Obsessive-compulsive symptoms and brain lesions compatible with multiple sclerosis.

Autoimmune-mediated obsessive-compulsive disorder (OCD) can occur in multiple sclerosis (MS). Here, a well-studied case study of a patient with OCD and MS-compatible diagnostic findings is presented. The 42-year-old female patient had displayed OCD symptoms for 6 years. Magnetic resonance imaging (MRI) identified several periventricular and one brainstem lesion suggestive of demyelination. Cerebrospinal fluid (CSF) analyses detected an increased white blood cell count, intrathecal immunoglobulin (Ig) G and IgM synthesis, CSF-specific oligoclonal bands, and a positive MRZ reaction. Neopterin was increased, but sarcoidosis was excluded. In the absence of neurological attacks and clues for MRI-based dissemination in time, a radiologically isolated syndrome, the pre-disease stage of MS, was diagnosed. Neurotransmitter measurements of CSF detected reduced serotonin levels. In the absence of visible strategic demyelinating lesions within the cortico-striato-thalamo-cortical circuits, OCD symptoms may relate to reduced intrathecal serotonin levels and mild neuroinflammatory processes. Serotonin abnormalities in MS should be studied further, as they could potentially explain the association between neuroinflammation and mental illnesses.

Plasma lipids paediatric reference intervals: Indirect estimation using a large 14-year database.

OBJECTIVES: Establishing direct reference intervals (RIs) for paediatric patients is a very challenging endeavour. Indirect RIs can address this problem, using existing clinical laboratory databases from real-world data research. Compared to the traditional direct method, the indirect approach is highly practical, widely applicable, and low-cost. Considering the relevance of dyslipidemia in the paediatric age, to provide better laboratory services to the local paediatric population, we established population-specific lipid RIs via data mining. METHODS: Our laboratory information system was searched for cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL) and high-density lipoprotein (HDL) of patients aged less than 18 years, performed from January 2009 until December 2022. RIs were estimated using RefineR algorithm. RESULTS: Values from 215,594 patients were initially collected. After refining data on the basis of specific exclusion criteria that left 17,933 patients, we determined the RIs for each analyte, including corresponding 95% confidence interval (95% CI). Age and sex partitions were required for proper stratification of the heterogenous subpopulations. Age-related variations in TC and TG values were observed mainly in children until 5 years. RIs were defined for children less than 3 years and for those of 3-18 years. In our population, the obtained RIs were comparable with those of the literature, but the upper TG limit in subjects under the age of 3 (2.03 mmol/L with 95% CI: 1.45-2.86) was lower than that previously reported. CONCLUSIONS: Our RIs, necessary for paediatric lipid monitoring, are tailored to the serviced patient population as should be done whenever possible.

Multiple Sclerosis: An Emergency Medicine-Focused Narrative Review.

BACKGROUND: Multiple sclerosis (MS) is a rare but serious condition associated with significant morbidity. OBJECTIVE: This review provides a focused assessment of MS for emergency clinicians, including the presentation, evaluation, and emergency department (ED) management based on current evidence. DISCUSSION: MS is an autoimmune disorder targeting the central nervous system (CNS), characterized by clinical relapses and radiological lesions disseminated in time and location. Patients with MS most commonly present with long tract signs (e.g., myelopathy, asymmetric spastic paraplegia, urinary dysfunction, Lhermitte's sign), optic neuritis, or brainstem syndromes (bilateral internuclear ophthalmoplegia). Cortical syndromes or multifocal presentations are less common. Radiologically isolated syndrome and clinically isolated syndrome (CIS) may or may not progress to chronic forms of MS, including relapsing remitting MS, primary progressive MS, and secondary progressive MS. The foundation of outpatient management involves disease-modifying therapy, which is typically initiated with the first signs of disease onset. Management of CIS and acute flares of MS in the ED includes corticosteroid therapy, ideally after diagnostic testing with imaging and lumbar puncture for cerebrospinal fluid analysis. Emergency clinicians should evaluate whether patients with MS are presenting with new-onset debilitating neurological symptoms to avoid unnecessary testing and admissions, but failure to appropriately diagnose CIS or MS flare is associated with increased morbidity. CONCLUSIONS: An understanding of MS can assist emergency clinicians in better diagnosing and managing this neurologically devastating disease.

