Cotton(Gossypium spp.) yield is reduced by stress. In this study, high temperature(HT) suppressed the expression of the jasmonic acid(JA) biosynthesis gene allene oxide cyclase 2(GhAOC2), reducing JA content and causi...Cotton(Gossypium spp.) yield is reduced by stress. In this study, high temperature(HT) suppressed the expression of the jasmonic acid(JA) biosynthesis gene allene oxide cyclase 2(GhAOC2), reducing JA content and causing male sterility in the cotton HT-sensitive line H05. Anther sterility was reversed by exogenous application of methyl jasmonate(MeJA) to early buds. To elucidate the role of GhAOC2 in JA biosynthesis and identify its putative contribution to the anther response to HT, we created gene knockout cotton plants using the CRISPR/Cas9 system. Ghaoc2 mutant lines showed male-sterile flowers with reduced JA content in the anthers at the tetrad stage(TS), tapetum degradation stage(TDS), and anther dehiscence stage(ADS). Exogenous application of MeJA to early mutant buds(containing TS or TDS anthers) rescued the sterile pollen and indehiscent anther phenotypes, while ROS signals were reduced in ADS anthers. We propose that HT downregulates the expression of GhAOC2 in anthers, reducing JA biosynthesis and causing excessive ROS accumulation in anthers, leading to male sterility. These findings suggest exogenous JA application as a strategy for increasing male fertility in cotton under HT.展开更多
Millimeter-Wave(mmWave)Massive MIMO is one of the most effective technology for the fifth-generation(5G)wireless networks.It improves both the spectral and energy efficiency by utilizing the 30–300 GHz millimeter-wav...Millimeter-Wave(mmWave)Massive MIMO is one of the most effective technology for the fifth-generation(5G)wireless networks.It improves both the spectral and energy efficiency by utilizing the 30–300 GHz millimeter-wave bandwidth and a large number of antennas at the base station.However,increasing the number of antennas requires a large number of radio frequency(RF)chains which results in high power consumption.In order to reduce the RF chain’s energy,cost and provide desirable quality-ofservice(QoS)to the subscribers,this paper proposes an energy-efficient hybrid precoding algorithm formm Wave massive MIMO networks based on the idea of RF chains selection.The sparse digital precoding problem is generated by utilizing the analog precoding codebook.Then,it is jointly solved through iterative fractional programming and successive convex optimization(SCA)techniques.Simulation results show that the proposed scheme outperforms the existing schemes and effectively improves the system performance under different operating conditions.展开更多
Millimeter wave communication works in the 30–300 GHz frequency range,and can obtain a very high bandwidth,which greatly improves the transmission rate of the communication system and becomes one of the key technolog...Millimeter wave communication works in the 30–300 GHz frequency range,and can obtain a very high bandwidth,which greatly improves the transmission rate of the communication system and becomes one of the key technologies of fifth-generation(5G).The smaller wavelength of the millimeter wave makes it possible to assemble a large number of antennas in a small aperture.The resulting array gain can compensate for the path loss of the millimeter wave.Utilizing this feature,the millimeter wave massive multiple-input multiple-output(MIMO)system uses a large antenna array at the base station.It enables the transmission of multiple data streams,making the system have a higher data transmission rate.In the millimeter wave massive MIMO system,the precoding technology uses the state information of the channel to adjust the transmission strategy at the transmitting end,and the receiving end performs equalization,so that users can better obtain the antenna multiplexing gain and improve the system capacity.This paper proposes an efficient algorithm based on machine learning(ML)for effective system performance in mmwave massive MIMO systems.The main idea is to optimize the adaptive connection structure to maximize the received signal power of each user and correlate the RF chain and base station antenna.Simulation results show that,the proposed algorithm effectively improved the system performance in terms of spectral efficiency and complexity as compared with existing algorithms.展开更多
基金funding support from the National Natural Science Foundation of China (32072024)the Fundamental Research Funds for the Central Universities (2021ZKPY019)the National Key Research and Development Program of China (2018YFD0100403, 2016YFD0101402)。
文摘Cotton(Gossypium spp.) yield is reduced by stress. In this study, high temperature(HT) suppressed the expression of the jasmonic acid(JA) biosynthesis gene allene oxide cyclase 2(GhAOC2), reducing JA content and causing male sterility in the cotton HT-sensitive line H05. Anther sterility was reversed by exogenous application of methyl jasmonate(MeJA) to early buds. To elucidate the role of GhAOC2 in JA biosynthesis and identify its putative contribution to the anther response to HT, we created gene knockout cotton plants using the CRISPR/Cas9 system. Ghaoc2 mutant lines showed male-sterile flowers with reduced JA content in the anthers at the tetrad stage(TS), tapetum degradation stage(TDS), and anther dehiscence stage(ADS). Exogenous application of MeJA to early mutant buds(containing TS or TDS anthers) rescued the sterile pollen and indehiscent anther phenotypes, while ROS signals were reduced in ADS anthers. We propose that HT downregulates the expression of GhAOC2 in anthers, reducing JA biosynthesis and causing excessive ROS accumulation in anthers, leading to male sterility. These findings suggest exogenous JA application as a strategy for increasing male fertility in cotton under HT.
基金This study was supported by the Institute for Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.2019-0-01343,Training Key Talents in Industrial Convergence Security).
文摘Millimeter-Wave(mmWave)Massive MIMO is one of the most effective technology for the fifth-generation(5G)wireless networks.It improves both the spectral and energy efficiency by utilizing the 30–300 GHz millimeter-wave bandwidth and a large number of antennas at the base station.However,increasing the number of antennas requires a large number of radio frequency(RF)chains which results in high power consumption.In order to reduce the RF chain’s energy,cost and provide desirable quality-ofservice(QoS)to the subscribers,this paper proposes an energy-efficient hybrid precoding algorithm formm Wave massive MIMO networks based on the idea of RF chains selection.The sparse digital precoding problem is generated by utilizing the analog precoding codebook.Then,it is jointly solved through iterative fractional programming and successive convex optimization(SCA)techniques.Simulation results show that the proposed scheme outperforms the existing schemes and effectively improves the system performance under different operating conditions.
基金Taif University Researchers Supporting Project Number(TURSP-2020/260),Taif University,Taif,Saudi Arabia.
文摘Millimeter wave communication works in the 30–300 GHz frequency range,and can obtain a very high bandwidth,which greatly improves the transmission rate of the communication system and becomes one of the key technologies of fifth-generation(5G).The smaller wavelength of the millimeter wave makes it possible to assemble a large number of antennas in a small aperture.The resulting array gain can compensate for the path loss of the millimeter wave.Utilizing this feature,the millimeter wave massive multiple-input multiple-output(MIMO)system uses a large antenna array at the base station.It enables the transmission of multiple data streams,making the system have a higher data transmission rate.In the millimeter wave massive MIMO system,the precoding technology uses the state information of the channel to adjust the transmission strategy at the transmitting end,and the receiving end performs equalization,so that users can better obtain the antenna multiplexing gain and improve the system capacity.This paper proposes an efficient algorithm based on machine learning(ML)for effective system performance in mmwave massive MIMO systems.The main idea is to optimize the adaptive connection structure to maximize the received signal power of each user and correlate the RF chain and base station antenna.Simulation results show that,the proposed algorithm effectively improved the system performance in terms of spectral efficiency and complexity as compared with existing algorithms.
