Relevant bibliographies by topics / Proton sensing

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Proton sensing'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Proton sensing"

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Mashiko, Misaki, Aya Kurosawa, Yuki Tani, Takashi Tsuji, and Shigeki Takeda. "GPR31 and GPR151 are activated under acidic conditions." Journal of Biochemistry 166, no. 4 (May 23, 2019): 317–22. http://dx.doi.org/10.1093/jb/mvz042.

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Abstract Recent studies have revealed that not only proton-sensing channels, but also one family of G protein-coupled receptors (GPCRs) comprising OGR1, GPR4, G2A and TDAG8 are responsible for the sensing of extracellular protons, or pH. Here, we report that two other GPCRs, GPR31 and GPR151, were also activated in acidic condition. Elevated pH of assay mixtures resulted in a remarkable increase in [35S]GTPγS binding by GPR31–Giα and GPR151–Giα fusion proteins in a narrow range between pH 6 and 5. Our reporter gene assays with CHO cells expressing recombinant GPR31 or GPR151 also showed that activation was maximal at pH ∼5.8. Although these results from in vitro and cellular assays revealed slightly different pH sensitivities, all of our results indicated that GPR31 and GPR151 sensed extracellular protons equally well as other proton-sensing GPCRs.
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Sherwood, Thomas W., Erin N. Frey, and Candice C. Askwith. "Structure and activity of the acid-sensing ion channels." American Journal of Physiology-Cell Physiology 303, no. 7 (October 1, 2012): C699—C710. http://dx.doi.org/10.1152/ajpcell.00188.2012.

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The acid-sensing ion channels (ASICs) are a family of proton-sensing channels expressed throughout the nervous system. Their activity is linked to a variety of complex behaviors including fear, anxiety, pain, depression, learning, and memory. ASICs have also been implicated in neuronal degeneration accompanying ischemia and multiple sclerosis. As a whole, ASICs represent novel therapeutic targets for several clinically important disorders. An understanding of the correlation between ASIC structure and function will help to elucidate their mechanism of action and identify potential therapeutics that specifically target these ion channels. Despite the seemingly simple nature of proton binding, multiple studies have shown that proton-dependent gating of ASICs is quite complex, leading to activation and desensitization through distinct structural components. This review will focus on the structural aspects of ASIC gating in response to both protons and the newly discovered activators GMQ and MitTx. ASIC modulatory compounds and their action on proton-dependent gating will also be discussed. This review is dedicated to the memory of Dale Benos, who made a substantial contribution to our understanding of ASIC activity.
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Sakata, Souhei, Tatsuki Kurokawa, Morten H. H. Nørholm, Masahiro Takagi, Yoshifumi Okochi, Gunnar von Heijne, and Yasushi Okamura. "Functionality of the voltage-gated proton channel truncated in S4." Proceedings of the National Academy of Sciences 107, no. 5 (December 14, 2009): 2313–18. http://dx.doi.org/10.1073/pnas.0911868107.

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The voltage sensor domain (VSD) is the key module for voltage sensing in voltage-gated ion channels and voltage-sensing phosphatases. Structurally, both the VSD and the recently discovered voltage-gated proton channels (Hv channels) voltage sensor only protein (VSOP) and Hv1 contain four transmembrane segments. The fourth transmembrane segment (S4) of Hv channels contains three periodically aligned arginines (R1, R2, R3). It remains unknown where protons permeate or how voltage sensing is coupled to ion permeation in Hv channels. Here we report that Hv channels truncated just downstream of R2 in the S4 segment retain most channel properties. Two assays, site-directed cysteine-scanning using accessibility of maleimide-reagent as detected by Western blotting and insertion into dog pancreas microsomes, both showed that S4 inserts into the membrane, even if it is truncated between the R2 and R3 positions. These findings provide important clues to the molecular mechanism underlying voltage sensing and proton permeation in Hv channels.
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Wobig, Lea, Thérèse Wolfenstetter, Sylvia Fechner, Wolfgang Bönigk, Heinz G. Körschen, Jan F. Jikeli, Christian Trötschel, et al. "A family of hyperpolarization-activated channels selective for protons." Proceedings of the National Academy of Sciences 117, no. 24 (May 28, 2020): 13783–91. http://dx.doi.org/10.1073/pnas.2001214117.

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Proton (H+) channels are special: They select protons against other ions that are up to a millionfold more abundant. Only a few proton channels have been identified so far. Here, we identify a family of voltage-gated “pacemaker” channels, HCNL1, that are exquisitely selective for protons. HCNL1 activates during hyperpolarization and conducts protons into the cytosol. Surprisingly, protons permeate through the channel’s voltage-sensing domain, whereas the pore domain is nonfunctional. Key to proton permeation is a methionine residue that interrupts the series of regularly spaced arginine residues in the S4 voltage sensor. HCNL1 forms a tetramer and thus contains four proton pores. Unlike classic HCN channels, HCNL1 is not gated by cyclic nucleotides. The channel is present in zebrafish sperm and carries a proton inward current that acidifies the cytosol. Our results suggest that protons rather than cyclic nucleotides serve as cellular messengers in zebrafish sperm. Through small modifications in two key functional domains, HCNL1 evolutionarily adapted to a low-Na+freshwater environment to conserve sperm’s ability to depolarize.
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Carvacho, Ingrid, I. Scott Ramsey, and David E. Clapham. "Voltage and proton gradient sensing in Hv1 proton channels." Biophysical Journal 96, no. 3 (February 2009): 484a. http://dx.doi.org/10.1016/j.bpj.2008.12.2495.

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Osmakov, Dmitry I., Sergey G. Koshelev, Igor A. Ivanov, Yaroslav A. Andreev, and Sergey A. Kozlov. "Endogenous Neuropeptide Nocistatin Is a Direct Agonist of Acid-Sensing Ion Channels (ASIC1, ASIC2 and ASIC3)." Biomolecules 9, no. 9 (August 22, 2019): 401. http://dx.doi.org/10.3390/biom9090401.

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Acid-sensing ion channel (ASIC) channels belong to the family of ligand-gated ion channels known as acid-sensing (proton-gated) ion channels. Only a few activators of ASICs are known. These are exogenous and endogenous molecules that cause a persistent, slowly desensitized current, different from an acid-induced current. Here we describe a novel endogenous agonist of ASICs—peptide nocistatin produced by neuronal cells and neutrophils as a part of prepronociceptin precursor protein. The rat nocistatin evoked currents in X. laevis oocytes expressing rat ASIC1a, ASIC1b, ASIC2a, and ASIC3 that were very similar in kinetic parameters to the proton-gated response. Detailed characterization of nocistatin action on rASIC1a revealed a proton-like dose-dependence of activation, which was accompanied by a dose-dependent decrease in the sensitivity of the channel to the protons. The toxin mambalgin-2, antagonist of ASIC1a, inhibited nocistatin-induced current, therefore the close similarity of mechanisms for ASIC1a activation by peptide and protons could be suggested. Thus, nocistatin is the first endogenous direct agonist of ASICs. This data could give a key to understanding ASICs activation regulation in the nervous system and also could be used to develop new drugs to treat pathological processes associated with ASICs activation, such as neurodegeneration, inflammation, and pain.
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Sisignano, Marco, Michael J. M. Fischer, and Gerd Geisslinger. "Proton-Sensing GPCRs in Health and Disease." Cells 10, no. 8 (August 10, 2021): 2050. http://dx.doi.org/10.3390/cells10082050.

