Merit Network

Abstract Disclosure: L. Miller-Wilson: Employee of Immunovant, Inc. N. Princic: Employee of Merative. K. Evans: Employee of Merative. N. Atreja: Employee of Immunovant, Inc. K. Tollefsen: Employee of Immunovant, Inc.. D. Toro-Tobón: None. Objectives: Graves’ disease (GD) is an autoimmune condition responsible for most cases of hyperthyroidism in the United States. An estimated 20%-50% of patients with GD will also develop thyroid eye disease (TED). Current treatments for GD focus on managing hyperthyroidism, but frequent monitoring, dose adjustments, and the high rate of relapse after discontinuation of antithyroid drugs can increase treatment costs and complicate long-term management. This study describes healthcare resource utilization (HCRU) and costs among patients with GD, with and without TED. Methods: This retrospective analysis utilized claims data from the Merative™ MarketScan® Research Databases. Individuals aged ≥18 years with ≥2 claims for a GD diagnosis from January 1, 2017-December 31, 2023 were included (first claim = index). To ensure a newly diagnosed cohort, patients with a history of GD-specific treatment or a GD diagnosis in the 12 months preceding the index were excluded. The final GD cohort was stratified into patients who had evidence of TED at any time during the 12-month pre-index period through 90 days post-index, and those who did not. Propensity score matching was used to balance patients with GD (with and without TED) with non-GD controls (1:3) on demographic and baseline clinical characteristics. Both GD cases (with and without TED) and controls were required to have ≥12 months of post-index follow-up. HCRU and costs were compared between cases and controls during a fixed 12-month post-index period. Categorical outcomes were compared using chi-squared tests, while continuous endpoints were compared using paired t-tests. Results: The analysis included 1,767 GD cases with TED matched with 5,301 controls (mean age, 53.1 years [standardized mean difference (SMD), 0.00]; 77% female [SMD, 0.00]; mean Charlson comorbidity score, 0.9 [SMD, 0.02]) and 43,120 GD cases without TED matched with 129,360 controls (mean age, 49.8 years [SMD, 0.00]; 78% female [SMD, 0.00]; mean Charlson comorbidity score, 0.7 [SMD, 0.01]). Compared with matched controls, patients with GD and TED had 78% more inpatient visits (0.16 vs 0.09), 104% more outpatient visits (65.06 vs 31.86), and 43% more prescriptions (30.21 vs 21.13) during the 12-month post-index period, while patients with GD without TED had 75% more inpatient visits (0.14 vs 0.08), 83% more outpatient visits (52.15 vs 28.48), and 38% more prescriptions (24.27 vs 17.56) (p<0.001 for all). Annual mean healthcare costs were 305% higher ($56,584 vs. $13,981) among GD patients with TED and 65% higher ($19,913 vs $12,076) in those without TED compared with matched controls. Conclusion: Patients with GD had significantly higher HCRU and healthcare costs than matched controls in the 12 months following diagnosis, and this difference was notably greater among GD patients who also had TED. More effective treatments are needed to address the health and financial burden of GD. Presentation: Monday, July 14, 2025

The rapid advancement of emerging technologies has significantly impacted the concept of digital sovereignty, presenting both opportunities and challenges for nations trying to control their digital ecosystems. This paper examines the relationship between digital sovereignty and cybersecurity, focusing on how technologies like Artificial Intelligence, Blockchain, and 5G are reshaping digital sovereignty and influencing national policies. It highlights both the transformative potential of these technologies and the associated risks. The paper also reviews global governance and collaborative cybersecurity frameworks in digital sovereignty, exploring how international cooperation can strengthen cybersecurity efforts. By analyzing different national approaches, the study highlights the need for governments and organizations to balance robust cybersecurity with maintaining control over their digital territories.

Thieno-pyrimidine derivatives have emerged as promising candidates for VEGFR-2 inhibition. This study aimed to design, synthesize, and evaluate novel thieno-pyrimidine derivatives for their anti-cancer potential. A series of thieno-pyrimidine compounds were synthesized and screened for in vitro cytotoxicity against MDA-231 and MCF-7 cell lines. The most active compound, 6b, was further analyzed for VEGFR-2 kinase inhibition, wound healing, apoptosis induction, and cell cycle arrest. Molecular docking, 200 ns molecular dynamics simulations, MM-GBSA, ProLIF PCAT, and FEL analyses were conducted to assess binding stability. DFT calculations evaluated electronic properties, while in silico ADMET profiling predicted pharmacokinetics and toxicity. Compound 6b exhibited potent cytotoxicity with IC50 values of 5.91 µM (MDA-231) and 7.16 µM (MCF-7). It demonstrated VEGFR-2 inhibition is comparable to sorafenib (IC50: 53.63 ± 3.14 nM). Wound healing assays showed significant inhibition of MDA-231 migration. Flow cytometry confirmed apoptosis induction (57.20% early apoptosis) and G1 phase arrest. Gene expression analysis revealed upregulation of pro-apoptotic markers and downregulation of Bcl-2. Computational studies confirmed stable VEGFR-2 binding, and ADMET predictions indicated a favorable safety profile. Compound 6b exhibits strong VEGFR-2 inhibition, potent anti-cancer effects, and a favorable toxicity profile, highlighting its potential for further therapeutic development.