Cluster-Based Strategy for Maximizing the Sum-Rate of a Distributed Reconfigurable Intelligent Surface (RIS)-Assisted Coordinated Multi-Point Non-Orthogonal Multiple-Access (CoMP-NOMA) System.

This article proposes a distributed intelligent Coordinated Multi-Point Non-Orthogonal Multiple-Access (CoMP-NOMA) collaborative transmission model with the assistance of reconfigurable intelligent surfaces (RISs) to address the issues of poor communication quality, low fairness, and high system power consumption for edge users in multi-cellular networks. By analyzing the interaction mechanisms and influencing factors among RIS signal enhancement, NOMA user scheduling, and multi-point collaborative transmission, the model establishes RIS-enhanced edge user grouping and coordinates NOMA user clusters based on this. In the multi-cell RIS-assisted JT-CoMP NOMA downlink transmission, joint optimization of the power allocation (PA), user clustering (UC), and RIS phase-shift matrix design (PS) poses a challenging Mixed-Integer Non-Linear Programming (MINLP) problem. The original problem is decomposed by optimizing the formulas into joint sub-problems of PA, UC, and PA and PS, and solved using an alternating optimization approach. Simulation results demonstrate that the proposed scheme effectively reduces the system's power consumption while significantly improving the system's throughput and rates.

Downlink Transmissions of UAV-RIS-Assisted Cell-Free Massive MIMO Systems: Location and Trajectory Optimization.

In this paper, we investigate a cell-free massive multiple-input multiple-output (CF-mMIMO) system with a reconfigurable intelligent surface (RIS) carried by an unmanned aerial vehicle (UAV), called the UAV-RIS. Compared with the RIS located on the ground, the UAV-RIS has a wider coverage that can reflect all signals from access points (APs) and user equipment (UE). By correlating the UAV location with the Rician K-factor, we derive a closed-form approximation of the UE achievable downlink rate. Based on this, we obtain the optimal UAV location and RIS phase shift that can maximize the UE sum rate through an alternating optimization method. Simulation results have verified the accuracy of the derived approximation and shown that the UE sum rate can be significantly improved with the obtained optimal UAV location and RIS phase shift. Moreover, we find that with a uniform UE distribution, the UAV-RIS should fly to the center of the system, while with an uneven UE distribution, the UAV-RIS should fly above the area where UEs are gathered. In addition, we also design the best trajectory for the UAV-RIS to fly from its initial location to the optimal destination while maintaining the maximum UE sum rate per time slot during the flight.

A Novel Two-Step Channel Estimation Method for RIS-Assisted mmWave Systems.

In this work, we resolve the cascaded channel estimation problem and the reflected channel estimation problem for the reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) systems. The novel two-step method contains modified multiple population genetic algorithm (MMPGA), least squares (LS), residual network (ResNet), and multi-task regression model. In the first step, the proposed MMPGA-LS optimizes the crossover strategy and mutation strategy. Besides, the ResNet achieves cascaded channel estimation by learning the relationship between the cascaded channel obtained by the MMPGA-LS and the channel of the user (UE)-RIS-base station (BS). Then, the proposed multi-task-ResNet (MTRnet) is introduced for the reflected channel estimation. Relying on the output of ResNet, the MTRnet with multiple output layers estimates the coefficients of reflected channels and reconstructs the channel of UE-RIS and RIS-BS. Remarkably, the proposed MTRnet is capable of using a lower optimization model to estimate multiple reflected channels compared with the classical neural network with the single output layer. A series of experimental results validate the superiority of the proposed method in terms of a lower norm mean square error (NMSE). Besides, the proposed method also obtains a low NMSE in the RIS with the formulation of the uniform planar array.

Joint Active and Passive Beamforming in RIS-Assisted Secure ISAC Systems.

