Monitor Network Research Articles

2.L. Round table: Monitoring health reforms to inform policy innovation: the Health Systems and Policies Monitor

Abstract Background The European Observatory on Health Systems and Policies’ (Observatory) Health Systems and Policy Monitor (HSPM) network convenes an international group of around 50 national health systems researchers from over 40 institutions across 31 high-income countries, including all EU countries, Canada, Israel, the UK and the US. In advance of the HSPM annual meeting, experts share the current year’s “top-three” health reforms in their countries via a standardized survey, soliciting details on content and general implementation processes. Reforms are iteratively organized into to 2 of 11 clusters, derived from the WHO health system building blocks. Experts then discuss background, status and content of national reforms in the year’s main clusters and initiate cross-country collaborations. A repository of over 650 reforms since 2018 has been created and is available online, showcasing reform patterns in HSPM countries. The collected data have informed studies on reform trends in HSPM countries from 2018-2019 and between 2020-2022, with work ongoing. Data are also consulted by researchers and policymakers wishing to learn about and from reforms in other countries. This workshop specifically aims to: • Inform participants about the methodologies used by the HSPM network to track reforms • Highlight common trends in major health reforms across countries since 2018 • Discuss national health reforms and the value of understanding and tracking these across countries • Identify research gaps in reform monitoring relevant to policy makers • Discuss how the health system research community can generate more relevant and actionable evidence Added-value The comparative analysis of reform trends provides insights into how systems operate and the types of reforms more likely to be implemented. The sharing of these insights also serves as a source of inspiration, allowing countries to learn from each other’s successes and failures and adopt proven best practices. This is relevant for policymakers and researchers striving to support health systems strengthening. Therefore, the workshop will support knowledge exchange between researchers and policymakers by (1) informing researchers and policymakers about similar ongoing reforms in other countries and (2) identifying areas where researchers can contribute to country reform agendas, and (3) stimulating interest in cross-country collaborative research on similar and topical reforms in different countries. Format This is a roundtable workshop chaired and moderated by the Observatory. An overview of reform trends and patterns since 2018 will set the scene, followed by an interactive discussion with policymakers and HSPM network members to (1) delve into trend dynamics and implications for health policy and research and (2) identify major (missing) research areas relevant to policymakers. In addition, we will leverage the geographic scope and expertise of the audience to reflect on the value of monitoring reforms. Key messages • Cross-country exchange of information on reform trends and implementation patterns can inspire future reforms and help policymakers identify promising policy approaches abroad. • Knowledge exchange between researchers and policymakers helps to determine evidence gaps and to generate relevant and actionable evidence that can drive policy innovation. Speakers/Panelists Reinhard Busse Berlin University of Technology, Berlin, Germany Ines Fronteira National School of Public Health - NOVA University of Lisbon, Lisbon, Portugal Iwona Kowalska-Bobko Jagiellonian University Medical College, Institute of Public Health, Cracow, Poland Isabel de La Mata DG SANTE, European Commission, Luxembourg, Luxembourg Vesna-Kestrin Petric Ministry of Health, Ljubljana, Slovenia

Integrated care in the Baltic states over a 5-year period: progress, challenges and future directions

Abstract Person-centered, multidisciplinary care is key to addressing evolving complex health needs. In Estonia, Latvia, and Lithuania, this pursuit of integrated care has gained momentum as an innovative approach to allocate resources more efficiently and improve patient outcomes. This study investigates the progress of integrating care in the Baltic countries from 2019 to 2024. It details key learnings, particularly for small European countries with limited resources. The Health Systems and Policy Monitor Network of the European Observatory on Health Systems and Policies undertook a cross-country study to better understand the progress in care integration in the Baltics. The Network’s Baltic country experts completed a 21-item questionnaire on the adoption of integrated care reforms. Responses were analyzed to capture reform characteristics and commonalities, countries’ political environments and their conduciveness to the uptake of integrated care. Country-specific experiences with the implementation of integrated care were further explored via case studies of pilot programs over the five years. The pace of implementing integrating care reforms varied in each country. Existing regulatory barriers, workforce challenges and payment schemes have impeded integration efforts across health and social care. Despite these obstacles, the political commitment to new and innovative service delivery and collaboration for chronic care management underscores an important prerequisite toward achieving more integrated and person-centered healthcare. The three case studies illustrate the hurdles that come with shifting care settings and expanding roles for some workers. Findings stress the role for integrated care to address organisational challenges in the Baltics. This study highlights innovative approaches and long-running efforts to provide multidisciplinary care for complex-needs patients and the implications of their results for broader health system adoption. Key messages • Small countries like the Baltics face structural challenges in integrating care to improve outcomes and efficiency. • Investment and innovative care approaches are key to further advancements.

