Elastic Net Regression Model Research Articles

Social Connection and Suicidal Thoughts and Behaviors in the Million Veteran Program Cohort

People with lower levels of social connection are at increased risk for suicidal thoughts and behaviors. This extends to populations at greater risk of death by suicide, including U.S. military veterans. Despite this well-established association, it is unclear which measures of social connection are most useful in identifying veterans who could benefit from intervention to prevent suicide. To this end, we used data from the Million Veteran Program (MVP) to investigate the measures of social connection most strongly associated with suicidal thoughts and behaviors. Our sample included 264,626 veterans who reported on three measures of social connection—marital status, household size, and perceived social support—and were assessed for suicidal thoughts and behaviors using electronic health records. Veterans who were partnered (OR = 0.78, 95% CI [0.76-0.80], p < .001), living with others (OR = 0.71, 95% CI [0.70-0.73], p < .001), or reported higher levels of social support (OR = 0.67, 95% CI [0.65-0.69], p < .001), were less likely to have suicidal thoughts and behaviors. These associations varied by veterans’ age, sex, and era of military service. When combined into a single risk score, lower levels of social connection were associated with greater likelihood of suicidal thoughts and behaviors (β = 1.42, 95% CI [1.40-1.43], p < .001). Social support, particularly positive social interactions, showed the strongest associations with suicidal thoughts and behaviors in elastic net regression models. Common measures of social connection, particularly social support, could be useful in assessing suicide risk and treatment needs for veterans.

Characterizing metabolomic signatures related to coffee and tea consumption and their association with incidence and dynamic progression of type 2 diabetes: A multi-state analysis.

Our study aimed to investigate the impact of tea and coffee consumption and related metabolomic signatures on dynamic transitions from diabetes-free status to incident type 2 diabetes (T2D), and subsequently to T2D-related complications and death. We included 438,970 participants in the UK Biobank who were free of diabetes and diabetes complications at baseline. Of these, 212,146 individuals had information on all metabolic biomarkers. We identified tea- and coffee-related metabolomic signatures using elastic net regression models. We examined associations of tea and coffee intake and related metabolomic signatures with the onset and progression of T2D using multi-state regression models. We observed that tea and coffee consumption and related metabolomic signatures were inversely associated with the risk of five T2D transitions. For example, HRs (95% CIs) per SD increase of the tea-related metabolomic signature were 0.87 (0.85, 0.89), 0.97 (0.95, 0.99), 0.91 (0.90, 0.92), 0.92 (0.91, 0.94), and 0.91 (0.90, 0.92) for transitions from diabetes-free state to incident T2D, from diabetes-free state to total death, from incident T2D to T2D complications, from incident T2D to death, and from T2D complications to death. These findings highlight the benefit of tea and coffee intake in reducing the risk of occurrence and progression of T2D.

Predicting Methanol Space-Time Yield from CO? Hydrogenation Using Machine Learning: Statistical Evaluation of Penalized Regression Techniques

This study investigates the effectiveness of machine learning techniques, specifically penalized regression models Ridge Regression, Lasso Regression, and Elastic Net Regression in predicting methanol space-time yield (STY) from CO? hydrogenation data. Using a dataset derived from Cu-based catalyst research, the study implemented a comprehensive preprocessing approach, including data cleaning, imputation, outlier removal, and normalization. The models were rigorously evaluated through 10-fold cross-validation and tested on unseen data. Ridge Regression outperformed the other models, achieving the lowest Root Mean Squared Error (RMSE) of 0.7706, Mean Absolute Error (MAE) of 0.5627, and Mean Squared Error (MSE) of 0.5938. In comparison, Lasso and Elastic Net Regression models exhibited higher error metrics. Feature importance analysis revealed that Gas Hourly Space Velocity (GHSV) and Molar Masses of Support significantly influence catalytic activity. These findings suggest that Ridge Regression is a promising tool for accurately predicting methanol production, providing valuable insights for optimizing catalytic processes and advancing sustainable practices in chemical engineering.

