Human neocortical expansion involves glutamatergic neuron diversification.

The neocortex is disproportionately expanded in human compared with mouse1,2, both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers composed of neurons that selectively make connections within the neocortex and with other telencephalic structures. Single-cell transcriptomic analyses of human and mouse neocortex show an increased diversity of glutamatergic neuron types in supragranular layers in human neocortex and pronounced gradients as a function of cortical depth3. Here, to probe the functional and anatomical correlates of this transcriptomic diversity, we developed a robust platform combining patch clamp recording, biocytin staining and single-cell RNA-sequencing (Patch-seq) to examine neurosurgically resected human tissues. We demonstrate a strong correspondence between morphological, physiological and transcriptomic phenotypes of five human glutamatergic supragranular neuron types. These were enriched in but not restricted to layers, with one type varying continuously in all phenotypes across layers 2 and 3. The deep portion of layer 3 contained highly distinctive cell types, two of which express a neurofilament protein that labels long-range projection neurons in primates that are selectively depleted in Alzheimer's disease4,5. Together, these results demonstrate the explanatory power of transcriptomic cell-type classification, provide a structural underpinning for increased complexity of cortical function in humans, and implicate discrete transcriptomic neuron types as selectively vulnerable in disease.

Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks.

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions.

Assessment of inter-centre agreement across multidisciplinary team meetings for patients with retroperitoneal sarcoma.

BACKGROUND: Decision-making in the management of patients with retroperitoneal sarcoma is complex and requires input from a number of different specialists. The aim of this study was to evaluate the levels of agreement in terms of resectability, treatment allocation, and organs proposed to be resected across different retroperitoneal sarcoma multidisciplinary team meetings. METHODS: The CT scans and clinical information of 21 anonymized retroperitoneal sarcoma patients were sent to all of the retroperitoneal sarcoma multidisciplinary team meetings in Great Britain, which were asked to give an opinion about resectability, treatment allocation, and organs proposed to be resected. The main outcome was inter-centre reliability, which was quantified using overall agreement, as well as the chance-corrected Krippendorff's alpha statistic. Based on the latter, the level of agreement was classified as: 'slight' (0.00-0.20), 'fair' (0.21-0.40), 'moderate' (0.41-0.60), 'substantial' (0.61-0.80), or 'near-perfect' (>0.80). RESULTS: Twenty-one patients were reviewed at 12 retroperitoneal sarcoma multidisciplinary team meetings, giving a total of 252 assessments for analysis. Consistency between centres was only 'slight' to 'fair', with rates of overall agreement and Krippendorff's alpha statistics of 85.4 per cent (211 of 247) and 0.37 (95 per cent c.i. 0.11 to 0.57) for resectability; 80.4 per cent (201 of 250) and 0.39 (95 per cent c.i. 0.33 to 0.45) for treatment allocation; and 53.0 per cent (131 of 247) and 0.20 (95 per cent c.i. 0.17 to 0.23) for the organs proposed to be resected. Depending on the centre that they had attended, 12 of 21 patients could either have been deemed resectable or unresectable, and 10 of 21 could have received either potentially curative or palliative treatment. CONCLUSIONS: Inter-centre agreement between retroperitoneal sarcoma multidisciplinary team meetings was low. Multidisciplinary team meetings may not provide the same standard of care for patients with retroperitoneal sarcoma across Great Britain.

An Adaptive Traffic-Flow Management System with a Cooperative Transitional Maneuver for Vehicular Platoons.

Globally, the increases in vehicle numbers, traffic congestion, and road accidents are serious issues. Autonomous vehicles (AVs) traveling in platoons provide innovative solutions for efficient traffic flow management, especially for congestion mitigation, thus reducing accidents. In recent years, platoon-based driving, also known as vehicle platoon, has emerged as an extensive research area. Vehicle platooning reduces travel time and increases road capacity by reducing the safety distance between vehicles. For connected and automated vehicles, cooperative adaptive cruise control (CACC) systems and platoon management systems play a significant role. Platoon vehicles can maintain a closer safety distance due to CACC systems, which are based on vehicle status data obtained through vehicular communications. This paper proposes an adaptive traffic flow and collision avoidance approach for vehicular platoons based on CACC. The proposed approach considers the creation and evolution of platoons to govern the traffic flow during congestion and avoid collisions in uncertain situations. Different obstructing scenarios are identified during travel, and solutions to these challenging situations are proposed. The merge and join maneuvers are performed to help the platoon's steady movement. The simulation results show a significant improvement in traffic flow due to the mitigation of congestion using platooning, minimizing travel time, and avoiding collisions.