IKern: Advanced Intrusion Detection and Prevention at the Kernel Level Using eBPF

The development of new technologies has significantly enhanced the monitoring and analysis of network traffic. Modern solutions like the Extended Berkeley Packet Filter (eBPF) demonstrate a clear advancement over traditional techniques, allowing for more customized and efficient filtering. These technologies are crucial for influencing system performance as they operate at the lowest layer of the operating system, such as the kernel. Network-based Intrusion Detection/Prevention Systems (IDPS), including Snort, Suricata, and Bro, passively monitor network traffic from terminal access points. However, most IDPS are signature-based and face challenges on large networks, where the drop rate increases due to limitations in capturing and processing packets. High throughput leads to overheads, causing IDPS buffers to drop packets, which can pose serious threats to network security. Typically, IDPS are targeted by volumetric and multi-vector attacks that overload the network beyond the reception and processing capacity of IDPS, resulting in packet loss due to buffer overflows. To address this issue, the proposed solution, iKern, utilizes eBPF and Virtual Network Functions (VNF) to examine and filter packets at the kernel level before forwarding them to user space. Packet stream inspection is performed within the iKern Engine at the kernel level to detect and mitigate volumetric floods and multi-vector attacks. The iKern detection engine, operating within the Linux kernel, is powered by eBPF bytecode injected from user space. This system effectively handles volumetric Distributed Denial of Service (DDoS) attacks. Real-time implementation of this scheme has been tested on a 1Gbps network and shows significant detection and reduction capabilities against volumetric and multi-vector floods.

Design of network security monitoring system based on CNN and exponential weighted D-S evidence theory

ABSTRACT To address the poor network security protection and low attack traffic identification, the study designs a network security monitoring system based on convolutional neural network and exponential weighted Dempster Shafer evidence theory. The validation of the UNSW-NB15 data set showed that the output of multi-source fusion after exponential weighted Dempster Shafer evidence theory was higher than the output of the feature fusion by 3.92%. The accuracy of the attack recognition was as high as 93.72%, which was 1.85% higher than feature fusion. The accuracy of the proposed network security monitoring system increased by 3.70% on average over other methods. The results indicate that the proposed network security monitoring system can effectively improve the efficiency of network attack identification, monitor network security in real-time, and effectively protect network operation. The system is feasible and reasonable in terms of network security situational awareness, which can provide effective situational analysis for network administrators.

Enhanced Dwarf Mongoose optimization algorithm with deep learning-based attack detection for drones

Security in smart cities is a challenging issue in urban environments as they depend upon interconnected technologies and data for effective services. To address security challenges, smart cities implement robust cybersecurity measures, including network monitoring, encryption, and intrusion detection systems. Detecting and mitigating possible security risks in drone network B5G is a crucial aspect of ensuring reliable and safe drone operation. It is necessary to establish sophisticated and robust attack detection techniques to defend against security threats as the use of drones becomes increasingly widespread and their applications diversify. This is due to the lack of privacy and security consideration in the drone’s system, including an inadequate computation capability and unsecured wireless channels to perform advanced cryptographic algorithms. Intrusion detection systems (IDS) and anomaly detection systems can identify suspicious activities and monitor network traffic, such as anomalous communication patterns or unauthorized access attempts. Therefore, the study presents an enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection (EDMOA-DLAD) in Networks B5G for the purpose of Drones technique. The presented EDMOA-DLAD technique aims to recognize the attacks and classifies them on the drone network B5G. Primarily, the EDMOA-DLAD technique designs a feature selection (FS) approach using EDMOA. To detect attacks, the EDMOA-DLAD technique uses a deep variational autoencoder (DVAE) classifier. Finally, the EDMOA-DLAD technique applies the beetle antenna search (BAS) technique for the optimum hyperparameter part of DVAE model. The outcome of EDMOA-DLAD approach can be verified on benchmark datasets. A wide range of simulations inferred that the EDMOA-DLAD method obtains enhanced performance of 99.79% over other classification techniques.

