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William M. Armstead - One of the best experts on this subject based on the ideXlab platform.
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k Channel Impairment determines sex and age differences in epinephrine mediated outcomes after brain injury
Journal of Neuroscience Research, 2017Co-Authors: William M. Armstead, John Riley, Monica S VavilalaAbstract:Traumatic brain injury (TBI) is the leading cause of injury-related death in children, with boys and children under 4 years having particularly poor outcomes. Activation of ATP- and calcium-sensitive (KATP and KCa ) Channels produces cerebrovasodilation and contributes to autoregulation, both of which are impaired after TBI, contributing to poor outcomes. Upregulation of the c-Jun-terminal kinase (JNK) isoform of mitogen-activated protein kinase produces K Channel function Impairment after CNS injury. Vasoactive agents can be used to normalize cerebral perfusion pressure. Epinephrine (EPI) prevents Impairment of cerebral autoregulation and hippocampal neuronal cell necrosis after TBI in female and male newborn and female juvenile but not male juvenile pigs via differential modulation of JNK. The present study used anesthetized pigs equipped with a closed cranial window to address the hypothesis that differential K Channel Impairment contributes to age and sex differences in EPI-mediated outcomes after brain injury. Results show that pial artery dilation in response to the KATP and KCa Channel agonists cromakalim and NS 1619 was impaired after TBI and that such Impairment was prevented by EPI in female and male newborn and female juvenile but not male juvenile pigs. Using vasodilation as an index of function, these data indicate that EPI protects cerebral autoregulation and limits histopathology after TBI through protection of K Channel function via blockade of JNK in an age- and sex-dependent manner. © 2017 Wiley Periodicals, Inc.
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Vasopressin-Induced Protein Kinase C–Dependent Superoxide Generation Contributes to ATP-Sensitive Potassium Channel but Not Calcium-Sensitive Potassium Channel Function Impairment After Brain Injury
2015Co-Authors: William M. ArmsteadAbstract:Background and Purpose—Pial artery dilation in response to activators of the ATP-sensitive K1 (KATP) and calcium-sensitive K1 (KCa) Channels is impaired after fluid percussion brain injury (FPI). Vasopressin, when coadministered with the KATP and KCa Channel agonists cromakalim and NS1619 in a concentration approximating that observed in cerebrospinal fluid (CSF) after FPI, blunted KATP and KCa Channel–mediated vasodilation. Vasopressin also contributes to impaired KATP and KCa Channel vasodilation after FPI. In addition, protein kinase C (PKC) activation generates superoxide anion (O22), which in turn contributes to KATP Channel Impairment after FPI. We tested whether vasopressin generates O22 in a protein kinase C (PKC)-dependent manner, which could link vasopressin release to impaired KATP and KCa Channel–induced pial artery dilation after FPI. Methods—Injury of moderate severity (1.9 to 2.1 atm) was produced with the lateral FPI technique in anesthetized newborn pigs equipped with a closed cranial window. Superoxide dismutase–inhibitable nitroblue tetrazolium (NBT) reduction was determined as an index of O22 generation. Results—Under sham injury conditions, topical vasopressin (40 pg/mL, the concentration present in CSF after FPI) increased superoxide dismutase–inhibitable NBT reduction from 161 to 2364 pmol/mm2. Chelerythrine (1027 mol/L, a PKC inhibitor) blunted such NBT reduction (161 to 962 pmol/mm2), whereas the vasopressin antagonist l-(b-mercapto-b,b-cyclopentamethylene propionic acid)2-(o-methyl)-Tyr-arginine vasopressin (MEAVP) blocked NB
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protein tyrosine kinase and mitogen activated protein kinase activation contribute to katp and kca Channel Impairment after brain injury
