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Sheh Rahman, Shaesta Khan, Noraziah Adzhar, and Nazri Ahmad Zamani. "Comparative Analysis of Machine Learning Models to Predict Common Vulnerabilities and Exposure." Malaysian Journal of Fundamental and Applied Sciences 20, no. 6 (December 16, 2024): 1410–19. https://doi.org/10.11113/mjfas.v20n6.3822.
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Predicting Common Vulnerabilities and Exposures (CVE) is a challenging task due to the increasing complexity of cyberattacks and the vast amount of threat data available. Effective prediction models are crucial for enabling cybersecurity teams to respond quickly and prevent potential exploits. This study aims to provide a comparative analysis of machine learning techniques for CVE prediction to enhance proactive vulnerability management and strengthening cybersecurity practices. The supervised machine learning model which is Gaussian Naive Bayes and unsupervised machine learning models that utilize clustering algorithms which are K-means and DBSCAN were employed for the predictive modelling. The performance of these models was compared using performance metrics such as accuracy, precision, recall, and F1-score. Among these models, the Gaussian Naive Bayes achieved an accuracy rate of 99.79%, and outperformed the clustering-based machine learning models in effectively determining the class labels or results of the data it was trained on or tested against. The outcome of this study will provide a proof of concept to Cybersecurity Malaysia, offering insights into the CVE model.
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Soo, Jhy-Charm, Perng-Jy Tsai, Shih-Chuan Lee, Shih-Yi Lu, Cheng-Ping Chang, Yuh-When Liou, and Tung-Sheng Shih. "Establishing aerosol exposure predictive models based on vibration measurements." Journal of Hazardous Materials 178, no. 1-3 (June 2010): 306–11. http://dx.doi.org/10.1016/j.jhazmat.2010.01.079.
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Zhang, Ying, Cheng Zhao, Yu Lei, Qilin Li, Hui Jin, and Qianjin Lu. "Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors." Lupus Science & Medicine 11, no. 2 (November 2024): e001311. http://dx.doi.org/10.1136/lupus-2024-001311.
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ObjectiveSystemic lupus erythematosus (SLE) is an autoimmune disease characterised by a loss of immune tolerance, affecting multiple organs and significantly impairing patients’ health and quality of life. While hereditary elements are essential in the onset of SLE, external environmental influences are also significant. Currently, there are few predictive models for SLE that takes into account the impact of occupational and living environmental exposures. Therefore, we collected basic information, occupational background and living environmental exposure data from patients with SLE to construct a predictive model that facilitates easier intervention.MethodsWe conducted a study comparing 316 individuals diagnosed with SLE and 851 healthy volunteers in a case–control design, collecting their basic information, occupational exposure history and environmental exposure data. Subjects were randomly allocated into training and validation groups using a 70/30 split. Using three-feature selection methods, we constructed four predictive models with multivariate logistic regression. Model performance and clinical utility were evaluated via receiver operating characteristic, calibration and decision curves. Leave-one-out cross-validation further validated the models. The best model was used to create a dynamic nomogram, visually representing the predicted relative risk of SLE onset.ResultsThe ForestMDG model demonstrated strong predictive ability, with an area under the curve of 0.903 (95% CI 0.880 to 0.925) in the training set and 0.851 (95% CI 0.809 to 0.894) in the validation set, as indicated by model performance evaluation. Calibration and decision curves demonstrated accurate results along with practical clinical value. Leave-one-out cross-validation confirmed that the ForestMDG model had the best accuracy (0.8338). Finally, we developed a dynamic nomogram for practical use, which is accessible via the following link:https://yingzhang99321.shinyapps.io/dynnomapp/.ConclusionWe created a user-friendly dynamic nomogram for predicting the relative risk of SLE onset based on occupational and living environmental exposures.Trial registration numberChiCTR2000038187.
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Aronoff-Spencer, Eliah, Sepideh Mazrouee, Rishi Graham, Mark S. Handcock, Kevin Nguyen, Camille Nebeker, Mohsen Malekinejad, and Christopher A. Longhurst. "Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting." PLOS ONE 18, no. 8 (August 18, 2023): e0287368. http://dx.doi.org/10.1371/journal.pone.0287368.
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Purpose Digital methods to augment traditional contact tracing approaches were developed and deployed globally during the COVID-19 pandemic. These “Exposure Notification (EN)” systems present new opportunities to support public health interventions. To date, there have been attempts to model the impact of such systems, yet no reports have explored the value of real-time system data for predictive epidemiological modeling. Methods We investigated the potential to short-term forecast COVID-19 caseloads using data from California’s implementation of the Google Apple Exposure Notification (GAEN) platform, branded as CA Notify. CA Notify is a digital public health intervention leveraging resident’s smartphones for anonymous EN. We extended a published statistical model that uses prior case counts to investigate the possibility of predicting short-term future case counts and then added EN activity to test for improved forecast performance. Additional predictive value was assessed by comparing the pandemic forecasting models with and without EN activity to the actual reported caseloads from 1–7 days in the future. Results Observation of time series presents noticeable evidence for temporal association of system activity and caseloads. Incorporating earlier ENs in our model improved prediction of the caseload counts. Using Bayesian inference, we found nonzero influence of EN terms with probability one. Furthermore, we found a reduction in both the mean absolute percentage error and the mean squared prediction error, the latter of at least 5% and up to 32% when using ENs over the model without. Conclusions This preliminary investigation suggests smartphone based ENs can significantly improve the accuracy of short-term forecasting. These predictive models can be readily deployed as local early warning systems to triage resources and interventions.
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Hosein, Roland, Paul Corey, Frances Silverman, Anthony Ayiomamitis, R. Bruce Urch, and Neil Alexis. "Predictive Models Based on Personal, Indoor and Outdoor Air Pollution Exposure." Indoor Air 1, no. 4 (December 1991): 457–64. http://dx.doi.org/10.1111/j.1600-0668.1991.00010.x.
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Wei, Chih-Chiang, and Wei-Jen Kao. "Establishing a Real-Time Prediction System for Fine Particulate Matter Concentration Using Machine-Learning Models." Atmosphere 14, no. 12 (December 13, 2023): 1817. http://dx.doi.org/10.3390/atmos14121817.
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With the rapid urbanization and industrialization in Taiwan, pollutants generated from industrial processes, coal combustion, and vehicle emissions have led to severe air pollution issues. This study focuses on predicting the fine particulate matter (PM2.5) concentration. This enables individuals to be aware of their immediate surroundings in advance, reducing their exposure to high concentrations of fine particulate matter. The research area includes Keelung City and Xizhi District in New Taipei City, located in northern Taiwan. This study establishes five fine prediction models based on machine-learning algorithms, namely, the deep neural network (DNN), M5’ decision tree algorithm (M5P), M5’ rules decision tree algorithm (M5Rules), alternating model tree (AMT), and multiple linear regression (MLR). Based on the predictive results from these five models, the study evaluates the optimal model for forecast horizons and proposes a real-time PM2.5 concentration prediction system by integrating various models. The results demonstrate that the prediction errors vary across different models at different forecast horizons, with no single model consistently outperforming the others. Therefore, the establishment of a hybrid prediction system proves to be more accurate in predicting future PM2.5 concentration compared to a single model. To assess the practicality of the system, the study process involved simulating data, with a particular focus on the winter season when high PM2.5 concentrations are prevalent. The predictive system generated excellent results, even though errors increased in long-term predictions. The system can promptly adjust its predictions over time, effectively forecasting the PM2.5 concentration for the next 12 h.