Spectra and Anisotropy of Solar Energetic Protons During GLE #65 on 28 October, 2003 and GLE #66 on 29 October, 2003

Solar Cycle 23 was the most active in ground-level enhancements (GLEs) with 16 events registered by the global neutron monitor network. In this paper, we study a very active period in October–November, 2003, which revealed an intense solar activity burst that led to several eruptive processes and produced a sequence of three GLEs. By applying state-of-the-art modelling to records from the global neutron monitor network as well as space-borne data, we derived the spectral and anisotropy characteristics of accelerated solar protons during the GLE #65 event on 28 October, 2003 and GLE #66 on 29 October, 2003. The spectra and the pitch angle distributions are obtained with a 5-min time resolution, providing their dynamical evolution throughout the event. The spectra are parameterised with a modified power-law rigidity spectrum, whilst the angular distribution with a Gaussian. The constraints and uncertainties of the derived characteristics are evaluated by corresponding modelling.

Applicances of Different Kind of Storage Systems for Network Traffic Analysis Results

Network Traffic Analysis (NTA) helps identify security threats, monitor network performance, and plan for future capacity. While real-time analysis is ideal, it can be difficult due to high data volume and complexity. Large amounts of traffic require parsing, and real-time data may miss hidden threats. Post-analysis can address these challenges. It hardly depends on choosing an effective and appropriate storage solutions. A variety of storage systems exist, each employing different approaches and formats to retain data. This article explores the applications of various storage systems for NTA results. Three different types of storage systems considered, including Greenplum, Nebula graph and OpenSearch. A comparative approach is employed, analyzing the same dataset across various storage systems.This allows to examine how different database structures and query capabilities influence the efficiency and accuracy of NTA. The resulting insights will not only provide valuable guidance for selecting the optimal storage solution for specific NTA tasks, but also serve as a foundation for future research in this area.

Public Health Benefits From Improved Identification of Severe Air Pollution Events With Geostationary Satellite Data.

Despite improvements in ambient air quality in the US in recent decades, many people still experience unhealthy levels of pollution. At present, national-level alert-day identification relies predominately on surface monitor networks and forecasters. Satellite-based estimates of surface air quality have rapidly advanced and have the capability to inform exposure-reducing actions to protect public health. At present, we lack a robust framework to quantify public health benefits of these advances in applications of satellite-based atmospheric composition data. Here, we assess possible health benefits of using geostationary satellite data, over polar orbiting satellite data, for identifying particulate air quality alert days (24hr PM2.5>35μgm-3) in 2020. We find the more extensive spatiotemporal coverage of geostationary satellite data leads to a 60% increase in identification of person-alerts (alert days×population) in 2020 over polar-orbiting satellite data. We apply pre-existing estimates of PM2.5 exposure reduction by individual behavior modification and find these additional person-alerts may lead to 1,200 (800-1,500) or 54% more averted PM2.5-attributable premature deaths per year, if geostationary, instead of polar orbiting, satellite data alone are used to identify alert days. These health benefits have an associated economic value of 13 (8.8-17) billion dollars ($2019) per year. Our results highlight one of many potential applications of atmospheric composition data from geostationary satellites for improving public health. Identifying these applications has important implications for guiding use of current satellite data and planning future geostationary satellite missions.

