Discrepant Estimates Research Articles

USMDA: Unsupervised Multisource Domain Adaptive ADHD prediction model using neuroimaging

There is an increasing number of large-scale cross-site database collections of neuroimaging markers (sMRI and fMRI) for studying neurodevelopmental illnesses (NDDs). Although a huge amount of data favors machine learning-based categorization algorithms, the unique heterogeneity of each site can impair cross-site generalization capacity. It is critical to create Unsupervised domain adaption methods for NDDs because obtaining appropriate diagnoses or labeling for NDDs might be problematic. In our work, we focus on Attention-deficit/hyperactivity disorder, which is the most common and frequently co-occurring NDD. We present an unsupervised multisource domain adaptation network (USMDA) with four primary components: Domain Alignment Module, Discrepancy Estimator, Pre-trained Model Generator, and Unsupervised Network. The Domain Alignment module is intended to incrementally and effectively align graph representations of the source and target domains. At the same time, the binary cross entropy regularizer is introduced for the first time during the training of a model learned on multiple source domains to improve existing feature alignment methods such as Transfer Joint Matching (TJM) and Joint Distribution Adaptation (JDA) by learning good unsupervised features. In an unsupervised network, the grid search optimization technique generates the optimal pseudo labels for unlabeled target data. We validate our proposed technique first on existing feature-level DA methods such as JDA, TJM, and Correlation alignment (CORAL), on the publically accessible dataset ADHD-200 and then by using binary cross-entropy in existing DA methods such as TJMCE and JDACE. The experimental results show that our proposed USMDAJDACE method when applied to multisite sMRI and fMRI ADHD data, can significantly outperform competitive methods for multi-center ADHD diagnosis.

Способи зниження складності обчислень при розрахунках потокорозподілу в мережах нестисливої рідини при фіксованих коефіцієнтах гідравлічного опору

Based on the analysis of the process of solving the problem of flow distribution calcula-tion in compressible fluid systems, which boils down to finding the root of a nonlinear system of equations using the Newton — Raphson method, methods of reducing the complexity of calculations at the stages of the iterative process are proposed. The studies belong to the class of hydraulic systems for which the total pressure change on the branch elements is a monotonic smooth function of the flow rate G. For the iterations of the Newton-Raphson method, the stages are analyzed: linearization of the nonlinear system of equations, determination of the discrepancy and error estimation, determination of cost growth and obtaining a new iterative cost value. A method and algorithm for determining the discrepancy and estimating the error with linear complexity relative to the number of branches of graph E is proposed. A method of reducing the number of unknowns in the system of flow distribution equations at the stage of forming a system of linear equations based on the use of the nodal convolution method is described. A number of variants of heuristic algorithms for determining the parameter that takes into account the rate of in-crease in costs when forming a new iterative value of costs and their analysis are present-ed.

Navigating uncertainty in maximum body size in marine metazoans.

Body size is a fundamental biological trait shaping ecological interactions, evolutionary processes, and our understanding of the structure and dynamics of marine communities on a global scale. Accurately defining a species' body size, despite the ease of measurement, poses significant challenges due to varied methodologies, tool usage, and subjectivity among researchers, resulting in multiple, often discrepant size estimates. These discrepancies, stemming from diverse measurement approaches and inherent variability, could substantially impact the reliability and precision of ecological and evolutionary studies reliant on body size data across extensive species datasets. This study examines the variation in reported maximum body sizes across 69,570 individual measurements of maximum size, ranging from <0.2 μm to >45 m, for 27,271 species of marine metazoans. The research aims to investigate how reported maximum size variations within species relate to organism size, taxonomy, habitat, and the presence of skeletal structures. The investigation particularly focuses on understanding why discrepancies in maximum size estimates arise and their potential implications for broader ecological and evolutionary studies relying on body size data. Variation in reported maximum sizes is zero for 38% of species, and low for most species, although it exceeds two orders of magnitude for some species. The likelihood of zero variation in maximum size decreased with more measurements and increased in larger species, though this varied across phyla and habitats. Pelagic organisms consistently had low maximum size range values, while small species with unspecified habitats had the highest variation. Variations in maximum size within a species were notably smaller than interspecific variation at higher taxonomic levels. Significant variation in maximum size estimates exists within marine species, and partially explained by organism size, taxonomic group, and habitat. Variation in maximum size could be reduced by standardized measurement protocols and improved meta-data. Despite the variation, egregious errors in published maximum size measurements are rare, and their impact on comparative macroecological and macroevolutionary research is likely minimal.