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Gomah, Mohamed Elgharib, Guichen Li, Naseer Muhammad Khan, Changlun Sun, Jiahui Xu, Ahmed A. Omar, Baha G. Mousa, Marzouk Mohamed Aly Abdelhamid, and Mohamed M. Zaki. "Prediction of Strength Parameters of Thermally Treated Egyptian Granodiorite Using Multivariate Statistics and Machine Learning Techniques." Mathematics 10, no. 23 (November 30, 2022): 4523. http://dx.doi.org/10.3390/math10234523.
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The mechanical properties of rocks, such as uniaxial compressive strength and elastic modulus of intact rock, must be determined before any engineering project by employing lab or in situ tests. However, there are some circumstances where it is impossible to prepare the necessary specimens after exposure to high temperatures. Therefore, the propensity to estimate the destructive parameters of thermally heated rocks based on non-destructive factors is a helpful research field. Egyptian granodiorite samples were heated to temperatures of up to 800 °C before being treated to two different cooling methods: via the oven (slow-cooling) and using water (rapid cooling). The cooling condition, temperature, mass, porosity, absorption, dry density (D), and P-waves were used as input parameters in the predictive models for the UCS and E of thermally treated Egyptian granodiorite. Multi-linear regression (MLR), random forest (RF), k-nearest neighbor (KNN), and artificial neural networks (ANNs) were used to create predictive models. The performance of each prediction model was also evaluated using the (R2), (RMSE), (MAPE), and (VAF). The findings revealed that cooling methods and mass as input parameters to predict UCS and E have a minor impact on prediction models. In contrast, the other parameters had a good relationship with UCS and E. Due to severe damage to granodiorite samples, many input and output parameters were impossible to measure after 600 °C. The prediction models were thus developed up to this threshold temperature. Furthermore, the comparative analysis of predictive models demonstrated that the ANN pattern for predicting the UCS and E is the most accurate model, with R2 of 0.99, MAPE of 0.25%, VAF of 97.22%, and RMSE of 2.04.
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Symanski, E., L. L. Kupper, I. Hertz-Picciotto, and S. M. Rappaport. "Comprehensive evaluation of long-term trends in occupational exposure: Part 2. Predictive models for declining exposures." Occupational and Environmental Medicine 55, no. 5 (May 1, 1998): 310–16. http://dx.doi.org/10.1136/oem.55.5.310.
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Moon, H., and M. Cong. "Predictive models of cytotoxicity as mediated by exposure to chemicals or drugs." SAR and QSAR in Environmental Research 27, no. 6 (June 2, 2016): 455–68. http://dx.doi.org/10.1080/1062936x.2016.1208272.
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Fu, Siheng. "Comparative Analysis of Expected Goals Models: Evaluating Predictive Accuracy and Feature Importance in European Soccer." Applied and Computational Engineering 117, no. 1 (December 19, 2024): 1–10. https://doi.org/10.54254/2755-2721/2024.18300.
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Expected Goals (xG) is a widely used metric in soccer analytics that estimates the probability of a shot resulting in a goal based on various characteristics of the shot. This study compares the predictive accuracy and feature importance of two prominent xG models: Opta and Understat. Using data from the top five European leagues from the 2017-2018 to the 2023-2024 seasons, we evaluate the predictive accuracy of each model using L1 and L2 loss metrics. Our findings indicate that Understat outperforms Opta in terms of lower prediction errors in the Bundesliga, Premier League, and Serie A, while Opta yields more stable predictions in La Liga and Ligue 1. We further analyze the factors influencing xG predictions through feature importance techniques using Random Forest and XGBoost models, complemented by SHAP (SHapley Additive exPlanations) analysis. Results reveal that goal exposure angle, shooting angle, and shot distance are key features in predicting goal probability, with differences in how categorical variables are weighted between the models. The study concludes with a discussion of the strengths, limitations, and league-specific applications of both models, highlighting the need for standardized data collection practices and expanded contextual features to enhance xG model utility and accuracy.
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Singhal, Sonalika, Nathan A. Ruprecht, Donald Sens, Mary Ann Sens, and Sandeep K. Singhal. "Meta analysis of arsenic exposed genes expression profiles to develop a bladder cancer predictor." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e16523-e16523. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e16523.
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e16523 Background: IARC classified arsenic (As) as “carcinogenic to humans”, but despite the health consequences, there is no molecular signature available yet to predict when exposure may lead to the disease development. First aim of this study is to investigate the genetic changes due to the exposure to carcinogenic compounds such as As. Secondly, how accurately we can predict the disease association when exposed to toxic compounds. Methods: The entire analysis was performed in-silico fashion and data was collected from the public resources such as NCBI database. Two Asian population datasets exposed As were used to find significantly differently express genes. In addition, four cancer cell lines with exposure of As compounds were used to identify the association with cancer. The human bladder cancer biopsy datasets were used to develop a risk predictive model. As per the requirements, numerous machine learning (ML) approaches such as random forest, hierarchical clustering, were used to find the classification and association between the samples and outcome. Statistical approaches such as T-Test, and ANOVA, applied to find the differentially expressed genes associated with different conditions and logistic regression models applied to develop risk prediction models. Results: We identified a set of 1183 genes which were common between both the populations and were significantly changed in humans exposed to As. A subset of 157 genes associated with As exposure and involved in cancer progression was selected for risk prediction model development. A set of four genes (NKIRAS2, AKTIP, HLA-DQA1 and TBC1D7) shows the highest prediction ability of primary bladder tumor with AUC 0.96 (95% CI: 0.82- 0.99) and reproducibility of 0.75 (0.34-0.93) when applied on different dataset. Conclusions: This study identified a list of genes and bladder cancer predictive models that would be very helpful to forecast the outcomes of As exposed in humans.
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Zhang, Yan, Weihua Yang, Günther Schauberger, Jianzhuang Wang, Jing Geng, Gen Wang, and Jie Meng. "Determination of Dose–Response Relationship to Derive Odor Impact Criteria for a Wastewater Treatment Plant." Atmosphere 12, no. 3 (March 12, 2021): 371. http://dx.doi.org/10.3390/atmos12030371.