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The group of proton-sensing G-protein coupled receptors (GPCRs) consists of the four receptors GPR4, TDAG8 (GPR65), OGR1 (GPR68), and G2A (GPR132). These receptors are cellular sensors of acidification, a property that has been attributed to the presence of crucial histidine residues. However, the pH detection varies considerably among the group of proton-sensing GPCRs and ranges from pH of 5.5 to 7.8. While the proton-sensing GPCRs were initially considered to detect acidic cellular environments in the context of inflammation, recent observations have expanded our knowledge about their physiological and pathophysiological functions and many additional individual and unique features have been discovered that suggest a more differentiated role of these receptors in health and disease. It is known that all four receptors contribute to different aspects of tumor biology, cardiovascular physiology, and asthma. However, apart from their overlapping functions, they seem to have individual properties, and recent publications identify potential roles of individual GPCRs in mechanosensation, intestinal inflammation, oncoimmunological interactions, hematopoiesis, as well as inflammatory and neuropathic pain. Here, we put together the knowledge about the biological functions and structural features of the four proton-sensing GPCRs and discuss the biological role of each of the four receptors individually. We explore all currently known pharmacological modulators of the four receptors and highlight potential use. Finally, we point out knowledge gaps in the biological and pharmacological context of proton-sensing GPCRs that should be addressed by future studies.
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Ludwig, Marie-Gabrielle, Miroslava Vanek, Danilo Guerini, Jürg A. Gasser, Carol E. Jones, Uwe Junker, Hans Hofstetter, Romain M. Wolf, and Klaus Seuwen. "Proton-sensing G-protein-coupled receptors." Nature 425, no. 6953 (September 2003): 93–98. http://dx.doi.org/10.1038/nature01905.

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Shapira, Barak, Eran Avraham, and Doron Aurbach. "Proton-selective electrode for pH sensing." Electrochemistry Communications 73 (December 2016): 80–84. http://dx.doi.org/10.1016/j.elecom.2016.11.007.

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Randolph, Aaron L., Carlos A. Villalba-Galea, and I. Scott Ramsey. "Voltage Sensing in Hv1 Proton Channels." Biophysical Journal 104, no. 2 (January 2013): 207a. http://dx.doi.org/10.1016/j.bpj.2012.11.1173.

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Dissertations / Theses on the topic "Proton sensing"

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Xin, Xie Zhu Da-Ming. "Current sensing atomic force microscope study of proton exchange membranes." Diss., UMK access, 2006.

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Thesis (M.S.)--Dept. of Physics. University of Missouri--Kansas City, 2006.
"A thesis in physics." Typescript. Advisor: Da-Ming Zhu. Vita. Title from "catalog record" of the print edition Description based on contents viewed Nov. 12, 2007. Includes bibliographical references (leaves 50-51). Online version of the print edition.
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Randolph, Aaron L. "Voltage Sensing Mechanism in the Voltage-gated and Proton (H+)-selective Ion Channel Hv1." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/582.

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Activation of the intrinsic aqueous water-wire proton conductance (GAQ) in Hv1 channels is controlled by changes in membrane potential and the transmembrane pH gradient (ΔpH). The mechanism by which changes in ΔpH affect the apparent voltage dependence of GAQ activation is not understood. In order to measure voltage sensor (VS) activation in Hv1, we mutated a conserved Arg residue in the fourth helical segment (S4) to His and measured H+ currents under whole-cell voltage clamp in transfected HEK-293 cells. Consistent with previous studies in VS domain containing proteins, we find that Hv1 R205H mediates a robust resting-state H+ ‘shuttle’ conductance (GSH) at negative membrane potentials. Voltage-dependent GSH gating is measured at more negative voltages than the activation GAQ, indicating that VS activation is thermodynamically distinct from opening of the intrinsic H+ permeation pathway. A hallmark biophysical feature of Hv1 channels is a ~-40 mV/pH unit shift in the apparent voltage dependence of GAQ gating. We show here that changes pHO are sufficient to cause similar shifts in GSH gating, indicating that GAQ inherits its pH dependence from an early step in the Hv1 activation pathway. Furthermore, we show for the first time that Hv1 channels manifest a form of electromechanical coupling VS activation and GAQ pore opening. Second-site mutations of D185 markedly alter GAQ gating without affecting GSH gating, indicating that D185 is required for a late step in the activation pathway that controls opening of the aqueous H+ permeation pathway. In summary, this work demonstrates that the Hv1 activation pathway contains multiple transitions with distinct voltage and pH dependencies that have not been previously identified. The results reported here novel insight into the mechanism of VS activation in Hv1 and raise fundamental questions about the nature of pH-dependent gating and electromechanical coupling in related VS domain-containing ion channels and phosphatases.
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Campion, Katherine. "Characterisation of calcium-sensing receptor extracellular pH sensitivity and intracellular signal integration." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/characterisation-of-calciumsensing-receptor-extracellular-ph-sensitivity-and-intracellular-signal-integration(e11adf01-4748-42ed-8679-f8b990d79dea).html.

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Parathyroid hormone (PTH) secretion maintains free-ionised extracellular calcium (Ca2+o) homeostasis under the control of the calcium-sensing receptor (CaR). In humans and dogs, blood acidosis and alkalosis is associated with increased or suppressed PTH secretion respectively. Furthermore, large (1.0 pH unit) changes in extracellular pH (pHo) alter Ca2+o sensitivity of the CaR in CaR-transfected HEK-293 cells (CaR-HEK). Indeed, it has been found in this laboratory that even pathophysiological acidosis (pH 7.2) renders CaR less sensitive to Ca2+o while pathophysiological alkalosis (pH 7.6) increases its Ca2+o sensitivity, both in CaR-HEK and parathyroid cells. If true in vivo, then CaR’s pHo sensitivity might represent a mechanistic link between metabolic acidosis and hyperparathyroidism in ageing and renal disease. However, in acidosis one might speculate that the additional H+ could displace Ca2+ bound to plasma albumin, thus increasing free-Ca2+ concentration and so compensating for the decreased CaR responsiveness. Therefore, I first demonstrated that a physiologically-relevant concentration of albumin (5% w/v) failed to overcome the inhibitory effect of pH 7.2 or stimulatory effect of pH 7.6 on CaR-induced intracellular Ca2+ (Ca2+i) mobilisation. Determining the molecular basis of CaR pHo sensitivity would help explain cationic activation of CaR and permit the generation of experimental CaR models that specifically lack pHo sensitivity. With extracellular histidine and free cysteine residues the most likely candidates for pHo sensing (given their sidechains’ pK values), all 17 such CaR residues were mutated to non-ionisable residues. However, none of the resulting CaR mutants exhibited significantly decreased CaR pHo sensitivity. Even co-mutation of the two residues whose individual mutation appeared to elicit modest reductions (CaRH429V and CaRH495V) failed to exhibit any change in CaR pHo sensitivity. I conclude therefore, that neither extracellular histidine nor free cysteine residues account for CaR pHo sensitivity. Next, it is known that cytosolic cAMP drives PTH secretion in vivo and that cAMP potentiates Ca2+o-induced Ca2+i mobilisation in CaR-HEK cells. Given the physiological importance of tightly controlled PTH secretion and Ca2+o homeostasis, here I investigated the influence of cAMP on CaR signalling in CaR-HEK cells. Agents that increase cytosolic cAMP levels such as forskolin and isoproterenol potentiated Ca2+o-induced Ca2+i mobilisation and lowered the Ca2+o threshold for Ca2+i mobilisation. Indeed, forskolin lowered the EC50 for Ca2+o on CaR (2.3 ± 0.1 vs. 3.0 ± 0.1 mM control, P<0.001). Forskolin also potentiated CaR-induced ERK phosphorylation; however protein kinase A activation appeared uninvolved in any of these effects. Pertussis toxin, used to block CaR-induced suppression of cAMP accumulation, also lowered the Ca2+o threshold for Ca2+i mobilisation though appeared to do so by increasing efficacy (Emax). Furthermore, mutation of the CaR’s two putative PKA consensus sequences (CaRS899 and CaRS900) to a non-phosphorylatable residue (alanine) failed to alter the potency of Ca2+o for CaR or attenuate the forskolin response. In contrast, phosphomimetic mutation of CaRS899 (to aspartate) did increase CaR sensitivity to Ca2+o. Together this suggests that PKA-mediated CaRS899 phosphorylation could potentiate CaR activity but that this does not occur following Ca2+o treatment in CaR-HEK cells. Together, these data show that cAMP regulates the Ca2+o threshold for Ca2+i mobilisation, thus helping to explain differential efficacy between CaR downstream signals. If true in vivo, this could help explain how multiple physiological signal inputs may be integrated in parathyroid cells.
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Nassios, Anaïs [Verfasser], and Stephan [Akademischer Betreuer] Schreml. "Expression of proton-sensing G-protein-coupled receptors in selected skin tumors / Anaïs Nassios ; Betreuer: Stephan Schreml." Regensburg : Universitätsbibliothek Regensburg, 2020. http://d-nb.info/121309612X/34.