This paper investigates joint beamforming in a secure integrated sensing and communications (ISAC) system assisted by reconfigurable intelligent surfaces (RIS). The system communicates with legitimate downlink users, detecting a potential target, which is a potential eavesdropper attempting to intercept the downlink communication information from the base station (BS) to legitimate users. To enhance the physical-layer secrecy of the system, we design and introduce interference signals at the BS to disrupt eavesdroppers' attempts to intercept legitimate communication information. The BS simultaneously transmits communication and interference signals, both utilized for communication and sensing to guarantee the sensing and communication quality. By jointly optimizing the BS active beamformer and the RIS passive beamforming matrix, we aim to maximize the achievable secrecy rate and radiation power of the system. We develop an effective scheme to find the active beamforming matrix through fractional programming (FP) and semi-definite programming (SDP) techniques and obtain the RIS phase shift matrix via a local search technique. Simulation results validate the effectiveness of the proposed methods in enhancing communication and sensing performance. Additionally, the results demonstrate the effectiveness of introducing the interference signals and RIS in enhancing the physical-layer secrecy of the ISAC system.

Enhancing Reconfigurable Intelligent Surface-Enabled Cognitive Radio Networks for Sixth Generation and Beyond: Performance Analysis and Parameter Optimization.

In this paper, we propose a novel system integrating reconfigurable intelligent surfaces (RISs) with cognitive radio (CR) technology, presenting a forward-looking solution aligned with the evolving standards of 6G and beyond networks. The proposed RIS-assisted CR networks operate with a base station (BS) transmitting signals to two users, the primary user (PU) and secondary user (SU), through direct and reflected signal paths, respectively. Our mathematical analysis focuses on deriving expressions for SU in the RIS-assisted CR system, validated through Monte Carlo simulations. The investigation covers diverse aspects, including the impact of the signal-to-noise ratio (SNR), power allocations, the number of reflected surfaces, and blocklength variations. The results provide nuanced insights into RIS-assisted CR system performance, highlighting its sensitivity to factors like the number of reflectors, fading severity, and correlation coefficient. Careful parameter selection, such as optimizing the configuration of reflectors, is shown to prevent a complete outage, showcasing the system's robustness. Additionally, the work suggests that the optimization of reflectors configuration can significantly enhance overall system performance, and RIS-assisted CR systems outperform reference schemes. This work contributes a thorough analysis of the proposed system, offering valuable insights for efficient performance evaluation and parameter optimization in RIS-assisted CR networks.

RIS-Assisted D2D Communication over Nakagami-m Fading with RSMA.

In this study, we investigated reconfigurable intelligent surface (RIS)-assisted device-to-device (D2D) communication systems over Nakagami-m fading channels. To enhance the reliability of RIS-assisted D2D communications, we utilized the rate-splitting multiple access (RSMA) technique to maximize the achievable ergodic rate for our considered systems. Specifically, both devices decoded the common symbol by treating private symbols as interference, and then each private symbol was decoded by treating the other as interference. In order to maximize the achievable ergodic rate at the destination, we analyzed the achievable ergodic rate of the RIS link and the D2D link, and the destination jointly decoded both symbols transmitted from the source and device by involving the maximum ratio combination (MRC). We obtained a closed-form expression for the achievable ergodic rate of the proposed RIS-assisted D2D communication system. Finally, we investigated the influence of power allocation factors and the number of reflective elements on the achievable ergodic rate. As seen by the numerical results, there was a good match between the analysis and simulation results, as well as significant superiority compared with existing works.

Statistics of the Sum of Double Random Variables and Their Applications in Performance Analysis and Optimization of Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface-Assisted Non-Orthogonal Multi-Access Systems.

For the future of sixth-generation (6G) wireless communication, simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) technology is emerging as a promising solution to achieve lower power transmission and flawless coverage. To facilitate the performance analysis of RIS-assisted networks, the statistics of the sum of double random variables, i.e., the sum of the products of two random variables of the same distribution type, become vitally necessary. This paper applies the statistics of the sum of double random variables in the performance analysis of an integrated power beacon (PB) energy-harvesting (EH)-based NOMA-assisted STAR-RIS network to improve its outage probability (OP), ergodic rate, and average symbol error rate. Furthermore, the impact of imperfect successive interference cancellation (ipSIC) on system performance is also analyzed. The analysis provides the closed-form expressions of the OP and ergodic rate derived for both imperfect and perfect SIC (pSIC) cases. All analyses are supported by extensive simulation results, which help recommend optimized system parameters, including the time-switching factor, the number of reflecting elements, and the power allocation coefficients, to minimize the OP. Finally, the results demonstrate the superiority of the proposed framework compared to conventional NOMA and OMA systems.

