & Development 20, no. 6 (December 2010): 684–93. http://dx.doi.org/10.1016/j.gde.2010.09.007.

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47

Beppu, Teruhiko. "Secondary metabolites as chemical signals for cellular differentiation." Gene 115, no. 1-2 (June 1992): 159–65. http://dx.doi.org/10.1016/0378-1119(92)90554-3.

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48

Mattiazzi Ušaj, Mojca, Metod Prelec, Mojca Brložnik, Cecilia Primo, Tomaž Curk, Janez Ščančar, Lynne Yenush, and Uroš Petrovič. "Yeast Saccharomyces cerevisiae adiponectin receptor homolog Izh2 is involved in the regulation of zinc, phospholipid and pH homeostasis." Metallomics 7, no. 9 (2015): 1338–51. http://dx.doi.org/10.1039/c5mt00095e.

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Izh2 acts as an integrator of intra- and extracellular signals. It dispatches a single input signal – a change in extracellular Zn concentration – into regulatory networks of several cellular processes, whereby it acts as the second line of cellular adaptation to perturbations to zinc homeostasis.

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Bharadwaj, Shruti, Rakesh Dubey, Md Iltaf Zafar, Saurabh Kr Tiwary, Rashid Aziz Faridi, and Susham Biswas. "A Novel Method to Determine the Optimal Location for a Cellular Tower by Using LiDAR Data." Applied System Innovation 5, no. 2 (February 23, 2022): 30. http://dx.doi.org/10.3390/asi5020030.

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The cellular industry faces challenges in controlling the quality of signals for all users, given its meteoric growth in the last few years. The service providers are required to place cellular towers at the optimal location for providing a strong cellular network in a particular region. However, due to buildings, roads, open spaces, etc., of varying topography in 3D (obstructing the signals) and varying densities of settlements, finding the optimal location for the tower becomes challenging. Further, in a bigger area, it is required to determine the optimum number and locations for setting up cellular towers to ensure improved quality. The determination of optimum solutions requires a signal strength prediction model that needs to integrate terrain data, information of cellular tower with users’ locations, along with tower signal strengths for predictions. Existing modeling practices face limitations in terms of the usage of 2D data, rough terrain inputs, and the inability to provide detailed shapefiles to GIS. The estimation of optimum distribution of cellular towers necessitates the determination of a model for the prediction of signal strength at users’ locations accurately. Better modeling is only possible with detailed and precise data in 3D. Considering the above needs, a LIDAR data-based cellular tower distribution modeling is attempted in this article. The locations chosen for this research are RGIPT, UP (45 Acre), and Shahganj, Agra, UP, India (6 km2). LiDAR data and google images for the project sites were classified as buildings and features. The edges of overground objects were extracted and used to determine the routes for transmission of a signal from the tower to user locations. The terrain parameters and transmission losses for every route are determined to model the signal strength for a user’s location. The ground strength of signals is measured over 1000 points in 3D at project sites to compare with modeled signal strengths (an RMSE error 3.45). The accurate model is then used to determine the optimum number and locations of cellular towers for each site. Modeled optimum solutions are compared with existing tower locations to estimate % over design or under design and the scope of improvement (80% users below −80 dB m improves to 70% users above −75 dB m).

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CHEN, AIMIN, JIAJUN ZHANG, ZHANJIANG YUAN, and TIANSHOU ZHOU. "NOISE-INDUCED ALTERNATIVE RESPONSE IN MAP KINASE PATHWAYS WITH MUTUAL INHIBITION." Journal of Biological Systems 17, no. 01 (March 2009): 125–40. http://dx.doi.org/10.1142/s021833900900282x.

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Abstract:

All organisms have the ability to detect and respond to changes in the environment for survival, and as a result, specific cellular signaling pathways have evolved by which organisms sense their environment and respond to signals that they detect. However, an important unsolved problem in cell biology is to understand how specificity from signal to cellular response is maintained between different signal transduction pathways that share similar or identical components. Here, we show, using mathematical and computational modeling, that two typical signaling pathways in a single cell, hyperosmolar and pheromone motigen-avtivated protein kinase in the yeast Saccharomyces cerevisiae with mutual inhibition, can respond alternatively to two costimulated signals in a stochastically fluctuated environment. Within a bistable region over two input signals, noise plays an essential role in achieving specificity of response, while outside it, these pathways achieve specificity by filtering out spurious crosstalk through mutual inhibition.

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Journal articles on the topic 'Cellular signals'

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