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Shvadchak, Volodymyr. "Two-color fluorescent dyes for sensing peptide interactions : application to the retroviral proteins." Strasbourg, 2009. https://publication-theses.unistra.fr/public/theses_doctorat/2009/SHVADCHAK_Volodymyr_2009.pdf.

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Lguensat, Redouane. "Learning from ocean remote sensing data." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0050/document.

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Reconstruire des champs géophysiques à partir d'observations bruitées et partielles est un problème classique bien étudié dans la littérature. L'assimilation de données est une méthode populaire pour aborder ce problème, et se fait par l'utilisation de techniques classiques, comme le filtrage de Kalman d’ensemble ou des filtres particulaires qui procèdent à une évaluation online du modèle physique afin de fournir une prévision de l'état. La performance de l'assimilation de données dépend alors fortement de du modèle physique. En revanche, la quantité de données d'observation et de simulation a augmenté rapidement au cours des dernières années. Cette thèse traite l'assimilation de données d'une manière data-driven et ce, sans avoir accès aux équations explicites du modèle. Nous avons développé et évalué l'assimilation des données par analogues (AnDA), qui combine la méthode des analogues et des méthodes de filtrage stochastiques (filtres Kalman, filtres à particules, chaînes de Markov cachées). Des applications aux modèles chaotiques simplifiés et à des études de cas de télédétection réelle (température de surface de lamer, anomalies du niveau de la mer), nous démontrons la pertinence d'AnDA pour l'interpolation de données manquantes des systèmes dynamiques non linéaires et à haute dimension à partir d'observations irrégulières et bruyantes.Motivé par l'essor du machine learning récemment, la dernière partie de cette thèse est consacrée à l'élaboration de modèles deep learning pour la détection et de tourbillons océaniques à partir de données de sources multiples et/ou multi temporelles (ex: SST-SSH), l'objectif général étant de surpasser les approches dites expertes
Reconstructing geophysical fields from noisy and partial remote sensing observations is a classical problem well studied in the literature. Data assimilation is one class of popular methods to address this issue, and is done through the use of classical stochastic filtering techniques, such as ensemble Kalman or particle filters and smoothers. They proceed by an online evaluation of the physical modelin order to provide a forecast for the state. Therefore, the performanceof data assimilation heavily relies on the definition of the physical model. In contrast, the amount of observation and simulation data has grown very quickly in the last decades. This thesis focuses on performing data assimilation in a data-driven way and this without having access to explicit model equations. The main contribution of this thesis lies in developing and evaluating the Analog Data Assimilation(AnDA), which combines analog methods (nearest neighbors search) and stochastic filtering methods (Kalman filters, particle filters, Hidden Markov Models). Through applications to both simplified chaotic models and real ocean remote sensing case-studies (sea surface temperature, along-track sea level anomalies), we demonstrate the relevance of AnDA for missing data interpolation of nonlinear and high dimensional dynamical systems from irregularly-sampled and noisy observations. Driven by the rise of machine learning in the recent years, the last part of this thesis is dedicated to the development of deep learning models for the detection and tracking of ocean eddies from multi-source and/or multi-temporal data (e.g., SST-SSH), the general objective being to outperform expert-based approaches
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Bin, Khayat Mohd Ezuan. "Protein kinase involvement in wild-type and mutant calcium-sensing receptor signalling." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/protein-kinase-involvement-in-wildtype-and-mutant-calciumsensing-receptor-signalling(b0189d85-400e-4b65-9412-bb0b3527b01d).html.

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The calcium-sensing receptor (CaR) is a G-protein coupled receptor that controls mammalian extracellular calcium (Ca2+o) homeostasis. CaR downstream signalling involves intracellular calcium (Ca2+i) mobilisation which can be negatively modulated by protein kinase C (PKC)-mediated phosphorylation of CaR residue Thr-888 (CaRT888). The nature of this regulation was investigated here using siRNA-based knockdown of individual PKC isotypes. Knocking down PKCα expression increased CaR-induced Ca2+i mobilisation in CaR-HEK cells, significantly lowering the EC50 for Ca2+o relative to control siRNA-transfected cells. In accordance, PKCα knockdown also decreased CaRT888 phosphorylation which also permitted the triggering of Ca2+i mobilisation in CaR-HEK cells at sub-threshold Ca2+o concentrations. Interestingly, PKCε knockdown attenuated CaR-induced Ca2+i mobilisation in CaR-HEK cells, significantly increasing the EC50 for Ca2+o. However, this knockdown was also also found to inhibit CaRT888 phosphorylation and this is the first time that CaRT888 phosphorylation has been shown to be dissociate from Ca2+i mobilisation. The results show the complexity of the interactions that potentially underlie the CaR’s pleiotropic signalling and provides novel targets for examining signal bias. Classically an increase in cAMP is known to trigger PTH seceretion. The observation in this study shows that raising intracellular cAMP levels with forskolin also decreased CaRT888 phosphorylation permitting increased Ca2+i mobilisation. This suggests that cAMP may stimulate the phosphatase (most likely protein phosphatase 2A (PP2A)). Nevertheless, knocking down Gα12, which has been shown to activate PP2A, resulted in increased CaRT888 phosphorylation and lower Ca2+i mobilisation (increased EC50 for Ca2+o). This suggests the possibility of CaR as a cAMP sensor that can detect an increase in intracellular cAMP in order to stop PTH serection. Three novel CaR effectors, P70 ribosamal protein S6 kinase, insulin-like growth factor receptor-1 and nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, were identified in CaR-HEK cells. It was shown that a) high Ca2+o stimulated the activation of these effectors and b) each effector was inhibited by knockdown of PKCα and Gα12, which further confirmed the association of these signals with CaR. These data show that CaR also plays an important role outside Ca2+o homeostasis, such as growth and inflammation. Finally, five CaR mutations associated with autosomal dominant hypocalcaemia (ADH) were found to increase Ca2+o-induced Ca2+i mobilisation, as well as ERK and p38MAPK activation, when transfected stably in HEK-293 cells. Cotreatment with the calcilytic NPSP795 inhibited ERK and p38MAPK phosphorylation in all 5 gain-of-function mutants and in the wild type CaR cells, with IC50s for the compound in the nanomolar range. These data highlight the potential utility of CaR negative allosteric modulators in the treatment of gain-of-function CaR mutations. Together these data enhance our understanding of CaRT888 phosphorylation and CaR signalling.
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Maggiori, Emmanuel. "Approches d'apprentissage pour la classification à large échelle d'images de télédétection." Thesis, Université Côte d'Azur (ComUE), 2017. http://www.theses.fr/2017AZUR4041/document.

