Elastic Net Approach Research Articles

Age, anticoagulants, hypertension and cardiovascular genetic traits predict cranial ischaemic complications in patients with giant cell arteritis

ObjectivesThis project aimed to determine whether cranial ischaemic complications at the presentation of giant cell arteritis (GCA) were associated with pre-existing cardiovascular (CV) risk factors, CV disease or genetic risk...

Enhancing personalized gene expression prediction from DNA sequences using genomic foundation models

Artificial intelligence/deep learning (AI/DL) models that predict molecular phenotypes like gene expression directly from DNA sequences have recently emerged. While these models have proven effective at capturing the variation across genes, their ability to explain inter-individual differences has been limited. We hypothesize that the performance gap can be narrowed through the use of pre-trained embeddings from the Nucleotide Transformer, a large foundation model trained on 3,000+ genomes. We train a transformer model using the pre-trained embeddings and compare its predictive performance to Enformer, the current state-of-the-art model, using genotype and expression data from 290 individuals. Our model significantly outperforms Enformer in terms of correlation across individuals and narrows the performance gap with an elastic net regression approach using just the genetic variants as predictors. Although simple regression models have their advantages in personalized prediction tasks, DL approaches based on foundation models pre-trained on diverse genomes have unique strengths in flexibility and interpretability. With further methodological and computational improvements with more training data, these models may someday predict molecular phenotypes from DNA sequences with accuracy surpassing that of regression-based approaches. Our work demonstrates the potential for large pre-trained AI/DL models to advance functional genomics.

Sensor-Based Multifaceted Feature Extraction and Ensemble Elastic Net Approach for Assessing Fall Risk in Community-Dwelling Older Adults.

Accurate identification of community-dwelling older adults at high fall risk can facilitate timely intervention and significantly reduce fall incidents. Analyzing gait and balance capabilities via feature extraction and modeling through sensor-based motion data has emerged as a viable approach for fall risk assessment. However, the existing approaches for extracting key features related to fall risk lack inclusiveness, with limited consideration of the non-linear characteristics of sensor signals, such as signal complexity, self-similarity, and local stability. In this study, we developed a multifaceted feature extraction scheme employing diverse feature types, including demographic, descriptive statistical, non-linear, spatiotemporal and spectral features, derived from three-axis accelerometers and gyroscope data. This study is the first attempt to investigate non-linear features related to fall risk in multi-task scenarios from a dynamic system perspective. Based on the extracted multifaceted features, we propose an ensemble elastic net (E-E-N) approach for handling imbalanced data and offering high model interpretability. The E-E-N utilizes bootstrap sampling to construct base classifiers and employs a weighting mechanism to aggregate the base classifiers. We conducted a set of validation experiments using real-world data for comprehensive comparative analysis. The results demonstrate that the E-E-N approach exhibits superior predictive performance on fall risk classification. Our proposed approach offers a cost-effective tool for accurately assessing fall risk and alleviating the burden of continuous health monitoring in the long term.

Factors associated with lower quarter performance-based balance and strength tests: a cross-sectional analysis from the project baseline health study.

Physical performance tests are predictive of mortality and may screen for certain health conditions (e.g., sarcopenia); however, their diagnostic and/or prognostic value has primarily been studied in age-limited or disease-specific cohorts. Our objective was to identify the most salient characteristics associated with three lower quarter balance and strength tests in a cohort of community-dwelling adults. We applied a stacked elastic net approach on detailed data on sociodemographic, health and health-related behaviors, and biomarker data from the first visit of the Project Baseline Health Study (N = 2,502) to determine which variables were most associated with three physical performance measures: single-legged balance test (SLBT), sitting-rising test (SRT), and 30-second chair-stand test (30CST). Analyses were stratified by age (<65 and ≥65). Female sex, Black or African American race, lower educational attainment, and health conditions such as non-alcoholic fatty liver disease and cardiovascular conditions (e.g., hypertension) were consistently associated with worse performance across all three tests. Several other health conditions were associated with either better or worse test performance, depending on age group and test. C-reactive protein was the only laboratory value associated with performance across age and test groups with some consistency. Our results highlighted previously identified and several novel salient factors associated with performance on the SLBT, SRT, and 30CST. These tests could represent affordable, noninvasive biomarkers of prevalent and/or future disease in adult individuals; future research should validate these findings. ClinicalTrials.gov, identifier NCT03154346, registered on May 15, 2017.

