A Distinct Mechanism to Achieve Efficient Signal Recognition Particle (SRP)-SRP Receptor Interaction by the Chloroplast SRP Pathway

Cotranslational protein targeting by the signal recognition particle (SRP) requires the SRP RNA, which accelerates the interaction between the SRP and SRP receptor 200-fold. This otherwise universally conserved SRP RNA is missing in the chloroplast SRP (cpSRP) pathway. Instead, the cpSRP and cpSRP receptor (cpFtsY) by themselves can interact 200-fold faster than their bacterial homologues. Here, cross-complementation analyses revealed the molecular origin underlying their efficient interaction. We found that cpFtsY is 5- to 10-fold more efficient than Escherichia coli FtsY at interacting with the GTPase domain of SRP from both chloroplast and bacteria, suggesting that cpFtsY is preorganized into a conformation more conducive to complex formation. Furthermore, the cargo-binding M-domain of cpSRP provides an additional 100-fold acceleration for the interaction between the chloroplast GTPases, functionally mimicking the effect of the SRP RNA in the cotranslational targeting pathway. The stimulatory effect of the SRP RNA or the M-domain of cpSRP is specific to the homologous SRP receptor in each pathway. These results strongly suggest that the M-domain of SRP actively communicates with the SRP and SR GTPases and that the cytosolic and chloroplast SRP pathways have evolved distinct molecular mechanisms (RNA vs. protein) to mediate this communication

Concerted Complex Assembly and GTPase Activation in the Chloroplast Signal Recognition Particle

The universally conserved signal recognition particle (SRP) and SRP receptor (SR) mediate the cotranslational targeting of proteins to cellular membranes. In contrast, a unique chloroplast SRP in green plants is primarily dedicated to the post-translational targeting of light harvesting chlorophyll a/b binding (LHC) proteins. In both pathways, dimerization and activation between the SRP and SR GTPases mediate the delivery of cargo; whether and how the GTPase cycle in each system adapts to its distinct substrate proteins were unclear. Here, we show that interactions at the active site essential for GTPase activation in the chloroplast SRP and SR play key roles in the assembly of the GTPase complex. In contrast to their cytosolic homologues, GTPase activation in the chloroplast SRP–SR complex contributes marginally to the targeting of LHC proteins. These results demonstrate that complex assembly and GTPase activation are highly coupled in the chloroplast SRP and SR and suggest that the chloroplast GTPases may forego the GTPase activation step as a key regulatory point. These features may reflect adaptations of the chloroplast SRP to the delivery of their unique substrate protein

Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework

To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.Comment: 15 pages, 10 figures. A short version will be submitted to IEEE GLOBECOM 202

Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance

We investigate the performance of multi-user multiple-antenna downlink systems in which a BS serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with M RF chains and N antennas, where M < N. Upon receiving pilot sequences to obtain the channel state information, the BS determines the best subset of M antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and QoS requirements. The JASPD overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tries all valid antenna subsets. Although approaching the (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a DNN to establish underlaying relations between the key system parameters and the selected antennas. The proposed L-ASPD is robust against the number of users and their locations, BS's transmit power, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves higher effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance.Comment: accepted to the IEEE Transactions on Wireless Communication

UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization

In this paper, a UAV is deployed as a flying base station to collect data from time-constrained IoT devices and then transfer the data to a ground gateway (GW). In general, the latency constraint at IoT users and the limited storage capacity of UAV highly hinder practical applications of UAV-assisted IoT networks. In this paper, full-duplex (FD) technique is adopted at the UAV to overcome these challenges. In addition, half-duplex (HD) scheme for UAV-based relaying is also considered to provide a comparative study between two modes. In this context, we aim at maximizing the number of served IoT devices by jointly optimizing bandwidth and power allocation, as well as the UAV trajectory, while satisfying the requested timeout (RT) requirement of each device and the UAV's limited storage capacity. The formulated optimization problem is troublesome to solve due to its non-convexity and combinatorial nature. Toward appealing applications, we first relax binary variables into continuous values and transform the original problem into a more computationally tractable form. By leveraging inner approximation framework, we derive newly approximated functions for non-convex parts and then develop a simple yet efficient iterative algorithm for its solutions. Next, we attempt to maximize the total throughput subject to the number of served IoT devices. Finally, numerical results show that the proposed algorithms significantly outperform benchmark approaches in terms of the number of served IoT devices and the amount of collected data.Comment: 30 pages, 11 figure

Mechanism of an ATP-independent Protein Disaggregase - I. Structure of a Membrane Protein Aggregate Reveals a Mechanism of Recognition by its Chaperone

Protein aggregation is detrimental to the maintenance of proper protein homeostasis in all cells. To overcome this problem, cells have evolved a network of molecular chaperones to prevent protein aggregation and even reverse existing protein aggregates. The most extensively studied disaggregase systems are ATP-driven macromolecular machines. Recently, we reported an alternative disaggregase system in which the 38-kDa subunit of chloroplast signal recognition particle (cpSRP43) efficiently reverses the aggregation of its substrates, the light-harvesting chlorophyll a/b-binding (LHC) proteins, in the absence of external energy input. To understand the molecular mechanism of this novel activity, here we used biophysical and biochemical methods to characterize the structure and nature of LHC protein aggregates. We show that LHC proteins form micellar, disc-shaped aggregates that are kinetically stable and detergent-resistant. Despite the nonamyloidal nature, the LHC aggregates have a defined global organization, displaying the chaperone recognition motif on its solvent-accessible surface. These findings suggest an attractive mechanism for recognition of the LHC aggregate by cpSRP43 and provide important constraints to define the capability of this chaperone

Prevalence and Determinants of Medication Adherence among Patients with HIV/AIDS in Southern Vietnam

This study was conducted to determine the prevalence and determinants of medication adherence among patients with HIV/AIDS in southern Vietnam. METHODS: A cross-sectional study was conducted in a hospital in southern Vietnam from June to December 2019 on patients who began antiretroviral therapy (ART) for at least 6 months. Using a designed questionnaire, patients were considered adherent if they took correct medicines with right doses, on time and properly with food and beverage and had follow-up visits as scheduled. Multivariable logistic regression was used to identify determinants of adherence. KEY FINDINGS: A total of 350 patients (from 861 medical records) were eligible for the study. The majority of patients were male (62.9%), and the dominant age group (≥35 years old) accounted for 53.7% of patients. Sexual intercourse was the primary route of transmission of HIV (95.1%). The proportions of participants who took the correct medicine and at a proper dose were 98.3% and 86.3%, respectively. In total, 94.9% of participants took medicine appropriately in combination with food and beverage, and 75.7% of participants were strictly adherent to ART. The factors marital status (odds ratio (OR) = 2.54; 95%CI = 1.51-4.28), being away from home (OR = 1.7; 95%CI = 1.03-2.78), substance abuse (OR = 2.7; 95%CI = 1.44-5.05), general knowledge about ART (OR = 2.75; 95%CI = 1.67-4.53), stopping medication after improvement (OR = 4.16; 95%CI = 2.29-7.56) and self-assessment of therapy adherence (OR = 9.83; 95%CI = 5.44-17.77) were significantly associated with patients' adherence. CONCLUSIONS: Three-quarters of patients were adherent to ART. Researchers should consider these determinants of adherence in developing interventions in further studies