Discrepancy of rational points in simple algebraic groups

The aim of the present paper is to derive effective discrepancy estimates for the distribution of rational points on general semisimple algebraic group varieties, in general families of subsets and at arbitrarily small scales. We establish mean-square, almost sure and uniform estimates for the discrepancy with explicit error bounds. We also prove an analogue of W. Schmidt's theorem, which establishes effective almost sure asymptotic counting of rational solutions to Diophantine inequalities in the Euclidean space. We formulate and prove a version of it for rational points on the group variety, with an effective bound which in some instances can be expected to be the best possible.

Quaia, the Gaia-unWISE Quasar Catalog: An All-sky Spectroscopic Quasar Sample

We present a new, all-sky quasar catalog, Quaia, that samples the largest comoving volume of any existing spectroscopic quasar sample. The catalog draws on the 6,649,162 quasar candidates identified by the Gaia mission that have redshift estimates from the space observatory’s low-resolution blue photometer/red photometer spectra. This initial sample is highly homogeneous and complete, but has low purity, and 18% of even the bright (G < 20.0) confirmed quasars have discrepant redshift estimates (∣Δz/(1 + z)∣ > 0.2) compared to those from the Sloan Digital Sky Survey (SDSS). In this work, we combine the Gaia candidates with unWISE infrared data (based on the Wide-field Infrared Survey Explorer survey) to construct a catalog useful for cosmological and astrophysical quasar studies. We apply cuts based on proper motions and colors, reducing the number of contaminants by approximately four times. We improve the redshifts by training a k-Nearest Neighbor model on SDSS redshifts, and achieve estimates on the G < 20.0 sample with only 6% (10%) catastrophic errors with ∣Δz/(1 + z)∣ > 0.2 (0.1), a reduction of approximately three times (approximately two times) compared to the Gaia redshifts. The final catalog has 1,295,502 quasars with G < 20.5, and 755,850 candidates in an even cleaner G < 20.0 sample, with accompanying rigorous selection function models. We compare Quaia to existing quasar catalogs, showing that its large effective volume makes it a highly competitive sample for cosmological large-scale structure analyses. The catalog is publicly available at 10.5281/zenodo.10403370.

Scale Reliability Evaluation Using Bayesian Analysis: A Latent Variable Modeling Procedure

ABSTRACT An application of Bayesian factor analysis for evaluation of scale reliability is discussed, which is developed within the framework of latent variable modeling. The method permits direct point and interval estimation of the reliability coefficient of multiple-component measuring instruments using Bayesian inference. The approach allows also point and interval estimation of the population discrepancy between the popular coefficient alpha and instrument reliability. The procedure is readily applied in empirical measurement research employing widely available statistical software. The outlined method is illustrated using numerical data.

Hydrodynamics of transient processes in a well with an electric submersible pump

The relevance. Associated with the problem of increase in efficiency of algorithms for monitoring and controlling the dynamic operation modes of wells equipped with an electric submersible pump with adjustable supply. The result summarizes and develops the previously published solution on the design of a dynamic well model, through the transition from linearized correlations to quadratic relationships between states at key points of a lift. These points properly reflect the observed forms of behavior in transient and equilibrium operating modes at full start-stop control ranges. The main aim. Updated description of a comprehensive quadratic barometric model of the «reservoir–lift–electric pump–well head» type well, focused on the tasks of automatic control and regulation in real time. Objects. Well with frequency-regulated submersible pump. Methods. Material balance, percolation, hydrostatics, quadratic kinetics of friction losses, numerical modeling of differential equations, finite-dimensional description of the complex hydrodynamics of the well, taking into account the potential and kinetic head losses in the lift. Results: (1) Updated mathematical description of the hydrodynamics of the well with an electric submersible pump creates the basis for: more reliable solution of inverse problems of parametric model support according to the data of field control, refined assessment of the adjustment potential, predicting the dynamics of a possible system output beyond the boundaries of functional stability, technologies of numerical and analytical design of optimal solutions when selecting the parameters of arrangement and laws of operational regulation, synthesis of dynamic observers of an extended state vector synchronously operating with the dynamics of controlling well-head and deep pressure. (2) The results of the computational analysis demonstrate a complex behavior that does not match the graph of the inflow and pump supply, when changing the frequency and wellhead pressure. This explains the observed effect of the multi-tempoity of transient processes during starts and stops and possible instability of the supply with a sharp decrease in frequency. (3) Estimates of discrepancy between the solutions for a quadratic model and a linearized prototype indicate an increase in errors of linearized analysis with strong deviations of operating modes from the equilibrium-calculated states. The refinements in dynamically disturbed operating modes delivered by the updated model are important for improving the reliability of operational control systems and parametric estimation for data control mode states.