Incidental demyelination in magnetic resonance imaging and 10-year risk of multiple sclerosis: A data lake cohort study.

BACKGROUND AND PURPOSE: There is an absence of data from large population-based cohort studies on the incidence of radiologically isolated syndrome (RIS). The incidence of RIS and the subsequent risk for multiple sclerosis (MS) were investigated. METHODS: A population-based, retrospective cohort study was conducted using a data-lake-based analysis of digitalized radiology reports. All brain and spinal cord magnetic resonance imaging (MRI) in people aged 16-70 during the years 2005-2010 (n = 102,224) were screened using optimized search terms to detect cases with RIS. The subjects with RIS were followed up until January 2022. RESULTS: The cumulative incidence of RIS was 0.03% when all MRI modalities were included and 0.06% when only brain MRI was included according to MAGNIMS 2018 recommendation criteria. With the Okuda 2009 criteria, the respective figures were 0.03% and 0.05% (86% concordance). The overall risk for MS after RIS was similar, 32% by using the MAGNIMS and 32% by using the Okuda definition of RIS. Individuals aged <35.5 years exhibited the most significant predisposition to MS (80%), whilst those >35.5 years had less than 10% risk of MS. MS diagnosed after RIS constituted 0.8% of the incident MS cases in the population during 2005-2010. CONCLUSIONS: A population-wide context was provided for the incidence of RIS and its relationship to MS. MAGNIMS recommendations were only slightly more sensitive to detect RIS compared to the Okuda criteria. RIS has a subtle effect on the overall incidence of MS, yet the risk for MS in individuals under the age of 35.5 years is substantial.

Mechanistic insight into humic acid-enhanced sonophotocatalytic removal of 17ß-estradiol: Formation and contribution of reactive intermediates.

In this study, humic acid (HA) enhanced 17ß-estradiol (17ß-E2) degradation by Er3+-CdS/MoS2 (ECMS) was investigated under ultrasonic and light conditions. The degradation reaction rate of 17ß-E2 was increased from (14.414 ± 0.315) × 10-3 min-1 to (122.677 ± 1.729) × 10-3 min-1 within 90 min sonophotocatalytic (SPC) reaction with the addition of HA. The results of quenching coupled with chemical probe experiments indicated that more reactive intermediates (RIs) including reactive oxygen species (ROSs) and triplet-excited states were generated in the HA-enhanced sonophotocatalytic system. The triplet-excited states of humic acid (3HA*), hydroxyl radical (•OH), and superoxide radical (•O2-) were the dominant RIs for 17ß-E2 elimination. In addition, the energy- and electron-transfer process via coexisting HA also account for 12.86% and 29.24% contributions, respectively. The quantum yields of RIs in the SPC-ECMS-HA system followed the order of 3HA* > H2O2 > 1O2 > â€¢O2-> •OH. Moreover, the spectral and fluorescence characteristics of HA were further analyzed during the sonophotocatalytic reaction process. The study expanded new insights into the comprehension of the effects of omnipresent coexisting HA and RIs formation for the removal of 17ß-E2 during the sonophotocatalytic process.

RIS-Assisted Cooperative Time-Division Multiple Access.

Reconfigurable intelligent surface-aided communication systems have been intensively investigated to improve capacity, coverage, and energy efficiency via optimal controlling of phase shifts for passive reflecting elements. However, there are few studies on cooperative transmission incorporating RIS in TDMA systems, because RIS reflects all the incident signals, and it inadvertently leads to a boost in interference signals. In this paper, we propose RIS-assisted cooperative time-division multiple access, in which the required SINR of all users is guaranteed as much as possible by opportunistic use of RIS for cooperative transmission. The proposed scheme's primary function is that some time slots, i.e., cooperative time slots, serve a pair of users, i.e., a strong- and a weak-channel-conditioned user, using RIS. To support this functionality, we propose scheduling for non-cooperative and cooperative time slots, user pairing for scheduled cooperative time slots, and transmit beamforming vector design for the pair of UEs in each cooperative time slot. The simulation and numerical results demonstrate that the proposed scheme guarantees QoS for all UE as much as possible and minimizes the remaining required capacity indicating the amount of capacity that was not achieved compared with the required capacity.