Predicting State-Level Firearm Suicide Rates: A Machine Learning Approach Using Public Policy Data

IntroductionOver 40,000 people die by suicide annually in the U.S., and firearms are the most lethal suicide method. There is limited evidence on the effectiveness of many state-level policies on reducing firearm suicide. The objective of this study was to identify public policies that best predict state-level firearm suicide rates. MethodsData from the Centers for Disease Control and Prevention's WONDER system and the State Firearm Law Database, a longitudinal catalog of 134 firearm safety laws, were analyzed. The analysis included 1,450 observations from 50 states spanning 1991-2019. An ElasticNet regression technique was used to analyze the relationship between the policy variables and firearm suicide rates. Nested cross-validation was performed to tune the model hyperparameters. The study data were collected and analyzed in 2023 and 2024. ResultsThe optimized ElasticNet approach had a mean MSE of 2.07, which was superior to non-regularized and dummy regressor models. The most influential policies for predicting the firearm suicide rate on average included laws requiring firearm dealers that sell handguns to have a state license and laws requiring individuals to obtain a permit to purchase a firearm through an approval process that includes law enforcement, among others. ConclusionsOn average, firearm suicide rates were lower in state-years that had each influential policy active. Notably, these analyses were ecological and non-causal. However, this study was able to use a supervised machine learning approach with inherent feature selection and many policy types to make predictions using unseen data (i.e., balancing Lasso and Ridge regularization penalties).

Combining genetic markers, on-farm information and infrared data for the in-line prediction of blood biomarkers of metabolic disorders in Holstein cattle

BackgroundVarious blood metabolites are known to be useful indicators of health status in dairy cattle, but their routine assessment is time-consuming, expensive, and stressful for the cows at the herd level. Thus, we evaluated the effectiveness of combining in-line near infrared (NIR) milk spectra with on-farm (days in milk [DIM] and parity) and genetic markers for predicting blood metabolites in Holstein cattle. Data were obtained from 388 Holstein cows from a farm with an AfiLab system. NIR spectra, on-farm information, and single nucleotide polymorphisms (SNP) markers were blended to develop calibration equations for blood metabolites using the elastic net (ENet) approach, considering 3 models: (1) Model 1 (M1) including only NIR information, (2) Model 2 (M2) with both NIR and on-farm information, and (3) Model 3 (M3) combining NIR, on-farm and genomic information. Dimension reduction was considered for M3 by preselecting SNP markers from genome-wide association study (GWAS) results.ResultsResults indicate that M2 improved the predictive ability by an average of 19% for energy-related metabolites (glucose, cholesterol, NEFA, BHB, urea, and creatinine), 20% for liver function/hepatic damage, 7% for inflammation/innate immunity, 24% for oxidative stress metabolites, and 23% for minerals compared to M1. Meanwhile, M3 further enhanced the predictive ability by 34% for energy-related metabolites, 32% for liver function/hepatic damage, 22% for inflammation/innate immunity, 42.1% for oxidative stress metabolites, and 41% for minerals, compared to M1. We found improved predictive ability of M3 using selected SNP markers from GWAS results using a threshold of > 2.0 by 5% for energy-related metabolites, 9% for liver function/hepatic damage, 8% for inflammation/innate immunity, 22% for oxidative stress metabolites, and 9% for minerals. Slight reductions were observed for phosphorus (2%), ferric-reducing antioxidant power (1%), and glucose (3%). Furthermore, it was found that prediction accuracies are influenced by using more restrictive thresholds (−log10(P-value) > 2.5 and 3.0), with a lower increase in the predictive ability.ConclusionOur results highlighted the potential of combining several sources of information, such as genetic markers, on-farm information, and in-line NIR infrared data improves the predictive ability of blood metabolites in dairy cattle, representing an effective strategy for large-scale in-line health monitoring in commercial herds.

Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort

Machine learning models are increasingly being used to estimate “brain age” from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic net regression approach, and then, we investigate its associations with mortality, cognitive status, and measures of health and disease in participants from Atherosclerosis Risk in Communities (ARIC) study who had a brain MRI at visit 5 of the study. Finally, we used the SOMAscan assay containing 4877 proteins to examine the proteomic associations with the MRI-defined BAG. Among N = 1849 participants (age, 76.4 (SD 5.6)), we found that increased values of BAG were strongly associated with increased mortality and increased severity of the cognitive status. Strong associations with mortality persisted when the analyses were performed in cognitively normal participants. In addition, it was strongly associated with BMI, diabetes, measures of physical function, hypertension, prevalent heart disease, and stroke. Finally, we found 33 proteins associated with BAG after a correction for multiple comparisons. The top proteins with positive associations to brain age were growth/differentiation factor 15 (GDF-15), Sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SEVP 1), matrilysin (MMP7), ADAMTS-like protein 2 (ADAMTS), and heat shock 70 kDa protein 1B (HSPA1B) while EGF-receptor (EGFR), mast/stem-cell-growth-factor-receptor (KIT), coagulation-factor-VII, and cGMP-dependent-protein-kinase-1 (PRKG1) were negatively associated to brain age. Several of these proteins were previously associated with dementia in ARIC. These results suggest that circulating proteins implicated in biological aging, cellular senescence, angiogenesis, and coagulation are associated with a neuroimaging measure of brain aging.

An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence.

This study aimed to establish and validate a prognostic model based on immune-related genes (IRGPM) for predicting disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy, and to elucidate the immune profiles associated with different prognostic outcomes. Transcriptomic and clinical data were sourced from the Gene Expression Omnibus (GEO) database and the West China Hospital database. We focused on genes from the RNA immune-oncology panel. The elastic net approach was employed to pinpoint immune-related genes significantly impacting DFS. We developed the IRGPM for rectal cancer using the random forest technique. Based on the IRGPM, we calculated prognostic risk scores to categorize patients into high-risk and low-risk groups. Comparative analysis of immune characteristics between these groups was conducted. In this study, 407 LARC samples were analyzed. The elastic net identified a signature of 20 immune-related genes, forming the basis of the IRGPM. Kaplan-Meier survival analysis revealed a lower 5-year DFS in the high-risk group compared to the low-risk group. The receiver operating characteristic (ROC) curve affirmed the model's robust predictive capability. Validation of the model was performed in the GSE190826 cohort and our institution's cohort. Gene expression differences between high-risk and low-risk groups predominantly related to cytokine-cytokine receptor interactions. Notably, the low-risk group exhibited higher immune scores. Further analysis indicated a greater presence of activated B cells, activated CD8 T cells, central memory CD8 T cells, macrophages, T follicular helper cells, and type 2 helper cells in the low-risk group. Additionally, immune checkpoint analysis revealed elevated PDCD1 expression in the low-risk group. The IRGPM, developed through random forest and elastic net methodologies, demonstrates potential in distinguishing DFS among LARC patients receiving standard treatment. Notably, the low-risk group, as defined by the IRGPM, showed enhanced activation of adaptive immune responses within the tumor microenvironment.

