Cumulative Metrics Research Articles

The X-Minute City: Analysing Accessibility to Essential Daily Destinations by Active Mobility in Seville

The concept of the “x-minute city” emphasises connected, mixed-use, and functionally dense urban areas where residents can access most daily necessities within a short walk or bike ride. By promoting proximity to essential destinations and sustainable transport options, this approach reduces the need for extensive travel and minimises environmental impact. This paper analyses the readiness of cities to function as x-minute cities and identifies necessary interventions. Using a reproducible and scalable methodology based on open data and software, the study assesses the accessibility of key urban amenities within specified timeframes. Cumulative accessibility metrics are calculated for different destination categories, considering both walking and cycling. In the case of Seville, accessibility requirements outlined in policy documents are already met for many essential services, particularly public facilities. The study identifies neighbourhoods that excel in accessibility and others that require improvement in adhering to x-minute city principles. The methodology and findings can inform planning and policy decisions in other cities, guiding efforts to enhance amenity provision, test accessibility scenarios, and target intervention areas.

Is Travel Associated With Match Performance in Elite North American Professional Soccer? An Exploratory Study.

Travel fatigue impacts cognitive and physiologic systems, but its association with elite soccer match performance is unclear. In this retrospective observational study, we aimed to explore the association between travel and match outcomes in elite North American soccer. Travel data and match outcomes(team points or goals scored and conceded) and physical performance outcomes from 26 elite professional soccer teams and their players were analyzed (148 matches [team-based data] and 1252 player matches from 297 players; age 22.7 [4.5]y). Player- and match-level correlations between performance measures and both acute and cumulated travel metrics were analyzed. Cumulative travel metrics were positively associated with team (travel distance [r = .20; 95% CI, .03-.25], travel time [r = .20; .06-.37], and time away [r = .20; .06-.37]) and individual player (travel distance, [r = .14; .08-.19], travel time [r = .17-.23], and time away [r = .13; .07-.18]) high-intensity running. Cumulative time away was negatively associated with team points (r = -.14; -.28 to -.001) and positively associated with goals conceded (r = .14; .01-.27); no clear association between acute travel metrics and match outcomes or physical performance was observed. As travel cumulated, away teams and their players ran more but for less reward (team points), although the magnitude of these associations was small. These data are exploratory and do not imply acausal relationship; however, further research should consider cumulation of travel.

Time-resolved clinical dose volume metrics, calculations and predictions based on source tracking measurements and uncertainties to aid treatment verification and error detection for HDR brachytherapy—a proof-of-principle study

Objective. High-dose-rate (HDR) brachytherapy lacks routinely available treatment verification methods. Real-time tracking of the radiation source during HDR brachytherapy can enhance treatment verification capabilities. Recent developments in source tracking allow for measurement of dwell times and source positions with high accuracy. However, more clinically relevant information, such as dose discrepancies, is still needed. To address this, a real-time dose calculation implementation was developed to provide more relevant information from source tracking data. A proof-of-principle of the developed tool was shown using source tracking data obtained from a 3D-printed anthropomorphic phantom. Approach. Software was developed to calculate dose-volume-histograms (DVH) and clinical dose metrics from experimental HDR prostate treatment source tracking data, measured in a realistic pelvic phantom. Uncertainty estimation was performed using repeat measurements to assess the inherent dose measuring uncertainty of the in vivo dosimetry (IVD) system. Using a novel approach, the measurement uncertainty can be incorporated in the dose calculation, and used for evaluation of cumulative dose and clinical dose-volume metrics after every dwell position, enabling real-time treatment verification. Main results. The dose calculated from source tracking measurements aligned with the generated uncertainty bands, validating the approach. Simulated shifts of 3 mm in 5/17 needles in a single plan caused DVH deviations beyond the uncertainty bands, indicating errors occurred during treatment. Clinical dose-volume metrics could be monitored in a time-resolved approach, enabling early detection of treatment plan deviations and prediction of their impact on the final dose that will be delivered in real-time. Significance. Integrating dose calculation with source tracking enhances the clinical relevance of IVD methods. Phantom measurements show that the developed tool aids in tracking treatment progress, detecting errors in real-time and post-treatment evaluation. In addition, it could be used to define patient-specific action limits and error thresholds, while taking the uncertainty of the measurement system into consideration.