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Municipal wastewater treatment plants (WWTPs) inside cities have been the major complained sources of odor pollution in China, whereas there is little knowledge about the dose–response relationship to describe the resident complaints caused by odor exposure. This study explored a dose–response relationship between the modelled exposure and the annoyance surveyed by questionnaires. Firstly, the time series of odor concentrations were preliminarily simulated by a dispersion model. Secondly, the perception-related odor exposures were further calculated by combining with the peak to mean factors (constant value 4 (Germany) and 2.3 (Italy)), different time periods of “a whole year”, “summer”, and “nighttime of summer”, and two approaches of odor impact criterion (OIC) (“odor-hour” and “odor concentration”). Thirdly, binomial logistic regression models were used to compare kinds of perception-related odor exposures and odor annoyance by odds ratio, goodness of fit and predictive ability. All perception-related odor exposures were positively associated with odor annoyance. The best goodness of fit was found when using “nighttime of summer” in predicting odor-annoyance responses, which highlights the importance of the time of the day and the time of the year weighting. The best predictive performance for odor perception was determined when the OIC was 4 ou/m3 at the 99th percentile for the odor exposure over time periods of nighttime of summer. The study of dose–response relationship could be useful for the odor management and control of WWTP to maximize the satisfaction of air quality for the residents inside city.
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Boaz, Ray, Andrew Lawson, and John Pearce. "2012 Multivariate air pollutant exposure prediction in South Carolina." Journal of Clinical and Translational Science 2, S1 (June 2018): 21. http://dx.doi.org/10.1017/cts.2018.98.
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OBJECTIVES/SPECIFIC AIMS: The objective of this project is the application of complex fusion models, which combine observed and modeled data, to areas with sparse monitoring networks with multiple chemical components is under-developed. Such models could provide improved accuracy and coverage for air quality measurement predictions, an area greatly limited by the amount of missing data. METHODS/STUDY POPULATION: This project focuses on the development of methods for improved estimation of pollutant concentrations when only sparse monitor networks are found. Sparse monitoring networks are defined as areas where fewer than three criteria air pollutants (based on EPA standards) are monitored. Particularly, a multivariate air pollutant statistical model to predict spatio-temporally resolved concentration fields for multiple pollutants simultaneously is developed and evaluated. The multivariate predictions allow monitored pollutants to inform the prediction of nonmonitored pollutants in sparse networks. RESULTS/ANTICIPATED RESULTS: Daily, ZIP code level pollutant concentration estimates will be provided for 8 pollutants across South Carolina, and goodness of fit metrics for model variants and previously established methods will be compared. DISCUSSION/SIGNIFICANCE OF IMPACT: These methods utilize only widely available data resources, meaning that the improved predictive accuracy of sparsely monitored pollutant concentrations can benefit future studies in any US area by improving estimation of health effects and saving resources needed for supplemental air pollutant monitoring campaigns. Our method for estimation attempts to improve predictive accuracy and data availability for sparsely monitored pollutants and areas.
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Kuo, Ching-Tang, Fen-Fen Chiu, Bo-Ying Bao, and Ta-Yuan Chang. "Determination and Prediction of Respirable Dust and Crystalline-Free Silica in the Taiwanese Foundry Industry." International Journal of Environmental Research and Public Health 15, no. 10 (September 25, 2018): 2105. http://dx.doi.org/10.3390/ijerph15102105.
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Background: Respirable crystalline silica (RCS) has been recognized as a human carcinogen; however, the measurement and analysis of RCS in small-scale foundries is rare and difficult. This study aimed to measure respirable dust and RCS levels among 236 foundry workers in Taiwan and used these data to establish predictive models for personal exposure. Methods: Personal sampling of various production processes were measured gravimetrically and analyzed using the X-ray diffraction method. Multiple linear regression was used to establish predictive models. Results: Foundry workers were exposed to geometric means and geometric standard deviations of 0.52 ± 4.0 mg/m3 and 0.027 ± 15 mg/m3 for respirable dust and RCS, respectively. The highest exposure levels were observed among workers in the sand blasting process, with geometric means of 1.6 mg/m3 and 0.099 mg/m3 for respirable dust and RCS, respectively. The predictive exposure model for respirable dust fitted the data well (R2 = 0.75; adjusted R2 = 0.64), and the predictive capacity for RCS was higher (R2 = 0.89; adjusted R2 = 0.84). Conclusions: Foundry workers in the sand blasting process may be exposed to the highest levels of respirable dust and RCS. The developed models can be applied to predict respirable dust and RCS levels adequately in small-scale foundry workers for epidemiological studies.
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Lang, Noémie, Aurélie Ayme, Chang Ming, Jean‑Damien Combes, Victor N. Chappuis, Alex Friedlaender, Aurélie Vuilleumier, et al. "Chemotherapy-related agranulocytosis as a predictive factor for germline BRCA1 pathogenic variants in breast cancer patients: a retrospective cohort study." Swiss Medical Weekly 153, no. 3 (March 30, 2023): 40055. http://dx.doi.org/10.57187/smw.2023.40055.
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BACKGROUND: Carriers of germline pathogenic variants of the BRCA1 gene (gBRCA1) tend to have a higher incidence of haematological toxicity upon exposure to chemotherapy. We hypothesised that the occurrence of agranulocytosis during the first cycle of (neo-)adjuvant chemotherapy (C1) in breast cancer (BC) patients could predict gBRCA1 pathogenic variants.
PATIENTS AND METHODS: The study population included non-metastatic BC patients selected for genetic counselling at Hôpitaux Universitaires de Genève (Jan. 1998 to Dec. 2017) with available mid-cycle blood counts performed during C1. The BOADICEA and Manchester scoring system risk-prediction models were applied. The primary outcome was the predicted likelihood of harbouring gBRCA1 pathogenic variants among patients presenting agranulocytosis during C1.
RESULTS: Three hundred seven BC patients were included: 32 (10.4%) gBRCA1, 27 (8.8%) gBRCA2, and 248 (81.1%) non-heterozygotes. Mean age at diagnosis was 40 years. Compared with non-heterozygotes, gBRCA1 heterozygotes more frequently had grade 3 BC (78.1%; p = 0.014), triple-negative subtype (68.8%; p <0.001), bilateral BC (25%; p = 0.004), and agranulocytosis following the first cycle of (neo-)adjuvant chemotherapy (45.8%; p = 0.002). Agranulocytosis and febrile neutropenia that developed following the first cycle of chemotherapy were independently predictive for gBRCA1 pathogenic variants (odds ratio: 6.1; p = 0.002). The sensitivity, specificity, positive predictive value, and negative predictive value for agranulocytosis predicting gBRCA1 were 45.8% (25.6–67.2%), 82.8% (77.5–87.3%), 22.9% (6.1–37.3%), and 93.4% (88.9–96.4%), respectively. Agranulocytosis substantially improved the positive predictive value of the risk-prediction models used for gBRCA1 evaluation.
CONCLUSION: Agranulocytosis following the first cycle of (neo-)adjuvant chemotherapy is an independent predictive factor for gBRCA1 detection in non-metastatic BC patients.
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Xu, Liuchang, Jie Wang, Dayu Xu, and Liang Xu. "Integrating Individual Factors to Construct Recognition Models of Consumer Fraud Victimization." International Journal of Environmental Research and Public Health 19, no. 1 (January 1, 2022): 461. http://dx.doi.org/10.3390/ijerph19010461.
