Pakistan Journal of Biological Sciences, Mar 15, 2022
Background and Objective: Plant genetic resources provide the raw material for crop improvement a... more Background and Objective: Plant genetic resources provide the raw material for crop improvement and plant breeding program largely depends on it. Therefore, the evaluation of plant genetic resources plays a critical role in crop improvement and also in conserving valuable genetic resources for the future. In this study, the genetic diversity of 16 Lactuca indica L. accessions collected in Vietnam was investigated by using ISSR and RAPD markers. Materials and Methods: Genetic diversity of 16 Lactuca sativa L. genotypes collected in Vietnam were evaluated using Random Amplified Polymorphic DNA (RAPD) and Inter-Simple Sequence Repeat (ISSR) molecular markers. Results: In this study, 42 RAPD and ISSR primers were initially used, of which 12 and 9 primers, respectively were finally selected as they produced scorable patterns. RAPD markers produced a total of 113 loci, out of which 52 loci (45.96%) were polymorphic. The average percentage of the polymorphic band for RAPD primer is 45.96% and the genetic similarity based on simple matching coefficient ranged from 69.0-94.7%. ISSR analysis detected a total of 60 loci, out of which 22 loci (36.32%) were polymorphic and the genetic similarity ranged from 56.7-95.0%. In general, ISSR markers amplified fewer loci and showed lower variation in the percentage of polymorphism compares to the RAPD assay. Conclusion: These results indicate that the 16 collected Indian lettuce genotypes are genetically diverse. Because of these genetic diversities, the collected genotypes could be used for preserving or crossing programs to improve this precious medicinal plant in Vietnam.
This paper proposes a novel cognitive cooperative transmission scheme by exploiting massive multi... more This paper proposes a novel cognitive cooperative transmission scheme by exploiting massive multiple-input multiple-output (MMIMO) and non-orthogonal multiple access (NOMA) radio technologies, which enables a macrocell network and multiple cognitive small cells to cooperate in dynamic spectrum sharing. The macrocell network is assumed to own the spectrum band and be the primary network (PN), and the small cells act as the secondary networks (SNs). The secondary access points (SAPs) of the small cells can cooperatively relay the traffic for the primary users (PUs) in the macrocell network, while concurrently accessing the PUs’ spectrum to transmit their own data opportunistically through MMIMO and NOMA. Such cooperation creates a “win-win” situation: the throughput of PUs will be significantly increased with the help of SAP relays, and the SAPs are able to use the PU’s spectrum to serve their secondary users (SUs). The interplay of these advanced radio techniques is analyzed in a systematic manner, and a framework is proposed for the joint optimization of cooperative relay selection, NOMA and MMIMO transmit power allocation, and transmission scheduling. Further, to model network-wide cooperation and competition, a two-sided matching algorithm is designed to find the stable partnership between multiple SAPs and PUs. The evaluation results demonstrate that the proposed scheme achieves significant performance gains for both primary and secondary users, compared to the baselines.
Chronic pain is a major healthcare issue posing a large burden on individuals and society. Conver... more Chronic pain is a major healthcare issue posing a large burden on individuals and society. Converging lines of evidence indicate that chronic pain is associated with substantial changes of brain structure and function. However, it remains unclear which neuronal measures relate to changes of clinical parameters over time and could thus monitor chronic pain and treatment responses. We therefore performed a longitudinal study in which we assessed clinical characteristics and resting-state electroencephalography data of 41 patients with chronic pain before and 6 months after interdisciplinary multimodal pain therapy. We specifically assessed electroencephalography measures that have previously been shown to differ between patients with chronic pain and healthy people. These included the dominant peak frequency; the amplitudes of neuronal oscillations at theta, alpha, beta, and gamma frequencies; as well as graph theory-based measures of brain network organization. The results show that pain intensity, pain-related disability, and depression were significantly improved after interdisciplinary multimodal pain therapy. Bayesian hypothesis testing indicated that these clinical changes were not related to changes of the dominant peak frequency or amplitudes of oscillations at any frequency band. Clinical changes were, however, associated with an increase in global network efficiency at theta frequencies. Thus, changes in chronic pain might be reflected by global network changes in the theta band. These longitudinal insights further the understanding of the brain mechanisms of chronic pain. Beyond, they might help to identify biomarkers for the monitoring of chronic pain.