An infinite interval version of the α-Kakutani equidistribution problem

In this article we extend results of Kakutani, Adler–Flatto, Smilansky and others on the classical α-Kakutani equidistribution result for sequences arising from finite partitions of the interval. In particular, we describe a generalization of the equidistribution result to infinite partitions. In addition, we give discrepancy estimates, extending results of Drmota–Infusino [8].

The productivity-environment nexus in space. Granularity bias, aggregation issues and spatial dependence within Italian farm-level data

This paper looks for empirical support to the existence of a positive nexus between economic and environmental performance in farming as implied by the Sustainable Intensification hypothesis. As the ecological scale at which this nexus actually occurs is unobservable, the paper juxtaposes its estimation at three spatial scales, the farm level and two regional levels. Starting with a common theoretical background, the paper estimates a dynamic spatial panel model on these alternative scales. Identification issues (granularity, aggregation bias and spatial dependence) may generate significantly discrepant estimates eventually questioning the reliability of these results. The empirical study investigates the relationship between total factor productivity, greenhouse gas emissions and crop diversity using a 2008–2018 panel of Italian farms. Results show that the productivity-environment nexus changes and may even revert its sign when passing from farm-level to aggregate data. The implications of these results for evidence-based policy making are discussed.

Discrepancy bounds for normal numbers generated by necklaces in arbitrary base

Mordechay B. Levin (1999) has constructed a number λ which is normal in base 2, and such that the sequence ({2nλ})n=0,1,2,… has very small discrepancy N⋅DN=O((log⁡N)2). This construction technique was generalized by Becher and Carton (2019), who generated normal numbers via nested perfect necklaces, for which the same upper discrepancy estimate holds. In this paper we derive an upper discrepancy bound for so-called semi-perfect nested necklaces and show that for Levin's normal number in arbitrary prime base p this upper bound for the discrepancy is best possible. This result generalizes a previous result by the authors (2022) in base 2.Our result for Levin's normal number in any prime base might support the guess that O((log⁡N)2) is the best order in N that can be achieved by a normal number, while generalizing the class of known normal numbers by introducing semi-perfect necklaces on the other hand might help for the search of normal numbers that satisfy smaller discrepancy bounds.

Advantages of Using Unweighted Approximation Error Measures for Model Fit Assessment

Fit indices are highly frequently used for assessing the goodness of fit of latent variable models. Most prominent fit indices, such as the root-mean-square error of approximation (RMSEA) or the comparative fit index (CFI), are based on a noncentrality parameter estimate derived from the model fit statistic. While a noncentrality parameter estimate is well suited for quantifying the amount of systematic error, the complex weighting function involved in its calculation makes indices derived from it challenging to interpret. Moreover, noncentrality-parameter-based fit indices yield systematically different values, depending on the indicators’ level of measurement. For instance, RMSEA and CFI yield more favorable fit indices for models with categorical as compared to metric variables under otherwise identical conditions. In the present article, approaches for obtaining an approximation discrepancy estimate that is independent from any specific weighting function are considered. From these unweighted approximation error estimates, fit indices analogous to RMSEA and CFI are calculated and their finite sample properties are investigated using simulation studies. The results illustrate that the new fit indices consistently estimate their true value which, in contrast to other fit indices, is the same value for metric and categorical variables. Advantages with respect to interpretability are discussed and cutoff criteria for the new indices are considered.

Reproducibility of the medical cost estimation from the Medicare Current Beneficiary Survey: Comparing claims and survey.