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L’analyse des images satellite et aériennes figure parmi les sujets fondamentaux du domaine de la télédétection. Ces dernières années, les avancées technologiques ont permis d’augmenter la disponibilité à large échelle des images, en comprenant parfois de larges étendues de terre à haute résolution spatiale. En plus des questions évidentes de complexité calculatoire qui en surgissent, un de plus importants défis est l’énorme variabilité des objets dans les différentes régions de la terre. Pour aborder cela, il est nécessaire de concevoir des méthodes de classification qui dépassent l’analyse du spectre individuel de chaque pixel, en introduisant de l’information contextuelle de haut niveau. Dans cette thèse, nous proposons d’abord une méthode pour la classification avec des contraintes de forme, basée sur l’optimisation d’une structure de subdivision hiérarchique des images. Nous explorons ensuite l’utilisation des réseaux de neurones convolutionnels (CNN), qui nous permettent d’apprendre des descripteurs hiérarchiques profonds. Nous étudions les CNN depuis de nombreux points de vue, ce qui nous permettra de les adapter à notre objectif. Parmi les sujets abordés, nous proposons différentes solutions pour générer des cartes de classification à haute résolution et nous étudions aussi la récolte des données d’entrainement. Nous avons également créé une base de données d’images aériennes sur des zones variées, pour évaluer la capacité de généralisation des CNN. Finalement, nous proposons une méthode pour polygonaliser les cartes de classification issues des réseaux de neurones, afin de pouvoir les intégrer dans des systèmes d’information géographique. Au long de la thèse, nous conduisons des expériences sur des images hyperspectrales, satellites et aériennes, toujours avec l’intention de proposer des méthodes applicables, généralisables et qui passent à l’échelle
The analysis of airborne and satellite images is one of the core subjects in remote sensing. In recent years, technological developments have facilitated the availability of large-scale sources of data, which cover significant extents of the earth’s surface, often at impressive spatial resolutions. In addition to the evident computational complexity issues that arise, one of the current challenges is to handle the variability in the appearance of the objects across different geographic regions. For this, it is necessary to design classification methods that go beyond the analysis of individual pixel spectra, introducing higher-level contextual information in the process. In this thesis, we first propose a method to perform classification with shape priors, based on the optimization of a hierarchical subdivision data structure. We then delve into the use of the increasingly popular convolutional neural networks (CNNs) to learn deep hierarchical contextual features. We investigate CNNs from multiple angles, in order to address the different points required to adapt them to our problem. Among other subjects, we propose different solutions to output high-resolution classification maps and we study the acquisition of training data. We also created a dataset of aerial images over dissimilar locations, and assess the generalization capabilities of CNNs. Finally, we propose a technique to polygonize the output classification maps, so as to integrate them into operational geographic information systems, thus completing the typical processing pipeline observed in a wide number of applications. Throughout this thesis, we experiment on hyperspectral, atellite and aerial images, with scalability, generalization and applicability goals in mind
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Matteo, Lionel. "De l’image optique "multi-stéréo" à la topographie très haute résolution et la cartographie automatique des failles par apprentissage profond." Thesis, Université Côte d'Azur, 2020. http://www.theses.fr/2020COAZ4099.

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Les failles sismogéniques sont la source des séismes. L'étude de leurs propriétés nous informe donc sur les caractéristiques des forts séismes qu'elles peuvent produire. Les failles sont des objets 3D qui forment des réseaux complexes incluant une faille principale et une multitude de failles et fractures secondaires qui "découpent" la roche environnante à la faille principale. Mon objectif dans cette thèse a été de développer des approches pour aider à étudier cette fracturation secondaire intense. Pour identifier, cartographier et mesurer les fractures et les failles dans ces réseaux, j'ai adressé deux défis :1) Les failles peuvent former des escarpements topographiques très pentus à la surface du sol, créant des "couloirs" ou des canyons étroits et profond où la topographie et donc, la trace des failles, peut être difficile à mesurer en utilisant des méthodologies standard (comme des acquisitions d'images satellites optiques stéréo et tri-stéréo). Pour répondre à ce défi, j'ai utilisé des acquisitions multi-stéréos avec différentes configurations (différents angles de roulis et tangage, différentes dates et modes d'acquisitions). Notre base de données constituée de 37 images Pléiades dans trois sites tectoniques différents dans l'Ouest américain (Valley of Fire, Nevada ; Granite Dells, Arizona ; Bishop Tuff, California) m'a permis de tester différentes configurations d'acquisitions pour calculer la topographie avec trois approches différentes. En utilisant la solution photogrammétrique open-source Micmac (IGN ; Rupnik et al., 2017), j'ai calculé la topographie sous la forme de Modèles Numériques de Surfaces (MNS) : (i) à partir de combinaisons de 2 à 17 images Pléiades, (ii) en fusionnant des MNS calculés individuellement à partir d'acquisitions stéréo et tri-stéréo, évitant alors l'utilisant d'acquisitions multi-dates et (iii) en fusionnant des nuages de points calculés à partir d'acquisitions tri-stéréos en suivant la méthodologie multi-vues développée par Rupnik et al. (2018). J’ai aussi combiné, dans une dernière approche (iv), des acquisitions tri-stéréos avec la méthodologie multi-vues stéréos du CNES/CMLA (CARS) développé par Michel et al. (2020), en combinant des acquisitions tri-stéréos. A partir de ces quatre approches, j'ai calculé plus de 200 MNS et mes résultats suggèrent que deux acquisitions tri-stéréos ou une acquisition stéréo combinée avec une acquisition tri-stéréo avec des angles de roulis opposés permettent de calculer les MNS avec la surface topographique la plus complète et précise.2) Couramment, les failles sont cartographiées manuellement sur le terrain ou sur des images optiques et des données topographiques en identifiant les traces curvilinéaires qu'elles forment à la surface du sol. Néanmoins, la cartographie manuelle demande beaucoup de temps ce qui limite notre capacité à produire cartographies et des mesures complètes des réseaux de failles. Pour s'affranchir de ce problème, j'ai adopté une approche d'apprentissage profond, couramment appelé un réseau de neurones convolutifs (CNN) - U-Net, pour automatiser l'identification et la cartographie des fractures et des failles dans des images optiques et des données topographiques. Volontairement, le modèle CNN a été entraîné avec une quantité modérée de fractures et failles cartographiées manuellement à basse résolution et dans un seul type d'images optiques (photographies du sol avec des caméras classiques). A partir d'un grand nombre de tests, j'ai sélectionné le meilleur modèle, MRef et démontre sa capacité à prédire des fractures et des failles précisément dans données optiques et topographiques de différents types et différentes résolutions (photographies prises au sol, avec un drone et par satellite). Le modèle MRef montre de bonnes capacités de généralisations faisant alors de ce modèle un bon outil pour cartographie rapidement et précisément des fractures et des failles dans des images optiques et des données topographiques
Seismogenic faults are the source of earthquakes. The study of their properties thus provides information on some of the properties of the large earthquakes they might produce. Faults are 3D features, forming complex networks generally including one master fault and myriads of secondary faults and fractures that intensely dissect the master fault embedding rocks. I aim in my thesis to develop approaches to help studying this intense secondary faulting/fracturing. To identify, map and measure the faults and fractures within dense fault networks, I have handled two challenges:1) Faults generally form steep topographic escarpments at the ground surface that enclose narrow, deep corridors or canyons, where topography, and hence fault traces, are difficult to measure using the available standard methods (such as stereo and tri-stereo of optical satellite images). To address this challenge, I have thus used multi-stéréo acquisitions with different configuration such as different roll and pitch angles, different date of acquisitions and different mode of acquisitions (mono and tri-stéréo). Our dataset amounting 37 Pléiades images in three different tectonic sites within Western USA (Valley of Fire, Nevada; Granite Dells, Arizona; Bishop Tuff, California) allow us to test different configuration of acquisitions to calculate the topography with three different approaches. Using the free open-source software Micmac (IGN ; Rupnik et al., 2017), I have calculated the topography in the form of Digital Surface Models (DSM): (i) with the combination of 2 to 17 Pleiades images, (ii) stacking and merging DSM built from individual stéréo or tri-stéréo acquisitions avoiding the use of multi-dates combinations, (iii) stacking and merging point clouds built from tri-stereo acquisitions following the multiview pipeline developped by Rupnik et al., 2018. We used the recent multiview stereo pipeling CARS (CNES/CMLA) developped by Michel et al., 2020 as a last approach (iv), combnining tri-stereo acquisitions. From the four different approaches, I have thus calculated more than 200 DSM and my results suggest that combining two tri-stéréo acquisitions or one stéréo and one tri-stéréo acquisitions with opposite roll angles leads to the most accurate DSM (with the most complete and precise topography surface).2) Commonly, faults are mapped manually in the field or from optical images and topographic data through the recognition of the specific curvilinear traces they form at the ground surface. However, manual mapping is time-consuming, which limits our capacity to produce complete representations and measurements of the fault networks. To overcome this problem, we have adopted a machine learning approach, namely a U-Net Convolutional Neural Network, to automate the identification and mapping of fractures and faults in optical images and topographic data. Intentionally, we trained the CNN with a moderate amount of manually created fracture and fault maps of low resolution and basic quality, extracted from one type of optical images (standard camera photographs of the ground surface). Based on the results of a number of performance tests, we select the best performing model, MRef, and demonstrate its capacity to predict fractures and faults accurately in image data of various types and resolutions (ground photographs, drone and satellite images and topographic data). The MRef predictions thus enable the statistical analysis of the fault networks. MRef exhibits good generalization capacities, making it a viable tool for fast and accurate extraction of fracture and fault networks from image and topographic data
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Girard, Nicolas. "Approches d'apprentissage et géométrique pour l'extraction automatique d'objets à partir d'images de télédétection." Thesis, Université Côte d'Azur, 2020. https://tel.archives-ouvertes.fr/tel-03177997.

