Browse Articles

Erratum: “Enhancement of second harmonic generation in two-dimensional monolayer materials empowered by merging BICs of photonic crystals” [Appl. Phys. Lett. <b>126</b> , 201702 (2025)]

Variant evolution graph: Can we infer how SARS-CoV-2 variants are evolving?

The SARS-CoV-2 virus has undergone extensive mutations over time, resulting in considerable genetic diversity among circulating strains. This diversity directly affects important viral characteristics, such as transmissibility and disease severity. During a viral outbreak, the rapid mutation rate produces a large cloud of variants, referred to as a viral quasispecies. However, many variants are lost due to the bottleneck of transmission and survival. Advances in next-generation sequencing have enabled continuous and cost-effective monitoring of viral genomes, but constructing reliable phylogenetic trees from the vast collection of sequences in GISAID (the Global Initiative on Sharing All Influenza Data) presents significant challenges. We introduce a novel graph-based framework inspired by quasispecies theory, the Variant Evolution Graph (VEG), to model viral evolution. Unlike traditional phylogenetic trees, VEG accommodates multiple ancestors for each variant and maps all possible evolutionary pathways. The strongly connected subgraphs in the VEG reveal critical evolutionary patterns, including recombination events, mutation hotspots, and intra-host viral evolution, providing deeper insights into viral adaptation and spread. We also derive the Disease Transmission Network (DTN) from the VEG, which supports the inference of transmission pathways and super-spreaders among hosts. We have applied our method to genomic data sets from five arbitrarily selected countries — Somalia, Bhutan, Hungary, Iran, and Nepal. Our study compares three methods for computing mutational distances to build the VEG, sourmash, pyani, and edit distance, with the phylogenetic approach using Maximum Likelihood (ML). Among these, ML is the most computationally intensive, requiring multiple sequence alignment and probabilistic inference, making it the slowest. In contrast, sourmash is the fastest, followed by the edit distance approach, while pyani takes more time due to its BLAST-based computations. This comparison highlights the computational efficiency of VEG, making it a scalable alternative for analyzing large viral data sets.

Dielectric breakdown of atmospheric-pressure grown hexagonal boron nitride single crystals

High-quality hexagonal boron nitride (hBN) has emerged as a reliable dielectric material for two-dimensional (2D) electronic devices because of its atomic flatness, ultrahigh optical transparency, wide energy bandgap, and high dielectric breakdown field. Traditionally, the best-quality hBN is mostly synthesized under high pressure and high temperature (HPHT). Because the HPHT method requires complex apparatus and limits the size of hBN crystals, it is desirable to grow large-area and high-quality hBN single crystals by a simpler method such as the synthesis under atmospheric pressure and high temperature (APHT). However, the comprehensive characterizations of APHT grown single crystals, particularly the dielectric breakdown information required for electronic applications, are still lacking. Here, we fabricate more than 30 graphite/hBN/graphite devices to systematically characterize the dielectric breakdown behaviors of APHT grown hBN crystals, along with other hBN crystals grown by different methods or vendors for direct comparison. The field values of dielectric breakdown, defined by the onset of leakage current, monotonically increase with the decrease in hBN thickness. Below the thickness of 10 nm, the field value reaches above 8 MV/cm. The dielectric breakdown behavior is comparable to that grown by HPHT. Our statistic results, along with the characterizations of X-ray diffraction, Raman spectroscopy, atomic force microscopy, and optical second harmonic generation microscopy, show that the hBN crystal under APHT is an ideal substitute for designing and fabricating the best-quality 2D electronic devices.

“We’re here to help them if they want to come”: A qualitative exploration of hospital staff perceptions and experiences with outpatient non-attendance