Monte Carlo Simulation and measurement of Calibration Neutron Monitor count rate dependence on proximity to water

Due to their global availability, neutron monitors play a crucial role in measuring time variations in the Galactic cosmic ray flux. A portable calibration neutron monitor (CalMon) is useful for intercalibrating various neutron monitors to ensure accurate measurements. A common technique to ensure that the calibration is done in a consistent environment is to place the CalMon at some height above a wide container (such as a portable swimming pool) filled with water. This study investigates the impact of CalMon height and water depth on the count rate ratio relative to a standard 18NM64 count rate recorded nearby (CalMon/18NM64). We compare simulated data from the FLUKA Monte Carlo package to experimental data from [1] to demonstrate the statistical accuracy of our simulation. Using the simulation results, we then extend the study of the proximity-to-water effect on the counting rate. In this work, we present a preliminary empirical model by analyzing the CalMon/18NM64 as a function of CalMon to water distance. Overall, our study enhances understanding of the response of calibration monitors (now often called “mini-neutron monitors”) operated in various locations worldwide, and validates the Monte Carlo techniques used to model the response of the global neutron monitor network.

Comparison of data-based and modeled-based analysis of aircraft departure noise using noise monitor network recordings

A comparison of data-based regression analysis and modeled-based analysis of aircraft departure noise variations using airport noise monitor network recordings is presented. Accurate modeling of aircraft noise is useful for the assessment of departure procedures and provides insight into the noise reduction potential of noise abatement flight procedures. In addition, many airports employ monitor networks that continuously record noise data produced by aircraft on departure, which can be used for assessing potential factors leading to variation in departure noise and for comparison to results from modeling methods. In this paper, departure noise variations of Boeing 737-800 aircraft are correlated to departure climb procedure factors using both data-based approaches incorporating recorded noise collected at the Seattle International Airport monitor network, and a method to model departure noise from on altitude, ground track, and ground speed from recorded operational surveillance data. Examples using both approaches indicate altitude and velocity result in the highest correlating factors for noise at the three noise monitor locations studied. Limitations and areas of needed improvement for the different noise modeling methods are highlighted.

Implementation of Security Information and Event Management (SIEM) for Monitoring IT Assets Using Alienvault OSSIM (Case Study: Udayana University Information Resources Unit)

One way that can be done to analyze cyber security equipment is by monitoring the logs it generates. Meanwhile, to be able to analyze the logs generated from each equipment requires a long time and has a high level of difficulty. When the management of the cyber security system is not going right, it causes the failure of the cyber security system. So a defense mechanism is needed on managing the log called Security Information and Event Management (SIEM) using Alienvault OSSIM tools. Threat Monitoring or monitoring of security threats in the Cyber world, is used to analyze, evaluate, and monitor network threats and as an end point for organizations to provide evidence of security threats, such as network intrusions, data exfiltration, ransomware and other malware attacks. The limitations of the problems carried out in this study were limited to Threat Monitoring using Alienvault OSSIM. There are 6 servers at the Udayana University Information Resources Unit (USDI) that are being monitored. Monitoring was carried out for 3 months. There were 230,622 Events or events that were collected as a whole. IT assets that have the most logs during monitoring are owned by DNS Servers with a total of 200,424 Events. There are 11 Event Names and 34 event logs that are discussed. The log is packaged in the form of a report along with an explanation, of course it can assist administrators in evaluating their IT assets. There is also an email notification feature using Gmail. Overall there are no attacks that are so significant with the low risk category. Alienvault OSSIM is proven to be able to carry out monitoring processes in real time properly and can help USDI to monitor the activities of its IT assets.