Artificial Intelligence for Quantifying Cumulative Small Bowel Disease Severity on CT-Enterography in Crohn's Disease.

Assessing the cumulative degree of bowel injury in ileal Crohn's disease (CD) is difficult. We aimed to develop machine learning (ML) methodologies for automated estimation of cumulative ileal injury on computed tomography-enterography (CTE) to help predict future bowel surgery. Adults with ileal CD using biologic therapy at a tertiary care center underwent ML analysis of CTE scans. Two fellowship-trained radiologists graded bowel injury severity at granular spatial increments along the ileum (1 cm), called mini-segments. ML segmentation methods were trained on radiologist grading with predicted severity and then spatially mapped to the ileum. Cumulative injury was calculated as the sum (S-CIDSS) and mean of severity grades along the ileum. Multivariate models of future small bowel resection were compared with cumulative ileum injury metrics and traditional bowel measures, adjusting for laboratory values, medications, and prior surgery at the time of CTE. In 229 CTE scans, 8,424 mini-segments underwent analysis. Agreement between ML and radiologists injury grading was strong (κ = 0.80, 95% confidence interval 0.79-0.81) and similar to inter-radiologist agreement (κ = 0.87, 95% confidence interval 0.85-0.88). S-CIDSS (46.6 vs 30.4, P = 0.0007) and mean cumulative injury grade scores (1.80 vs 1.42, P < 0.0001) were greater in CD biologic users that went to future surgery. Models using cumulative spatial metrics (area under the curve = 0.76) outperformed models using conventional bowel measures, laboratory values, and medical history (area under the curve = 0.62) for predicting future surgery in biologic users. Automated cumulative ileal injury scores show promise for improving prediction of outcomes in small bowel CD. Beyond replicating expert judgment, spatial enterography analysis can augment the personalization of bowel assessment in CD.

Adopting the RE-AIM analytic framework for rural program evaluation: experiences from the Advance Care Planning via Group Visits (ACP-GV) national evaluation.

To support rigorous evaluation across a national portfolio of grants, the United States Department of Veterans Affairs (VA) Office of Rural Health (ORH) adopted an analytic framework to guide their grantees' evaluation of initiatives that reach rural veterans and to standardize the reporting of outcomes and impacts. Advance Care Planning via Group Visits (ACP-GV), one of ORH's Enterprise-Wide Initiatives, also followed the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. ACP-GV is a national patient-centered intervention delivered in a large, veterans integrated healthcare system. This manuscript describes how RE-AIM was used to evaluate this national program and lessons learned from ORH's annual reporting feedback to ACP-GV on their use of the framework to describe evaluation impacts. We used patient, provider, and site-level administrative health care data from the VA Corporate Data Warehouse and national program management databases for federal fiscal years (FY) spanning October 1, 2018-September 30, 2023. Measures included cumulative and past FY metrics developed to assess program impacts. RE-AIM constructs included the following cumulative and annual program evaluation results. ACP-GV reached 54,167 unique veterans, including 19,032 unique rural veterans between FY 2018 to FY 2023. During FY 2023, implementation adherence to the ACP-GV model was noted in 91.7% of program completers, with 55% of these completers reporting a knowledge increase and 14% reporting a substantial knowledge increase (effectiveness). As of FY 2023, 66 ACP-GV sites were active, and 1,556 VA staff were trained in the intervention (adoption). Of the 66 active sites in FY 2023, 27 were sites previously funded by ORH and continued to offer ACP-GV after the conclusion of three years of seed funding (maintenance). Lessons learned developing RE-AIM metrics collaboratively with program developers, implementers, and evaluators allowed for a balance of clinical and scientific input in decision-making, while the ORH annual reporting feedback provided specificity and emphasis for including both cumulative, annual, and rural specific metrics. ACP-GV's use of RE-AIM metrics is a key step towards improving rural veteran health outcomes and describing real world program impacts.