Background Patient non-attendance remains a major challenge for health services. Few studies have examined how health service providers think about, potentially address, and prioritise non-attendance within the scope of their practice. This study aimed to (1) explore healthcare professionals’ perspectives, beliefs, and opinions about the impact of patient non-attendance within a publicly-funded outpatient physiotherapy clinic context; (2) explore perceived barriers and facilitators associated with the implementation of non-attendance mitigation strategies; and (3) identify health service staff generated solutions to address perceived barriers and enhance facilitators. Methods A focus group discussion and semi-structured interviews were conducted between June 2023 to January 2024 with 27 physiotherapy department clinic outpatient staff involved in operationalising clinic referral processing, appointment scheduling, or providing care to patients. Data was analysed using a hybrid inductive/deductive framework analysis approach. Results Participants indicated that non-attendance had predominantly negative implications for the health service, healthcare provider, and patient. The interconnected issue of non-attendance encompassed multiple areas and were broadly categorised into five inductively identified themes: impact of non-attendance, perceptions of value, material deprivation, service delivery and built environment, and professional role and identity. Non-attendance mitigation strategies generated by participants were deductively mapped to the theoretical domains framework (TDF) to explore behavioural determinants that may influence successful implementation. This included knowledge, reinforcement, goals, optimism, memory, attention and decision-making, environmental resources and context, and emotions. Conclusions Staff identified multiple strategies for reducing non-attendance; implementing many of these strategies would require additional resourcing. Research determining the effectiveness of such strategies both in the short-term and long-term following implementation into practice remains a priority for future investigation.

Double-channel AlGaN/GaN Schottky barrier diode with AlN super back barrier on SiC substrate

In this work, high-performance double-channel AlGaN/GaN Schottky barrier diodes with AlN super back barrier are demonstrated, and the corresponding current transport mechanisms at various anode bias voltages are studied. Benefiting from the electric field-modulated GaN double channel via polarized charge, high output current of 339 mA/mm and high breakdown voltage of 2.18 kV are obtained. The initial leakage current is dominated by thermionic emission, when the anode is biased at a small voltage. With the increase in anode voltage, electrons in the GaN channel are partly depleted, and two-dimensional variable-range hopping, through high-density trap states in the GaN-based material, dominates the total leakage current. When the anode reverse bias is large enough to fully deplete electrons in channel, the temperature-dependent leakage current shows little dependence on the applied anode voltage, and the trap-assisted tunneling model satisfies.

In silico analysis of quorum sensing modulators: Insights into molecular docking and dynamics and potential therapeutic applications

Quorum sensing (QS) regulates bacterial functions like virulence and biofilm formation, mediated by proteins such as LasI and QscR in Pseudomonas aeruginosa. This study investigates the structural dynamics of LasI and QscR proteins in complex with Sulfamerazine and Sulfaperin, using AiiA lactonase as a negative control, through molecular dynamics (MD) simulations to identify potential QS modulators. Molecular docking and MD simulations assessed binding affinity and structural dynamics, analyzing parameters like docking scores, root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), radius of gyration (Rg), principal component analysis (PCA), and covariance analysis. Sulfamerazine exhibited the highest binding affinity for LasI based on docking scores, indicating strong ligand-protein interactions. MD simulations revealed stability in the LasI-Sulfamerazine complex, with lower RMSD compared to LasI-Sulfaperin and QscR complexes. RMSF analysis indicated greater flexibility in ligand-binding regions of LasI-Sulfaperin and QscR complexes, suggesting weaker binding. SASA showed a decrease in solvent-accessible surface area for the LasI-Sulfamerazine complex, supporting a compact structure. Rg values confirmed this, with the LasI-Sulfamerazine complex being more compact (~2.00 nm) than QscR-ligand complexes (2.10–2.30 nm). PCA revealed significant conformational changes in the LasI-Sulfamerazine complex, with PC1 explaining 57.26% variance. Covariance analysis indicated stronger residue coupling in the LasI-Sulfamerazine complex, suggesting higher rigidity, while LasI-Sulfaperin and QscR complexes exhibited flexible dynamics. AiiA lactonase was used as a negative control due to its established quorum quenching activity, which hydrolyzes AHL molecules and disrupts QS signaling. Unlike LasI and QscR, AiiA does not rely on small molecule binding for activation. However, a known LasI or QscR inhibitor would have served as a more appropriate positive control, which will be considered in future studies. These findings suggest the LasI-Sulfamerazine complex’s stability and rigidity make Sulfamerazine a promising QS modulator. Computational analyses highlight its potential to disrupt bacterial communication. Further experimental validation is needed to confirm its therapeutic implications.

<i>In situ</i> growth of carbon nanotubes in biomass carbon for energy storage: The denser the better?