LINKING PLASMA PROTEOMICS TO BRAIN ACCELERATED/RESILIENT AGING IN THE ATHEROSCLEROSIS RISK IN COMMUNITY STUDY

Abstract Machine learning models are increasingly being used to estimate ‘brain age’ from neuroimaging data. The gap between chronological age and the estimated brain age (BAG) is potentially a measure of accelerated/resilient brain aging, and we estimated BAG based on an elastic net regression approach. Here, we report associations between this brain age measure and plasma protein levels (measured using an aptamer-based proteomic platform) in the Atherosclerosis Risk in Communities (ARIC) Study to determine whether BAG was associated with proteins linked to biologic aging. We used brain MRI scans from 1507 ARIC visit 5 participants: 938 were Cognitively Normal (CN), 495 had mild cognitive impairment (MCI) and 74 had dementia. The proteomic dataset contains 4877 plasma proteins in these same individuals. We fitted univariate linear regression models adjusting for age, race, smoking, education, sex, diabetes, hypertension and intra-cranial volume. A Bonferroni correction was applied to account for multiple testing to maintain an analysis-wise α=0.05. In total 31 proteins were significantly associated with BAG. Among these SEVP1, GDF-15, pleiotrophin, MMP7, Natriuretic-peptides and Hsp70 were found to be associated with accelerated BAG, whereas EGF-receptor, mast/stem-cell-growth-factor-receptor (KIT), Coagulation-factor-VII and cGMP-dependent-protein-kinase-1 were associated to resilient BAG. The analysis based only on CN participants produced 3 significant proteins: retinoblastoma-2 and SEVP1, which were associated with accelerated BAG and Coagulation-factor-VII, which was associated to resilient BAG. These results suggest circulating proteins implicated in biological aging, cellular senescence, angiogenesis and coagulation are associated with a neuroimaging measure of accelerated brain aging.

Developing a DNAm Surrogate to Predict Cognitive Functioning among Older Adults

AbstractBackgroundBlood‐based epigenetic measures are being developed and used to predict aging‐related health outcomes. Specifically, researchers have developed “epigenetic clocks” based on predictions of chronological age and age‐related health declines by DNA methylation. However, these markers are meant to predict aging per se and are not disease specific. Recently, scholars have developed “DNAm surrogates” to predict specific outcomes. This methodological technique has yet to be applied to cognitive functioning despite its potential to improve prediction of cognitive health in later life and provide insight into the role of biological aging.MethodThis study developed a novel DNAm surrogate measure for cognitive functioning (DNAmCogScore) based on an elastic net approach linking using 833,865 CpG sites predicting the Telephone Interview Cognitive Status score (TICS) available in the Health and Retirement Study (HRS), a nationally representative sample of older adults in the United States. Additionally, we evaluate the association of this DNAm surrogate and other neuropathology biomarkers. We used used ridge regression to create DNAmCogScore with 50% of the sample for training and 50% for testing (N = 2009 for each subsample). The Irish Longitudinal Study of Aging (TILDA) will be used for validation (results forthcoming).ResultWe find a positive association and modest predictive value between DNAmCogScore and TICS (see Figure 1, r = .4). Table 1 shows the strong associations with the DNAmCogScore and demographic characteristics. Overall, we find a strong positive coefficient between DNAmCogScore with attenuation after controlling for demographic characteristics, from 1.12 in Model 1 to .20 in Model 3, but remaining statistically significant throughout. Table 3 shows modest negative correlations between DNAmCogScore and NfL, pTau181, and GFAP (ranging from ‐.24 to ‐.36) and no correlation with AB42:40.ConclusionThis study is among the first to create a DNA methylation surrogate measure of cognitive functioning. Our measure is associated with cognitive functioning, even after adjusting for key demographic characteristics. And it has the expected association with other more neuropathology biomarkers. Overall, it suggests that changes in DNA methylation may be a molecular‐level mechanisms by which biological aging impacts cognitive health, and this measure may provide useful prediction of cognitive impairment before the onset of symptoms.

Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies

Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19.