Numerical solution-based algorithm assisted development of a continuous flow microwave reactor

Continuous flow microwave heating is a sustainable technology favored in the chemical and food processing sectors due to its short residence time and relatively low environmental footprint. To address the complexity of fluid dynamics and electromagnetic field interactions, nuanced evaluation methods beyond conventional reflection coefficient analysis are necessary to predict its efficiency and uniformity. In this study, a numerical solution-based algorithm is utilized to propose synchrony and cumulative index metrics for performance analysis. Although identical S-parameter systems demonstrate approximate electromagnetic power loss density in the fluid region, significant disparities in heating performance are observed, evidenced by outlet temperatures ranging between 108.10 °C and 147.09 °C, and covariances ranging from 0.07 to 0.46. The power loss density is corrected based on velocity to introduce the synchrony index, which is further calculated into the cumulative index. This approach provides accurate predictions of efficiency and uniformity, informing system designs that align with specific performance objectives. To achieve the best uniformity in simulated two-part systems, a combination of 63 % GIM system and 37 % EBH-2 system in terms of power input was found to be optimal. However, when considering temperature dependent dielectric properties, the optimal combination was adjusted to 60 % GIM system and 40 % EBH-4 system. The numerical solution-based algorithm facilitates optimization of complex continuous flow microwave systems, providing invaluable insights into the time–space electromagnetic response mechanism of fluids.

Phenotypic lags influence rapid evolution throughout a drought cycle.

Climate anomalies are increasing and posing strong selection, which can lead to rapid evolution. This is occurring on a backdrop of interannual variability that might weaken or even reverse selection. However, the effect of interannual climatic variability on rapid evolution is rarely considered. We study the climatic differences that contribute to rapid evolution throughout a 7-year period, encompassing a severe drought across 12 populations of Mimulus cardinalis (scarlet monkeyflower). Plants were grown in a common greenhouse environment under wet and dry treatments, where specific leaf area and date of flowering were measured. We examine the association between trait values and different climate metrics at different time periods, including the collection year, prior years, and cumulative metrics across sequential years. Of the climatic variables we assessed, we find that anomalies in mean annual precipitation best describe trait differences over our study period. Past climates, of 1-2 years prior, are often related to trait values in a conflicting direction to collection-year climate. Uncovering these complex climatic impacts on evolution is critical to better predict and interpret the impacts of climate change.

Assessment of noise complaints as an indicator of long-term effects of aircraft noise on communities surrounding aerodromes

Aircraft noise is considered by many as the most burdensome part of aircraft operations. Communities neighbouring airports often express concerns about the possible health effects of chronic exposure to noise. To quantify the long-term effects of environmental noise exposure, researchers often use the metric of annoyance. Annoyance is widely considered to be the most well-corroborated health effect of aircraft noise and a moderating factor for other suspected health effects. Annoyance can also be correlated to average cumulative noise levels, such that higher levels of chronic noise likely evoke higher levels of annoyance within the population. Annoyance data is typically collected using extensive surveys and/or interviews, which are costly and time-consuming. In the absence of annoyance data, complaints are often used as a proxy for annoyance. This research used complaint, noise and annoyance data to demonstrate that complaints do not equate to annoyance, nor do they correlate to cumulative noise metrics. Thus, while complaint data may prove useful in analyzing short-term response to operations, it should not be relied upon for the assessment of long-term impacts from aircraft noise exposure, nor should it be used to direct noise and annoyance mitigation initiatives.

A generalized cumulative noise exposure metric for community response analysis.

In community noise studies, there is often a desire to understand how the annoyance response to multiple noise events aggregates over a long period of time. Many cumulative response metrics, such as day-night level (DNL), are based on the idea that humans respond, on average, to the sum of frequency-weighted acoustic energy over time. This paper introduces a generalization of DNL that includes a parameter, b, that ranges between zero and one. When b equals zero, the metric returns the maximum level of the events. When b equals 0.5, the metric reproduces the equal-energy-based output of DNL. When b = 0, 0.5, and 1, the metric returns a value that more harshly penalizes the number of events. In this way, these common possible hypotheses are organized onto a single scale, one that may be used to craft effective noise mitigation techniques or implement regulations. The analysis is demonstrated in two ways: first, on synthetic datasets to show the utility and consistency of the metric, and second, on limited quiet-supersonic response data gathered during the Quiet Supersonic Flights 2018 community study.