Augmenting the availability of historical GDP per capita estimates through machine learning

Can we use data on the biographies of historical figures to estimate the GDP per capita of countries and regions? Here, we introduce a machine learning method to estimate the GDP per capita of dozens of countries and hundreds of regions in Europe and North America for the past seven centuries starting from data on the places of birth, death, and occupations of hundreds of thousands of historical figures. We build an elastic net regression model to perform feature selection and generate out-of-sample estimates that explain 90% of the variance in known historical income levels. We use this model to generate GDP per capita estimates for countries, regions, and time periods for which these data are not available and externally validate our estimates by comparing them with four proxies of economic output: urbanization rates in the past 500 y, body height in the 18th century, well-being in 1850, and church building activity in the 14th and 15th century. Additionally, we show our estimates reproduce the well-known reversal of fortune between southwestern and northwestern Europe between 1300 and 1800 and find this is largely driven by countries and regions engaged in Atlantic trade. These findings validate the use of fine-grained biographical data as a method to augment historical GDP per capita estimates. We publish our estimates with CI together with all collected source data in a comprehensive dataset.

Association of metabolic signatures of air pollution with MASLD: Observational and Mendelian randomization study

Background & AimsTo identify metabolic signatures associated with exposure to ambient air pollution and to explore their associations with risk of metabolic dysfunction-associated steatotic liver disease (MASLD). MethodsWe utilized data from the UK Biobank Cohort. Annual mean concentrations of PM2.5, PM10, NO2 and NOx were assessed for each participant using bilinear interpolation. The Elastic Net regression model was used to identify metabolites associated with four air pollutants and to construct metabolic signatures, respectively. Associations between air pollutants, metabolic signatures and MASLD were analyzed using Cox models. Mendelian randomization (MR) analysis was used to examine potential causality. Mediation analysis was employed to examine the role of metabolic signatures in the association between air pollutants and MASLD. ResultsA total of 244,842 participants from the UK Biobank were included in this analysis. We identified 87, 65, 76, and 71 metabolites as metabolic signatures of PM2.5, PM10, NO2, and NOx, respectively. Metabolic signatures were associated with risk of MASLD, with hazard ratios (HRs) and 95% confidence intervals (95% CIs) were 1.10 (1.06, 1.14), 1.06 (1.02, 1.10), 1.24 (1.20, 1.29) and 1.14 (1.10, 1.19). The four pollutants were associated with increased risk of MASLD, with HRs (95% CIs) of 1.03 (1.01, 1.05), 1.02 (1.01, 1.04), 1.01 (1.01, 1.02) and 1.01 (1.00, 1.01). MR analysis indicated an association between PM2.5, NO2 and NOx-related metabolic signatures and MASLD. Metabolic signatures mediated the association of PM2.5, PM10, NO2 and NOx with MASLD. ConclusionThere may be association between PM2.5, PM10, NO2 and NOx-related metabolic signatures and MASLD, and metabolic signatures mediate the increase of PM2.5, PM10, NO2 and NOx in the risk of MASLD. Impact and implicationsAir pollution is a significant public health issue and an important risk factor for metabolic dysfunction-associated steatotic liver disease (MASLD), however, the mechanism by which air pollution affects MASLD remains unclear. Our study used integrated serological metabolic data of 251 metabolites from a large-scale cohort study to demonstrate that metabolic signatures play a crucial role in the elevated risk of MASLD caused by air pollution. These results are relevant to patients and policymakers because they suggest that air pollution-related metabolic signatures are not only potentially associated with MASLD but also involved in mediating the process by which PM2.5, PM10, NO2, and NOx increase the risk of MASLD. Focusing on changes in air pollution-related metabolic signatures may offer a new perspective for preventing air pollution-induced MASLD and serve as protective measures to address this emerging public health challenge.