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Créer un double numérique de la Terre sous forme de cartes a de nombreuses applications comme la conduite autonome, la planification urbaine, les télécommunications, la gestion des catastrophes naturelles, etc. Les systèmes d'information géographique (SIG) sont utilisés pour intégrer des données géolocalisées sous forme de cartes. Les SIG utilisent une représentation vectorielle pour les objets, prenant peu d'espace mémoire et rendant leur modification plus facile que des données raster. Avec la quantité croissante d'images satellites et aériennes capturées chaque jour, des méthodes automatiques sont en cours de développement pour extraire les informations de ces images de télédétection. Les méthodes d'apprentissage profond pour la segmentation d'images sont capables de délimiter les formes des objets, mais elles le font avec une représentation raster, sous la forme d'une carte de probabilité. Des méthodes de vectorisation post-traitement convertissent ensuite cette représentation raster en une représentation vectorielle compatible avec les SIG. Un autre défi de la télédétection est de gérer un certain type de bruit dans les données, qui est le désalignement entre différentes couches d'informations géolocalisées (par exemple entre les images et les cadastres des bâtiments). Ce type de bruit est fréquent en raison de diverses erreurs introduites lors du traitement des données de télédétection. Cette thèse développe des approches combinées d'apprentissage et géométriques dans le but d'améliorer l'automatisation du processus de cartographie SIG à partir d'images de télédétection.Nous proposons d'abord une méthode pour corriger une carte mal alignée sur une image, pur faire correspondre ces deux données géolocalisées, et aussi pour créer des jeu de données de télédétection pour la segmentation d'images avec une vérité terrain corrigé. En effet, entraîner un modèle sur une vérité terrain mal alignée ne mènerait pas à de bonnes segmentations. Au cours de ce travail, nous avons également observé un effet de débruitage par notre modèle d'alignement et l'avons utilisé pour débruiter un jeu de données mal aligné de manière auto-supervisée, ce qui signifie que seul le jeu de données mal aligné a été utilisé pour l'apprentissage.Nous proposons ensuite une approche simple pour utiliser un réseau de neurones produisant directement une représentation vectorielle de l'objet à détecter, afin de contourner l'étape de vectorisation post-traitement. Nous démontrons qu'il est possible d'apprendre à régresser les coordonnées de polygones (avec un nombre de sommets fixes dans notre cas), produisant directement des sorties cartographiques vectorielles.Bien que les méthodes plus récentes d'apprentissage directement en représentation vectorielle sont maintenant plus évoluées, elles ont encore d'autres limitations en termes de type de formes d'objets qu'elles peuvent prédire. Des cas topologiques plus complexes tels que des objets avec des trous ou des bâtiments se touchant ayant un mur mitoyen ne sont pas gérés par ces méthodes d'apprentissage. Nous proposons ainsi une approche hybride palliant ces limitations en entraînant un réseau de neurones pour produire une carte de probabilité de segmentation comme usuellement, mais aussi pour produire un “frame field” (4 champs vectoriels superposés) aligné avec les contours des objets détectés. Ce “frame field” encode des informations géométriques supplémentaires apprises par le réseau. Nous proposons ensuite notre méthode de polygonisation parallélisable pour exploiter ce “frame field” pour vectoriser efficacement la carte de probabilité de segmentation. Notre méthode de polygonisation ayant accès à des informations supplémentaires sous la forme d'un “frame field” elle peut être moins complexe que d'autres méthodes de vectorisation avancées et donc plus rapide. De plus calculer ce “frame field” n'augmente pratiquement pas le temps d'inférence, il n'est que bénéfique
Creating a digital double of the Earth in the form of maps has many applications in e.g. autonomous driving, automated drone delivery, urban planning, telecommunications, and disaster management. Geographic Information Systems (GIS) are the frameworks used to integrate geolocalized data and represent maps. They represent shapes of objects in a vector representation so that it is as sparse as possible while representing shapes accurately, as well as making it easier to edit than raster data. With the increasing amount of satellite and aerial images being captured every day, automatic methods are being developed to transfer the information found in those remote sensing images into Geographic Information Systems. Deep learning methods for image segmentation are able to delineate the shapes of objects found in images however they do so with a raster representation, in the form of a mask. Post-processing vectorization methods then convert that raster representation into a vector representation compatible with GIS. Another challenge in remote sensing is to deal with a certain type of noise in the data, which is the misalignment between different layers of geolocalized information (e.g. between images and building cadaster data). This type of noise is frequent due to various errors introduced during the processing of remote sensing data. This thesis develops combined learning and geometric approaches with the purpose to improve automatic GIS mapping from remote sensing images.We first propose a method for correcting misaligned maps over images, with the first motivation for them to match, but also with the motivation to create remote sensing datasets for image segmentation with alignment-corrected ground truth. Indeed training a model on misaligned ground truth would not lead to great performance, whereas aligned ground truth annotations will result in better models. During this work we also observed a denoising effect of our alignment model and use it to denoise a misaligned dataset in a self-supervised manner, meaning only the misaligned dataset was used for training.We then propose a simple approach to use a neural network to directly output shape information in the vector representation, in order to by-pass the post-processing vectorization step. Experimental results on a dataset of solar panels show that the proposed network succeeds in learning to regress polygon coordinates, yielding directly vectorial map outputs. Our simple method is limited to predicting polygons with a fixed number of vertices though.While more recent methods for learning directly in the vector representation do not have this limitation, they still have other limitations in terms of the type of object shapes they can predict. More complex topological cases such as objects with holes or buildings touching each other (with a common wall which is very typical of European city centers) are not handled by these fully deep learning methods. We thus propose a hybrid approach alleviating those limitations by training a neural network to output a segmentation probability map as usual and also to output a frame field aligned with the contours of detected objects (buildings in our case). That frame field constitutes additional shape information learned by the network. We then propose our highly parallelizable polygonization method for leveraging that frame field information to vectorize the segmentation probability map efficiently. Because our polygonization method has access to additional information in the form of a frame field, it can be less complex than other advanced vectorization methods and is thus faster. Lastly, requiring an image segmentation network to also output a frame field only adds two convolutional layers and virtually does not increase inference time, making the use of a frame field only beneficial
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Books on the topic "Proton sensing"