An Optimization Framework for Data Collection in Software Defined Vehicular Networks.

A Software Defined Vehicular Network (SDVN) is a new paradigm that enhances programmability and flexibility in Vehicular Adhoc Networks (VANETs). There exist different architectures for SDVNs based on the degree of control of the control plane. However, in vehicular communication literature, we find that there is no proper mechanism to collect data. Therefore, we propose a novel data collection methodology for the hybrid SDVN architecture by modeling it as an Integer Quadratic Programming (IQP) problem. The IQP model optimally selects broadcasting nodes and agent (unicasting) nodes from a given vehicular network instance with the objective of minimizing the number of agents, communication delay, communication cost, total payload, and total overhead. Due to the dynamic network topology, finding a new solution to the optimization is frequently required in order to avoid node isolation and redundant data transmission. Therefore, we propose a systematic way to collect data and make optimization decisions by inspecting the heterogeneous normalized network link entropy. The proposed optimization model for data collection for the hybrid SDVN architecture yields a 75.5% lower communication cost and 32.7% lower end-to-end latency in large vehicular networks compared to the data collection in the centralized SDVN architecture while collecting 99.9% of the data available in the vehicular network under optimized settings.

Enhancing Handover for 5G mmWave Mobile Networks Using Jump Markov Linear System and Deep Reinforcement Learning.

The Fifth Generation (5G) mobile networks use millimeter waves (mmWaves) to offer gigabit data rates. However, unlike microwaves, mmWave links are prone to user and topographic dynamics. They easily get blocked and end up forming irregular cell patterns for 5G. This in turn causes too early, too late, or wrong handoffs (HOs). To mitigate HO challenges, sustain connectivity, and avert unnecessary HO, we propose an HO scheme based on a jump Markov linear system (JMLS) and deep reinforcement learning (DRL). JMLS is widely known to account for abrupt changes in system dynamics. DRL likewise emerges as an artificial intelligence technique for learning highly dimensional and time-varying behaviors. We combine the two techniques to account for time-varying, abrupt, and irregular changes in mmWave link behavior by predicting likely deterioration patterns of target links. The prediction is optimized by meta training techniques that also reduce training sample size. Thus, the JMLS-DRL platform formulates intelligent and versatile HO policies for 5G. When compared to a signal and interference noise ratio (SINR) and DRL-based HO scheme, our HO scheme becomes more reliable in selecting reliable target links. In particular, our proposed scheme is able to reduce wasteful HO to less than 5% within 200 training episodes compared to the DRL-based HO scheme that needs more than 200 training episodes to get to less than 5%. It supports longer dew time between HOs and high sum rates by ably averting unnecessary HOs with almost half the HOs compared to a DRL-based HO scheme.

Automatic Assessment of Aphasic Speech Sensed by Audio Sensors for Classification into Aphasia Severity Levels to Recommend Speech Therapies.