392. MACHINE LEARNING TO PREDICT LYMPH NODE METASTASIS IN T1 ESOPHAGEAL SQUAMOUS CELL CARCINOMA: A MULTICENTER STUDY

Abstract Background Lymph node metastases (LNM) is a relatively uncommon but possible complication of T1 esophageal squamous cell carcinoma (ESCC). Existing models do poorly when it comes to quantifying this risk. As a branch of artificial intelligence, machine learning (ML) algorithms have developed and become more prominent. Up to now, no individualized ML prediction model aimed at predicting LNM in T1 esophageal squamous cell carcinoma patients is available. This study aimed to develop a machine learning model for LNM in patients with T1 esophageal squamous cell carcinoma. Methods Patients with T1 squamous cell carcinoma treated with surgery between January 2010 and September 2021 from 3 institutions were included. Machine-learning models were developed using data on patients’ age and sex, depth of tumor invasion, tumor size, tumor location, macroscopic tumor type, lymphatic and vascular invasion, and histologic grade. Elastic net regression (ELR), random forest (RF), extreme gradient boosting (XGB), and a combined (ensemble) model of these was generated. Use Area Under Curve (AUC) to evaluate the predictive ability of models. The contribution of each factor was calculated. To meet clinical needs, we designed the model as a user-friendly website. Results 926 and 341 patients were included in the derivation and validation cohorts. The lymph node metastasis rate was 14.8%. The models all exhibited potent discriminating power. The elastic net regression model performed best with externally validated AUC of 0.803 (AUC 0.773 for RF, 0.791 for XGB, 0.799 for ensemble) whereas the NCCN guidelines identified patients with LNM with an AUC of 0.576 and logistic model with an AUC of 0. 670. The most important features were lymphatic and vascular invasion as well as depth of tumor invasion. Conclusions The model created utilizing machine learning approaches had excellent performance estimating the likelihood of LNM in T1 ESCC, which can help patients achieve personalized treatment options.

Visual analytics of an interpretable prediction model for the glass transition temperature of fluoroelastomers

In development, precise measurement and control of Tg is essential for researchers to thoroughly understand and master how variations in formulation and production processes impact product performance. Given the complexity of industrial production, most manufacturers repeatedly produce fluorine materials and test their Tg until it satisfies the application environment’s demands. Consequently, this study utilized an FKM dataset from actual synthesis trials, subjected to data classification, alignment, and feature filtering, to develop a predictive model based on Elastic Net Regression, enhancing the accuracy of Tg predictions for FKM. We evaluated the performance of several regression machine learning algorithms, including Random Forest, Support Vector Machines, Decision Trees, Extreme Gradient Boosting, and Ensemble Learning. The findings show that Random Forest and Elastic Net models exceeded other models by approximately 5% to 10 % in the coefficient of determination, indicating superior predictive capabilities. The Elastic Net Regression model excelled particularly in terms of evaluation coefficients and error rates. To boost the model’s interpretability, this research developed and implemented a visualization technology scheme for the excellently performing models, integrating SHAP value analysis to vividly depict the feature space, prediction processes, and experimental outcomes, thereby aiding the exploration of Tg influencing factors. This research not only offers an innovative machine learning framework for Tg prediction in FKM but also enhances the model’s transparency and credibility using visualization technology, holding significant theoretical and practical implications for materials science research.

A Penalized Regression Method for Genomic Prediction Reduces Mismatch between Training and Testing Sets.