Predicting Duodenal Cancer Risk in Patients with Familial Adenomatous Polyposis Using Machine Learning Model.

The aim of this study was to both classify data of familial adenomatous polyposis patients with and without duode- nal cancer and to identify important genes that may be related to duodenal cancer by XGboost model. The current study was performed using expression profile data from a series of duodenal samples from familial adenomatous polyposis patients to explore variations in the familial adenomatous polyposis duodenal adenoma-carcinoma sequence. The expression profiles obtained from cancerous, adenomatous, and normal tissues of 12 familial adenomatous polyposis patients with duodenal cancer and the tissues of 12 familial adenomatous polyposis patients without duodenal cancer were compared. The ElasticNet approach was utilized for the feature selection. Using 5-fold cross-validation, one of the machine learning approaches, XGboost, was utilized to classify duodenal cancer. Accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score performance metrics were assessed for model performance. According to the variable importance obtained from the modeling, ADH1C, DEFA5, CPS1, SPP1, DMBT1, VCAN-AS1, APOB genes (cancer vs. adenoma); LOC399753, APOA4, MIR548X, and ADH1C genes (adenoma vs. adenoma); SNORD123, CEACAM6, SNORD78, ANXA10, SPINK1, and CPS1 (normal vs. adenoma) genes can be used as predictive biomarkers. The proposed model used in this study shows that the aforementioned genes can forecast the risk of duodenal cancer in patients with familial adenomatous polyposis. More comprehensive analyses should be performed in the future to assess the reliability of the genes determined.

A machine learning-derived neuroanatomical pattern predicts delayed reward discounting in the Human Connectome Project Young Adult sample.

Delayed reward discounting (DRD) is defined as the extent to which person favors smaller rewards that are immediately available over larger rewards available in the future. Higher levels of DRD have been identified in individuals with a wide range of clinical disorders. Although there have been studies adopting larger samples and using only gray matter volume to characterize the neuroanatomical correlates of DRD, it is still unclear whether previously identified relationships are generalizable (out-of-sample) and how cortical thickness and cortical surface area contribute to DRD. In this study, using the Human Connectome Project Young Adult dataset (N=1038), a machine learning cross-validated elastic net regression approach was used to characterize the neuroanatomical pattern of structural magnetic resonance imaging variables associated with DRD. The results revealed a multi-region neuroanatomical pattern predicted DRD and this was robust in a held-out test set (morphometry-only R2 = 3.34%, morphometry + demographics R2 =6.96%). The neuroanatomical pattern included regions implicated in the default mode network, executive control network, and salience network. The relationship of these regions with DRD was further supported by univariate linear mixed effects modeling results, in which many of the regions identified as part of this pattern showed significant univariate associations with DRD. Taken together, these findings provide evidence that a machine learning-derived neuroanatomical pattern encompassing various theoretically relevant brain networks produces robustly predicts DRD in a large sample of healthy young adults.

Comparative Analysis of Statistical and Machine Learning Techniques for Rice Yield Forecasting for Chhattisgarh, India