In this paper, we investigate a cloud radio access network (Cloud-RAN) in which both fronthaul an... more In this paper, we investigate a cloud radio access network (Cloud-RAN) in which both fronthaul and radio access links use massive MIMO millimeter-wave (mmWave) transmissions. Such an all-mmWave Cloud-RAN architecture provides a flexible and cost-effective means for deployment of next-generation (5G and beyond) cellular networks to meet the demands of fast-growing mobile data traffic. Nevertheless, the design of transmit power allocation schemes for multiple massive MIMO beams on both the fronthaul and access links in a mmWave Cloud-RAN is challenging. In particular, the traffic and wireless channel states of multiple mobile terminals (MTs) change over time, while their statistics may not be known a priori. We formulate the joint fronthaul-access link massive MIMO beam power allocation problem as a Markov decision process (MDP) with an objective to optimize the long-term quality of service to all the MTs in a Cloud-RAN. A reinforcement learning algorithm is designed, which learns the optimal beam power allocation policy on the fly and adapts to the network dynamics. Further, by leveraging the structure of the underlying problem, a post-decision state is introduced and a function decomposition technique is developed to reduce the search space during the learning process. The evaluation results validate the convergence of our proposed scheme and demonstrate its superior performance over the state-of-the-art baselines.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) uses radioactive contrast agents a... more Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) uses radioactive contrast agents as a tracer to provide tumor morphology and contrast kinetics information of tumor regions, which are crucial in breast cancer diagnosis and treatment. The effectiveness of the imaging modality relies on its capacity to acquire dynamic data at a sufficient sampling rate to gain the desired temporal resolution. This can be achieved by sparsely sampling the k-space data and applying advanced image reconstruction method that exploits compressed sensing, such as the recently proposed GenSeT (Generalized Series with Temporal constraint) method. Due to the highly nonlinear nature of compressed-sensing-based approach, computational complexity of the reconstruction algorithm is a practical challenge, especially for large breast DCE-MRI datasets. In this study, the GenSeT algorithm was implemented in GPU using CUDA platform to significantly reduce reconstruction time, yielding a much more practical solution. Experimental results showed that for a breast DCE-MRI data, the proposed GPU-based GenSeT implementation achieved approximately 48 times faster in the reconstruction time as compared to the CPU approach, without sacrificing the image quality. Although this work focuses on accelerating image reconstruction for sparsely-sampled breast DCE-MRI, the proposed GPU-based algorithm can be easily applied for sparsely sampled DCE-MRI of other organs.
Chronic pain is a highly prevalent and severely disabling disease that is associated with substan... more Chronic pain is a highly prevalent and severely disabling disease that is associated with substantial changes of brain function. Such changes have mostly been observed when analyzing static measures of resting-state brain activity. However, brain activity varies over time, and it is increasingly recognized that the temporal dynamics of brain activity provide behaviorally relevant information in different neuropsychiatric disorders. Here, we therefore investigated whether the temporal dynamics of brain function are altered in chronic pain. To this end, we applied microstate analysis to eyes-open and eyes-closed resting-state electroencephalography data of 101 patients suffering from chronic pain and 88 age- and sex-matched healthy controls. Microstate analysis describes electroencephalography activity as a sequence of a limited number of topographies termed microstates that remain stable for tens of milliseconds. Our results revealed that sequences of 5 microstates, labelled with the letters A to E, consistently described resting-state brain activity in both groups in the eyes-closed condition. Bayesian analysis of the temporal characteristics of microstates revealed that microstate D has a less predominant role in patients than in controls. As microstate D has previously been related to attentional networks and functions, these abnormalities might relate to dysfunctional attentional processes in chronic pain. Subgroup analyses replicated microstate D changes in patients with chronic back pain, while patients with chronic widespread pain did not show microstates alterations. Together, these findings add to the understanding of the pathophysiology of chronic pain and point to changes of brain dynamics specific to certain types of chronic pain.
Massive multiple-input multiple-output (MMIMO) is a key technology for 5G mobile communication sy... more Massive multiple-input multiple-output (MMIMO) is a key technology for 5G mobile communication systems, which enables to simultaneously form and transmit multiple directional signal beams to multiple mobile terminals (MTs) on the same frequency channel with high array beamforming gains and throughput. One of the challenges in MMMIO beamforming is how to allocate the transmit power to multiple beams sent from a MMIMO base station to multiple MTs and schedule data transmissions, given heterogeneous traffic and channel conditions of multiple MTs. Furthermore, the statistics of users’ packet arrivals and channel states may not be known a priori and vary over time. In this paper, we propose a framework to optimize MMIMO beam power allocation and transmission scheduling in millimeter wave networks with time-varying traffic and channel conditions. The optimization problem is formulated as a Markov decision process (MDP) with the objective to minimize the overall queueing delay of multiple MTs by taking their heterogeneous and dynamic traffic and channel states into account. An online reinforcement learning scheme is designed which allows achieving the long-term optimal system performance with no requirement for a priori knowledge of user traffic statistics and wireless network states. Evaluation results show that our proposed scheme outperforms the state-of-the-art baselines.