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lantbruksuniversitet, Sveriges, ed. Isolation, cloning and tissue distribution of a 500 kDa protein with Ca²⁺ sensing properties. Uppsala: Swedish University of Agricultural Sciences, Genetic Center, Dept. of Cell Research, 1995.

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Raju, Raghavan, and Irshad H. Chaudry. The host response to hypoxia in the critically ill. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0305.

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The hypoxic response of the host is complex. While the oxygen-sensing intracellular machinery attempts to restore cellular homeostasis by augmenting respiration and blood flow, events such as severe haemorrhage lead to whole body hypoxia and decreased mitochondrial function. Immunological perturbations following severe haemorrhage may result in multiple organ dysfunction and sepsis, while impaired perfusion may lead to microvascular injury and local hypoxia. Trauma-haemorrhage or hypoxic exposure in animals causes a systemic inflammatory response, decreased antigen presentation by peritoneal macrophages, hypoxaemia and initiation of endoplasmic reticulum stress. In response, the protein level of the oxygen-sensing transcription factor, hypoxia inducible factor (HIF)-1 increases; this leads to the regulation of expression of a number of genes resulting in decreased mitochondrial ATP production, but enhanced glycolytic processes, thus shifting the energy balance. In addition, sustained tissue hypoxia leads to increased free radical production and cellular apoptosis. Though the initial host response to hypoxia may be protective, sustained hypoxia becomes detrimental to the tissues and the organism as a whole.
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Newell-Price, John, Alia Munir, and Miguel Debono. Primary hyperparathyroidism. Edited by Patrick Davey and David Sprigings. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199568741.003.0187.

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Primary hyperparathyroidism is a disorder of bone mineralization and renal physiology due to excess parathyroid hormone secretion. Parathyroid hormone (PTH) is produced and released by the parathyroid chief cells, under regulation of the G- protein-coupled calcium-sensing receptor. Primary hyperparathyroidism occurs when there is a loss of the inhibitory feedback of PTH release by extracellular calcium. The rise in PTH levels is initially associated with a normal serum calcium, and then over time with hypercalcaemia. The most common cause of primary hyperparathyroidism is a benign solitary adenoma (80%). Other causes include multiple adenomas and hyperplasia. This chapter reviews the causes, clinical features, and management of primary hyperparathyroidism.
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Simpson, Stephen J., Carlos Ribeiro, and Daniel González-Tokman. Feeding behavior. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797500.003.0008.

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Insects need to ingest nutrients at appropriate levels to attain a balanced diet and maximize fitness. They do so by integrated responses that involve physiological mechanisms for sensing current nutritional needs, releasing systemic signals, and producing specific appetites for key required nutrients. Historically, the study of insect feeding behavior was appreciated for its importance in the understanding and control of crop pests and disease vectors. However, current evidence has shown that some mechanisms regulating feeding are highly conserved in animals, from insects to humans, bringing additional interest in insects as models in medicine. The study of insect feeding behavior and nutrition has also given rise to an integrative modelling approach called the geometric framework for nutrition. This approach has proven useful beyond the insects, and allows the understanding of the impact of multiple nutrients on individuals and their interactions in populations, communities, and ecosystems.
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Book chapters on the topic "Proton sensing"

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Seuwen, Klaus, and Marie-Gabrielle Ludwig. "Proton-Sensing GPCRs." In Encyclopedia of Molecular Pharmacology, 1309–13. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57401-7_200.

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Seuwen, Klaus, and Marie-Gabrielle Ludwig. "Proton-Sensing GPCRs." In Encyclopedia of Molecular Pharmacology, 1–5. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-21573-6_200-1.

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Sun, Wei-Hsin, and Shih-Ping Dai. "Tackling Pain Associated with Rheumatoid Arthritis: Proton-Sensing Receptors." In Advances in Pain Research: Mechanisms and Modulation of Chronic Pain, 49–64. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1756-9_5.

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Pesci, Everett C., and Barbara H. Iglewski. "Quorum Sensing." In Bacterial Protein Toxins, 55–65. Washington, DC, USA: ASM Press, 2014. http://dx.doi.org/10.1128/9781555817893.ch4.

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Hanus, Kasper, and Emilia Smagur. "Pre- and Proto-Historic Anthropogenic Landscape Modifications in Siem Reap Province (Cambodia) as Seen Through Satellite Imagery." In Digital Methods and Remote Sensing in Archaeology, 229–46. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40658-9_10.

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Picking, William D., and Michael L. Barta. "The Tip Complex: From Host Cell Sensing to Translocon Formation." In Bacterial Type III Protein Secretion Systems, 173–99. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/82_2019_171.

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Jastrzebska, Beata. "Class A GPCR: Light Sensing G Protein-Coupled Receptor – Focus on Rhodopsin Dimer." In G-Protein-Coupled Receptor Dimers, 79–97. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60174-8_4.

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Findakly, T. "PROTON-EXCHANGED INTEGRATED OPTICAL COMPONENTS." In Optical Fiber Rotation Sensing, 337–51. Elsevier, 1994. http://dx.doi.org/10.1016/b978-0-12-146075-4.50016-5.

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Koltai, Tomas, Larry Fliegel, Fátima Baltazar, Stephan J. Reshkin, Khalid O. Alfarouk, Rosa Angela Cardone, and Julieta Afonso. "Membrane proton sensing potentiates the pro-tumoral effects of extracellular acidity." In pH Deregulation as the Eleventh Hallmark of Cancer, 163–72. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-443-15461-4.00012-6.

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Aanniz, Tarik, Wissal Bakri, Safae El Mazouri, Hajar Wakrim, Ilham Kandoussi, Lahcen Belyamani, Mouna Ouadghiri, and Azeddine Ibrahimi. "Biofilm and Quorum Sensing in Helicobacter pylori." In Bacterial Biofilms [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.104568.