Aphasia is a type of speech disorder that can cause speech defects in a person. Identifying the severity level of the aphasia patient is critical for the rehabilitation process. In this research, we identify ten aphasia severity levels motivated by specific speech therapies based on the presence or absence of identified characteristics in aphasic speech in order to give more specific treatment to the patient. In the aphasia severity level classification process, we experiment on different speech feature extraction techniques, lengths of input audio samples, and machine learning classifiers toward classification performance. Aphasic speech is required to be sensed by an audio sensor and then recorded and divided into audio frames and passed through an audio feature extractor before feeding into the machine learning classifier. According to the results, the mel frequency cepstral coefficient (MFCC) is the most suitable audio feature extraction method for the aphasic speech level classification process, as it outperformed the classification performance of all mel-spectrogram, chroma, and zero crossing rates by a large margin. Furthermore, the classification performance is higher when 20 s audio samples are used compared with 10 s chunks, even though the performance gap is narrow. Finally, the deep neural network approach resulted in the best classification performance, which was slightly better than both K-nearest neighbor (KNN) and random forest classifiers, and it was significantly better than decision tree algorithms. Therefore, the study shows that aphasia level classification can be completed with accuracy, precision, recall, and F1-score values of 0.99 using MFCC for 20 s audio samples using the deep neural network approach in order to recommend corresponding speech therapy for the identified level. A web application was developed for English-speaking aphasia patients to self-diagnose the severity level and engage in speech therapies.

Forecasting spread of COVID-19 using google trends: A hybrid GWO-deep learning approach.

The recent outbreak of COVID-19 has brought the entire world to a standstill. The rapid pace at which the virus has spread across the world is unprecedented. The sheer number of infected cases and fatalities in such a short period of time has overwhelmed medical facilities across the globe. The rapid pace of the spread of the novel coronavirus makes it imperative that its' spread be forecasted well in advance in order to plan for eventualities. An accurate early forecasting of the number of cases would certainly assist governments and various other organizations to strategize and prepare for the newly infected cases, well in advance. In this work, a novel method of forecasting the future cases of infection, based on the study of data mined from the internet search terms of people in the affected region, is proposed. The study utilizes relevant Google Trends of specific search terms related to COVID-19 pandemic along with European Centre for Disease prevention and Control (ECDC) data on COVID-19 spread, to forecast the future trends of daily new cases, cumulative cases and deaths for India, USA and UK. For this purpose, a hybrid GWO-LSTM model is developed, where the network parameters of Long Short Term Memory (LSTM) network are optimized using Grey Wolf Optimizer (GWO). The results of the proposed model are compared with the baseline models including Auto Regressive Integrated Moving Average (ARIMA), and it is observed that the proposed model achieves much better results in forecasting the future trends of the spread of infection. Using the proposed hybrid GWO-LSTM model incorporating online big data from Google Trends, a reduction in Mean Absolute Percentage Error (MAPE) values for forecasting results to the extent of about 98% have been observed. Further, reduction in MAPE by 74% for models incorporating Google Trends was observed, thus, confirming the efficacy of utilizing public sentiments in terms of search frequencies of relevant terms online, in forecasting pandemic numbers.

A Comprehensive Open-Source Simulation Framework for LiFi Communication.

Light Fidelity (LiFi) is a new candidate for wireless networking that utilizes the visible light spectrum and exploits the existing lighting infrastructure in the form of light-emitting diodes (LEDs). It provides point-to-point and point-to-multipoint communication on a bidirectional channel at very high data rates. However, the LiFi has small coverage, and its optical gain is closely related to the receiver's directionality vis-à-vis the transmitter, therefore it can experience frequent service outages. To provide reliable coverage, the LiFi is integrated with other networking technologies such as wireless fidelity (WiFi) thus forming a hybrid system. The hybrid LiFi/WiFi system faces many challenges including but not limited to seamless integration with the WiFi, support for mobility, handover management, resource sharing, and load balancing. The existing literature has addressed one or the other aspect of the issues facing LiFi systems. There are limited free source tools available to holistically address these challenges in a scalable manner. To this end, we have developed an open-source simulation framework based on the network simulator 3 (ns-3), which realizes critical aspects of the LiFi wireless network. Our developed ns-3 LiFi framework provides a fully functional AP equipped with the physical layer and medium access control (MAC), a mobility model for the user device, and integration between LiFi and WiFi with a handover facility. Simulation results are produced to demonstrate the mobility and handover capabilities, and the performance gains from the LiFi-WiFi hybrid system in terms of packet delay, throughput, packet drop ratio (PDR), and fairness between users. The source code of the framework is made available for the use of the research community.

Modelling of Chest Wall Motion for Cardiorespiratory Activity for Radar-Based NCVS Systems.

