Annals of Applied Statistics最新文献

{"title":"Small Area Estimation of Education Levels in Low- and Middle-Income Countries.","authors":"Yunhan Wu, Ameer Dharamshi, Jon Wakefield","doi":"10.1214/25-aoas2135","DOIUrl":"10.1214/25-aoas2135","url":null,"abstract":"<p><p>Education is a key driver of social and economic mobility, yet disparities in attainment persist, particularly in low- and middle-income countries (LMICs). Existing indicators, such as mean years of schooling for adults aged 25 and older (MYS25) and expected years of schooling (EYS), offer a snapshot of an educational system, but lack either cohort-specific or temporal granularity. To address these limitations, we introduce the ultimate years of schooling (UYS)-a birth cohort-based metric targeting the final educational attainment of any individual cohort, including those with ongoing schooling trajectories. As with many attainment indicators, we propose to estimate UYS with cross-sectional household surveys. However, for younger cohorts, estimation fails, because these individuals are right-censored leading to severe downwards bias. To correct for this, we propose to re-frame educational attainment as a time-to-event process and deploy discrete-time survival models that explicitly account for censoring in the observations. At the national level, we estimate the parameters of the model using survey-weighted logistic regression, while for finer spatial resolutions, where sample sizes are smaller, we embed the discrete-time survival model within a Bayesian spatiotemporal framework to improve stability and precision. Applying our proposed methods to data from the 2022 Tanzania Demographic and Health Surveys, we estimate female educational trajectories corrected for censoring biases, and reveal substantial subnational disparities. By providing a dynamic, bias-corrected, and spatially disaggregated measure, our approach enhances education monitoring; it equips policymakers and researchers with a more precise tool for monitoring current progress towards education goals, and for designing future targeted policy interventions in LMICs.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"20 1","pages":"833-855"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13090004/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TEMPORAL MODELS FOR ESTIMATION AND SHORT-TERM FORECASTING OF NEONATAL MORTALITY RATES IN SUB-SAHARAN AFRICA.","authors":"Katherine R Paulson, Geir-Arne Fuglstad, Zehang Richard Li, Jonathan Wakefield","doi":"10.1214/25-aoas2100","DOIUrl":"10.1214/25-aoas2100","url":null,"abstract":"<p><p>Accurate estimation and forecasts for neonatal mortality rates (NMRs) in low- and middle-income countries is an urgent problem. Much of child mortality is preventable, and understanding temporal trends is of great interest when evaluating past performance and planning future policy or programming. In countries without robust vital registration, we rely on modeled estimates based on survey data to understand trends. A toolkit of compelling temporal models exists, but these methods have not been comprehensively evaluated for their application for the estimation of the NMR in low- and middle-income countries using household survey data. Using Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) data from 41 countries in sub-Saharan Africa, we estimate and forecast the national-level NMR for 1970-2030 separately with random walk, auto-regressive, penalized spline, natural spline, and logit-linear latent temporal models. We examine the statistical behavior of these temporal models with both an out-of-sample analysis using the DHS and MICS data and a simulation study. We find that the second-order random walk and the penalized spline have the least bias, and short-term forecasts from the penalized spline tend to have narrower intervals with better out-of-sample performance. From the analysis of the NMR in sub-Saharan Africa, we estimate that 6 or fewer of the 41 countries included are on track to achieve the Sustainable Development Goals target of 12 neonatal deaths per 1000 live births by 2030.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"20 1","pages":"322-345"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13096879/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147788192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BRIDGING THE GAP: ENHANCING THE GENERALIZABILITY OF EPIGENETIC CLOCKS THROUGH TRANSFER LEARNING.","authors":"Lan Luo, Lulu Shang, Jaclyn M Goodrich, Karen E Peterson, Peter X K Song","doi":"10.1214/26-aoas2136","DOIUrl":"10.1214/26-aoas2136","url":null,"abstract":"<p><p>Changes in DNA methylation patterns exhibit a high correlation with chronological age. Epigenetic clocks, developed through statistical models that estimate epigenetic age using the methylation levels of cytosine-guanine dinucleotide (CpG) sites, have emerged as powerful tools for understanding aging and age-related diseases. Despite their popularity, the generalizability of these clocks across diverse populations remains a challenge. Some of the widely used epigenetic clocks, such as Horvath's clock (<i>Genome Biol</i>. <b>14</b> (2013) 1-20) and the PedBE clock (<i>Proc. Natl. Acad. Sci. USA</i> <b>117</b> (2020) 23329-23335), are shown to perform poorly in our target cohort. This loss of prediction accuracy raises concerns about their viability in calculating biological age in distinct