Genomic selection (GS) is changing plant breeding by significantly reducing the resources needed for phenotyping. However, its accuracy can be compromised by mismatches between training and testing sets, which impact efficiency when the predictive model does not adequately reflect the genetic and environmental conditions of the target population. To address this challenge, this study introduces a straightforward method using binary-Lasso regression to estimate β coefficients. In this approach, the response variable assigns 1 to testing set inputs and 0 to training set inputs. Subsequently, Lasso, Ridge, and Elastic Net regression models use the inverse of these β coefficients (in absolute values) as weights during training (WLasso, WRidge, and WElastic Net). This weighting method gives less importance to features that discriminate more between training and testing sets. The effectiveness of this method is evaluated across six datasets, demonstrating consistent improvements in terms of the normalized root mean square error. Importantly, the model's implementation is facilitated using the glmnet library, which supports straightforward integration for weighting β coefficients.

An elastic net regression model for predicting the risk of ICU admission and death for hospitalized patients with COVID-19

This study aimed to develop and validate prediction models to estimate the risk of death and intensive care unit admission in COVID-19 inpatients. All RT-PCR-confirmed adult COVID-19 inpatients admitted to Fujian Provincial Hospital from October 2022 to April 2023 were considered. Elastic Net Regression was used to derive the risk prediction models. Potential risk factors were considered, which included demographic characteristics, clinical symptoms, comorbidities, laboratory results, treatment process, prognosis. A total of 1906 inpatients were included finally by inclusion/exclusion criteria and were divided into derivation and test cohorts in a ratio of 8:2, where 1526 (80%) samples were used to develop prediction models under a repeated cross-validation framework and the remaining 380 (20%) samples were used for performance evaluation. Overall performance, discrimination and calibration were evaluated in the validation set and test cohort and quantified by accuracy, scaled Brier score (SbrS), the area under the ROC curve (AUROC), and Spiegelhalter-Z statistics. The models performed well, with high levels of discrimination (AUROCICU [95%CI]: 0.858 [0.803,0.899]; AUROCdeath [95%CI]: 0.906 [0.850,0.948]); and good calibrations (Spiegelhalter-ZICU: − 0.821 (p-value: 0.412); Spiegelhalter-Zdeath: 0.173) in the test set. We developed and validated prediction models to help clinicians identify high risk patients for death and ICU admission after COVID-19 infection.

MDB-42. RADIOMIC TEXTURAL AND MORPHOLOGICAL-BASED FEATURES TO RISK-STRATIFY PEDIATRIC MEDULLOBLASTOMA PATIENTS

Abstract BACKGROUND One key determinate in the treatment pathway for pediatric medulloblastoma (MB), the most frequent malignant brain tumor in children, is accurate risk-stratification. MB tumors are classified as standard- or high-risk based on current approaches (Chang’s/molecular stratification). However, there is still a need for additional attributes towards reliable risk-stratification. This work presents a radiomic-based approach that combines textural- and morphological-based attributes to risk-stratify MB patients. Our rationale is that coupling texture and morphology attributes from the intra-tumoral regions that quantify the heterogeneity and disorderly nature of aggressive tumors can transcend the current clinical approaches in accurately stratifying tumors into high- and standard-risk. METHODS T1-weighted MRI scans of 119 MB patients (2–18 years) were collected from Cincinnati Children’s Hospital Medical Center (Site1-n=42), Children’s Hospital Los Angeles (Site2-n=47), and Children’s Hospital of Philadelphia (Site3-n=30), used interchangeably for training and testing. Following preprocessing and performing annotations for the enhancing lesion and edema sub-compartments, 232 textural and morphological features were extracted from each sub-compartment. Namely, 214 textural (gradient, Haralick, intensity, Gabor, Laws, entropy) as well as 34 morphological (4 surface-based (e.g., curvedness and sharpness), 18 global (e.g., roundness, elongation, compactness, flatness)) features were extracted. Features were then fed into Elastic-Net regression models for survival analysis. RESULTS Our analysis revealed that texture and morphology features, combined, yielded Concordance index (CI) and p-value of 0.52, 0.0001, respectively, for the enhancing lesion, using Sites1,3 for training, and CI=0.61, p=0.0065 using Site3 for testing. Additionally, for edema, CI and p-value of 0.514, 0.0001 were obtained when employing Sites2,3 for training and CI=0.61, p=0.00096 using Site1 for testing. Results were not significant when using Chang’s or molecular stratification alone for survival analysis. CONCLUSIONS Our study shows that radiomic-based morphological and textural features show promise towards reliable MB risk-stratification.