Brain Research, 2002Co-Authors: William M. ArmsteadAbstract:Abstract Previous studies have observed that pial artery dilation to activators of the ATP sensitive K (K ATP ) and calcium sensitive K (K ca ) Channel was blunted following fluid percussion brain injury (FPI) in the piglet. In recent studies in the rat, protein tyrosine kinase (PTK) activation was observed to contribute to K ATP Channel Impairment after FPI, but such a role in K ca Channel Impairment was unclear. This study investigated the role of PTK and mitogen activated protein kinase (MAPK) activation in blunted pial dilation to K ATP and K ca Channel agonists following FPI in piglets equipped with a closed cranial window. Cromakalim and NS1619 (10 −8 , 10 −6 M) induced pial artery dilation was blunted after FPI, but partially restored by the PTK inhibitors genistein (10 −6 M) and tyrphostin A23 (10 −5 M) (10±1 and 19±1%, sham control; 2±1 and 4±1%, FPI; and 7±1 and 11±1% FPI-genistein pretreated for NS1619 10 −8 , 10 −6 M, respectively). Cromakalim- and NS1619-induced pial dilation was also partially restored after FPI by pretreatment with the MAPK inhibitors U0126 (10 −6 M) and PD98059 (10 −5 M) (12±1 and 21±1%, sham control; 2±1 and 4±1%, FPI; and 6±1 and 10±2%, FPI-U0126 pretreated for NS1619 10 −8 , 10 −6 M, respectively). These data suggest that PTK and MAPK activation contribute to K ATP and K ca Channel Impairment following FPI.
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vasopressin induced protein kinase c dependent superoxide generation contributes to atp sensitive potassium Channel but not calcium sensitive potassium Channel function Impairment after brain injury
Stroke, 2001Co-Authors: William M. ArmsteadAbstract:Background and Purpose—Pial artery dilation in response to activators of the ATP-sensitive K+ (KATP) and calcium-sensitive K+ (KCa) Channels is impaired after fluid percussion brain injury (FPI). Vasopressin, when coadministered with the KATP and KCa Channel agonists cromakalim and NS1619 in a concentration approximating that observed in cerebrospinal fluid (CSF) after FPI, blunted KATP and KCa Channel–mediated vasodilation. Vasopressin also contributes to impaired KATP and KCa Channel vasodilation after FPI. In addition, protein kinase C (PKC) activation generates superoxide anion (O2−), which in turn contributes to KATP Channel Impairment after FPI. We tested whether vasopressin generates O2− in a protein kinase C (PKC)-dependent manner, which could link vasopressin release to impaired KATP and KCa Channel–induced pial artery dilation after FPI. Methods—Injury of moderate severity (1.9 to 2.1 atm) was produced with the lateral FPI technique in anesthetized newborn pigs equipped with a closed cranial win...
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age dependent Impairment of katp Channel function following brain injury
Journal of Neurotrauma, 1999Co-Authors: William M. ArmsteadAbstract:ABSTRACT Previous studies observed that endothelin-1 (ET-1) contributed to ATP-sensitive K+ (KATP) Channel Impairment 1 h following fluid percussion brain injury (FPI) in the newborn pig. The prese...
Dongseong Kim - One of the best experts on this subject based on the ideXlab platform.
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chain net learning deep model for modulation classification under synthetic Channel Impairment
arXiv: Signal Processing, 2020Co-Authors: Thien Huynhthe, Vansang Doan, Camhao Hua, Quocviet Pham, Dongseong KimAbstract:Modulation classification, an intermediate process between signal detection and demodulation in a physical layer, is now attracting more interest to the cognitive radio field, wherein the performance is powered by artificial intelligence algorithms. However, most existing conventional approaches pose the obstacle of effectively learning weakly discriminative modulation patterns. This paper proposes a robust modulation classification method by taking advantage of deep learning to capture the meaningful information of modulation signal at multi-scale feature representations. To this end, a novel architecture of convolutional neural network, namely Chain-Net, is developed with various asymmetric kernels organized in two processing flows and associated via depth-wise concatenation and element-wise addition for optimizing feature utilization. The network is evaluated on a big dataset of 14 challenging modulation formats, including analog and high-order digital techniques. The simulation results demonstrate that Chain-Net robustly classifies the modulation of radio signals suffering from a synthetic Channel deterioration and further performs better than other deep networks.