demographic and ethnic groups. Technically, the feature space of existing clocks is yielded with an obsolete technique, potentially leading to systematic bias in the analysis of all target data generated by the EPIC 850K array. To address both population heterogeneity and technological advances, we adopt a transfer learning framework to calibrate existing epigenetic clocks by borrowing shared knowledge from diverse datasets. Furthermore, our transfer learning is built on kriging- and DNN-based methods for feature adaptation, to close the gap between existing clocks and our target data. We analyze data collected from 523 blood samples from a cohort of children and adolescents in the Early Life Exposure in Mexico to Environmental Toxicants (ELEMENT) study and show that our proposed transfer learning methods significantly improve prediction performance compared to existing clocks. Performance is further enhanced by using the CpG sites profiled on the higher-resolution EPIC array. More importantly, calibrated clocks produce epigenetic age accelerations that correlate better with stages of sexual maturation. Our methodology demonstrates the potential to bridge the gap between different DNA methylation datasets and various profiling platforms, thereby enhancing the applicability of epigenetic clocks across diverse population groups and contributing to more accurate aging research.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"20 1","pages":"68-89"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13299310/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148347149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"JOINT MODELING FOR LEARNING DECISION-MAKING DYNAMICS IN BEHAVIORAL EXPERIMENTS.","authors":"Yuan Bian, Xingche Guo, Yuanjia Wang","doi":"10.1214/25-aoas2112","DOIUrl":"10.1214/25-aoas2112","url":null,"abstract":"<p><p>Major depressive disorder (MDD), a leading cause of disability and mortality, is associated with reward-processing abnormalities and concentration issues. Motivated by the probabilistic reward task from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, we propose a novel framework that integrates the reinforcement learning (RL) model and drift-diffusion model (DDM) to jointly analyze reward-based decision-making with response times. To account for emerging evidence suggesting that decision-making may alternate between multiple interleaved strategies, we model latent state switching using a hidden Markov model (HMM). In the engaged state, decisions follow an RL-DDM, simultaneously capturing reward processing, decision dynamics, and temporal structure. In contrast, in the lapsed state, decision-making is modeled using a simplified DDM, where specific parameters are fixed to approximate random guessing with equal probability. The proposed method is implemented using a computationally efficient generalized expectation-maximization (EM) algorithm with forward-backward procedures. Through extensive numerical studies, we demonstrate that our proposed method outperforms competing approaches across various reward-generating distributions, under both strategy-switching and non-switching scenarios, as well as in the presence of input perturbations. When applied to the EMBARC study, our framework reveals that MDD patients exhibit lower overall engagement than healthy controls and experience longer responses when they do engage. Additionally, we show that neuroimaging measures of brain activities are associated with decision-making characteristics in the engaged state but not in the lapsed state, providing evidence of brain-behavior association specific to the engaged state.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 4","pages":"3372-3393"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12814034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146012947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MULTI-OBJECT DATA INTEGRATION IN THE STUDY OF PRIMARY PROGRESSIVE APHASIA.","authors":"Rene Gutierrez, Aaron Scheffler, Rajarshi Guhaniyogi, Maria Luisa Gorno-Tempini, Maria Luisa Mandelli, Giovanni Battistella","doi":"10.1214/25-aoas2071","DOIUrl":"10.1214/25-aoas2071","url":null,"abstract":"<p><p>This article focuses on a multi-modal imaging data application where structural/anatomical information from gray matter (GM) and brain connectivity information in the form of a brain connectome network from functional magnetic resonance imaging (fMRI) are available for a number of subjects with different degrees of primary progressive aphasia (PPA), a neurodegenerative disorder (ND) measured through a speech rate measure on motor speech loss. The clinical/scientific goal in this study becomes the identification of brain regions of interest significantly related to the speech rate measure to gain insight into ND patterns. Viewing the brain connectome network and GM images as objects, we develop an integrated object response regression framework of network and GM images on the speech rate measure. A novel integrated prior formulation is proposed on network and structural image coefficients in order to exploit network information of the brain connectome while leveraging the interconnections among the two objects. The principled Bayesian framework allows the characterization of uncertainty in ascertaining a region being actively related to the speech rate measure. Our framework yields new insights into the relationship of brain regions associated with PPA, offering a deeper understanding of neuro-degenerative patterns of PPA. The supplementary file adds details about posterior computation and additional empirical results.