Carbon nanotubes (CNTs) and their allotropes have been regarded as promising materials for electric double-layer supercapacitors (EDLCs). It is well accepted that the higher surface area of carbon will enable a higher capacity of energy storage. An interesting question is what could happen if the half channel size is less than the Stern layer spacing. In this study, we grew CNTs into carbonized basswood for EDLCs, in which the CNT density varied in bass carbon sheets with different thicknesses, eventually giving various pore/channel sizes. Electrochemical analyses revealed that the bass carbon with a higher CNT density exhibits inferior performance compared to specimens with lower CNT densities. Further investigation through electrochemical impedance spectroscopy suggests that such anomalous behavior in energy storage is attributed to the restricted ion diffusion within the densely packed CNTs in bass carbon channels.

Investigating methods to enhance interpretability and performance in cardiac MRI for myocardial scarring diagnosis using convolutional neural network classification and One Match

Machine learning (ML) classification of myocardial scarring in cardiac MRI is often hindered by limited explainability, particularly with convolutional neural networks (CNNs). To address this, we developed One Match (OM), an algorithm that builds on template matching to improve on both the explainability and performance of ML myocardial scaring classification. By incorporating OM, we aim to foster trust in AI models for medical diagnostics and demonstrate that improved interpretability does not have to compromise classification accuracy. Using a cardiac MRI dataset from 279 patients, this study evaluates One Match, which classifies myocardial scarring in images by matching each image to a set of labeled template images. It uses the highest correlation score from these matches for classification and is compared to a traditional sequential CNN. Enhancements such as autodidactic enhancement (AE) and patient-level classifications (PLCs) were applied to improve the predictive accuracy of both methods. Results are reported as follows: accuracy, sensitivity, specificity, precision, and F1-score. The highest classification performance was observed with the OM algorithm when enhanced by both AE and PLCs, 95.3% accuracy, 92.3% sensitivity, 96.7% specificity, 92.3% precision, and 92.3% F1-score, marking a significant improvement over the base configurations. AE alone had a positive impact on OM increasing accuracy from 89.0% to 93.2%, but decreased the accuracy of the CNN from 85.3% to 82.9%. In contrast, PLCs improved accuracy for both the CNN and OM, raising the CNN’s accuracy by 4.2% and OM’s by 7.4%. This study demonstrates the effectiveness of OM in classifying myocardial scars, particularly when enhanced with AE and PLCs. The interpretability of OM also enabled the examination of misclassifications, providing insights that could accelerate development and foster greater trust among clinical stakeholders.

Quantitative characterization of ZrO2 gate dielectric interface with tellurium

Tellurium (Te) has recently emerged as a promising p-type semiconductor that can be processed at low temperatures, compatible with back end of line CMOS integration. Characterization of tellurium–dielectric interfaces is essential for further device advancements. Here, the interface quality of Te with ZrO2 gate dielectric is studied in a metal-oxide semiconductor capacitor structure. The interface trap density (Dit) is measured as a function of atomic layer deposition (ALD) temperature, without the use of a seed layer. Given the low thermal budget of Te, the ALD temperature is shown to be particularly important. The lowest Dit of 5 × 1012 states/cm2 eV is obtained at a low ALD process temperature of 120 °C. To further assess the impact of Dit on device performance, field-effect transistors (FETs) were fabricated. The subthreshold swing and effective hole mobility of the FETs were analyzed in relation to Dit, emphasizing the importance of a defect-minimized interface for enhancing Te transistor performance.

Correction: Hyper-induction of IL-6 after TLR1/2 stimulation in calves with bovine respiratory disease

The chronic degeneration mechanism of NbOx-based artificial neurons in the active state

The stable operation of the neuromorphic system is significantly constrained by the response imbalance of artificial neurons, which originates from the degradation of devices as switching voltage shift (SVS). In this Letter, we systematically investigate the chronic degradation mechanism of the NbOx threshold switching (TS) device in integrate-and-fire (LIF) neurons by sustained operations. The SVS of the device is solidly verified and attributed to the changes of the TS region. Particularly, the biased evolution of the TS region diameter during operation dominates the different SVS in the reverse response. This reveals the irreversible degradation of the devices and neurons and further demonstrates their limited self-repair ability by implementing symmetric pulse stimulation. This work clarifies the SVS mechanism of NbOx-based TS devices in LIF neurons, providing support for extending neuronal lifespan and developing reliable neuron-based hardware.