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Consumer financial fraud has become a serious problem because it often causes victims to suffer economic, physical, mental, social, and legal harm. Identifying which individuals are more likely to be scammed may mitigate the threat posed by consumer financial fraud. Based on a two-stage conceptual framework, this study integrated various individual factors in a nationwide survey (36,202 participants) to construct fraud exposure recognition (FER) and fraud victimhood recognition (FVR) models by utilizing a machine learning method. The FER model performed well (f1 = 0.727), and model interpretation indicated that migration status, financial status, urbanicity, and age have good predictive effects on fraud exposure in the Chinese context, whereas the FVR model shows a low predictive effect (f1 = 0.565), reminding us to consider more psychological factors in future work. This research provides an important reference for the analysis of individual differences among people vulnerable to consumer fraud.
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Rasool, Muhammad F., Sundus Khalid, Abdul Majeed, Hamid Saeed, Imran Imran, Mohamed Mohany, Salim S. Al-Rejaie, and Faleh Alqahtani. "Development and Evaluation of Physiologically Based Pharmacokinetic Drug–Disease Models for Predicting Rifampicin Exposure in Tuberculosis and Cirrhosis Populations." Pharmaceutics 11, no. 11 (November 5, 2019): 578. http://dx.doi.org/10.3390/pharmaceutics11110578.
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The physiologically based pharmacokinetic (PBPK) approach facilitates the construction of novel drug–disease models by allowing incorporation of relevant pathophysiological changes. The aim of the present work was to explore and identify the differences in rifampicin pharmacokinetics (PK) after the application of its single dose in healthy and diseased populations by using PBPK drug–disease models. The Simcyp® simulator was used as a platform for modeling and simulation. The model development process was initiated by predicting rifampicin PK in healthy population after intravenous (i.v) and oral administration. Subsequent to successful evaluation in healthy population, the pathophysiological changes in tuberculosis and cirrhosis population were incorporated into the developed model for predicting rifampicin PK in these populations. The model evaluation was performed by using visual predictive checks and the comparison of mean observed/predicted ratios (ratio(Obs/pred)) of the PK parameters. The predicted PK parameters in the healthy population were in adequate harmony with the reported clinical data. The incorporation of pathophysiological changes in albumin concentration in the tuberculosis population revealed improved prediction of clearance. The developed PBPK drug–disease models have efficiently described rifampicin PK in tuberculosis and cirrhosis populations after administering single drug dose, as the ratio(Obs/pred) for all the PK parameters were within a two-fold error range. The mechanistic nature of the developed PBPK models may facilitate their extension to other diseases and drugs.
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Sauve, Jean-Francois, Fantine Kollar, and Gautier Mater. "102 Enhancing the coverage of a multi-agent exposure assessment tool through the modelling of over 100,000 measurements." Annals of Work Exposures and Health 68, Supplement_1 (June 1, 2024): 1. http://dx.doi.org/10.1093/annweh/wxae035.046.
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Abstract Introduction The INRS developed an online tool (https://www.inrs.fr/outil110) that provides descriptive statistics of concentrations for work situations defined by users using up to ten exposure variables. Statistics are calculated from measurements collected in French workplaces and recorded in the Colchic database. However, a given work situation may have only few measurements available for reporting reliable descriptive statistics. We explored the use of mixed-effects models to address this issue. Methodology We selected individual measurements collected between 2010- 2022. We defined a core model that contained all variables available in the tool: industry, occupation, task, and product associated with exposure (random effects), number of workers, general ventilation, local exhaust ventilation, exposure frequency, process, and sampling duration (fixed effects). We modeled each agent separately (minimum 100 measurements) after excluding highly correlated variables. Models were cross-validated and predicted concentrations were compared to those recorded in Colchic from January to July 2023 (minimum 10 measurements per agent). Results We fitted models to 134 agents with 101 to 11,819 measurements (median 405). The median cross-validation R2 across agents was 0.45 (interquartile interval 0.35-0.56). Predictive performance for 2023 concentrations by agent (n=48) was lower (median R2 0.10, interquartile interval 0.02-0.22; median relative bias -15%); coverage of the 80% prediction intervals was adequate (median 79%, interquartile interval 69-88%). Conclusions While the cross-validation showed promising results, the performance of the models to predict new concentrations was less satisfactory. Factors associated with poorer predictive performance should be investigated and remediated prior to integrating these models in the tool.
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Paulik, Ryan, Shaun Williams, and Benjamin Popovich. "Spatial Transferability of Residential Building Damage Models between Coastal and Fluvial Flood Hazard Contexts." Journal of Marine Science and Engineering 11, no. 10 (October 11, 2023): 1960. http://dx.doi.org/10.3390/jmse11101960.
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This study investigates residential building damage model transferability between coastal and fluvial flood hazard contexts. Despite the frequency of damaging coastal flood events, empirical damage models from fluvial flooding are often applied in quantitative coastal flood risk assessments. This assumes that building damage response is similar from the exposure to different flood sources. Here, we use empirical data from coastal, riverine and riverine-levee breach flooding events to analyse residential building damage. Damage is analysed by applying univariable and multivariable learning models to determine the importance of explanatory variables for relative damage prediction. We observed that the larger explanatory variable range considered in multivariable models led to higher predictive accuracy than univariable models in all flood contexts. Transfer analysis using multivariable models showed that models trained on event-specific damage data had higher predictive accuracy than models learned on all damage data or on data from other events and locations. This finding highlights the need for damage models to replicate local damage factors for reliable application across different flood hazard contexts.
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Jakasa, I., and S. Kezic. "Evaluation of in-vivo animal and in-vitro models for prediction of dermal absorption in man." Human & Experimental Toxicology 27, no. 4 (April 2008): 281–88. http://dx.doi.org/10.1177/0960327107085826.
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Risk assessment of dermal exposure to chemicals requires percutaneous absorption data to link the external exposure to the systemic uptake. The most reliable data on percutaneous absorption are obtained from in-vivo human volunteer studies. In addition to ethical constrains, the conduct of these studies is not feasible for the large number of industrial chemicals in use today. Therefore, there is an increasing need for alternative methods to determine percutaneous absorption such as in-vitro assays and methods performed in vivo in experimental animals. In this article, recent comparative in-vitro and in-vivo studies on percutaneous absorption have been addressed with emphasis on the factors that may affect the predictive value of the in-vitro models. Furthermore, the use of animal models, in particular the rat skin, in prediction of percutaneous absorption in the human skin has been reviewed. In-vitro assays showed to be largely influenced by the experimental circumstances, such as type and thickness of the skin, receptor fluid, and the way in which percutaneous absorption is calculated. Rat skin showed consistently to be more permeable than human skin. However, the difference between human and rat skin does not show a consistent pattern between chemicals hampering prediction of human percutaneous absorption. To increase predictive value of in-vitro and animal models, the influence of experimental factors on the percutaneous absorption should be systematically investigated by comparison with human in-vivo data, resulting in more prescriptive guidelines.
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Zaitseva, N. V., M. A. Zemlyanova, Yu V. Koldibekova, and E. V. Peskova. "Scientific and methodological grounds for iterative prediction of risk and harm to human health under chemical environmental exposures: From protein targets to systemic metabolic disorders." Health Risk Analysis, no. 2 (June 2024): 18–31. http://dx.doi.org/10.21668/health.risk/2024.2.02.