The Medicare Current Beneficiary Survey (MCBS) limited-access data provides the unique opportunity to utilize administrative claims and adjusted survey data to investigate trends in utilization and medical expenditure across time. The adjusted survey data is a synthesized, matched version of the original survey data and claims. Researchers may choose adjusted survey data or original claims for cost evaluations according to their research purpose. However, limited research has examined methodological issues when estimating medical cost using different MCBS data sources. The study objective was to examine the reproducibility of individual-level medical cost using both MCBS data sources: adjusted survey and claims data. This serial cross-sectional study design analyzed 2006-2012 MCBS data. The sample included noninstitutionalized older Medicare beneficiaries (≥65 years old), with a cancer diagnosis and annually enrolled in Medicare Parts A, B, and D. The population was stratified by diabetes diagnosis. The primary outcome was annual medical cost. We investigated the discrepancies of medical cost estimated from the adjusted survey and original claims data. The agreement between cost estimates from the two sources in each year was determined using the Wilcoxon signed-rank test. A total of 4918 eligible Medicare beneficiaries were included in this study, and 26% of beneficiaries also had diabetes (N = 1275). Significant disagreements in cost estimates between adjusted survey and claims data were present regardless of disease complexity (with or without diabetes). Significant disagreements in medical cost estimates were present in most years, except in 2010 (p = 0.467) and 2011 (p = 0.098), for beneficiaries with cancer and diabetes (p < 0.001 for all). Significant disagreements in medical cost estimates were present in all years for beneficiaries with cancer without diabetes (p < 0.001 for all). Based on discrepant cost estimates across data sources, researchers using MCBS to estimate costs should be cautious when using claims or adjusted survey data alone.

Evaluating the Discrepancy Between Scale Reliability and Cronbach’s Coefficient Alpha Using Latent Variable Modeling

ABSTRACT This article outlines a readily applicable procedure for point and interval estimation of the population discrepancy between reliability and the popular Cronbach’s coefficient alpha for unidimensional multi-component measuring instruments with uncorrelated errors, which are widely used in behavioral and social research. The method is developed within the latent variable modeling framework and can be used to evaluate the degree to which coefficient alpha underestimates scale reliability in empirical measurement research employing such instruments. The approach is straight-forwardly utilized with readily available software and is illustrated using a numerical example.

Quantitative inductive estimates for Green’s functions of non-self-adjoint matrices

We provide quantitative inductive estimates for Green's functions of matrices with (sub)expoentially decaying off diagonal entries in higher dimensions. Together with Cartan's estimates and discrepancy estimates, we establish explicit bounds for the large deviation theorem for non-self-adjoint Toeplitz operators. As applications, we obtain the modulus of continuity of the integrated density of states with explicit bounds and the pure point spectrum property for analytic quasi-periodic operators. Moreover, our inductions are self-improved and work for perturbations with low complexity interactions.

Utility of the Age Discrepancy between Frailty-Based Biological Age and Expected Life Age in Patients with Urological Cancers.

Background: The estimation of biological age is challenging in patients with cancers. We aimed to investigate frailty-based biological ages using frailty-discriminant scores (FDS) and examined the effect of biological-expected life age discrepancy on the prognosis of patients with urological cancers. Methods: We retrospectively evaluated frailty in 1035 patients having urological cancers. Their frailty-based biological age was then defined by the FDS, which is a comprehensive frailty assessment tool, using 1790 noncancer individuals as controls. An expected life age (=chronological age + life expectancy) was subsequently calculated using the 2019 life expectancy table. The primary outcome was the estimation of the biological-expected life age discrepancy between the frailty-based biological age and expected life age in patients with urological cancers. Secondary outcomes were the evaluation of the effect of the biological-expected life age discrepancy on overall survival. Results: We included 405, 466, and 164 patients diagnosed with prostate cancer, urothelial carcinoma, and renal cell carcinoma, respectively. The median chronological age, life expectancy, and estimated frailty-based biological age were 71, 17, and 83 years, respectively. The biological-expected life age discrepancy in any urological cancers, localized diseases, and metastatic diseases was -4.8, -6.3, and +0.15 years, respectively. The biological-expected life age discrepancy of &gt;5 years was significantly associated with poor overall survival. Conclusions: The biological-expected life age discrepancy between frailty-based biological age and expected life age may be helpful in understanding the role of frailty and patient/doctor conversation.