Aggressive network scanners, i.e., ones with immoderate and persistent behaviors, ubiquitously search the Internet to identify insecure and publicly accessible hosts. These scanners generally lie within two main categories; i) benign research-oriented probers; ii) nefarious actors that forage for vulnerable victims and host exploitation. However, the origins, characteristics and the impact on real networks of these aggressive scanners are not well understood. In this paper, via the vantage point of a large network telescope, we provide an extensive longitudinal empirical analysis of aggressive IPv4 scanners that spans a period of almost two years. Moreover, we examine their network impact using flow and packet data from two academic ISPs. To our surprise, we discover that a non-negligible fraction of packets processed by ISP routers can be attributed to aggressive scanners. Our work aims to raise the network community's awareness for these ''heavy hitters'', especially the miscreant ones, whose invasive and rigorous behavior i) makes them more likely to succeed in abusing the hosts they target and ii) imposes a network footprint that can be disruptive to critical network services by incurring consequences akin to denial of service attacks.

Cloud computing has dramatically changed service deployment patterns. In this work, we analyze how attackers identify and target cloud services in contrast to traditional enterprise networks and network telescopes. Using a diverse set of cloud honeypots in 5 providers and 23 countries as well as 2 educational networks and 1 network telescope, we analyze how IP address assignment, geography, network, and service-port selection, influence what services are targeted in the cloud. We find that scanners that target cloud compute are selective: they avoid scanning networks without legitimate services and they discriminate between geographic regions. Further, attackers mine Internet-service search engines to find exploitable services and, in some cases, they avoid targeting IANA-assigned protocols, causing researchers to misclassify at least 15% of traffic on select ports. Based on our results, we derive recommendations for researchers and operators.

Distributed Denial of Service (DDoS) attacks continue to be a severe threat to the Internet, and have been evolving both in traffic volume and in sophistication. While many attack detection approaches exist, few of them provide easily interpretable and actionable network-level signatures. Further, most tools are either not scalable or are prohibitively expensive, and thus are not broadly available to network operators. We bridge this gap by proposing AMON-SENSS, an open-source system for scalable, accurate DDoS detection and signature generation in large networks. AMON-SENSS employs hash-based binning with multiple bin layers for scalability, observes traffic at multiple granularities, and deploys traffic volume and traffic asymmetry change-point detection techniques to identify attacks. It proactively devises network-level attack signatures, which can be used to filter attack traffic. We evaluate AMON-SENSS against two commercial defense systems, using 37 days of real traffic from a mid-size Internet Service Provider (ISP). We find that our proposed approach exhibits superior performance in terms of accuracy, detection time and network signature quality over commercial alternatives. AMON-SENSS is deployable today, it is free, and requires no hardware or routing changes.

The bi-directional communication capabilities that emerged into the smart power grid play a critical role in the grid's secure, reliable and efficient operation. Nevertheless, the data communication functionalities introduced to Advanced Metering Infrastructure (AMI) nodes end the grid's isolation, and expose the network into an array of cyber-security threats that jeopardize the grid's stability and availability. For instance, malware amenable to inject false data into the AMI can compromise the grid's state estimation process and lead to catastrophic power outages. In this paper, we explore several statistical spatio-temporal models for efficient diagnosis of false data injection attacks in smart grids. The proposed methods leverage the data co-linearities that naturally arise in the AMI measurements of the electric network to provide forecasts for the network's AMI observations, aiming to quickly detect the presence of "bad data". We evaluate the proposed approaches with data tampered with stealth attacks compiled via three different attack strategies. Further, we juxtapose them against two other forecasting-aided detection methods appearing in the literature, and discuss the trade-offs of all techniques when employed on real-world power grid data, obtained from a large university campus.

The smart power grid is a synergistic system that integrates diverse network components for power generation, transmission, and distribution. Its advanced metering infrastructure (AMI) enables the grid's efficient and reliable operation. Nevertheless, it is amenable to advanced cyber threats; malicious actors can compromise vulnerable meters and arbitrarily alter their readings. These orchestrated “false data injection attacks” can lead to power outages and service interruption. We propose a framework that uses measurements from trusted (secure) nodes in order to detect abnormal “spoofing” activity of other nodes, possibly tampered. Our model considers the structural similarities in the electricity consumption of AMI nodes, and exploits the spatial correlation amongst meters. To alleviate the problem's large-dimensionality aspect, the meters are clustered into classes of similar energy patterns. We evaluate our algorithms using real-world building data obtained from a large university campus.

‘Chisholm’ (Reg. No. CV-273, PI 666039) is a zoysiagrass (Zoysia japonica Steud.) approved for release by Texas A&M AgriLife Research, Dallas, TX, and the Kansas Agricultural Experiment Station, Manhattan, KS. Chisholm is a clonal selection made in 1997 from a germplasm nursery established in 1983 in Dallas. It was originally evaluated under the designation TAES 4436 and included in the 2002 National Turfgrass Evaluation Program Zoysiagrass Variety Test under the designation DALZ 0102. Chisholm has a texture coarser than ‘Meyer’ and intermediate to ‘JaMur’ and ‘Palisades’. Freezing tolerance of Chisholm is equivalent to, or slightly poorer than, Meyer, but it is superior to Meyer for establishment rate from vegetative plugs; turfgrass quality; growth under shade; fall color; resistance to bluegrass billbug (Sphenophorus parvulus Gyllenhal), zoysiagrass mite (Eriophyes zoysiae Baker, Kono, and O'Neill), and take all patch [caused by Gaeumannomyces graminis var. graminis (Sacc.) Arx & D.L. Olivier]; recovery from divots; and seed head production (fewer produced). Improvement over Meyer in these traits will potentially increase the utility of zoysiagrass in the transition zone. Chisholm is well suited for use on lawns and golf courses in the transition zone, particularly those areas in zones 6b or higher on the USDA Plant Hardiness Zone map.