Cardiovascular disease risk among Australian unpaid carers – A survival analysis using 15 waves of the HILDA survey

Background Engaging in chronically stressful behaviours has been hypothesised to increase the risk of experiencing cardiovascular disease (CVD). Providing unpaid care is known to be a stressful activity, but it is not clear whether this caregiving is associated with CVD. This study filled a gap in the existing literature by examining the association between providing unpaid care and incident cardiovascular disease among a nationally representative sample of Australian adults. Methods 11,123 adult participants aged over 18 years from the Household Income and Labour Dynamics in Australia (HILDA) survey were followed for up to 14 years from baseline (2003) until 2017. Gender-stratified survival analysis models used self-reported caregiving and heart disease statuses as well as time-varying covariates, to assess the association between providing high-intensity or low-intensity unpaid care (to an elderly or disabled relative) and incident CVD in comparison with a non-caregiving control. Results Among females, there was weak evidence that CVD was associated with high-intensity unpaid care (HR = 1.27, 95% CI = [0.83, 1.95]) and no evidence for low-intensity unpaid care (HR = 0.79, 95% CI = [0.50, 1.26]) in comparison with non-carers after adjusting for confounders. There was no association between caregiving and incident CVD for high-intensity (HR = 0.82, 95% CI = [0.47, 1.42]) or low-intensity (HR = 0.84, 95% CI = [0.55, 1.28]) caregiving males in the adjusted models. Conclusions These findings do not provide strong evidence to reject the null hypothesis that providing unpaid care does not increase risk of developing CVD in the Australian population. Given that these findings are somewhat inconsistent with the extant literature from other populations, further research is necessary, both in Australia and internationally, to build on the findings of this study and improve understanding of the nature of the association between caregiving and incident CVD.

Influence of different imaging scales on machine learning-based determination of magnetic parameters from magnetic images

The determination of material parameters is significantly important in material science, which is often a challenging task. Recently, advancements have shown that magnetic parameters, such as the Dzyaloshinskii–Moriya interaction (DMI), can be estimated from a magnetic domain image using machine learning (ML). This development suggests a potential shift in how magnetic parameters are determined, moving away from traditional measurement techniques to more innovative methods involving image-based inputs processed by ML. In previous studies, the test images used for estimation always matched the training images in size. However, since the image size is contingent on the microscopy technique used, the ability to accurately estimate parameters from images of varying sizes is essential. Here, we investigated the feasibility of estimating the DMI constant and saturation magnetization from magnetic domain images of different sizes using ML. We demonstrated that it is possible to estimate these parameters even when the imaging sizes differ between training and test datasets. Additionally, our comparison of the estimation accuracy for the DMI constant and saturation magnetization revealed that the tolerance for differences in the image size varies depending on the specific parameter being estimated. These findings could have a significant impact on the future methods of determining magnetic parameters.

Utilizing Multi-omics analysis to elucidate the role of mitochondrial gene defects in Gastric cancer progression

Background Gastric cancer is a leading cause of cancer-related mortality worldwide, with poor survival outcomes despite advances in diagnostic and therapeutic methods. Mitochondrial autophagy, or mitophagy, is crucial for maintaining cellular homeostasis and has significant implications in tumor biology. DUSP1, a bispecific phosphatase regulating MAP kinase activity, has been associated with various cancers, but its role in GC remains unclear. Materials and methods In order to gain a deeper understanding of gastric cancer cells, this study utilized bulk RNA-seq data from TCGA and GEO, combined with the MSigDB database, to screen for mitophagy-related genes. Univariate Cox regression and LASSO analysis were employed to further identify key mitophagy-related genes. Single-cell RNA sequencing data from the database was analyzed using Seurat software to investigate the mitochondrial autophagy genes in each candidate gastric tissue. To clarify the functional pathways involved, enrichment analysis and differential gene expression analysis were conducted. The characteristics of the immune microenvironment were assessed using the CIBERSORT R package. Additionally, both the ssGSEA algorithm and the CIBERSORT algorithm were utilized to evaluate changes and effects in immunological characteristics during gastric cancer pathogenesis. Results We identified eight prognostic genes—STX10, CDC37, VPS35, RCAN1, TRIM25, DUSP1, SEC23A, and GLT8D1—using LASSO-Cox regression analysis. RCAN1 and DUSP1 are strongly positively correlated, while DUSP1 is strongly negatively correlated with TRIM25, and CDC37 is strongly negatively correlated with SEC23A. By incorporating mitochondrial autophagy scores and clinical characteristics, we established a prognostic model that accurately predicts the 3-year survival status of gastric cancer (GC) patients. Additionally, our single-cell analysis identified DUSP1 as a key mitophagy-related gene. Functional studies demonstrated that DUSP1 knockdown significantly inhibits GC cell proliferation and migration. Conclusion In this study, we developed a risk score based on eight mitochondrial autophagy-related genes and analyzed their expression across different cell types using single-cell analysis. DUSP1 stood out as a key player in gastric cancer progression, with higher expression in tumor tissues and a significant role in cell proliferation, apoptosis, and drug resistance. Our research also linked this risk score to tumor microenvironment immune cell infiltration and tumor mutational burden, revealing distinct high and low-risk groups in gastric cancer patients. This risk score holds potential for improving patient survival assessment and guiding personalized treatment, including enhancing immunotherapy efficacy.