Thien Huynhthe - One of the best experts on this subject based on the ideXlab platform.
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chain net learning deep model for modulation classification under synthetic Channel Impairment
arXiv: Signal Processing, 2020Co-Authors: Thien Huynhthe, Vansang Doan, Camhao Hua, Quocviet Pham, Dongseong KimAbstract:Modulation classification, an intermediate process between signal detection and demodulation in a physical layer, is now attracting more interest to the cognitive radio field, wherein the performance is powered by artificial intelligence algorithms. However, most existing conventional approaches pose the obstacle of effectively learning weakly discriminative modulation patterns. This paper proposes a robust modulation classification method by taking advantage of deep learning to capture the meaningful information of modulation signal at multi-scale feature representations. To this end, a novel architecture of convolutional neural network, namely Chain-Net, is developed with various asymmetric kernels organized in two processing flows and associated via depth-wise concatenation and element-wise addition for optimizing feature utilization. The network is evaluated on a big dataset of 14 challenging modulation formats, including analog and high-order digital techniques. The simulation results demonstrate that Chain-Net robustly classifies the modulation of radio signals suffering from a synthetic Channel deterioration and further performs better than other deep networks.
Xiangjun Xin - One of the best experts on this subject based on the ideXlab platform.
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200 gbit s λ pdm pam 4 pon system based on intensity modulation and coherent detection
IEEE\ OSA Journal of Optical Communications and Networking, 2020Co-Authors: Jiao Zhang, Kaihui Wang, Wen Zhou, Jiangnan Xiao, Li Zhao, Xiaolong Pan, Bo Liu, Xiangjun XinAbstract:A low-complexity and cost-efficient coherent detection-based 100 Gb/s or beyond passive optical network (PON) has attracted a lot of attention in recent years, as this technology offers high receiver sensitivity, colorless frequency selectivity, and linear detection enabling Channel Impairment compensation in the digital domain. We experimentally demonstrate the first single-wavelength 200 Gb/s coherent PON over 20 km downstream transmission with polarization division multiplexed four-level pulsed amplitude modulation (PDM-PAM-4) signals in the C-band. The intensity modulator replaces the costly in-phase/quadrature modulator, and hardware-efficient carrier recovery technologies are used for recovery of PAM-4 symbols. By using optimized Nyquist pulse shaping, the transceiver bandwidth can be reduced to within 50 GHz. A maximum power budget of 32.5 dB can be achieved for 200 Gb/s/λ PDM-PAM-4 at a bit error rate of $ 1 \times 10^{ - 2}$ over 20 km fiber transmission. The possibility of a 200 Gb/s/λ PON is investigated using intensity modulation and coherent detection for the first time to our knowledge.
Quocviet Pham - One of the best experts on this subject based on the ideXlab platform.
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chain net learning deep model for modulation classification under synthetic Channel Impairment
arXiv: Signal Processing, 2020Co-Authors: Thien Huynhthe, Vansang Doan, Camhao Hua, Quocviet Pham, Dongseong KimAbstract:Modulation classification, an intermediate process between signal detection and demodulation in a physical layer, is now attracting more interest to the cognitive radio field, wherein the performance is powered by artificial intelligence algorithms. However, most existing conventional approaches pose the obstacle of effectively learning weakly discriminative modulation patterns. This paper proposes a robust modulation classification method by taking advantage of deep learning to capture the meaningful information of modulation signal at multi-scale feature representations. To this end, a novel architecture of convolutional neural network, namely Chain-Net, is developed with various asymmetric kernels organized in two processing flows and associated via depth-wise concatenation and element-wise addition for optimizing feature utilization. The network is evaluated on a big dataset of 14 challenging modulation formats, including analog and high-order digital techniques. The simulation results demonstrate that Chain-Net robustly classifies the modulation of radio signals suffering from a synthetic Channel deterioration and further performs better than other deep networks.