Associations between exposure to pesticides mixture and semen quality among the non-occupationally exposed males: Four statistical models

Most epidemiological studies on the associations between pesticides exposure and semen quality have been based on a single pesticide, with inconsistent major results. In contrast, there was limited human evidence on the potential effect of pesticides mixture on semen quality. Our study aimed to investigate the relationship of pesticide profiles with semen quality parameters among 299 non-occupationally exposed males aged 25–50 without any clinical abnormalities. Serum concentrations of 21 pesticides were quantified by gas chromatography-tandem mass spectrometry (GC-MS/MS). Semen quality parameters were abstracted from medical records. Generalized linear regression models (GLMs) and three mixture approaches, including weighted quantile sum regression (WQS), elastic net regression (ENR) and Bayesian kernel machine regression (BKMR), were applied to explore the single and mixed effects of pesticide exposure on semen quality. In GLMs, as the serum levels of Bendiocarb, β-BHC, Clomazone, Dicrotophos, Dimethenamid, Paclobutrazole, Pentachloroaniline and Pyrimethanil increased, the straight-line velocity (VSL), linearity (LIN) and straightness (STR) decreased. This negative association also occurred between the concentration of β-BHC, Pentachloroaniline, Pyrimethanil and progressive motility, total motility. In the WQS models, pesticides mixture was negatively associated with total motility and several sperm motility parameters (β: −3.07∼-1.02 per decile, FDR-P<0.05). After screening the important pesticides derived from the mixture by ENR model, the BKMR models showed that the decreased qualities for VSL, LIN, and STR were also observed when pesticide mixtures were at ≥ 70th percentiles. Clomazone, Dimethenamid, and Pyrimethanil (Posterior inclusion probability, PIP: 0.2850–0.8900) were identified as relatively important contributors. The study provides evidence that exposure to single or mixed pesticide was associated with impaired semen quality.

Greening Automation: Policy Recommendations for Sustainable Development in AI-Driven Industries

This study delves into the dynamic relationship between artificial intelligence (AI) and environmental performance, with a specific focus on greenhouse gas (GHG) emissions across European countries from 2012 to 2022. Utilizing data on industrial robots, AI companies, and AI investments, we examine how AI adoption influences GHG emissions. Preliminary analyses, including ordinary least squares (OLS) regression and diagnostic assessments, were conducted to ensure data adequacy and model readiness. Subsequently, the Elastic Net (ENET) regression model was employed to mitigate overfitting issues and enhance model robustness. Our findings reveal intriguing trends, such as a downward trajectory in GHG emissions correlating with increased AI investment levels and industrial robot deployment. Graphical representations further elucidate the evolution of coefficients and cross-validation errors, providing valuable insights into the relationship between AI and environmental sustainability. These findings offer policymakers actionable insights for leveraging AI technologies to foster sustainable development strategies.

Exogenous factors associated with legacy PFAS concentrations in the general U.S. population: NHANES 1999-2018