“I Am a Poor Man with a Family”: Rangers, Social Welfare, and Monitor Networks in the Forests of Interwar Zlatna, Romania

In the interwar period, forestry emerged as a powerful tool through which state institutions attempted to shape social and natural environments in rural Romania. This article evaluates how criminality, poverty, surveillance, exploitation, and labor intersected in Romania’s forests through the figure of the forest ranger, responsible for a range of tasks from managing forest ecology to serving as the state’s representative in the woodlands. Using a microhistorical approach, the article recounts the story of a Zlatna ranger investigated for theft in the late 1920s. His story and the individuals involved offer important insight into the asymmetrical power relations and local social factors that negotiated the forests’ legibility. Rangers served as part crucial asset, part embarrassing liability, existing in a liminal space between the state and local society. Through local forestry agents, their informal networks, and the “involuntary monitors” these invariably created, state forestry officials had valuable points of contact with rural society. This article demonstrates the value of rural perspectives in historical studies, challenging prevailing notions that conceptualize rural people as simple, irrelevant, or singlemindedly opposed to the encroachment of the modern state.

Data-Driven Placement of PM2.5 Air Quality Sensors in the United States: An Approach to Target Urban Environmental Injustice.

In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low-cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low-cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low-cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost-constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM2.5 on timescales ranging from 1day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM2.5 extremes and increasing pollution monitoring in low-income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low-income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low-cost sensors in less privileged communities.

The U.S. and Global Neutron Monitor Network for Heliophysics and Space Weather

The U.S. and Global Neutron Monitor Network for Heliophysics and Space Weather

Maintainable Log Datasets for Evaluation of Intrusion Detection Systems

Intrusion detection systems (IDS) monitor system logs and network traffic to recognize malicious activities in computer networks. Evaluating and comparing IDSs with respect to their detection accuracies is thereby essential for their selection in specific use-cases. Despite a great need, hardly any labeled intrusion detection datasets are publicly available. As a consequence, evaluations are often carried out on datasets from real infrastructures, where analysts cannot control system parameters or generate a reliable ground truth, or private datasets that prevent reproducibility of results. As a solution, we present a collection of maintainable log datasets collected in a testbed representing a small enterprise. Thereby, we employ extensive state machines to simulate normal user behavior and inject a multi-step attack. For scalable testbed deployment, we use concepts from model-driven engineering that enable automatic generation and labeling of an arbitrary number of datasets that comprise repetitions of attack executions with variations of parameters. In total, we provide 8 datasets containing 20 distinct types of log files, of which we label 8 files for 10 unique attack steps. We publish the labeled log datasets and code for testbed setup and simulation online as open-source to enable others to reproduce and extend our results.

Monitoring of heliosphere, magnetosphere and atmosphere via cosmic ray effects in August 2018

Using data of ground-level cosmic ray measurements on the neutron monitor network, along with data of Yakutsk muon telescope complex and muon hodoscope URAGAN (Moscow) uncorrected for temperature, we used modified spectrographic global survey method for an August 2018 event to split cosmic ray variations into components of primary, magnetospheric and atmospheric origin. Time evolutions of isotropic primary flux for different rigidities, pitch-angle anisotropy of cosmic rays, interplanetary magnetic field orientation were obtained, variations in geomagnetic cut-off rigidity in Irkutsk and average-mass temperature over charged components’ measurement sites were also shown.

Selection of the Solar-Diurnal Anisotropy of Cosmic Rays by Local and Global Methods

According to the data of Moscow neutron monitor, using harmonic analysis, the characteristics ofthe solar-diurnal anisotropy of cosmic rays on quiet days have been obtained for a long period from 1965 to2020. It has been established that the average diurnal variation of cosmic rays at the Moscow neutron monitoris almost completely described by two harmonics of the solar-diurnal anisotropy and does not contain signsof other influences. A comparison with the average daily characteristics of the equatorial component of cosmicrays vector anisotropy, obtained from the data of the worldwide neutron monitor network using the globalsurvey method showed a good agreement between the results of the two methods. From a comparison of localand global results, estimates were obtained for the coupling coefficients of the first harmonic of the cosmicray anisotropy for the Moscow neutron monitor, and a new experimental method for calculating the couplingcoefficients of individual detectors was proposed. The limitations of the local method, as well as the possibilityof continuing and expanding this study, have been discussed and justified

Efficacy and Safety of DaxibotulinumtoxinA for Injection Over Successive Treatments in Adults With Cervical Dystonia in the Phase 3 ASPEN-1 and ASPEN-OLS Trials (P11-11.006)