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Helicobacter pylori (H. pylori) is a gram-negative bacterium living in the human gastrointestinal tract considered as the most common cause of gastritis. H. pylori was listed as the main risk factor for gastric cancer. Triple therapy consisting of a proton pump inhibitor and combinations of antibiotics is the main treatment used. However, this line of therapy has proven less effective mainly due to biofilm formation. Bacteria can regulate and synchronize the expression of multiple genes involved in virulence, toxin production, motility, chemotaxis, and biofilm formation by quorum sensing (QS), thus contributing to antimicrobial resistance. Henceforth, the inhibition of QS called quorum quenching (QQ) is a promising target and alternative to fight H. pylori resistance to antimicrobials. Many phytochemicals as well as synthetic compounds acting as quorum quenchers in H. pylori were described in vitro and in vivo. Otherwise, many other compounds known as quorum quenchers in other species and inhibitors of biofilm formation in H. pylori could act as quorum quenchers in H. pylori. Here, we summarize and discuss the latest findings on H. pylori’s biofilm formation, QS sensing, and QQ mechanisms.
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Conference papers on the topic "Proton sensing"

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Vu, Paul, Boyd Fowler, Brian Rodricks, Janusz Balicki, Steve Mims, and Wang Li. "Evaluation of 10MeV proton irradiation on 5.5 Mpixel scientific CMOS image sensor." In Remote Sensing, edited by Roland Meynart, Steven P. Neeck, and Haruhisa Shimoda. SPIE, 2010. http://dx.doi.org/10.1117/12.868160.

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Сурнин and S. Surnin. "Crystal structure of a proton." In XXIV International Conference. Москва: Infra-m, 2016. http://dx.doi.org/10.12737/22881.

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Is represented by the system of models the crystalline structure of a Proton, consistent with the known empirical data on sensing a Proton, proton and anti-Proton annihilation, strong nuclear interactions and process of disintegration of peonies.
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Boucher, Richard H., Warren F. Woodward, Terrence S. Lomheim, Ralph M. Shima, David J. Asman, Kevin M. Killian, Jason LeGrand, and Gregory J. Goellner. "Proton-induced degradation in interferometric fiber optic gyroscopes." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Edward W. Taylor. SPIE, 1995. http://dx.doi.org/10.1117/12.210543.

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Tang, Xiling, Kurtis Remmel, Daniel Sandker, Zhi Xu, and Junhang Dong. "Proton conducting perovskite-type ceramics for fiber optic sensors for hydrogen monitoring at high temperature." In SPIE Defense, Security, and Sensing, edited by Xudong Fan, Hai Xiao, and Anbo Wang. SPIE, 2010. http://dx.doi.org/10.1117/12.850919.

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Tsurumaki, Hiroaki, Chihiro Mogi, Haruka Saito-Aoki, Koichi Sato, Takashi Nakakura, Masakiyo Yatomi, Yasuhiko Koga, et al. "Absence of proton-sensing TDAG8 protects against ovalbumin-induced allergic airway inflammation." In ERS International Congress 2017 abstracts. European Respiratory Society, 2017. http://dx.doi.org/10.1183/1393003.congress-2017.pa2020.

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Cherniatiev, B. V., Grigorii M. Chernyavskiy, Nicholas L. Johnson, and Darren S. McKnight. "Identification and resolution of an orbital debris problem with the proton launch vehicle." In Optical Engineering and Photonics in Aerospace Sensing, edited by Firooz A. Allahdadi. SPIE, 1993. http://dx.doi.org/10.1117/12.156549.

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Kissa, Karl M., Hogan Eng, David K. Lewis, Vincent D. Rodino, Paul G. Suchoski, Jr., and Nancy A. Koziarz. "Reliability of lithium niobate Annealed Proton Exchanged integrated optical circuits." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Andrew R. Pirich. SPIE, 1995. http://dx.doi.org/10.1117/12.212700.

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Taylor, Edward W., S. P. Chapman, Anthony D. Sanchez, Michael A. Kelly, Jonathan Stohs, and Douglas M. Craig. "Radiation-induced crosstalk in a proton-exchanged LiNbO3 directional coupler." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Edward W. Taylor. SPIE, 1995. http://dx.doi.org/10.1117/12.210541.

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Lee, Yu Jin, Kyun Heo, Soo-Ah Park, Dong-Young Noh, Kyong-Tai Kim, Sung Ho Ryu, and Pann-Ghill Suh Suh. "Abstract 3950: Extracellular protons promote the metastasis of breast cancerviaactivation of the proton-sensing receptor G-protein coupled receptor 4." In Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1538-7445.am2013-3950.

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Tsurumaki, Hiroaki, Takeshi Hisada, Chihiro Mogi, Haruka Saito-Aoki, Masakiyo Yatomi, Yousuke Kamide, Masayuki Tobo, et al. "Proton-sensing TDAG8 exhibits the protective role in lipopolysaccharide-induced acute lung injury." In ERS International Congress 2016 abstracts. European Respiratory Society, 2016. http://dx.doi.org/10.1183/13993003.congress-2016.pa943.

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Reports on the topic "Proton sensing"

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Fernando, P. U. Ashvin Iresh, Gilbert Kosgei, Matthew Glasscott, Garrett George, Erik Alberts, and Lee Moores. Boronic acid functionalized ferrocene derivatives towards fluoride sensing. Engineer Research and Development Center (U.S.), July 2022. http://dx.doi.org/10.21079/11681/44762.

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In this technical report (TR), a robust, readily synthesized molecule with a ferrocene core appended with one or two boronic acid moieties was designed, synthesized, and used toward F- (free fluoride) detection. Through Lewis acid-base interactions, the boronic acid derivatives are capable of binding with F- in an aqueous solution via ligand exchange reaction and is specific to fluoride ion. Fluoride binding to ferrocene causes significant changes in fluorescence or electrochemical responses that can be monitored with field-portable instrumentation at concentrations below the WHO recommended limit. The F- binding interaction was further monitored via proton nuclear magnetic resonance spectroscopy (1H-NMR). In addition, fluorescent spectroscopy of the boronic acid moiety and electrochemical monitoring of the ferrocene moiety will allow detection and estimation of F- concentration precisely in a solution matrix. The current work shows lower detection limit (LOD) of ~15 μM (285 μg/L) which is below the WHO standards. Preliminary computational calculations showed the boronic acid moieties attached to the ferrocene core interacted with the fluoride ion. Also, the ionization diagrams indicate the amides and the boronic acid groups can be ionized forming strong ionic interactions with fluoride ions in addition to hydrogen bonding interactions.
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Tian, Yongchi, and Jia Chen. Protein-Assisted Redox Sensing at Biomimetic Electrode. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada412000.

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Sohn, Lydia, T. C. Messina, L. N. Dunkleberger, G. A. Mensing, A. S. Kalmbach, Ron Weiss, and D. J. Beebe. Probing Interactions at the Nanoscale. Sensing Protein Molecules. Office of Scientific and Technical Information (OSTI), September 2003. http://dx.doi.org/10.2172/940829.

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Khaneja, Navin. Intelligent Sensing and Probing with Applications to Protein NMR Spectroscopy and Laser Chemistry. Fort Belvoir, VA: Defense Technical Information Center, August 2006. http://dx.doi.org/10.21236/ada463606.

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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, September 2021. http://dx.doi.org/10.46337/210930.

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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.
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Borrett, Veronica, Melissa Hanham, Gunnar Jeremias, Jonathan Forman, James Revill, John Borrie, Crister Åstot, et al. Science and Technology for WMD Compliance Monitoring and Investigations. The United Nations Institute for Disarmament Research, December 2020. http://dx.doi.org/10.37559/wmd/20/wmdce11.