The 2023 New Zealand Ground-Motion Database

ABSTRACT This article summarizes the development of the 2023 New Zealand ground-motion database (NZGMDB). A preceding version was formally used as the central ground-motion database in the ground-motion characterization modeling for the 2022 New Zealand (NZ) National Seismic Hazard Model (NSHM) revision. The database contains ground motions for events with a moment magnitude greater than ∼3.0 from the years 2000 to the end of 2022. Several challenges associated with NZ earthquake source metadata are explained, including determination of earthquake location, magnitude, tectonic classification, and finite-fault geometry, among others. The site table leverages the site database developed as a part of the 2022 NZ NSHM revision, and several definitions of source-to-site distance are computed for the propagation path table. The ground-motion quality classification was initially assessed using a neural network. Subsequent waveform quality verification was conducted and additional quality criteria were enforced to ensure a sufficiently high-quality database. Standard processing techniques were applied to the ground motions before intensity measure (IM) calculation. IMs in the database include peak ground acceleration, 5%-damped pseudoacceleration response spectra, smoothed Fourier amplitude spectra, and other cumulative and duration-related metrics. The NZGMDB is publicly available and routinely updated as new and higher quality data become available.

Abstract 19079: Assessment of Heart Sounds, Physiologic Changes, and Sleep Performance Using Non-Invasive Monitoring in a Volume Depleted Heart Failure Patient

Case: A 46-year-old male with EF 15% who was co-enrolled in two non-invasive remote monitoring clinical trials, using the WHOOP strap and Nanowear vest to track heart failure (HF) progression at Hershey Medical Center was readmitted for volume depletion. Our patient presented to his PCP’s office complaining of daytime fatigue. Review of systems was negative including no dizziness or light headedness. Blood pressures during this visit was 85/50 and 79/49. He was sent to the emergency department where he had persistent hypotension with a creatinine elevation from 2.4 to 3.71. He was admitted for IV fluid resuscitation and diuretic optimization. Methods: We report cumulative metrics pre- and post-hospitalization showing significant changes in sleep performance and S3 heart sound magnitude. Other measured variables with WHOOP included heart rate (HR), respiratory rate (RR), rapid eye movement (REM) sleep, and heart rate variability (HRV). Results: Following adequate hydration, return to euvolemia, and diuretic adjustment, the patient had statistically significant improvement in sleep metrics (Figure 1, Graph C). Patient’s daily strain, a surrogate of HR elevation over time, was also significantly decreased post discharge (5.19 to 0.73, p &lt;0.01). Patient’s S3 heart sounds were measured during both hospitalizations and showed a decrease in amplitude consistent with volume depletion (Figure 1, Graphs A/B). Conclusion: We report physiologic trends that correlate with clinical status as measured using two novel non-invasive monitoring strategies. Poor sleep secondary to orthopnea and paroxysmal nocturnal dyspnea are considered signs that indicate volume overload. This case, indicates that volume depletion in patients with severely reduced EFs could also contribute to poor sleep performance, indicating a narrow therapeutic window. Further studies may validate these devices as remote monitoring tools for HF management.

Brachial Plexus Tolerance to Standard Fractionation Re-Irradiation: The Ohio State University Experience