To reduce per- and polyfluoroalkyl substances (PFAS) exposure through legislation and other interventions, we must understand factors contributing to individual body burden. Identifying factors associated with serum perfluorinated compounds (PFAS) concentrations also aids in identifying groups at higher risk of adverse health outcomes due to elevated exposure. This exploratory analysis provides initial findings on exposure-related factors associated with legacy PFAS concentrations in the general United States (U.S.) population. We obtained National Health and Nutrition Examination Survey (NHANES) datasets with individual serum PFAS measurements from cycle years 1999-2018 (N = 14,961), excluding the 2001-2002 cycle due to pooled sampling. Over 100 features were evaluated for associations with PFAS concentrations. Data were singly imputed using hot deck and predictive mean matching (PMM) methods, and model performance by imputation method was compared using elastic net regression (ENR) models. Hot-deck imputation explained the most variance in predictive models; thus, a dataset imputed via hot-deck was used for feature selection. ENR models were employed to identify the top variables associated with legacy PFAS concentrations, and selected features were put into linear mixed models to obtain beta estimates and standard errors. Survey year, demographic, income, place of birth and citizenship status, household size, dietary, health, food insecurity, general health and healthcare, housing, social and behavioral, and other characteristics were important factors associated with legacy PFAS concentrations in this nationally representative study population. A better understanding of exogenous factors associated with PFAS concentrations can influence future epidemiological studies, guiding decisions on adjustment for confounding, and advancing our understanding of factors that affect chemical half-lives and toxicokinetics.

Multi-polygenic prediction of frailty highlights chronic pain and educational attainment as key risk and protective factors.

Frailty is a complex trait. Twin studies and recent Genome-Wide Association Studies have demonstrated a strong genetic basis of frailty but there remains a lack of genetic studies exploring genetic prediction of Frailty. Previous work has shown that a single polygenic predictor - represented by a Frailty polygenic score - predicts Frailty, measured via the frailty index, in independent samples within the United Kingdom. We extended this work, using a multi-polygenic score (MPS) approach to increase predictive power. Predictor variables - twenty-six polygenic scores (PGS) were modelled in regularised Elastic net regression models, with repeated cross-validation, to estimate joint prediction of the polygenic scores and order the predictions by their contributing strength to Frailty in two independent cohorts aged 65+ - the English Longitudinal Study of Ageing (ELSA) and Lothian Birth Cohort 1936 (LBC1936). Results showed that the MPS explained 3.6% and 4.7% of variance compared to the best single-score prediction of 2.6% and 2.2% of variance in ELSA and LBC1936 respectively. The strongest polygenic predictors of worsening frailty came from PGS for Chronic pain, Frailty and Waist circumference; whilst PGS for Parental Death, Educational attainment, and Rheumatoid Arthritis were found to be protective to frailty. Results from the predictors remaining in the final model were then validated using the longitudinal LBC1936, with equivalent PGS scores from the same GWAS summary statistics. Thus, this MPS approach provides new evidence for the genetic contributions to frailty in later life and sheds light on the complex structure of the Frailty Index measurement.

Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy.

Surgical resection is the standard of care for patients with large or symptomatic brain metastases (BMs). Despite improved local control after adjuvant stereotactic radiotherapy, the risk of local failure (LF) persists. Therefore, we aimed to develop and externally validate a pre-therapeutic radiomics-based prediction tool to identify patients at high LF risk. Data were collected from A Multicenter Analysis of Stereotactic Radiotherapy to the Resection Cavity of BMs (AURORA) retrospective study (training cohort: 253 patients from 2 centers; external test cohort: 99 patients from 5 centers). Radiomic features were extracted from the contrast-enhancing BM (T1-CE MRI sequence) and the surrounding edema (T2-FLAIR sequence). Different combinations of radiomic and clinical features were compared. The final models were trained on the entire training cohort with the best parameter set previously determined by internal 5-fold cross-validation and tested on the external test set. The best performance in the external test was achieved by an elastic net regression model trained with a combination of radiomic and clinical features with a concordance index (CI) of 0.77, outperforming any clinical model (best CI: 0.70). The model effectively stratified patients by LF risk in a Kaplan-Meier analysis (P < .001) and demonstrated an incremental net clinical benefit. At 24 months, we found LF in 9% and 74% of the low and high-risk groups, respectively. A combination of clinical and radiomic features predicted freedom from LF better than any clinical feature set alone. Patients at high risk for LF may benefit from stricter follow-up routines or intensified therapy.