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Increasing the predictive potential of early diagnostics and correction of negative outcomes is becoming especially relevant for preventing and reducing personalized and population health risks, including those caused by environmental exposures. The purpose of the study was to develop scientific and methodological grounds for iterative numerical prediction of risk and harm to human health under chemical environmental exposures. The study design was based on the consistent implementation of an algorithm for system analysis of the development of negative effects under a chemical environmental exposure, from protein targets to systemic metabolic disorders. The in-depth examination covered more than 1 million people living under real long-term combined inhalation exposure at doses up to 5–10 RfC. About 350 digital multifactor models, including about 5.5 thousand parameters, were evaluated. Structural bioinformation matrices were constructed to identify the sequence of response events at the molecular-cellular level. These events are initiated by the transformation of the protein-peptide profile of human blood plasma, which determine the metabolome. The study clarifies the elements of involvement of 20 target proteins in the pathogenesis of metabolic disorders associated with hypertension, dyslipidemia, obesity, hepatosis, and cognitive dysfunction associated with chemical combined exposure. The criteria for the safe content of 10 contaminants were substantiated, taking into account their combinations in human biological media. Predictive assessments of pathogenetic pathways were confirmed by the facts of their implementation at the cellular-tissue, organ and body level as metabolic disorders and existing diseases of the cardiovascular, nervous systems, lipoprotein metabolism, etc., proven to be associated with effects produced by airborne pollutants, including combined ones. The study expands the existing methodological approaches to assessing combined effects of chemicals taking into account parameterized cause-effect relations of biomarkers of exposure and effects and quantitative assessment of additional cases of risk occurrence. Assessment of the developed digital models revealed that combined chemical exposures were predominantly synergic and emergent (up to 70 % cases). We developed conceptual grounds and architecture of iterative risk prediction and the development of risk-associated diseases, including real harm to health upon elevated expression of protein targets. Thus, the digitized version of the forecast (digital platform), as a multi-level cascade model, is a tool for scientific analysis of a hygienic situation with the parameterization of expected negative outcomes. It determines methods for their correction and prevention, which increases the reliability of hygienic assessments and the validity of management decisions.
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Zaitseva, N. V., M. A. Zemlyanova, Yu V. Koldibekova, and E. V. Peskova. "Scientific and methodological grounds for iterative prediction of risk and harm to human health under chemical environmental exposures: From protein targets to systemic metabolic disorders." Health Risk Analysis, no. 2 (June 2024): 18–31. http://dx.doi.org/10.21668/health.risk/2024.2.02.eng.
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Increasing the predictive potential of early diagnostics and correction of negative outcomes is becoming especially relevant for preventing and reducing personalized and population health risks, including those caused by environmental exposures. The purpose of the study was to develop scientific and methodological grounds for iterative numerical prediction of risk and harm to human health under chemical environmental exposures. The study design was based on the consistent implementation of an algorithm for system analysis of the development of negative effects under a chemical environmental exposure, from protein targets to systemic metabolic disorders. The in-depth examination covered more than 1 million people living under real long-term combined inhalation exposure at doses up to 5–10 RfC. About 350 digital multifactor models, including about 5.5 thousand parameters, were evaluated. Structural bioinformation matrices were constructed to identify the sequence of response events at the molecular-cellular level. These events are initiated by the transformation of the protein-peptide profile of human blood plasma, which determine the metabolome. The study clarifies the elements of involvement of 20 target proteins in the pathogenesis of metabolic disorders associated with hypertension, dyslipidemia, obesity, hepatosis, and cognitive dysfunction associated with chemical combined exposure. The criteria for the safe content of 10 contaminants were substantiated, taking into account their combinations in human biological media. Predictive assessments of pathogenetic pathways were confirmed by the facts of their implementation at the cellular-tissue, organ and body level as metabolic disorders and existing diseases of the cardiovascular, nervous systems, lipoprotein metabolism, etc., proven to be associated with effects produced by airborne pollutants, including combined ones. The study expands the existing methodological approaches to assessing combined effects of chemicals taking into account parameterized cause-effect relations of biomarkers of exposure and effects and quantitative assessment of additional cases of risk occurrence. Assessment of the developed digital models revealed that combined chemical exposures were predominantly synergic and emergent (up to 70 % cases). We developed conceptual grounds and architecture of iterative risk prediction and the development of risk-associated diseases, including real harm to health upon elevated expression of protein targets. Thus, the digitized version of the forecast (digital platform), as a multi-level cascade model, is a tool for scientific analysis of a hygienic situation with the parameterization of expected negative outcomes. It determines methods for their correction and prevention, which increases the reliability of hygienic assessments and the validity of management decisions.
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Hong, Hyunsu, IlHwan Choi, Hyungjin Jeon, Yumi Kim, Jae-Bum Lee, Cheong Hee Park, and Hyeon Soo Kim. "An Air Pollutants Prediction Method Integrating Numerical Models and Artificial Intelligence Models Targeting the Area around Busan Port in Korea." Atmosphere 13, no. 9 (September 9, 2022): 1462. http://dx.doi.org/10.3390/atmos13091462.
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Exposure to air pollutants, such as PM2.5 and ozone, has a serious adverse effect on health, with more than 4 million deaths, including early deaths. Air pollution in ports is caused by exhaust gases from various elements, including ships, and to reduce this, the International Maritime Organization (IMO) is also making efforts to reduce air pollution by regulating the sulfur content of fuel used by ships. Nevertheless, there is a lack of measures to identify and minimize the effects of air pollution. The Community Multiscale Air Quality (CMAQ) model is the most used to understand the effects of air pollution. In this paper, we propose a hybrid model combining the CMAQ model and RNN-LSTM, an artificial neural network model. Since the RNN-LSTM model has very good predictive performance, combining these two models can improve the spatial distribution prediction performance of a large area at a relatively low cost. In fact, as a result of prediction using the hybrid model, it was found that IOA improved by 0.235~0.317 and RMSE decreased by 4.82~8.50 μg/m3 compared to the case of using only CMAQ. This means that when PM2.5 is predicted using the hybrid model, the accuracy of the spatial distribution of PM2.5 can be improved. In the future, if real-time prediction is performed using the hybrid model, the accuracy of the calculation of exposure to air pollutants can be increased, which can help evaluate the impact on health. Ultimately, it is expected to help reduce the damage caused by air pollution through accurate predictions of air pollution.
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Nastić, Filip. "Predlog modela za predviđanje koncentracije suspendovanih (PM2.5) čestica u vazduhu." Energija, ekonomija, ekologija XXV, no. 3 (2023): 39–44. http://dx.doi.org/10.46793/eee23-3.39n.
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Increasing number of studies indicate the negative influence of Particulate Matter on human health. One of the ways to avoid their negative consequences is a timely prediction of airborne PM2.5 concentrations. Knowing hourly PM2.5 concentrations, people could organize their daily activities to reduce exposure to intensive pollution. With the goal to train an optimal predictive model, the predictive performances of three machine learning algorithms were analysed: „Random forest“, „XGBoost“, and „Light gradient boosting machine“. Using mentioned regression algorithms in combination with meteorological and chronological data, the models were trained to predict hourly airborne PM2.5 concentrations with relatively high accuracy. The data about airborne PM2.5 concentrations were collected using the laser sensor in the city of Kragujevac, Serbia. The trained models were evaluated using the coefficient of determination (R2), mean absolute error (MAE), and rootmean-square error (RMSE).