Factors contributing to brachial plexopathy (BPP) in the re-irradiation setting need further assessment. Preliminary work revealed that higher the doses and the use of concurrent cisplatin were associated with higher risk of BPP. Here, we expand our cohort and increase our follow-up duration, given the late nature of BPP development. Sixty-two BP sites with 41 patients (pts), treated between 2015 and 2020 for recurrent H&N cancer, were assessed. Contours and plans were reviewed via a prospective multidisciplinary chart rounds prior to treatment delivery and re-verified by two authors prior to this analysis. Kaplan-Meier and logistic regression were used to test the correlation between variables and outcomes. ROC was used to evaluate the cutoff values. Common terminology criteria of adverse events were used to define BPP. The median age of pts was 63 (29-78) and 65% were males. Median prescription dose was 70 Gy (60-70) for the 1st course and 66 Gy (44-70) for the 2nd course. 13% received intraoperative radiotherapy (IORT) during salvage treatment ranging from 10-15 Gy. 30% of pts in the 1st course and 80% in the 2nd course of pts had surgery. Concurrent chemotherapy was delivered to 71% of pts in the 1st course (Cisplatin 34%, Cetuximab 22%, Carboplatin +/- Paclitaxel 15%) and 76% in the 2nd course (Carboplatin/Paclitaxel 41%, Cisplatin 19%, Cetuximab 12%, Nivolumab 4%). The median interval between courses was 26.5 months (8-221). The median cumulative Dmax (0.03cc) and mean dose to the BP were 96.5 Gy (51-144) and 61.5 Gy (15-110), respectively. The median V60, V70, V80, V90, and V100 were 3.6cc (0.03-10.4), 2.4cc (0-9.9), 1.3cc (0-9.3), 0.6cc (0-8.4), and 0.0015cc (0-7.2), respectively. The median follow-up after the completion of the 2nd RT course was 19 months (1.4-71.5). The 1-yr incidence of BPP was 17%, with a median time to onset of 8.9 months (1-16.6). Factors associated with development of BPP were cumulative Dmax > 100 Gy (HR 1.06, [CI 1.014-1.1], p = 0.009), cumulative mean dose >70 Gy (HR 1.05, [CI 1.01-1.09], p = 0.03), V80 > 1.6cc (HR 1.2. [CI 0.99- 1.52], p = 0.06), V90 > 1cc (HR 1.3, [CI 1.013-1.58], p = 0.038), V100 > 0.3cc (HR 1.33, [1.044-1.69], p = 0.021) and the usage of concurrent cisplatin during the 2nd course (HR 8.9, [CI 2.36-33.75], p = 0.001). Other factors including gender, age, surgery, treatment interval between the two courses, IORT, V60, and V70 were not associated with increased risk of BPP. The incidence of grade 1, grade 2, and grade 3 BPP were 9.6%, 3% and 2.4%, respectively. The 1-yr OS was 70% and LC was 60%. At a median follow up of 19 months, the 1-yr incidence of grade 2 and 3 BPP was approximately 5%. Cisplatin during the 2nd course, cumulative metrics of Dmax 100 Gy, mean 70 Gy, V80 1.6cc, V90 1cc and V100 0.3cc were associated with development of BPP. Prospective study, longer follow up, and higher numbers are warranted. To our knowledge, this represents the largest cohort and longest follow up yet reported for BPP in the re-irradiation setting.

Authorship and citation patterns of highly cited biomedical researchers: a cross-sectional study

BackgroundScientific productivity is often evaluated by means of cumulative citation metrics. Different metrics produce different incentives. The H-index assigns full credit from a citation to each coauthor, and thus may encourage multiple collaborations in mid-list author roles. In contrast, the Hm-index assigns only a fraction 1/k of citation credit to each of k coauthors of an article, and thus may encourage research done by smaller teams, and in first or last author roles. Whether H and Hm indices are influenced by different authorship patterns has not been examined.MethodsUsing a publicly available Scopus database, I examined associations between the numbers of research articles published as single, first, mid-list, or last author between 1990 and 2019, and the H-index and the Hm-index, among 18,231 leading researchers in the health sciences.ResultsAdjusting for career duration and other article types, the H-index was negatively associated with the number of single author articles (partial Pearson r -0.06) and first author articles (-0.08), but positively associated with the number of mid-list (0.64) and last author articles (0.21). In contrast, all associations were positive for the Hm-index (0.04 for single author articles, 0.18 for first author articles, 0.24 for mid-list articles, and 0.46 for last author articles).ConclusionThe H-index and the Hm-index do not reflect the same authorship patterns: the full-credit H-index is predominantly associated with mid-list authorship, whereas the partial-credit Hm-index is driven by more balanced publication patterns, and is most strongly associated with last-author articles. Since performance metrics may act as incentives, the selection of a citation metric should receive careful consideration.

SSC risk significance in risk-informed, performance-based licensing of non-LWRs

The main criteria used in NEI 18–04 to define SSCs as risk-significant include (1) the SSC is required to keep all LBEs within the F–C target, and (2) the total frequency with the SSC failed exceeds 1% of the limit for at least one of the three cumulative risk metrics used for evaluating the integrated plant risk. The first one is a reasonable criterion in determining the risk significant SSCs. However, the second criterion may not be adequate to serve the purpose of determining the risk significance of SSCs. In the second criterion, the cumulative risk metric values representing the integrated plant risk (less the preventive and mitigative effects of the SSC being evaluated) are compared to a risk limit that represents a very small contribution to the overall integrated plant risk, which corresponds appropriately to the contributions from individual SSCs. The easiest approach to redefine the NEI 18-04 definition of risk-significant SSCs in relation to the integrated plant risk metrics is to compare the difference, between the risk metric value calculated with the SSC failed and the risk metric value calculated with the SSC credited, with 1% of the risk limit established for the integrated plant risk metrics.