Sc0.2Al0.8N Lamb wave filter with a high temperature stability using the strategy of double-layer SiO2 and groove structure

The Lamb wave filter (LWF) has a moderate bandwidth, and the conventional temperature compensation method with one layer of SiO2 on the Lamb wave resonator (LWR) will significantly reduce its effective electromechanical coupling coefficient. This, in turn, narrows the bandwidth of the LWF and limits its application range. This paper achieves high temperature stability for both the LWR and LWF by employing a strategy that combines a double-layer SiO2 and a groove structure based on a Sc0.2Al0.8N film. Finite element analysis is employed to investigate the effect of adding single or double layers of SiO2 films on the temperature stability of the Sc0.2Al0.8N LWR. The simulation results show that applying SiO2 to both the top and bottom of the Sc0.2Al0.8N film can mitigate the reduction in k2 while enhancing temperature stability. The groove structure in the piezoelectric layer can simultaneously improve the temperature coefficient of frequency (TCF) and k2 by altering the electric field distribution and boundary conditions. Measurement results show that the double-layer SiO2 improves the TCF of the LWR to approximately –10 ppm/°C. The grooves can further improve the TCF toward 0 ppm/°C and increase the k2 of LWR.

Kinesthetic illusions induced by muscle tendon vibration: The orientation of the vibration motor as a new methodological factor?

Purpose/aim To investigate the impact of changing the rotational orientation of the vibrating motor on kinesthestic illusions. Materials and methods Twenty healthy individuals received vibration over the wrist flexor muscles of dominant and non-dominant sides (80 Hz, 1 mm, 10 seconds) using four conditions (3 trials/conditions) defined by the rotational direction of the vibrator’s eccentric rotating mass according to the anatomical position: (1) proximodistal, (2) distoproximal, (3) mediolateral and (4) lateromedial. Non-parametric statistical analyses were used to compare illusion characteristics across conditions. Results Lateromedial rotation created illusions that were more often in unexpected directions compared to the other rotational orientations. Distoproximal rotation was more likely to evoke kinesthetic illusions of wrist extension (76.8%) compared to lateromedial rotation (57.7%; p = 0.009). The latter led more frequently to complex/combined movement illusions (26.1%) especially with an ulnar deviation component (17.7%) compared to the other rotational directions. Conclusion Results from the present study demonstrated that the rotational orientation can influence illusory perceptions, but not to a great extent. Distoproximal rotation was more effective to elicit the expected illusions of wrist extension, compared to the lateromedial orientation that more often caused complex and variable perceptions of movement. Distoproximal rotation should thus be preferred if clear and reproducible perceptions are required and lateromedial might serve as a way of creating illusions more akin to everyday functional movements. Although the exact underlying mechanisms remain unclear, our work raises awareness on the importance of gaining a better understanding and control over methodological factors that could affect kinesthetic illusions.

Twist angle dependence of flatbands in trilayer graphene: A first-principles study

Among the most classical two-dimensional (2D) heterostructures, graphene stands out to exhibit unique electronic properties through manipulations of stacking layers, twist angles, strain, and external electric and magnetic fields. In this work, we present a theoretical investigation of twisted trilayer graphene (tTLG) using first-principles calculations, considering various twist angles θ12, which is the angle between the bottom and middle layers, and θ23, which is the angle between the top and middle layers. For trilayer configurations AÃA, where θ23 = θ12, and AÃÃ′, where θ23≠θ12, supercells as large as containing 23 338 atoms are built to capture the impact of twist angles on the electronic properties in tTLG using RESCU+. We discover that flatbands (with bandwidths less than 100 meV) can emerge when the twist angles approach certain magic angles while θ12⋅θ23 &amp;gt; 0. The bandwidth minimizes near these magic angles and varies smoothly with the twist angle, exhibiting no abrupt changes. Despite the symmetry being affected by the shift of the bottom and top layers leading to the changes in the Dirac cones, the flatbands exhibit remarkable robustness and retain their distinct properties. Our findings explain why the correlated states can also exist at twist angles slightly away from the exact magic angles.