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Zhou, Tianyi, Yaojia Shen, Jinlang Lyu, Li Yang, Hai-Jun Wang, Shenda Hong, and Yuelong Ji. "Medication Usage Record-Based Predictive Modeling of Neurodevelopmental Abnormality in Infants under One Year: A Prospective Birth Cohort Study." Healthcare 12, no. 7 (March 24, 2024): 713. http://dx.doi.org/10.3390/healthcare12070713.
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Early identification of children with neurodevelopmental abnormality is a major challenge, which is crucial for improving symptoms and preventing further decline in children with neurodevelopmental abnormality. This study focuses on developing a predictive model with maternal sociodemographic, behavioral, and medication-usage information during pregnancy to identify infants with abnormal neurodevelopment before the age of one. In addition, an interpretable machine-learning approach was utilized to assess the importance of the variables in the model. In this study, artificial neural network models were developed for the neurodevelopment of five areas of infants during the first year of life and achieved good predictive efficacy in the areas of fine motor and problem solving, with median AUC = 0.670 (IQR: 0.594, 0.764) and median AUC = 0.643 (IQR: 0.550, 0.731), respectively. The final model for neurodevelopmental abnormalities in any energy region of one-year-old children also achieved good prediction performance. The sensitivity is 0.700 (IQR: 0.597, 0.797), the AUC is 0.821 (IQR: 0.716, 0.833), the accuracy is 0.721 (IQR: 0.696, 0.739), and the specificity is 0.742 (IQR: 0.680, 0.748). In addition, interpretable machine-learning methods suggest that maternal exposure to drugs such as acetaminophen, ferrous succinate, and midazolam during pregnancy affects the development of specific areas of the offspring during the first year of life. This study established predictive models of neurodevelopmental abnormality in infants under one year and underscored the prediction value of medication exposure during pregnancy for the neurodevelopmental outcomes of the offspring.
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M. Dzhambov, Angel, Donka D. Dimitrova, and Tanya H. Turnovska. "Improving Traffic Noise Simulations Using Space Syntax: Preliminary Results from Two Roadway Systems." Archives of Industrial Hygiene and Toxicology 65, no. 3 (September 29, 2014): 259–72. http://dx.doi.org/10.2478/10004-1254-65-2014-2469.
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AbstractNoise pollution is one of the four major pollutions in the world. In order to implement adequate strategies for noise control, assessment of traffic-generated noise is essential in city planning and management. The aim of this study was to determine whether space syntax could improve the predictive power of noise simulation. This paper reports a record linkage study which combined a documentary method with space syntax analysis. It analyses data about traffic flow as well as field-measured and computer-simulated traffic noise in two Bulgarian agglomerations. Our findings suggest that space syntax might have a potential in predicting traffic noise exposure by improving models for noise simulations using specialised software or actual traffic counts. The scientific attention might need to be directed towards space syntax in order to study its further application in current models and algorithms for noise prediction.
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Jankowska, Agnieszka, Sławomir Czerczak, Małgorzata Kucharska, Wiktor Wesołowski, Piotr Maciaszek, and Małgorzata Kupczewska-Dobecka. "Application of predictive models for estimation of health care workers exposure to sevoflurane." International Journal of Occupational Safety and Ergonomics 21, no. 4 (October 2, 2015): 471–79. http://dx.doi.org/10.1080/10803548.2015.1086183.
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Trinh, Tung X., and Jongwoon Kim. "Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity." Nanomaterials 11, no. 1 (January 7, 2021): 124. http://dx.doi.org/10.3390/nano11010124.
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Co-exposure of nanomaterials and chemicals can cause mixture toxicity effects to living organisms. Predictive models might help to reduce the intensive laboratory experiments required for determining the toxicity of the mixtures. Previously, concentration addition (CA), independent action (IA), and quantitative structure–activity relationship (QSAR)-based models were successfully applied to mixtures of organic chemicals. However, there were few studies concerning predictive models for toxicity of nano-mixtures before June 2020. Previous reviews provided comprehensive knowledge of computational models and mechanisms for chemical mixture toxicity. There is a gap in the reviewing of datasets and predictive models, which might cause obstacles in the toxicity assessment of nano-mixtures by using in silico approach. In this review, we collected 183 studies of nano-mixture toxicity and curated data to investigate the current data and model availability and gap and to derive research challenges to facilitate further experimental studies for data gap filling and the development of predictive models.
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Trinh, Tung X., and Jongwoon Kim. "Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity." Nanomaterials 11, no. 1 (January 7, 2021): 124. http://dx.doi.org/10.3390/nano11010124.
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Co-exposure of nanomaterials and chemicals can cause mixture toxicity effects to living organisms. Predictive models might help to reduce the intensive laboratory experiments required for determining the toxicity of the mixtures. Previously, concentration addition (CA), independent action (IA), and quantitative structure–activity relationship (QSAR)-based models were successfully applied to mixtures of organic chemicals. However, there were few studies concerning predictive models for toxicity of nano-mixtures before June 2020. Previous reviews provided comprehensive knowledge of computational models and mechanisms for chemical mixture toxicity. There is a gap in the reviewing of datasets and predictive models, which might cause obstacles in the toxicity assessment of nano-mixtures by using in silico approach. In this review, we collected 183 studies of nano-mixture toxicity and curated data to investigate the current data and model availability and gap and to derive research challenges to facilitate further experimental studies for data gap filling and the development of predictive models.
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Clarke, Erik, Kathleen None Chiotos, James Harrigan, Ebbing Lautenbach, Emily Reesey, Magda Wernovsky, Pam Tolomeo, et al. "Comparison of Respiratory Microbiome Disruption Indices to Predict VAP and VAE risk at LTACH Admission." Infection Control & Hospital Epidemiology 41, S1 (October 2020): s179—s180. http://dx.doi.org/10.1017/ice.2020.711.