Dynamic Pricing with Unknown Nonparametric Demand and Limited Price Changes

Dynamic pricing with demand learning is a very common problem that retailers face. In this problem, the retailer aims to maximize cumulative revenue collected over a finite time horizon by balancing two objectives: learning demand and maximizing revenue. In their paper “Dynamic Pricing with Unknown Nonparametric Demand and Limited Price Changes,” Perakis and Singhvi study this problem when the retailer makes no parametric assumption on the demand and seeks to reduce the amount of price experimentation because of the potential costs associated with price changes. They construct a pricing policy that uses second order approximations of the unknown demand function and establish when the proposed policy achieves near-optimal rate of regret while making very limited price changes. They also perform extensive numerical experiments to show that their proposed policy substantially improves over existing methods in terms of the total price changes, with comparable performance on the cumulative regret metric.

Graph Neural Network-Guided Contrastive Learning for Sequential Recommendation

Sequential recommendation uses contrastive learning to randomly augment user sequences and alleviate the data sparsity problem. However, there is no guarantee that the augmented positive or negative views remain semantically similar. To address this issue, we propose graph neural network-guided contrastive learning for sequential recommendation (GC4SRec). The guided process employs graph neural networks to obtain user embeddings, an encoder to determine the importance score of each item, and various data augmentation methods to construct a contrast view based on the importance score. Experimental validation is conducted on three publicly available datasets, and the experimental results demonstrate that GC4SRec improves the hit rate and normalized discounted cumulative gain metrics by 1.4% and 1.7%, respectively. The model can enhance recommendation performance and mitigate the data sparsity problem.

Incorporating Social Vulnerability Variables in Measures to Quantify Access to Opportunities

This study quantifies access to travel opportunities to understand what societal factors are linked with local access and to identify communities with reduced access. We introduce a method to compare accessibility across all census tracts in the United States that can be used across geographically diverse communities ranging from sparsely to densely populated areas. This study considers six key opportunities which we consider essential for all communities (grocery stores, public schools, daycares, primary care doctors, pharmacies, and parks), and six additional destinations which can be viewed as a social safety net (homeless shelters, women’s shelters, food pantries, libraries, vocational schools, and banks). We quantify accessibility to these opportunities within a 15 min walk, transit trip, bicycle ride, and automobile drive for every census tract in the United States, and observe a decrease in vehicle miles traveled and vehicle ownership in tracts with increased walkability. Through analysis at the census tract level, this study incorporates variables of social vulnerability with these cumulative opportunity metrics to better understand diminished accessibility as attributed to social and racial inequities. As example findings, we find decreased access to financial services in communities with high minority and limited English speaking populations, no apparent change in access for childcare in communities with high percentages of single-parent families, and potentially increased or decreased access to women’s healthcare resources for Black women depending on the travel mode.

Joint and Limb Loading during Gait in Adults with ACL Reconstruction: Comparison between Single-Step and Cumulative Load Metrics.

Individuals with anterior cruciate ligament reconstruction (ACLR) generally exhibit limb underloading behaviors during walking, but most research focuses on per-step comparisons. Cumulative loading metrics offer unique insight into joint loading as magnitude, duration, and total steps are considered, but few studies have evaluated if cumulative loads are altered post-ACLR. Here, we evaluated if underloading behaviors are apparent in ACLR limbs when using cumulative load metrics and how load metrics change in response to walking speed modifications. Treadmill walking biomechanics were evaluated in 21 participants with ACLR at three speeds (self-selected (SS); 120% SS and 80% SS). Cumulative loads per step and per kilometer were calculated using knee flexion and adduction moment (KFM and KAM) and vertical ground reaction force (GRF) impulses. Traditional magnitude metrics for KFM, KAM, and GRF were also calculated. The ACLR limb displayed smaller KFM and GRF in early and late stances, but larger KFM and GRF during midstance compared with the contralateral limb ( P < 0.01). Only GRF cumulative loads (per step and per kilometer) were reduced in the ACLR limb ( P < 0.01). In response to speed modifications, load magnitudes generally increased with speed. Conversely, cumulative load metrics (per step and per kilometer) decreased at faster speeds and increased at slow speeds ( P < 0.01). Patients with ACLR underload their knee in the sagittal plane per step, but cumulatively over the course of many steps/distance, this underloading phenomenon was not apparent. Furthermore, cumulative load increased at slower speeds, opposite to what is identified with traditional single-step metrics. Assessing cumulative load metrics may offer additional insight into how load outcomes may be impacted in injured populations or in response to gait modifications.