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 4","pages":"3282-3303"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12707422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145776604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PERSONALIZED RISK PREDICTION FOR CANCER SURVIVORS: A GENERALIZED BAYESIAN SEMI-PARAMETRIC MODEL OF RECURRENT EVENTS WITH COMPETING OUTCOMES.","authors":"Nam Hoai Nguyen, Seung Jun Shin, Elissa Dodd-Eaton, Jing Ning, Wenyi Wang","doi":"10.1214/25-AOAS2083","DOIUrl":"10.1214/25-AOAS2083","url":null,"abstract":"<p><p>Multiple primary cancers are increasingly more frequent due to improved survival of cancer patients. Characteristics of the first primary cancer largely impact the risk of developing subsequent primary cancers. Hence, model-based risk characterization of cancer survivors that captures patient-specific variables is needed for healthcare policy making. We propose a Bayesian semi-parametric framework, where the occurrence processes of the competing cancer types follow independent non-homogeneous Poisson processes and adjust for covariates including the type and age at diagnosis of the first primary. Applying this framework to a historically collected cohort with families presenting a highly enriched history of multiple primary tumors and diverse cancer types, we have derived a suite of age-to-onset penetrance curves for cancer survivors. This includes penetrance estimates for second primary lung cancer, potentially impactful to ongoing cancer screening decisions. Using Receiver Operating Characteristic (ROC) curves, we have validated the good predictive performance of our models in predicting second primary lung cancer, sarcoma, breast cancer, and all other cancers combined, with areas under the curves (AUCs) at 0.89, 0.91, 0.76 and 0.68, respectively. In conclusion, our framework provides covariate-adjusted quantitative risk assessment for cancer survivors, hence moving a step closer to personalized health management for this unique population.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 4","pages":"3091-3112"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12955820/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147357368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SUPERVISED LEARNING OF OUTCOME-RELEVANT ITEMS FROM A QUESTIONNAIRE VIA MIXED INTEGER OPTIMIZATION.","authors":"Leyao Zhang, Wen Wang, Mengtong Hu, Alan P Baptist, Peng Wang, Peter X K Song","doi":"10.1214/25-AOAS2093","DOIUrl":"10.1214/25-AOAS2093","url":null,"abstract":"<p><p>Questionnaires are among the oldest and most widely used instruments in practice to measure variables relevant to traits of interest that cannot be easily measured by physical devices, for example, depression. In many clinical settings, the scope of an existing questionnaire is often unfit to apply to a new study population, whose underlying characteristics are different from those of the original population used for the questionnaire's development and/or validation. Motivated by a cohort study of elderly asthma patients, we aim to examine associations between clinical outcomes and quality of life (QoL) measured by a QoL questionnaire. To increase comparability, we consider a supervised learning method to identify a subset of questions whose summary score is strongly associated with a specific clinical outcome under investigation. The resultant set of selected items gives an optimal summary metric of the questionnaire, which improves both statistical power and clinical interpretation. Our item extraction procedure is built upon the best subset algorithm implemented by a mixed integer programming, which enjoys both theoretical guarantee of selection consistency and flexibility of handling nonresponse missing data. Moreover, estimation uncertainty is analyzed by the means of noise perturbation. Our methodology is first evaluated by extensive simulation studies with comparisons to existing methods and then applied to derive tailored QoL scores adaptive to two clinical outcomes of lung function measure (FEV1) and asthma control test (ACT), respectively, among elderly people with persistent asthma.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 4","pages":"3157-3178"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12869357/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146127254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH.","authors":"Mingcong Wu, Yang Li, Shuangge Ma, Mengyun Wu","doi":"10.1214/25-aoas2097","DOIUrl":"10.1214/25-aoas2097","url":null,"abstract":"<p><p>Identifying genes that display spatial patterns is critical to investigating expression interactions within a spatial context and further dissecting biological understanding of complex mechanistic functionality. Despite the increase in statistical methods designed to identify spatially variable genes, they are mostly based on marginal analysis and share the limitation that the dependence (network) structures among genes are not well accommodated, where a biological process usually involves changes in multiple genes that interact in a complex network. In addition, the latent cellular composition within the spots can introduce confounding variations, negatively affecting the accuracy of the identification. In this study we develop a novel Bayesian regularization approach for spatial transcriptomic data, with confounding variations induced by varying cellular distributions effectively corrected. Significantly advancing from existing studies, a thresholded graph Laplacian regularization is proposed to simultaneously identify spatially variable genes and accommodate the network structure among genes. The proposed method is based on a zero-inflated negative binomial distribution, effectively accommodating the count nature, zero inflation, and overdispersion of spatial transcriptomic data. Extensive simulations and applications to real data demonstrate the competitive performance of the proposed method.