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Background: Healthcare exposure results in significant microbiome disruption, particularly in the setting of critical illness, which may contribute to risk for healthcare-associated infections (HAIs). Patients admitted to long-term acute-care hospitals (LTACHs) have extensive prior healthcare exposure and critical illness; significant microbiome disruption has been previously documented among LTACH patients. We compared the predictive value of 3 respiratory tract microbiome disruption indices—bacterial community diversity, dominance, and absolute abundance—as they relate to risk for ventilator-associated pneumonia (VAP) and adverse ventilator-associated events (VAE), which commonly complicate LTACH care. Methods: We enrolled 83 subjects on admission to an academic LTACH for ventilator weaning and performed longitudinal sampling of endotracheal aspirates, followed by 16S rRNA gene sequencing (Illumina HiSeq), bacterial community profiling (QIIME2) for diversity, and 16S rRNA quantitative PCR (qPCR) for total bacterial abundance. Statistical analyses were performed with R and Stan software. Mixed-effects models were fit to relate the admission MDIs to subsequent clinically diagnosed VAP and VAE. Results: Of the 83 patients, 19 had been diagnosed with pneumonia during the 14 days prior to LTACH admission (ie, “recent past VAP”); 23 additional patients were receiving antibiotics consistent with empiric VAP therapy within 48 hours of admission (ie, “empiric VAP therapy”); and 41 patients had no evidence of VAP at admission (ie, “no suspected VAP”). We detected no statistically significant differences in admission Shannon diversity, maximum amplicon sequence variant (ASV)–level proportional abundance, or 16S qPCR across the variables of interest. In isolation, all 3 admission microbiome disruption indices showed poor predictive performance, though Shannon diversity performed better than maximum ASV abundance. Predictive models that combined (1) bacterial diversity or abundance with (2) recent prior VAP diagnosis and (3) concurrent antibiotic exposure best predicted 14-day VAP (type S error < 0.05) and 30-day VAP (type S error < 0.003). In this cohort, VAE risk was paradoxically associated with higher admission Shannon diversity and lower admission maximum ASV abundance. Conclusions: In isolation, respiratory tract microbiome disruption indices obtained at LTACH admission showed poor predictive performance for subsequent VAP and VAE. But diversity and abundance models incorporating recent VAP history and admission antibiotic exposure performed well predicting 14-day and 30-day VAP.Disclosures: NoneFunding: None
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Ruan, Yanmei, Guanhao Huang, Jinwei Zhang, Shiqi Mai, Chunrong Gu, Xing Rong, Lili Huang, Wenfeng Zeng, and Zhi Wang. "Risk analysis of noise-induced hearing loss of workers in the automobile manufacturing industries based on back-propagation neural network model: a cross-sectional study in Han Chinese population." BMJ Open 14, no. 5 (May 2024): e079955. http://dx.doi.org/10.1136/bmjopen-2023-079955.
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ObjectivesThis study aims to predict the risk of noise-induced hearing loss (NIHL) through a back-propagation neural network (BPNN) model. It provides an early, simple and accurate prediction method for NIHL.DesignPopulation based, a cross sectional study.SettingHan, China.ParticipantsThis study selected 3266 Han male workers from three automobile manufacturing industries.Primary outcome measuresInformation including personal life habits, occupational health test information and occupational exposure history were collected and predictive factors of NIHL were screened from these workers. BPNN and logistic regression models were constructed using these predictors.ResultsThe input variables of BPNN model were 20, 16 and 21 important factors screened by univariate, stepwise and lasso-logistic regression. When the BPNN model was applied to the test set, it was found to have a sensitivity (TPR) of 83.33%, a specificity (TNR) of 85.92%, an accuracy (ACC) of 85.51%, a positive predictive value (PPV) of 52.85%, a negative predictive value of 96.46% and area under the receiver operating curve (AUC) is: 0.926 (95% CI: 0.891 to 0.961), which demonstrated the better overall properties than univariate-logistic regression modelling (AUC: 0.715) (95% CI: 0.652 to 0.777). The BPNN model has better predictive performance against NIHL than the stepwise-logistic and lasso-logistic regression model in terms of TPR, TNR, ACC, PPV and NPV (p<0.05); the area under the receiver operating characteristics curve of NIHL is also higher than that of the stepwise and lasso-logistic regression model (p<0.05). It was a relatively important factor in NIHL to find cumulative noise exposure, auditory system symptoms, age, listening to music or watching video with headphones, exposure to high temperature and noise exposure time in the trained BPNN model.ConclusionsThe BPNN model was a valuable tool in dealing with the occupational risk prediction problem of NIHL. It can be used to predict the risk of an individual NIHL.
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Bloomfield, Celeste, Christine E. Staatz, Sean Unwin, and Stefanie Hennig. "Assessing Predictive Performance of Published Population Pharmacokinetic Models of Intravenous Tobramycin in Pediatric Patients." Antimicrobial Agents and Chemotherapy 60, no. 6 (March 21, 2016): 3407–14. http://dx.doi.org/10.1128/aac.02654-15.
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Several population pharmacokinetic models describe the dose-exposure relationship of tobramycin in pediatric patients. Before the implementation of these models in clinical practice for dosage adjustment, their predictive performance should be externally evaluated. This study tested the predictive performance of all published population pharmacokinetic models of tobramycin developed for pediatric patients with an independent patient cohort. A literature search was conducted to identify suitable models for testing. Demographic and pharmacokinetic data were collected retrospectively from the medical records of pediatric patients who had received intravenous tobramycin. Tobramycin exposure was predicted from each model. Predictive performance was assessed by visual comparison of predictions to observations, by calculation of bias and imprecision, and through the use of simulation-based diagnostics. Eight population pharmacokinetic models were identified. A total of 269 concentration-time points from 41 pediatric patients with cystic fibrosis were collected for external evaluation. Three models consistently performed best in all evaluations and had mean errors ranging from −0.4 to 1.8 mg/liter, relative mean errors ranging from 4.9 to 29.4%, and root mean square errors ranging from 47.8 to 66.9%. Simulation-based diagnostics supported these findings. Models that allowed a two-compartment disposition generally had better predictive performance than those that used a one-compartment disposition model. Several published models of the pharmacokinetics of tobramycin showed reasonable low levels of bias, although all models seemed to have some problems with imprecision. This suggests that knowledge of typical pharmacokinetic behavior and patient covariate values alone without feedback concentration measurements from individual patients is not sufficient to make precise predictions.
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Wu, Wenzhu, Jing Xu, Yezhi Dou, Jia Yu, Deyang Kong, and Lixiang Zhou. "Bioaccumulation of Pyraoxystrobin and Its Predictive Evaluation in Zebrafish." Toxics 10, no. 1 (December 24, 2021): 5. http://dx.doi.org/10.3390/toxics10010005.
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This paper aims to understand the bioaccumulation of pyraoxystrobin in fish. Using a flow-through bioconcentration method, the bioconcentration factor (BCF) and clearance rate of pyraoxystrobin in zebrafish were measured. The measured BCF values were then compared to those estimated from three commonly used predication models. At the exposure concentrations of 0.1 μg/L and 1.0 μg/L, the maximum BCF values for pyraoxystrobin in fish were 820.8 and 265.9, and the absorption rate constants (K1) were 391.0 d−1 and 153.2 d−1, respectively. The maximum enrichment occurred at 12 d of exposure. At the two test concentrations, the clearance rate constant (K2) in zebrafish was 0.5795 and 0.4721, and the half-life (t1/2) was 3.84 d and 3.33 d, respectively. The measured BCF values were close to those estimated from bioconcentration predication models.
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Lourenço, Vanessa S. C., Neusa L. Figueiredo, and Michiel A. Daam. "Application of General Unified Threshold Models to Predict Time-Varying Survival of Mayfly Nymphs Exposed to Three Neonicotinoids." Water 16, no. 8 (April 10, 2024): 1082. http://dx.doi.org/10.3390/w16081082.