Standardized Cumulative Metrics of Excess Mortality to Monitor Health System Resilience Throughout COVID-19 and Other Respiratory Virus Resurgences.

Monitoring morbidity and mortality in resurgences of respiratory infections poses significant challenges, as shown by coronavirus disease 2019 (COVID-19). For example, case fatality rates and deaths attributed to specific respiratory pathogens are known to suffer from significant biases undermining their comparability through time and space. As a result, it is difficult to evaluate the protective effect of public health interventions or quantify the impact of a resurgence on the general population through direct recording of COVID-19 related deaths. To overcome these limitations, more robust, less-biased metrics, such as all-cause deaths, have been proposed for monitoring the effect of an epidemic over a population and over time. More specifically, metrics of excess mortality over time, which have been used for influenza surveillance in the past, are increasingly considered important for COVID-19 surveillance. Here, we discuss excess mortality surveillance focusing on standardized single-point and standardized cumulative metrics that allow comparability of excess mortality through space and time. We explain why z score allows for comparison of excess mortality between countries and different periods, while cumulative z score allows assessment of excess mortality over long periods. Our commentary reiterates the importance of standardized statistics of excess mortality for COVID-19 surveillance as we move toward a coexistence with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that will allow drawing conclusions from best practices in different health systems and different periods.

Abstract 3124: Identification of sub-clonal loss of heterozygosity (LOH) in glioblastomas analyzed with spatial transcriptomics

Abstract We propose a method to leverage spatial transcriptomics and bioinformatic analysis to identify sub-clonal loss of heterozygosity that could otherwise be missed by standard bulk sequencing in glioblastoma tumors. Glioblastomas are highly invasive and aggressive brain tumors with a low five-year survival rate of less than 10%. Current treatment strategies are not sufficient for long term disease outcome, and innovative approaches to analyze glioblastoma are imperative for future improved options. Many factors contribute to difficulty of treatment, though one critical consideration is the diffuse nature and known intratumoral heterogeneity of this cancer. It is important to determine LOH heterogeneity, as this genomic alteration is associated with copy number variation and specific classifications of gliomas. Loss of heterozygosity is an irreversible deletion of DNA that is a common alteration in cancer. Application of spatial transcriptomics can increase our understanding of genomic complexity across a tissue, and unsupervised graph clustering can computationally separate spatial regions with similar gene expression patterns. Our method investigates spatial clusters individually at known heterozygous SNP sites to identify evidence for LOH with Bayesian inference and a hidden Markov model. Our analysis centers on the balance of reference (A) and non-reference (B) spatial transcriptomics read counts at SNPs of interest. B allele frequency is the number of reads aligned to the alternative allele divided by the total number of reads counted at the SNP. At a normal two-copy state at a heterozygous site, B allele frequency is ideally 50 percent. LOH can be identified as this value deviates toward 0 or 100, and copy number alterations impact this frequency. Our method utilizes Bayes factor values, or likelihood ratios between two models, using allele counts to provide support for either LOH or a heterozygous event. For our analysis, spatial transcriptomics BAMs are processed with a graph clustering algorithm and split into cluster-specific BAMs. We calculate allele count coverage for each cluster at pre-selected heterozygous SNP positions identified through germline exome sequencing. Bayes factor values are then calculated at each SNP, resulting in evidence for LOH or a heterozygous event. Chromosomal regions are segmented by a hidden Markov approach. Cumulative metrics for each segment are evaluated to determine a “state” label of heterozygous, LOH, or undefined. We tested our method on three WHO grade 4 IDH wild-type EGFRvIII positive fresh frozen glioblastoma tissue samples. Sub-clonal heterogeneity was identified in two samples and confirmed by bulk exome sequencing, bulk RNA sequencing, and analysis of initial clinical reports. Citation Format: Michelle G. Webb, Frances Chow, Carmel McCullough, Bohan Zhang, Kyle Hurth, John Carpten, Gabriel Zada, David W. Craig. Identification of sub-clonal loss of heterozygosity (LOH) in glioblastomas analyzed with spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3124.