Performance of a Double RIS Communication System Aided by Partially Active Elements.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology to enhance the spectral efficiency of wireless communication systems. However, if there are many obstacles between the RIS and users, a single RIS may not provide sufficient performance. For this reason, a double RIS-aided communication system is proposed in this paper. However, this system also has a problem: the signal is attenuated three times due to the three channels created by the double RIS. To overcome these attenuations, an active RIS is proposed in this paper. An active RIS is almost the same as a conventional RIS, except for the included amplifier. Comprehensively, the proposed system overcomes various obstacles and attenuations. In this paper, an active RIS is applied to the second RIS. To reduce the power consumption of active elements, a partially active RIS is applied. To optimize the RIS elements, the sum of the covariance matrix is found by using channels related to each RIS, and the right singular vector is exploited using singular value decomposition for the sum of the covariance matrix. Then, the singular value of the sum of the covariance value is checked to determine which element is the active element. Simulation results show that the proposed system has better sum rate performance compared to a single RIS system. Although it has a lower sum rate performance compared to a double RIS with fully active elements, the proposed system will be more attractive in the future because it has much better energy efficiency.

Deep Learning-Based Link Quality Estimation for RIS-Assisted UAV-Enabled Wireless Communications System.

In recent years, unmanned aerial vehicles (UAVs) have become a valuable platform for many applications, including communication networks. UAV-enabled wireless communication faces challenges in complex urban and dynamic environments. UAVs can suffer from power limitations and path losses caused by non-line-of-sight connections, which may hamper communication performance. To address these issues, reconfigurable intelligent surfaces (RIS) have been proposed as helpful technologies to enhance UAV communication networks. However, due to the high mobility of UAVs, complex channel environments, and dynamic RIS configurations, it is challenging to estimate the link quality of ground users. In this paper, we propose a link quality estimation model using a gated recurrent unit (GRU) to assess the link quality of ground users for a multi-user RIS-assisted UAV-enabled wireless communication system. Our proposed framework uses a time series of user channel data and RIS phase shift information to estimate the quality of the link for each ground user. The simulation results showed that the proposed GRU model can effectively and accurately estimate the link quality of ground users in the RIS-assisted UAV-enabled wireless communication network.

A Codeword-Independent Localization Technique for Reconfigurable Intelligent Surface Enhanced Environments Using Adversarial Learning.

Reconfigurable Intelligent Surfaces (RISs) not only enable software-defined radio in modern wireless communication networks but also have the potential to be utilized for localization. Most previous works used channel matrices to calculate locations, requiring extensive field measurements, which leads to rapidly growing complexity. Although a few studies have designed fingerprint-based systems, they are only feasible under an unrealistic assumption that the RIS will be deployed only for localization purposes. Additionally, all these methods utilize RIS codewords for location inference, inducing considerable communication burdens. In this paper, we propose a new localization technique for RIS-enhanced environments that does not require RIS codewords for online location inference. Our proposed approach extracts codeword-independent representations of fingerprints using a domain adversarial neural network. We evaluated our solution using the DeepMIMO dataset. Due to the lack of results from other studies, for fair comparisons, we define oracle and baseline cases, which are the theoretical upper and lower bounds of our system, respectively. In all experiments, our proposed solution performed much more similarly to the oracle cases than the baseline cases, demonstrating the effectiveness and robustness of our method.

A Survey on Reinforcement Learning for Reconfigurable Intelligent Surfaces in Wireless Communications.

A reconfigurable intelligent surface (RIS) is a development of conventional relay technology that can send a signal by reflecting the signal received from a transmitter to a receiver without additional power. RISs are a promising technology for future wireless communication due to their improvement of the quality of the received signal, energy efficiency, and power allocation. In addition, machine learning (ML) is widely used in many technologies because it can create machines that mimic human mindsets with mathematical algorithms without requiring direct human assistance. Meanwhile, it is necessary to implement a subfield of ML, reinforcement learning (RL), to automatically allow a machine to make decisions based on real-time conditions. However, few studies have provided comprehensive information related to RL algorithms-especially deep RL (DRL)-for RIS technology. Therefore, in this study, we provide an overview of RISs and an explanation of the operations and implementations of RL algorithms for optimizing the parameters of RIS technology. Optimizing the parameters of RISs can offer several benefits for communication systems, such as the maximization of the sum rate, user power allocation, and energy efficiency or the minimization of the information age. Finally, we highlight several issues to consider in implementing RL algorithms for RIS technology in wireless communications in the future and provide possible solutions.