Chest wall motion can provide information on critical vital signs, including respiration and heartbeat. Mathematical modelling of chest wall motion can reduce an extensive requirement of human testing in the development of many biomedical applications. In this paper, we propose a mathematical model that simulates a chest wall motion due to cardiorespiratory activity. Chest wall motion due to respiration is simulated based on the optimal chemical-mechanical respiratory control-based mechanics. The theory of relaxation oscillation system is applied to model the motion due to cardiac activity. The proposed mathematical chest wall model can be utilized in designing and optimizing different design parameters for radar-based non-contact vital sign (NCVS) systems.

Design and Implementation of a Self-Powered Smart Water Meter.

Smart cities require interactive management of water supply networks and water meters play an important role in such a task. As compared to fully mechanical water meters, electromechanical water meters or fully electronic water meters can collect real-time information through automatic meter reading (AMR), which makes them more suitable for smart cities applications. In this paper, we first study the design principles of existing water meters, and then present our design and implementation of a self-powered smart water meter. The proposed water meter is based on a water turbine generator, which serves for two purposes: (i) to sense the water flow through adaptive signal processing performed on the generated voltage; and (ii) to produce electricity to charge batteries for the smart meter to function properly. In particular, we present the design considerations and implementation details. The wireless transceiver is integrated in the proposed water meter so that it can provide real-time water flow information. In addition, a mobile phone application is designed to provide a user with a convenient tool for water usage monitoring.

Visible Light Communication: A System Perspective-Overview and Challenges.

Visible light communication (VLC) is a new paradigm that could revolutionise the future of wireless communication. In VLC, information is transmitted through modulating the visible light spectrum (400⁻700 nm) that is used for illumination. Analytical and experimental work has shown the potential of VLC to provide high-speed data communication with the added advantage of improved energy efficiency and communication security/privacy. VLC is still in the early phase of research. There are fewer review articles published on this topic mostly addressing the physical layer research. Unlike other reviews, this article gives a system prespective of VLC along with the survey on existing literature and potential challenges toward the implementation and integration of VLC.

A clinician-centred programme for behaviour change in the optimal use of staging investigations for newly diagnosed prostate cancer.

OBJECTIVES: To improve imaging utilisation and reduce the widespread overuse of staging investigations, in the form of computed tomography (CT) and whole-body bone scans for men with newly diagnosed prostate cancer in the Hunter region of NSW, Australia, by implementation of a multifaceted clinician-centred behaviour change programme. PATIENTS AND METHODS: Records of all patients with a new diagnosis of prostate cancer were reviewed prior to the intervention (July 2014 to July 2015), and the results of this audit were presented to participating urologists by a clinical champion. Urologists then underwent focused education based on current guidelines. Patterns of imaging use for staging were then re-evaluated (November 2015 to July 2016). Patients were stratified into low-, intermediate- and high-risk groups as described by the D'Amico classification system. RESULTS: A total of 144 patients were retrospectively enrolled into the study cohort. The use of diagnostic imaging for staging purposes significantly decreased in men with low- and intermediate-risk disease post intervention. In low-risk patients, the use of CT decreased from 43% to 0% (P = 0.01). A total of 21% of patients underwent bone scans in the pre-intervention group compared with18% in the post-intervention group (P = 0.84). In intermediate-risk patients, the use of CT decreased from 89% to 34% (P < 0.001), whilst the use of bone scan decreased from 63% to 37% (P = 0.02). In high-risk patients, the appropriate use of imaging was maintained, with CT performed in 87% compared with 85% and bone scan in 87% compared with 65% (P = 0.07). CONCLUSION: Our results show that a focused, clinician-centred education programme can lead to improved guideline adherence at a regional level. The assessment of trends and application of such a programme at a state-based or national level could be further assessed in the future with the help of registry data. This will be particularly important in future with the advent of advanced imaging, such as prostate-specific membrane antigen positron-emission tomography.

Multifunctional Hyperbolic Nanogroove Metasurface for Submolecular Detection.