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The integration of novel technologies for monitoring and investigating compliance can enhance the effectiveness of regimes related to weapons of mass destruction (WMD). This report looks at the potential role of four novel approaches based on recent technological advances – remote sensing tools; open-source satellite data; open-source trade data; and artificial intelligence (AI) – in monitoring and investigating compliance with WMD treaties. The report consists of short essays from leading experts that introduce particular technologies, discuss their applications in WMD regimes, and consider some of the wider economic and political requirements for their adoption. The growing number of space-based sensors is raising confidence in what open-source satellite systems can observe and record. These systems are being combined with local knowledge and technical expertise through social media platforms, resulting in dramatically improved coverage of the Earth’s surface. These open-source tools can complement and augment existing treaty verification and monitoring capabilities in the nuclear regime. Remote sensing tools, such as uncrewed vehicles, can assist investigators by enabling the remote collection of data and chemical samples. In turn, this data can provide valuable indicators, which, in combination with other data, can inform assessments of compliance with the chemical weapons regime. In addition, remote sensing tools can provide inspectors with real time two- or three-dimensional images of a site prior to entry or at the point of inspection. This can facilitate on-site investigations. In the past, trade data has proven valuable in informing assessments of non-compliance with the biological weapons regime. Today, it is possible to analyse trade data through online, public databases. In combination with other methods, open-source trade data could be used to detect anomalies in the biological weapons regime. AI and the digitization of data create new ways to enhance confidence in compliance with WMD regimes. In the context of the chemical weapons regime, the digitization of the chemical industry as part of a wider shift to Industry 4.0 presents possibilities for streamlining declarations under the Chemical Weapons Convention (CWC) and for facilitating CWC regulatory requirements.
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McInerney, Michael K., and John M. Carlyle. : Demonstration of Acoustic Sensing Techniques for Fuel-Distribution System Condition Monitoring : Final Report on Project F07-AR07. Engineer Research and Developmenter Center (U.S.), January 2021. http://dx.doi.org/10.21079/11681/39560.

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Leaks in fuel storage tanks and distribution piping systems have been identified as a mission-critical problem by the Department of Defense and the U.S. Army. Fuel system leaks are often hard to locate and virtually inaccessible for efficient repair because the piping is often installed under a concrete pad or tarmac. Leak repair could cost up to $2,000, and the cost of cleanup and re-mediation for fuel spills can exceed $50,000. In this project an acoustic remote sensing system was installed to monitor an Army heliport refueling system to determine whether it could detect and accurately locate fuel leaks using computer software technolo-gies to distinguish acoustic leakage signatures from normal fuel system operational noise. Demonstration and validation efforts were disadvantaged by the fact that no fuel leaks occurred in the monitored system for the duration of the project. However, the monitoring system did identify several unusual acoustic events within the fueling system and interpret them as indications of intermittent malfunctions of a check valve and a fuel pump. The 30-year ROI is about 6.42. Further work is required before the technology can be fully implemented: its ability to detect fluid leaks must be proven, and the system specifications must be certified through an EPA third party.
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Zarrieß, Benjamin, and Jens Claßen. Verification of Knowledge-Based Programs over Description Logic Actions. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.216.

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A knowledge-based program defines the behavior of an agent by combining primitive actions, programming constructs and test conditions that make explicit reference to the agent’s knowledge. In this paper we consider a setting where an agent is equipped with a Description Logic (DL) knowledge base providing general domain knowledge and an incomplete description of the initial situation. We introduce a corresponding new DL-based action language that allows for representing both physical and sensing actions, and that we then use to build knowledge-based programs with test conditions expressed in the epistemic DL. After proving undecidability for the general case, we then discuss a restricted fragment where verification becomes decidable. The provided proof is constructive and comes with an upper bound on the procedure’s complexity.
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Douglas, Thomas, and Caiyun Zhang. Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41222.

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The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.
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Coplin, David L., Shulamit Manulis, and Isaac Barash. roles Hrp-dependent effector proteins and hrp gene regulation as determinants of virulence and host-specificity in Erwinia stewartii and E. herbicola pvs. gypsophilae and betae. United States Department of Agriculture, June 2005. http://dx.doi.org/10.32747/2005.7587216.bard.

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Gram-negative plant pathogenic bacteria employ specialized type-III secretion systems (TTSS) to deliver an arsenal of pathogenicity proteins directly into host cells. These secretion systems are encoded by hrp genes (for hypersensitive response and pathogenicity) and the effector proteins by so-called dsp or avr genes. The functions of effectors are to enable bacterial multiplication by damaging host cells and/or by blocking host defenses. We characterized essential hrp gene clusters in the Stewart's Wilt of maize pathogen, Pantoea stewartii subsp. stewartii (Pnss; formerly Erwinia stewartii) and the gall-forming bacterium, Pantoea agglomerans (formerly Erwinia herbicola) pvs. gypsophilae (Pag) and betae (Pab). We proposed that the virulence and host specificity of these pathogens is a function of a) the perception of specific host signals resulting in bacterial hrp gene expression and b) the action of specialized signal proteins (i.e. Hrp effectors) delivered into the plant cell. The specific objectives of the proposal were: 1) How is the expression of the hrp and effector genes regulated in response to host cell contact and the apoplastic environment? 2) What additional effector proteins are involved in pathogenicity? 3) Do the presently known Pantoea effector proteins enter host cells? 4) What host proteins interact with these effectors? We characterized the components of the hrp regulatory cascade (HrpXY ->7 HrpS ->7 HrpL ->7 hrp promoters), showed that they are conserved in both Pnss and Fag, and discovered that the regulation of the hrpS promoter (hrpSp) may be a key point in integrating apoplastic signals. We also analyzed the promoters recognized by HrpL and demonstrated the relationship between their composition and efficiency. Moreover, we showed that promoter strength can influence disease expression. In Pnss, we found that the HrpXY two-component signal system may sense the metabolic status of the bacterium and is required for full hrp gene expression in planta. In both species, acyl-homoserine lactone-mediated quorum sensing may also regulate epiphytic fitness and/or pathogenicity. A common Hrp effector protein, DspE/WtsE, is conserved and required for virulence of both species. When introduced into corn cells, Pnss WtsE protein caused water-soaked lesions. In other plants, it either caused cell death or acted as an Avr determinant. Using a yeast- two-hybrid system, WtsE was shown to interact with a number of maize signal transduction proteins that are likely to have roles in either programmed cell death or disease resistance. In Pag and Pab, we have characterized the effector proteins HsvG, HsvB and PthG. HsvG and HsvB are homologous proteins that determine host specificity of Pag and Pab on gypsophila and beet, respectively. Both possess a transcriptional activation domain that functions in yeast. PthG was found to act as an Avr determinant on multiple beet species, but was required for virulence on gypsophila. In addition, we demonstrated that PthG acts within the host cell. Additional effector genes have been characterized on the pathogenicity plasmid, pPATHₚₐg, in Pag. A screen for HrpL- regulated genes in Pnsspointed up 18 candidate effector proteins and four of these were required for full virulence. It is now well established that the virulence of Gram-negative plant pathogenic bacteria is governed by Hrp-dependent effector proteins. However; the mode of action of many effectors is still unresolved. This BARD supported research will significantly contribute to the understanding of how Hrp effectors operate in Pantoea spp. and how they control host specificity and affect symptom production. This may lead to novel approaches for genetically engineering plants resistant to a wide range of bacterial pathogens by inactivating the Hrp effectors with "plantabodies" or modifying their receptors, thereby blocking the induction of the susceptible response. Alternatively, innovative technologies could be used to interfere with the Hrp regulatory cascade by blocking a critical step or mimicking plant or quorum sensing signals.
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