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 4","pages":"2705-2723"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13170318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147943565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TREE-REGULARIZED BAYESIAN LATENT CLASS ANALYSIS FOR IMPROVING WEAKLY SEPARATED DIETARY PATTERN SUBTYPING IN SMALL-SIZED SUBPOPULATIONS.","authors":"By Mengbing Li, Briana Stephenson, Zhenke Wu","doi":"10.1214/25-aoas2067","DOIUrl":"10.1214/25-aoas2067","url":null,"abstract":"<p><p>Dietary patterns synthesize multiple related diet components, which can be used by nutrition researchers to examine diet-disease relationships. Latent class models (LCMs) have been used to derive dietary patterns from dietary intake assessment, where each class profile represents the probabilities of exposure to a set of diet components. However, LCM-derived dietary patterns can exhibit strong similarities, or weak separation, resulting in numerical and inferential instabilities that challenge scientific interpretation. This issue is exacerbated in small-sized subpopulations. To address these issues, we provide a simple solution that empowers LCMs to improve dietary pattern estimation. We develop a tree-regularized Bayesian LCM that shares statistical strength between dietary patterns to make better estimates using limited data. This is achieved via a Dirichlet diffusion tree process that specifies a prior distribution for the unknown tree over classes. Dietary patterns that share proximity to one another in the tree are shrunk toward ancestral dietary patterns a priori, with the degree of shrinkage varying across prespecified food groups. Using dietary intake data from the Hispanic Community Health Study/Study of Latinos, we apply the proposed approach to a sample of 496 U.S. adults of South American ethnic background to identify and compare dietary patterns.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 4","pages":"3003-3022"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12867110/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146121126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FAST VARIABLE SELECTION FOR DISTRIBUTIONAL REGRESSION WITH APPLICATION TO CONTINUOUS GLUCOSE MONITORING DATA.","authors":"Alexander Coulter, Rashmi N Aurora, Naresh M Punjabi, Irina Gaynanova","doi":"10.1214/25-aoas2038","DOIUrl":"10.1214/25-aoas2038","url":null,"abstract":"<p><p>With the growing prevalence of diabetes and the associated public health burden, it is crucial to identify modifiable factors that could improve patients' glycemic control. In this work, we seek to examine associations between medication usage, concurrent comorbidities, and glycemic control, utilizing data from continuous glucose monitors (CGMs). CGMs provide high-frequency interstitial glucose measurements, but reducing data to simple statistical summaries is common in clinical studies, resulting in substantial information loss. Recent advancements in the Fréchet regression framework allow to utilize more information by treating the full distributional representation of CGM data as the response, while sparsity regularization enables variable selection. However, the methodology does not scale to large datasets. Crucially, rigorous inference is not possible because the asymptotic behavior of the underlying estimates is unknown, while the application of resampling-based inference methods is computationally infeasible. We develop a new algorithm for sparse distributional regression by deriving a new explicit characterization of the gradient and Hessian of the underlying objective function, while also utilizing rotations on the sphere to perform feasible updates. The updated method is up to 10000+ fold faster than the original approach, opening the door for applying sparse distributional regression to large-scale datasets and enabling previously unattainable resampling-based inference. We combine our algorithm with stability selection to perform variable selection inference on CGM data from patients with type 2 diabetes and obstructive sleep apnea. We find a significant association between sulfonylurea medication and glucose variability without evidence of association with glucose mean. We also find that overnight oxygen desaturation variability has a stronger association with glucose regulation than overall oxygen desaturation levels.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 3","pages":"2105-2128"},"PeriodicalIF":1.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12700301/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145758247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}