Non-intrusive estimation of model error and discrepancy in dynamics models

Errors in formulating the system physics model, due to inability to rigorously account for nonlinear behavior, boundary conditions, and multi-component and multi-physics interactions result in discrepancies between model predictions of system responses and the corresponding experimentally measured values. In our previous work, we have developed a Bayesian state estimation-based framework for the estimation of these model errors, and the subsequent prediction of model discrepancies at unmeasured locations, for untested inputs, and for untested configurations. In this approach, we represent model errors as external inputs in the dynamic system model, and estimate them using Bayesian state estimation. However, this approach is intrusive in the sense that it requires access to the governing system of equations, the ability to modify the system model by introducing an external system input, and the ability to represent system inputs and responses as probabilistic quantities. In this paper, we develop semi-intrusive and non-intrusive approaches to extend the model error estimation to black-box models. The semi-intrusive method does not need access to the governing equations, but requires the ability to interrupt the simulation at any time instant, update the system responses at that moment, and resume the simulation using the updated value of system responses. For situations where this is not permitted, we propose a fully non-intrusive approach where, a surrogate model is constructed and used in the Bayesian state estimation procedure. The proposed approaches are illustrated with three examples: structural deformation of a Timoshenko beam, heat transfer across a rigid bar, and deformation of a Timoshenko beam subjected to heating.

Balanced Sex Ratios and the Autism Continuum

SEE CORRESPONDING ARTICLE ON PAGE 654 A Data-Driven Approach in an Unbiased Sample Reveals Equivalent Sex Ratio of Autism Spectrum Disorder–Associated Impairment in Early ChildhoodBiological PsychiatryVol. 92Issue 8PreviewSex differences in the prevalence of neurodevelopmental disorders are particularly evident in autism spectrum disorder (ASD). Heterogeneous symptom presentation and the potential of measurement bias hinder early ASD detection in females and may contribute to discrepant prevalence estimates. We examined trajectories of social communication (SC) and restricted and repetitive behaviors (RRBs) in a sample of infant siblings of children with ASD, adjusting for age- and sex-based measurement bias. We hypothesized that leveraging a prospective elevated familial likelihood sample, deriving data-driven behavioral constructs, and accounting for measurement bias would reveal less discrepant sex ratios than are typically seen in ASD. Full-Text PDF

A Data-Driven Approach in an Unbiased Sample Reveals Equivalent Sex Ratio of Autism Spectrum Disorder–Associated Impairment in Early Childhood

BackgroundSex differences in the prevalence of neurodevelopmental disorders are particularly evident in autism spectrum disorder (ASD). Heterogeneous symptom presentation and the potential of measurement bias hinder early ASD detection in females and may contribute to discrepant prevalence estimates. We examined trajectories of social communication (SC) and restricted and repetitive behaviors (RRBs) in a sample of infant siblings of children with ASD, adjusting for age- and sex-based measurement bias. We hypothesized that leveraging a prospective elevated familial likelihood sample, deriving data-driven behavioral constructs, and accounting for measurement bias would reveal less discrepant sex ratios than are typically seen in ASD. MethodsWe conducted direct assessments of ASD symptoms at 6 to 9, 12 to 15, 24, and 36 to 60 months of age (total nobservations = 1254) with infant siblings of children with ASD (n = 377) and a lower ASD-familial-likelihood comparison group (n = 168; nobservations = 527). We established measurement invariance across age and sex for separate models of SC and RRB. We then conducted latent class growth mixture modeling with the longitudinal data and evaluated for sex differences in trajectory membership. ResultsWe identified 2 latent classes in the SC and RRB models with equal sex ratios in the high-concern cluster for both SC and RRB. Sex differences were also observed in the SC high-concern cluster, indicating that girls classified as having elevated social concerns demonstrated milder symptoms than boys in this group. ConclusionsThis novel approach for characterizing ASD symptom progression highlights the utility of assessing and adjusting for sex-related measurement bias and identifying sex-specific patterns of symptom emergence.

Let's Talk About Sex: Improving Measurement of Contraceptive Use in Cross-Sectional Surveys by Accounting for Sexual Activity Recency.