The oncologic burden of residual disease in incidental gallbladder cancer: An elastic net regression model to profile high-risk features

IntroductionIncidental Gallbladder Cancer (IGBC) following cholecystectomy constitutes a significant portion of gallbladder cancer diagnoses. Re-exploration is advocated to optimize disease clearance and enhance survival rates. The consistent association of residual disease (RD) with inferior oncologic outcomes prompts a critical examination of re-resection's role as a modifying factor in the natural history of IGBC. MethodsAll patients diagnosed with gallbladder cancer between 2012 and 2022 were included. An elastic net regularized regression model was employed to profile high-risk predictors of RD within the IGBC group. Survival outcomes were assessed based on resection margins and RD. ResultsAmong the 181 patients undergoing re-exploration for IGBC, 133 (73.5 %) harbored RD, while 48 (26.5 %) showed no evidence. The elastic net model, utilizing a selected λ = 0.029, identified six coefficients associated with the risk of RD: aspiration from cholecystectomy (0.141), hepatic tumor origin (1.852), time to re-exploration >8 weeks (1.879), positive margin status (2.575), higher T stage (1.473), and poorly differentiated tumors (2.241). Furthermore, the study revealed a median overall survival of 44 months (CI 38–60) for IGBC patients with no evidence of RD, compared to 31 months (23–42) for those with RD (p < 0.001). ConclusionRe-resection revealed a high incidence of RD (73.5 %), significantly correlating with poorer survival outcomes. The preoperative identification of high-risk features provides a reliable biological disease profile. This aids in strategic preselection of patients who may benefit from re-resection, underscoring the need to consolidate outcomes with tailored chemotherapy for those with unfavorable characteristics.

Impact of climate change and genetic development on Iowa corn yield

The vulnerability of corn yield to high temperature and insufficient rainfall in the US mid-west is widely acknowledged. The impact of extreme weather and genetic development on corn yield is less well known. One of the main reasons is that the multicollinearity in the variables can lead to confounding results. Here we model the impact of climate and genetic development by employing an elastic net regression model to address the multicollinearity issue. This allows us to develop a more robust multiple regression model with higher predictive accuracy. Using granular data for Iowa from 1981-2018, we find that corn yield is vulnerable to high mean summer temperatures particularly in July, a widening diurnal temperature range in June and dry summer conditions (due to extremely low rainfall) from June-August. We find that overall climate impact reduced average annual yield by 0.7%. We also find that genetic development which led to earlier planting dates, widening duration of the reproductive interval, higher growing degree day accumulation and larger net planted area had a beneficial impact on the Iowa corn yield during 1981-2018 resulting in an average annual yield improvement of 1.8% per annum. This provides a basis for optimism that these genetic developments and management practices will continue to adapt and improve in the future to counter the impact of climate change on corn yield. We have also modelled the impact of future climate change using the latest climate projections from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). These climate projections show that the average temperature during the growing season (MayO-October) will increase by 2.4 -2.9 o C by mid-century while the average spring temperature (March and April) will increase by a relatively slower 1.9 -2.3 o C by mid-century. Additionally, climate projections show that both temperature and rainfall will also become more extreme in the future with the changes varying from spring to summer. Our results show that, just due to climate change alone in Iowa corn yield will decline between 1.4-1.7% per annum until mid-century (or 1.2-2.1% per annum until the late twenty first century).

Association of Metal Exposure with Novel Immunoinflammatory Indicators.