Crop yield forecasting before harvesting is critical for the creation, implementation, and optimization of policies related to food safety as well as for agro-product storage and marketing. Crop growth and development are influenced by the weather. Therefore, models using weather variables can provide reliable predictions of crop yields. It can be tough to select the best crop production forecasting model. Therefore, in this study, five alternative models, viz., stepwise multiple linear regression (SMLR), an artificial neural network (ANN), the least absolute shrinkage and selection operator (LASSO), an elastic net (ELNET), and ridge regression, were compared in order to discover the best model for rice yield prediction. The outputs from individual models were used to build ensemble models using the generalized linear model (GLM), random forest (RF), cubist and ELNET methods. For the previous 21 years, historical rice yield statistics and meteorological data were collected for three districts under three separate agro-climatic zones of Chhattisgarh, viz., Raipur in the Chhattisgarh plains, Surguja in the northern hills, and Bastar in the southern plateau. The models were calibrated using 80% of these datasets, and the remaining 20% was used for the validation of models. The present study concluded that for rice crop yield forecasting, the performance of the ANN was good for the Raipur (Rcal2 = 1, Rval2= 1 and RMSEcal = 0.002, RMSEval = 0.003) and Surguja (Rcal2 = 1, Rval2= 0.99 and RMSEcal = 0.004, RMSEval = 0.214) districts as compared to the other models, whereas for Bastar, ELNET (Rcal2 = 90, Rval2= 0.48) and LASSO (Rcal2 = 93, Rval2= 0.568) performed better. The performance of the ensemble model was better compared to the individual models. For Raipur and Surguja, the performance of all the ensemble methods was comparable, whereas for Bastar, random forest (RF) performed better, with R2 = 0.85 and 0.81 for calibration and validation, respectively, as compared to the GLM, cubist, and ELNET approach.

Retracted] Heterogeneous Network‐Based Inductive Matrix Methods for Predicting Biomedical Gene Disease

Prediction of gene‐disease associations has grown in popularity in recent biomedical research. However, positive and unlabeled (PU) issues and limited gene‐disease association data are common concerns with present association prediction algorithms. A gene‐disease association prediction approach based on Katz‐enhanced inductive matrix completion is suggested in light of the abovementioned flaws. Preestimate based on the Katz technique and refined estimation based on the inductive matrix completion approach makes the model. The Katz technique is utilized to preestimate the gene‐disease association on the basis of gene‐disease heterogeneous network to mitigate the effects of association data‐sparse and PU issues. The Katz technique, however, necessarily introduces some noise when predicting gene‐disease connections due to the similarity network’s quality limitations. Therefore, the elastic net regularization approach is utilized to increase the resilience of the conventional inductive matrix completion model. As a result, the prediction effect of gene‐disease connections is increased using robustness and a better inductive matrix completion model. The experimental findings demonstrate that the proposed model has dramatically increased recall and precision compared to widely used gene‐disease association prediction approaches. It can also resolve the typical cold‐start issue in association prediction. The proposed KIMC method may consider integrating more diverse biological data sources in the future and also aid in the effective extraction of the feature data of genes and diseases with higher correlation from this biological data to improve the prediction effect.

Estimation of glomerular filtration rate in a pediatric population using non-contrast kidney phase contrast magnetic resonance imaging.

Glomerular filtration rate (GFR) is a key measure of kidney function but often inaccurately ascertained by serum creatinine and cystatin C in pediatrics. In this pilot trial, we evaluated the relationship between GFR calculated by using phase-contrast MRI (PC-MRI) biomarkers and GFR by 125I-iothalamate clearance in youth undergoing bone marrow transplantation (BMT). A total of twenty-one pediatric BMT candidates (8-21years of age) were recruited for a research kidney PC-MRI. After completion of 125I-iothalamate clearance, same-day PC-MRI measurements were completed of the kidney circulation without a gadolinium-based contrast agent. MRI included a non-contrast balanced-SSFP-triggered angiography to position ECG-gated breath-held 2D PC-MRI flow measurements (1.2 × 1.2 × 6 mm3). A multivariate model of MRI biomarkers estimating GFR (GFR-MRI) was selected using the elastic net approach. The GFR-MRI variables selected by elastic net included average heart rate during imaging (bpm), peak aorta flow below the kidney artery take-offs (ml/s), average kidney artery blood flow, average peak kidney vein blood flow, and average kidney vein blood flow (ml/s). The GFR-MRI model demonstrated strong agreement with GFR by 125I-iothalamate (R2 = 0.65), which was stronger than what was observed with eGFR by the full age spectrum and Chronic Kidney Disease in Children under 25 (CKiD U25) approaches. In this pilot study, noninvasive GFR-MRI showed strong agreement with gold standard GFR in youth scheduled for BMT. Further work is needed to evaluate whether non-contrast GFR-MRI holds promise to become a superior alternative to eGFR and GFR by clearance techniques. A higher resolution version of the Graphical abstract is available as Supplementary information.