Efficacy and Safety of DaxibotulinumtoxinA for Injection Over Successive Treatments in Adults With Cervical Dystonia in the Phase 3 ASPEN-1 and ASPEN-OLS Trials (P11-11.006)

Blepharoclonus as a Potential Novel Clinical Marker in Parkinson’s Disease (P3-11.009)

Blepharoclonus as a Potential Novel Clinical Marker in Parkinson’s Disease (P3-11.009)

OPTN: A Possible Genetic Cause of Both CBS and ALS (P9-11.013)

<h3>Objective:</h3> To report a patient with a mutation typically associated with familial amyotrophic lateral sclerosis (ALS), but instead presenting with the corticobasal syndrome (CBS) phenotype. <h3>Background:</h3> CBS is defined by atypical parkinsonism and specific examination findings indicating cortical dysfunction. In contrast, corticobasal degeneration (CBD) is a pathological entity and is the principal pathology underlying CBS. CBD and frontotemporal dementia (FTD) often involve tau deposition, but in different topographical distributions, and also in different molecular forms. Many cases of CBD and FTD may, in addition, overlap clinically. By comparison, there is significantly less clinical overlap between ALS and the pathologies that cause CBS. Here we report a case of CBS linked to a mutation in the OPTN gene. This individual has several close relatives diagnosed with familial ALS who also have been found to carry the same OPTN mutation. <h3>Design/Methods:</h3> NA <h3>Results:</h3> We report a 49-year-old man with 6 months of involuntary movements of the right upper extremity, followed by gait unsteadiness and memory changes. Family history was notable for two siblings with ALS linked to a mutation in the OPTN gene. On exam, we noted right hemidystonia, as well as bradykinesia and brisk reflexes in the right arm. There was gait ataxia, as well as right-sided astereognosis, agraphesthesia, tactile extinction, and ideomotor apraxia. No ocular abnormalities, orthostasis, or other upper-motor neuron features were seen. MRI showed left peri-Rolandic fissure atrophy. Gene testing revealed homozygosity for a loss of function splice-site mutation in the OPTN gene. <h3>Conclusions:</h3> This case illustrates differential effects of the same gene mutation in different family members. It is interesting that the pathology causing CBS in this patient shares the same mutation with an inherited form of ALS. The heterogeneity of OPTN phenotypes may be a consequence of the same neuropathology, but with differential topographical distribution in the nervous system. <b>Disclosure:</b> Dr. Feldman has nothing to disclose. Dr. Benatar has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Biogen. The institution of Dr. Benatar has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Arrowhead. Dr. Benatar has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Alexion. Dr. Benatar has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Annexon. Dr. Benatar has received personal compensation in the range of $500-$4,999 for serving as a Consultant for UCB. Dr. Benatar has received personal compensation in the range of $500-$4,999 for serving as a Consultant for UniQure. Dr. Benatar has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Immunovant. Dr. Benatar has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Alector. Dr. Benatar has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Sanofi. Dr. Benatar has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Novartis. Dr. Benatar has received intellectual property interests from a discovery or technology relating to health care. Dr. Benatar has received intellectual property interests from a discovery or technology relating to health care. Dr. Saraf Lavi has nothing to disclose. Dr. Latchman has nothing to disclose. Dr. Singer has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Amneal. Dr. Singer has received personal compensation in the range of $10,000-$49,999 for serving as a Video Panel with CNS Ratings LLC. Dr. Singer has received personal compensation in the range of $500-$4,999 for serving as a Course Organiztion with Omniprex Scientific Medical Eduaction. Dr. Singer has received personal compensation in the range of $500-$4,999 for serving as a Reviewer with Health Monitor Network. Dr. Singer has received personal compensation in the range of $500-$4,999 for serving as a Speaker with Movement Disorders Society. Dr. Singer has received personal compensation in the range of $500-$4,999 for serving as a Workshop Moderator with International Neurotoxin Association Inc. Dr. Singer has received personal compensation in the range of $500-$4,999 for serving as a Lecturer with CME Outfitteres LLC.