RIS-Assisted Hybrid Beamforming and Connected User Vehicle Localization for Millimeter Wave MIMO Systems.

A reconfigurable intelligent surface (RIS) is a type of metasurface that can dynamically control the reflection and transmission of electromagnetic waves, such as radio waves, by changing its physical properties. Recently, RISs have played an important role in intelligently reshaping wireless propagation environments to improve the received signal gain as well as spectral efficiency performance. In this paper, we consider a millimeter wave (mmWave) vehicle-to-vehicle (V2V) multiple-input multiple-output (MIMO) system in which, an RIS is deployed to aid downlink V2V data transmission. In particular, the line-of-sight path of the mmWave system is affected by blockages, resulting in higher signaling overhead. Thus, the system performance may suffer due to interruptions caused by static or mobile blockers, such as buildings, trees, vehicles, and pedestrians. In this paper, we propose an RIS-assisted hybrid beamforming scheme for blockage-aware mmWave V2V MIMO systems to increase communication service coverage. First, we propose a conjugate gradient and location-based hybrid beamforming (CG-LHB) algorithm to solve the user sub-rate maximization problem. We then propose a double-step iterative algorithm that utilizes an error covariance matrix splitting method to minimize the effect of location error on the passive beamforming. The proposed algorithms perform quite well when the channel uncertainty is smaller than 10%. Finally, the simulation results validate the proposed CG-LHB algorithm in terms of the RIS-assisted equivalent channel for mmWave V2V MIMO communications.

Joint Beamforming Design for RIS-Assisted Integrated Satellite-HAP-Terrestrial Networks Using Deep Reinforcement Learning.

In this paper, we consider reconfigurable intelligent surface (RIS)-assisted integrated satellite high-altitude platform terrestrial networks (IS-HAP-TNs) that can improve network performance by exploiting the HAP stability and RIS reflection. Specifically, the reflector RIS is installed on the side of HAP to reflect signals from the multiple ground user equipment (UE) to the satellite. To aim at maximizing the system sum rate, we jointly optimize the transmit beamforming matrix at the ground UEs and RIS phase shift matrix. Due to the limitation of the unit modulus of the RIS reflective elements constraint, the combinatorial optimization problem is difficult to tackle effectively by traditional solving methods. Based on this, this paper studies the deep reinforcement learning (DRL) algorithm to achieve online decision making for this joint optimization problem. In addition, it is verified through simulation experiments that the proposed DRL algorithm outperforms the standard scheme in terms of system performance, execution time, and computing speed, making real-time decision making truly feasible.

RIS-Aided Proactive Mobile Network Downlink Interference Suppression: A Deep Reinforcement Learning Approach.

A proactive mobile network (PMN) is a novel architecture enabling extremely low-latency communication. This architecture employs an open-loop transmission mode that prohibits all real-time control feedback processes and employs virtual cell technology to allocate resources non-exclusively to users. However, such a design also results in significant potential user interference and worsens the communication's reliability. In this paper, we propose introducing multi-reconfigurable intelligent surface (RIS) technology into the downlink process of the PMN to increase the network's capacity against interference. Since the PMN environment is complex and time varying and accurate channel state information cannot be acquired in real time, it is challenging to manage RISs to service the PMN effectively. We begin by formulating an optimization problem for RIS phase shifts and reflection coefficients. Furthermore, motivated by recent developments in deep reinforcement learning (DRL), we propose an asynchronous advantage actor-critic (A3C)-based method for solving the problem by appropriately designing the action space, state space, and reward function. Simulation results indicate that deploying RISs within a region can significantly facilitate interference suppression. The proposed A3C-based scheme can achieve a higher capacity than baseline schemes and approach the upper limit as the number of RISs increases.