Pakistan Journal of Biological Sciences, Mar 15, 2022
Background and Objective: Plant genetic resources provide the raw material for crop improvement a... more Background and Objective: Plant genetic resources provide the raw material for crop improvement and plant breeding program largely depends on it. Therefore, the evaluation of plant genetic resources plays a critical role in crop improvement and also in conserving valuable genetic resources for the future. In this study, the genetic diversity of 16 Lactuca indica L. accessions collected in Vietnam was investigated by using ISSR and RAPD markers. Materials and Methods: Genetic diversity of 16 Lactuca sativa L. genotypes collected in Vietnam were evaluated using Random Amplified Polymorphic DNA (RAPD) and Inter-Simple Sequence Repeat (ISSR) molecular markers. Results: In this study, 42 RAPD and ISSR primers were initially used, of which 12 and 9 primers, respectively were finally selected as they produced scorable patterns. RAPD markers produced a total of 113 loci, out of which 52 loci (45.96%) were polymorphic. The average percentage of the polymorphic band for RAPD primer is 45.96% and the genetic similarity based on simple matching coefficient ranged from 69.0-94.7%. ISSR analysis detected a total of 60 loci, out of which 22 loci (36.32%) were polymorphic and the genetic similarity ranged from 56.7-95.0%. In general, ISSR markers amplified fewer loci and showed lower variation in the percentage of polymorphism compares to the RAPD assay. Conclusion: These results indicate that the 16 collected Indian lettuce genotypes are genetically diverse. Because of these genetic diversities, the collected genotypes could be used for preserving or crossing programs to improve this precious medicinal plant in Vietnam.
This paper proposes a novel cognitive cooperative transmission scheme by exploiting massive multi... more This paper proposes a novel cognitive cooperative transmission scheme by exploiting massive multiple-input multiple-output (MMIMO) and non-orthogonal multiple access (NOMA) radio technologies, which enables a macrocell network and multiple cognitive small cells to cooperate in dynamic spectrum sharing. The macrocell network is assumed to own the spectrum band and be the primary network (PN), and the small cells act as the secondary networks (SNs). The secondary access points (SAPs) of the small cells can cooperatively relay the traffic for the primary users (PUs) in the macrocell network, while concurrently accessing the PUs’ spectrum to transmit their own data opportunistically through MMIMO and NOMA. Such cooperation creates a “win-win” situation: the throughput of PUs will be significantly increased with the help of SAP relays, and the SAPs are able to use the PU’s spectrum to serve their secondary users (SUs). The interplay of these advanced radio techniques is analyzed in a systematic manner, and a framework is proposed for the joint optimization of cooperative relay selection, NOMA and MMIMO transmit power allocation, and transmission scheduling. Further, to model network-wide cooperation and competition, a two-sided matching algorithm is designed to find the stable partnership between multiple SAPs and PUs. The evaluation results demonstrate that the proposed scheme achieves significant performance gains for both primary and secondary users, compared to the baselines.
Chronic pain is a major healthcare issue posing a large burden on individuals and society. Conver... more Chronic pain is a major healthcare issue posing a large burden on individuals and society. Converging lines of evidence indicate that chronic pain is associated with substantial changes of brain structure and function. However, it remains unclear which neuronal measures relate to changes of clinical parameters over time and could thus monitor chronic pain and treatment responses. We therefore performed a longitudinal study in which we assessed clinical characteristics and resting-state electroencephalography data of 41 patients with chronic pain before and 6 months after interdisciplinary multimodal pain therapy. We specifically assessed electroencephalography measures that have previously been shown to differ between patients with chronic pain and healthy people. These included the dominant peak frequency; the amplitudes of neuronal oscillations at theta, alpha, beta, and gamma frequencies; as well as graph theory-based measures of brain network organization. The results show that pain intensity, pain-related disability, and depression were significantly improved after interdisciplinary multimodal pain therapy. Bayesian hypothesis testing indicated that these clinical changes were not related to changes of the dominant peak frequency or amplitudes of oscillations at any frequency band. Clinical changes were, however, associated with an increase in global network efficiency at theta frequencies. Thus, changes in chronic pain might be reflected by global network changes in the theta band. These longitudinal insights further the understanding of the brain mechanisms of chronic pain. Beyond, they might help to identify biomarkers for the monitoring of chronic pain.