Standard measures of contraceptive prevalence have known biases given that they do not reflect sexual activity and true exposure to the risk of an unintended pregnancy. In this study, we aim to understand the extent to which women protect themselves against unintended pregnancy, taking into account exposure to sex, by examining contraceptive use patterns by marital status and recent sexual activity. Data come from population-based surveys of reproductive age women in 9 low-resource settings. We estimated contraceptive prevalence using (1) the standard current contraceptive use measure, (2) a new measure of contraceptive use at last sex, and (3) a comprehensive measure that combines current use and use at last sex. Analyses are stratified by site and examine patterns by marital status only, and by both marital status and sexual activity separately. We then examined method mix by each contraceptive measure. Study findings reveal distinct patterns in contraceptive use in relation to marital status and sexual recency across sites. Overall, married women tended to report higher levels of current contraceptive use compared to use at last sex, whereas unmarried women reported higher levels of contraceptive use at last sex. When examining these measures by sexual activity and marital status, results indicate lower levels of contraceptive use among women who had not had sex in the month prior to the survey, for both married and unmarried women. The comprehensive measure of contraceptive use yielded the highest estimates, by design. Method mix varied consistently by contraceptive measure, with current use tending to capture more permanent and long-acting methods and use at last sex more likely to capture short-acting and coital-dependent methods. These findings have important implications for how the family planning field evaluates unintended pregnancy risk and unmet need for contraception within low-resource settings, given different estimates yield discrepant estimates for who is "at risk."

Abstract P1-02-10: Breast tumor measurement after neoadjuvant chemotherapy using the 8th edition of the American Joint Committee on cancer staging system: Impact on estimates of tumor size and discrepancies with residual cancer burden class

Abstract Background: In the 8th Edition of the American Joint Committee on Cancer Staging System (yAJCC) for breast cancer after neoadjuvant chemotherapy (NAC), tumor size is based on the largest focus of residual tumor, excluding treatment-related fibrosis. The goal of this study was to assess the impact of strict adherence to yAJCC criteria on residual breast tumor size and discrepancies between yAJCC stage and Residual Cancer Burden (RCB) class. Design: Breast cancer resections after NAC from 2016-2020 were identified in the Anatomic Pathology laboratory information system. Stage IV, pT4b, pT4d, pCR, neoadjuvant endocrine therapy alone and no response to NAC were exclusion criteria. Tumor size, ypT category and focality were reassessed using current yAJCC criteria and compared to the original reports. A median of 29 slides per case were reviewed (range, 9-113). Results: A total of 189 cases met inclusion criteria, including 58 (31%) TNBCs, 50 (26%) HER2+ and 81 (43%) hormone receptor-positive/HER2-negative (HR+/HER2-). The reassessed tumor size and ypT category differed from the original report in 113 (60%) and 107 (57%) cases, respectively. In all of these cases the tumor size or ypT category was smaller/lower on review. The 107 cases with lower ypT categories included 41% of TNBCs, 62% of HER2+ and 64% of HR+/HER2- (p=0.02) In the original report, 36 (19%) cases were classified as multifocal versus 131 (69%) after review (p=0.00001). yAJCC staging based on the reassessed ypT categories differed from the original report in 38 (20%) cases (all down-staged). There were 77/189 (41%) cases with yAJCC/RCB Class discrepancies in the original reports versus 95/189 (50%) after slide review (p=0.08). The yAJCC/RCB discrepant cases in the original reports included 47% of TNBCs, 38% of HER2+ and 38% of HR+/HER2- versus 47% of TNBCs, 48% of HER2+ and 54% of HR+/HER2- after review (p=0.62). The higher proportion of discrepancies among HR+/HER2- after review did not reach statistical significance (p=0.06). Conclusions: Strict adherence to 8th Ed. yAJCC criteria for measurement of the residual breast tumor after NAC resulted in smaller tumor size, lower ypT category, lower yAJCC stage and more tumors classified as multifocal. Changes in ypT category occurred more often among HER2+ and HR+/HER2- than TNBC. There was no significant change in the frequency of yAJCC/RCB discrepancies. Citation Format: Dawn Harter, Siobhan M O'Connor, Johann D Hertel, Benjamin C Calhoun. Breast tumor measurement after neoadjuvant chemotherapy using the 8th edition of the American Joint Committee on cancer staging system: Impact on estimates of tumor size and discrepancies with residual cancer burden class [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-02-10.