Predictors of Economic Outcomes among Romanian Youth: The Influence of Education-An Empirical Approach Based on Elastic Net Regression.

Young people have to be provided with opportunities to access prosperous, resilient and fulfilling lives. Investing in education and skills is considered one of the most important ways to support young people’s well-being and to enable them to enjoy good career prospects. Using the framework of human capital theory, we explored the role of education among the factors explaining wage variation among Romanian youth. We built our analysis on micro-data for Romania from the EU Statistics on Income and Living Conditions 2020. In order to identify the most important factors influencing the wage distribution, we employed the elastic net regression approach. Moreover, we considered the phenomenon of expansion of education and ran the analysis by alternately using a traditional measure for education and a relative measure reflecting the theory of education as positional good. We ran the analysis for different cohorts of the population, focusing the discussion on the results for young people. Our findings confirm the importance of education for wage distribution together with other factors of influence, such as gender, degree of urbanization, region, sector of employment and working experience. Our conclusions are relevant for designing more effective educational and social policies to deal with various disadvantages faced by youth in Romania.

Spatial and sociodemographic determinants of community loudness perception

Urban noise is an increasingly important public health concern, but few studies have characterized drivers of community loudness perception rather than objective measures of sound, which may be an important element of the associated health effects. In this study, we used survey responses from the 2015–2016 Greater Boston Neighborhood Noise Survey (n = 898) to determine the key geospatial determinants of community loudness perception at both the street and neighborhood level. We gathered numerous land use and sociodemographic covariates and used an elastic net selection approach to determine the subset to include in final prediction models. Individual noise sensitivity and proximity to bus stops (<150 m) was associated with perceived loudness at the street and neighborhood level. In addition, at the street level, the restaurant and transportation infrastructure within close proximity (<200 m) of the residence were predictive of perceived loudness. At the neighborhood level, there was a larger and more complex set of predictors in the final prediction model, including multiple commercial/business sources at larger distances (500–1000 m buffers) from the residence. In general, these predictors overlapped with those in existing models predicting objective sound across multiple frequencies (A-weighted sound. low-frequency sound, mid-frequency sound, and high frequency sound), especially for transportation sources, although some predictors of noise perception differed from those for sound measurements. These results emphasize the importance of characterizing both perceived noise and objective sound, with the combined insights helping to develop soundscape maps and determine which urban areas require intervention to improve community public health.

Exploring the Systemic Risk of Domestic Banks with ΔCoVaR and Elastic-Net

We analyze the systemic risk of Italian banks with the ΔCoVaR from a bivariate normal GARCH model. The results show that it is a good measure of systemic risk and is applicable to the ranking of Italian other systemically important institutions. Using an elastic-net approach, we identify the balance sheet and market variables that explain the ΔCoVaR of Italian banks. The analysis confirms that these variables are key determinants of systemic importance and highlights how higher capitalization is beneficial to tackling systemic risk. And, we detect a connection between ΔCoVaR and some variables for trading and investment banking.

Constrained Bayesian doubly elastic net Lasso for linear quantile mixed models

In this article, we propose a novel constrained Bayesian elastic net approach for linear quantile mixed model shrinkage. A partially collapsed Gibbs sampling algorithm is developed for efficient posterior computation based on a modified Cholesky decomposition for the covariance matrix of random effects and an asymmetric Laplace distribution for the error distribution. We demonstrate the proposed method based on simulated data and an experimental dataset from a longitudinal study of age-related macular degeneration trial. Both simulation studies and real data analysis indicate that the proposed constrained Bayesian elastic net approach is competitive with the existing methods under a variety of scenarios, such as presence of a large number of covariates and collinearity.