In this paper, we investigate a cloud radio access network (Cloud-RAN) in which both fronthaul an... more In this paper, we investigate a cloud radio access network (Cloud-RAN) in which both fronthaul and radio access links use massive MIMO millimeter-wave (mmWave) transmissions. Such an all-mmWave Cloud-RAN architecture provides a flexible and cost-effective means for deployment of next-generation (5G and beyond) cellular networks to meet the demands of fast-growing mobile data traffic. Nevertheless, the design of transmit power allocation schemes for multiple massive MIMO beams on both the fronthaul and access links in a mmWave Cloud-RAN is challenging. In particular, the traffic and wireless channel states of multiple mobile terminals (MTs) change over time, while their statistics may not be known a priori. We formulate the joint fronthaul-access link massive MIMO beam power allocation problem as a Markov decision process (MDP) with an objective to optimize the long-term quality of service to all the MTs in a Cloud-RAN. A reinforcement learning algorithm is designed, which learns the optimal beam power allocation policy on the fly and adapts to the network dynamics. Further, by leveraging the structure of the underlying problem, a post-decision state is introduced and a function decomposition technique is developed to reduce the search space during the learning process. The evaluation results validate the convergence of our proposed scheme and demonstrate its superior performance over the state-of-the-art baselines.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) uses radioactive contrast agents a... more Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) uses radioactive contrast agents as a tracer to provide tumor morphology and contrast kinetics information of tumor regions, which are crucial in breast cancer diagnosis and treatment. The effectiveness of the imaging modality relies on its capacity to acquire dynamic data at a sufficient sampling rate to gain the desired temporal resolution. This can be achieved by sparsely sampling the k-space data and applying advanced image reconstruction method that exploits compressed sensing, such as the recently proposed GenSeT (Generalized Series with Temporal constraint) method. Due to the highly nonlinear nature of compressed-sensing-based approach, computational complexity of the reconstruction algorithm is a practical challenge, especially for large breast DCE-MRI datasets. In this study, the GenSeT algorithm was implemented in GPU using CUDA platform to significantly reduce reconstruction time, yielding a much more practical solution. Experimental results showed that for a breast DCE-MRI data, the proposed GPU-based GenSeT implementation achieved approximately 48 times faster in the reconstruction time as compared to the CPU approach, without sacrificing the image quality. Although this work focuses on accelerating image reconstruction for sparsely-sampled breast DCE-MRI, the proposed GPU-based algorithm can be easily applied for sparsely sampled DCE-MRI of other organs.
Chronic pain is a highly prevalent and severely disabling disease that is associated with substan... more Chronic pain is a highly prevalent and severely disabling disease that is associated with substantial changes of brain function. Such changes have mostly been observed when analyzing static measures of resting-state brain activity. However, brain activity varies over time, and it is increasingly recognized that the temporal dynamics of brain activity provide behaviorally relevant information in different neuropsychiatric disorders. Here, we therefore investigated whether the temporal dynamics of brain function are altered in chronic pain. To this end, we applied microstate analysis to eyes-open and eyes-closed resting-state electroencephalography data of 101 patients suffering from chronic pain and 88 age- and sex-matched healthy controls. Microstate analysis describes electroencephalography activity as a sequence of a limited number of topographies termed microstates that remain stable for tens of milliseconds. Our results revealed that sequences of 5 microstates, labelled with the letters A to E, consistently described resting-state brain activity in both groups in the eyes-closed condition. Bayesian analysis of the temporal characteristics of microstates revealed that microstate D has a less predominant role in patients than in controls. As microstate D has previously been related to attentional networks and functions, these abnormalities might relate to dysfunctional attentional processes in chronic pain. Subgroup analyses replicated microstate D changes in patients with chronic back pain, while patients with chronic widespread pain did not show microstates alterations. Together, these findings add to the understanding of the pathophysiology of chronic pain and point to changes of brain dynamics specific to certain types of chronic pain.
Massive multiple-input multiple-output (MMIMO) is a key technology for 5G mobile communication sy... more Massive multiple-input multiple-output (MMIMO) is a key technology for 5G mobile communication systems, which enables to simultaneously form and transmit multiple directional signal beams to multiple mobile terminals (MTs) on the same frequency channel with high array beamforming gains and throughput. One of the challenges in MMMIO beamforming is how to allocate the transmit power to multiple beams sent from a MMIMO base station to multiple MTs and schedule data transmissions, given heterogeneous traffic and channel conditions of multiple MTs. Furthermore, the statistics of users’ packet arrivals and channel states may not be known a priori and vary over time. In this paper, we propose a framework to optimize MMIMO beam power allocation and transmission scheduling in millimeter wave networks with time-varying traffic and channel conditions. The optimization problem is formulated as a Markov decision process (MDP) with the objective to minimize the overall queueing delay of multiple MTs by taking their heterogeneous and dynamic traffic and channel states into account. An online reinforcement learning scheme is designed which allows achieving the long-term optimal system performance with no requirement for a priori knowledge of user traffic statistics and wireless network states. Evaluation results show that our proposed scheme outperforms the state-of-the-art baselines.
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