A provably secure coercion-resistant e-voting scheme with confidentiality, anonymity, unforgeability, and CAI verifiability

Ensuring both cast-as-intended (CAI) verifiability and coercion-resistance in e-voting remains a critical challenge. The e-voting scheme proposed by Finogina and Herranz in 2023 represents the first notable advancement in reconciling these conflicting requirements. CAI verifiability allows voters to confirm that their intended vote has been correctly recorded, even without a secure channel to the election committee, while coercion-resistance prevents external influence and vote-selling. However, essential security properties such as confidentiality, anonymity, unforgeability, and double-voting prevention fall outside the scope of Finogina and Herranz’s e-voting scheme, leaving significant gaps in its security guarantees. To address this limitation, we propose a novel e-voting scheme that simultaneously achieves CAI verifiability, coercion-resistance, confidentiality, anonymity, unforgeability, and double-voting prevention while maintaining an asymptotic complexity of 𝒪(n). To the best of our knowledge, no existing scheme satisfies all these properties concurrently. Moreover, we establish that anonymity inherently implies CAI verifiability in e-voting schemes, a result of independent interest. By strengthening security and privacy guarantees, our work bridges existing gaps and provides a comprehensive security model that serves as a foundation for the design of future e-voting systems.

Ferroelectric polarization in nylon-3

It has been theoretically predicted that remanent polarization in odd nylons increases in a linear fashion with decreasing the molecular size of the amide unit, and can reach values comparable to those of inorganic ferroelectrics for lower order odd-nylons. However, such large remanent polarizations have never been experimentally measured due to challenges in processing low order nylons such as nylon-3 into films, and into their ferroelectric phase. Here, we report solution-processed ferroelectric capacitors fabricated from linear chain nylon-3 with excellent dielectric performance and demonstrate a giant remanent polarization of 16.1 ± 0.5 μC cm−2, which is the record high remanent polarization reported for ferroelectric polymers and is comparable to values that are reported for inorganic ferroelectric ceramics. Based on our analysis of ferroelectric odd nylons, we predict that nylon-1 could achieve remanent polarization values approaching 40 μC cm−2, far exceeding those reported for any ferroelectric polymer to date.

Enhanced Surveillance for Meningococcal Disease—United States, 2015–2019

Background High quality surveillance data are important to monitor U.S. epidemiology of meningococcal disease. Enhanced Meningococcal Disease Surveillance (EMDS) was established in 2015 to collect additional data and isolates from reported cases. Methods Epidemiologic information and isolates obtained through EMDS for meningococcal disease cases reported through the National Notifiable Diseases Surveillance System (NNDSS) during 2015–2019 were included in the analysis. Isolates were characterized by serogrouping and molecular typing using either Sanger sequencing or whole genome sequencing. Case fatality ratios (CFRs) were calculated using cases with known outcome as the denominator. Odds ratios (ORs) were calculated using logistic regression. Results A total of 1,806 meningococcal disease cases were reported during 2015–2019. The average annual incidence was 0.11 cases per 100,000 population. For key variables already collected through NNDSS, EMDS improved completeness over NNDSS by 21%-39% each year during 2015–2019. Completeness of key variables in EMDS improved over time by an average of 18%. Testing of isolates submitted through EMDS showed that serogroups B and C were predominant in all five years. For serogroup B, the most common clonal complex (CC) was CC32 in 2015–2017 and CC41/44 in 2018–2019; for serogroup C, CC11 was most common in 2015–2017 and CC103 in 2018–2019. The CFR was significantly lower for CC103 (6.7%) compared to other CCs (13.6%) (OR 0.44, 95% CI: 0.22–0.89). Conclusions U.S. incidence of meningococcal disease remained low during 2015–2019. Data and isolates obtained from EMDS supplement NNDSS and are critical to monitor U.S. meningococcal disease epidemiology.