Metasurface serves as a promising plasmonic sensing platform for engineering the enhanced light-matter interactions. Here, a hyperbolic metasurface with the nanogroove structure in the subwavelength scale is designed. This metasurface is able to modify the wavefront and wavelength of surface plasmon wave with the variation of the nanogroove width or periodicity. At the specific optical frequency, surface plasmon polaritons are tightly confined and propagated with a diffraction-free feature due to the epsilon-near-zero effect. Most importantly, the groove hyperbolic metasurface can enhance the plasmonic sensing with an ultrahigh phase sensitivity of 30 373 deg RIU-1 and Goos-Hänchen shift sensitivity of 10.134 mm RIU-1 . The detection resolution for refractive index change of glycerol solution is achieved as 10-8 RIU based on the phase measurement. The detection limit of bovine serum albumin (BSA) molecule is measured as low as 0.1 × 10-18 m (1 × 10-19 mol L-1 ), which corresponds to a submolecular detection level (0.13 BSA mm-2 ). As for low-weight biotin molecule, the detection limit is estimated below 1 × 10-15 m (1 × 10-15 mol L-1 , 1300 biotin mm-2 ). This enhanced plasmonic sensing performance is two orders of magnitude higher than those with current state-of-art plasmonic metamaterials and metasurfaces.

Population-scale three-dimensional reconstruction and quantitative profiling of microglia arbors.

MOTIVATION: The arbor morphologies of brain microglia are important indicators of cell activation. This article fills the need for accurate, robust, adaptive and scalable methods for reconstructing 3-D microglial arbors and quantitatively mapping microglia activation states over extended brain tissue regions. RESULTS: Thick rat brain sections (100-300 µm) were multiplex immunolabeled for IBA1 and Hoechst, and imaged by step-and-image confocal microscopy with automated 3-D image mosaicing, producing seamless images of extended brain regions (e.g. 5903 × 9874 × 229 voxels). An over-complete dictionary-based model was learned for the image-specific local structure of microglial processes. The microglial arbors were reconstructed seamlessly using an automated and scalable algorithm that exploits microglia-specific constraints. This method detected 80.1 and 92.8% more centered arbor points, and 53.5 and 55.5% fewer spurious points than existing vesselness and LoG-based methods, respectively, and the traces were 13.1 and 15.5% more accurate based on the DIADEM metric. The arbor morphologies were quantified using Scorcioni's L-measure. Coifman's harmonic co-clustering revealed four morphologically distinct classes that concord with known microglia activation patterns. This enabled us to map spatial distributions of microglial activation and cell abundances. AVAILABILITY AND IMPLEMENTATION: Experimental protocols, sample datasets, scalable open-source multi-threaded software implementation (C++, MATLAB) in the electronic supplement, and website (www.farsight-toolkit.org). http://www.farsight-toolkit.org/wiki/Population-scale_Three-dimensional_Reconstruction_and_Quanti-tative_Profiling_of_Microglia_Arbors CONTACT: broysam@central.uh.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A pilot, multisite, randomized controlled trial of a self-directed coping skills training intervention for couples facing prostate cancer: accrual, retention, and data collection issues.

PURPOSE: To examine the acceptability of the methods used to evaluate Coping-Together, one of the first self-directed coping skill intervention for couples facing cancer, and to collect preliminary efficacy data. METHODS: Forty-two couples, randomized to a minimal ethical care (MEC) condition or to Coping-Together, completed a survey at baseline and 2 months after, a cost diary, and a process evaluation phone interview. RESULTS: One hundred seventy patients were referred to the study. However, 57 couples did not meet all eligibility criteria, and 51 refused study participation. On average, two to three couples were randomized per month, and on average it took 26 days to enrol a couple in the study. Two couples withdrew from MEC, none from Coping-Together. Only 44 % of the cost diaries were completed, and 55 % of patients and 60 % of partners found the surveys too long, and this despite the follow-up survey being five pages shorter than the baseline one. Trends in favor of Coping-Together were noted for both patients and their partners. CONCLUSIONS: This study identified the challenges of conducting dyadic research, and a number of suggestions were put forward for future studies, including to question whether distress screening was necessary and what kind of control group might be more appropriate in future studies.