Objective: We aimed to investigate the relationship between metal exposure and novel immunoinflammatory indicators. Methods: Data on adults participating in the National Health and Nutrition Examination Survey (NHANES) from 2009 to 2018 were analyzed. Various statistical models were employed to assess the association between metal exposure and novel immune-inflammation-related indicators. Additionally, the impact of metal exposure on inflammation in different gender populations was explored. Results: This study included 4482 participants, of whom 51.1% were male. Significant correlations were observed among various metals. Both elastic net (ENET) and linear regression models revealed robust associations between cadmium (Cd), cobalt (Co), arsenic (As), mercury (Hg), and immunoinflammatory indicators. Weighted quantile sum (WQS) and Quantile g-computation (Q-gcomp) models demonstrated strong associations between barium (Ba), Co, and Hg and immunoinflammatory indicators. Bayesian kernel machine regression (BKMR) analysis indicated an overall positive correlation between in vivo urinary metal levels and systemic inflammation response index (SIRI) and aggregate index of systemic inflammation (AISI). Furthermore, Co, As, and Hg emerged as key metals contributing to changes in novel immunoinflammatory indicators. Conclusions: Metals exhibit associations with emerging immunoinflammatory indicators, and concurrent exposure to mixed metals may exacerbate the inflammatory response. Furthermore, this relationship varies across gender populations.

Association between multipollutant exposure and thyroid hormones in elderly people: A cross-sectional study in China

Environmental chemicals have been indicated to cause disruption of thyroid homeostasis in human populations. However, previous studies mostly focused on single group of chemicals. Herein, we investigate the independent and combined effects of multiple pollutants on thyroid homeostasis, including thyroid-stimulating hormone (TSH), total and free thyroxine (tT4 and fT4) and total and free triiodothyronine (tT3 and fT3) in elderly people. These environmental pollutants (n = 144) are from ten categories, including phenols, parabens, perfluoroalkyl substances (PFASs), polychlorinated biphenyls (PCBs), phthalate esters (PAEs), polycyclic aromatic hydrocarbons (PAHs), organochlorine pesticides (OCPs), organophosphate pesticides (OPPs), synthetic pyrethroids (SPs), herbicides, and metals. Few studies have evaluated the health risks of these 144 chemicals, especially their joint effects. In single-pollutant evaluations, multiple linear regression (MLR) models were used to estimate the independent associations between multiple exposures and thyroid biomarkers. In multi-pollutant evaluations, elastic net regression and Bayesian kernel machine regression (BKMR) models were used to estimate the combined associations. The MLR models showed that 41 chemicals were significantly related to THs levels. BKMR models revealed the most important chemical groups: metals for TSH, PAHs, SPs and PCBs for tT4, herbicides and SPs for tT3. This study will contribute to the understanding of multipollutant exposure and help prioritize specific chemical groups related to thyroid hormone disruption.

Comparison and analysis of the accuracy of Lasso regression, Ridge regression and Elastic Net regression models in predicting students' teaching quality achievement

In this paper, the "Student Performance Data Set" data set of Kaggle competition is used to conduct correlation analysis on multiple attributes such as students' personal information, school information and students' school performance, and a correlation heat map is drawn. The results show that there is a strong positive correlation among the attributes. We then divided the data set into training, validation, and test sets in a 6:2:2 ratio, and used the Lasso regression model, the Elastic Net model, and the Ridge regression model to make predictions. After training 50 epochs, we evaluated and compared the models. The results show that Lasso regression model has the lowest prediction error and the best error effect. Elastic Net was the next best predictor, while Ridge regression model had the largest prediction error and was the worst. To sum up, Lasso regression model has the best Performance in grade prediction based on the "Student Performance Data Set" dataset. This conclusion is of great significance for schools and educational institutions, as it can help them better understand students' learning and improve the quality and effectiveness of teaching. At the same time, this conclusion is also valuable for data scientists and machine learning researchers, because it can guide them to choose the most appropriate model and algorithm on similar data sets, improving the accuracy and effectiveness of predictions. In general, this paper analyzes and discusses the problem of Student achievement prediction, puts forward the Lasso regression model based on the "Student Performance Data Set" data set, which has the best performance prediction effect, and analyzes the principles of the three models. This conclusion has practical applications for schools and educational institutions, as well as providing a reference for data scientists and machine learning researchers.