Improving sexual health in men with prostate cancer: randomised controlled trial of exercise and psychosexual therapies.

BACKGROUND: Despite being a critical survivorship care issue, there is a clear gap in current knowledge of the optimal treatment of sexual dysfunction in men with prostate cancer. There is sound theoretical rationale and emerging evidence that exercise may be an innovative therapy to counteract sexual dysfunction in men with prostate cancer. Furthermore, despite the multidimensional aetiology of sexual dysfunction, there is a paucity of research investigating the efficacy of integrated treatment models. Therefore, the purpose of this study is to: 1) examine the efficacy of exercise as a therapy to aid in the management of sexual dysfunction in men with prostate cancer; 2) determine if combining exercise and brief psychosexual intervention results in more pronounced improvements in sexual health; and 3) assess if any benefit of exercise and psychosexual intervention on sexual dysfunction is sustained long term. METHODS/DESIGN: A three-arm, multi-site randomised controlled trial involving 240 prostate cancer survivors will be implemented. Participants will be randomised to: 1) 'Exercise' intervention; 2) 'Exercise + Psychosexual' intervention; or 3) 'Usual Care'. The Exercise group will receive a 6-month, group based, supervised resistance and aerobic exercise intervention. The Exercise + Psychosexual group will receive the same exercise intervention plus a brief psychosexual self-management intervention that addresses psychological and sexual well-being. The Usual Care group will maintain standard care for 6 months. Measurements for primary and secondary endpoints will take place at baseline, 6 months (post-intervention) and 1 year follow-up. The primary endpoint is sexual health and secondary endpoints include key factors associated with sexual health in men with prostate cancer. DISCUSSION: Sexual dysfunction is one of the most prevalent and distressing consequences of prostate cancer. Despite this, very little is known about the management of sexual dysfunction and current health care services do not adequately meet sexual health needs of survivors. This project will examine the potential role of exercise in the management of sexual dysfunction and evaluate a potential best-practice management approach by integrating pharmacological, physiological and psychological treatment modalities to address the complex and multifaceted aetiology of sexual dysfunction following cancer. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12613001179729.

Phosphoglycerate mutase deficiency with tubular aggregates in a patient from Panama.

INTRODUCTION: Phosphoglycerate mutase deficiency (PGAM) is a rare metabolic myopathy that results in terminal block in glycogenolysis. Clinically, patients with PGAM deficiency are asymptomatic, except when they engage in brief, strenuous efforts, which may trigger myalgias, cramps, muscle necrosis, and myoglobinuria. An unusual pathologic feature of PGAM deficiency is the association with tubular aggregates. METHODS: We report an African-American patient from Panama with partial deficiency of PGAM who presented with asymptomatic elevation of creatine kinase levels and tubular aggregates on muscle biopsy. RESULTS: Muscle biopsies showed subsarcolemmal and sarcolemmal tubular aggregates in type 2 fibers. Muscle PGAM enzymatic activity was decreased and gene sequencing revealed a heterozygous mutation in codon 78 of exon 1 of the PGAM2 gene, which is located on the short arm of chromosome 7. CONCLUSIONS: PGAM deficiency has been reported in 14 patients, 9 of whom were of African-American ethnicity, and in 5 (36%) tubular aggregates were seen on muscle biopsy. Contrary to previously reported cases, our patient was initially asymptomatic. This further expands the PGAM deficiency phenotype.

Beneficial effects of Rituximab in patients with anti-MAG (myelin-associated glycoprotein) neuropathy: case reports.

Anti-myelin-associated glycoprotein (MAG) neuropathy is a primary demyelinating sensorimotor polyneuropathy that can be very debilitating and is known to be resistant to treatment. There are only a few conflicting reports on the effect of Rituximab in anti-MAG neuropathy. We present three patients who improved remarkably with Rituximab infusions. Until the safety and efficacy of this drug are determined in larger controlled studies, use of Rituximab should be limited to patients with significant neurologic deficits.