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Pesticide exposure patterns tested in laboratory bioassays often do not match real-world pesticide exposure profiles in edge-of-field waterbodies. Toxicokinetic–toxicodynamic (TKTD) models are therefore increasingly used, as they allow for predictions of the toxic effects under actual time-variable field exposures. The TKTD models from the General Unified Threshold models of Survival (GUTS), for example, are considered ready for use by regulators for calculating the survival rates for any time-variable exposure profile. However, questions remain regarding their predictive power for compounds showing increased toxicity over time, such as neonicotinoid insecticides. The aim of the present study was therefore to compare the GUTS-predicted 28 d toxicity values of three neonicotinoids (imidacloprid, clothianidin, and thiamethoxam) for the common New Zealand mayfly genus Deleatidium spp. with those observed in a previously published study. Overall, the GUTS modeling results underestimated the toxicity values derived experimentally. From the three neonicotinoids, clothianidin showed the best fit between the estimated and observed 28 d LC50 (median-lethal-concentration) values. Shortcomings of the modeling exercise, future research needs, and implications for the application of GUTS models in regulatory risk assessment are discussed.
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Ho, Vikki, Coraline Danieli, Michal Abrahamowicz, Anne-Sophie Belanger, Vanessa Brunetti, Edgard Delvin, Julie Lacaille, and Anita Koushik. "Predicting serum vitamin D concentrations based on self-reported lifestyle factors and personal attributes." British Journal of Nutrition 120, no. 7 (August 6, 2018): 803–12. http://dx.doi.org/10.1017/s000711451800199x.
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AbstractEvidence supports the role of vitamin D in various conditions of development and ageing. Serum 25-hydroxyvitamin D (25(OH)D) is the best indicator for current vitamin D status. However, the cost of its measurement can be prohibitive in epidemiological research. We developed and validated multivariable regression models that quantified the relationships between vitamin D determinants, measured through an in-person interview, and serum 25(OH)D concentrations. A total of 200 controls participating in a population-based case–control study in Montreal, Canada, provided a blood specimen and completed an in-person interview on socio-demographic, reproductive, medical and lifestyle characteristics and personal attributes. Serum 25(OH)D concentrations were quantified by liquid chromatography–tandem MS. Multivariable least squares regression was used to build models that predict 25(OH)D concentrations from interview responses. We assessed high-order effects, performed sensitivity analysis using the lasso method and conducted cross-validation of the prediction models. Prediction models were built for users and non-users of vitamin D supplements separately. Among users, alcohol intake, outdoor time, sun protection, dose of supplement use, menopausal status and recent vacation were predictive of 25(OH)D concentrations. Among non-users, BMI, sun sensitivity, season and recent vacation were predictive of 25(OH)D concentrations. In cross-validation, 46–47 % of the variation in 25(OH)D concentrations were explained by these predictors. In the absence of 25(OH)D measures, our study supports that predicted 25(OH)D scores may be used to assign exposure in epidemiological studies that examine vitamin D exposure.
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Mari, Lorenzo, Enrico Bertuzzo, Flavio Finger, Renato Casagrandi, Marino Gatto, and Andrea Rinaldo. "On the predictive ability of mechanistic models for the Haitian cholera epidemic." Journal of The Royal Society Interface 12, no. 104 (March 2015): 20140840. http://dx.doi.org/10.1098/rsif.2014.0840.
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Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. We address the above issue in a formal model comparison framework and provide a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels and coupling mechanisms. Reference is made to records of the recent Haiti cholera epidemics. Our intensive computations and objective model comparisons show that spatially explicit models accounting for spatial connections have better explanatory power than spatially disconnected ones for short-to-intermediate calibration windows, while parsimonious, spatially disconnected models perform better with long training sets. On average, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management.
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Virji, Mohammed Abbas, Caroline Groth, Xiaoming Liang, and Paul Henneberger. "147 Association between mixed exposures to cleaning chemicals and asthma outcomes." Annals of Work Exposures and Health 68, Supplement_1 (June 1, 2024): 1. http://dx.doi.org/10.1093/annweh/wxae035.219.
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Abstract Introduction Cleaning products are complex mixtures of chemicals and their use in healthcare is associated with elevated prevalence/odds of asthma outcomes in models with single exposure variables. To better represent workplace mixed exposures, we explored the use of Bayesian Kernel Machine Regression (BKMR) combined with prior toxicological knowledge on asthmagenic potential to understand the combined effect of correlated mixtures. Methods Healthcare workers from nine occupations completed a questionnaire with modules on cleaning and disinfecting tasks and product use. Quantitative exposure to total and eight specific volatile organic compounds were assessed at five different hospitals and were assigned to participants based on predictive statistical models and responses on questionnaires. Exposures were classified as asthmagens or non-classified based on toxicological evidence. A five-level asthma outcome variable was created by hierarchical clustering of 27 respiratory symptom/care variables. Exposure-response relationships for asthma clusters were explored using BKMR for mixed exposures. All models were adjusted for age, sex, education, smoking and allergic status. Results Group probability of inclusion was always higher for asthmagens compared to non-classified for all asthma clusters. Within asthmagens, acetone and chloroform had the higher probabilities of inclusion for several asthma clusters. Higher levels of acetone had the highest risk for all health clusters when evaluating the effect of mixed asthmagens. Among non-classified, BTEX chemicals and methyl-methacrylate had the higher probabilities of inclusion for several asthma clusters. Conclusions Multipollutant models combined with toxicological knowledge is a more realistic approach to model workplace mixed exposures although the effect of correlated variables cannot be entirely eliminated.
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De Vito, Saverio, Elena Esposito, Ettore Massera, Fabrizio Formisano, Grazia Fattoruso, Sergio Ferlito, Antonio Del Giudice, et al. "Crowdsensing IoT Architecture for Pervasive Air Quality and Exposome Monitoring: Design, Development, Calibration, and Long-Term Validation." Sensors 21, no. 15 (July 31, 2021): 5219. http://dx.doi.org/10.3390/s21155219.
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A pervasive assessment of air quality in an urban or mobile scenario is paramount for personal or city-wide exposure reduction action design and implementation. The capability to deploy a high-resolution hybrid network of regulatory grade and low-cost fixed and mobile devices is a primary enabler for the development of such knowledge, both as a primary source of information and for validating high-resolution air quality predictive models. The capability of real-time and cumulative personal exposure monitoring is also considered a primary driver for exposome monitoring and future predictive medicine approaches. Leveraging on chemical sensing, machine learning, and Internet of Things (IoT) expertise, we developed an integrated architecture capable of meeting the demanding requirements of this challenging problem. A detailed account of the design, development, and validation procedures is reported here, along with the results of a two-year field validation effort.
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Baliashvili, Davit, Francisco Averhoff, Ana Kasradze, Stephanie J. Salyer, Giorgi Kuchukhidze, Amiran Gamkrelidze, Paata Imnadze, et al. "Risk factors and genotype distribution of hepatitis C virus in Georgia: A nationwide population-based survey." PLOS ONE 17, no. 1 (January 21, 2022): e0262935. http://dx.doi.org/10.1371/journal.pone.0262935.
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In preparation for the National Hepatitis C Elimination Program in the country of Georgia, a nationwide household-based hepatitis C virus (HCV) seroprevalence survey was conducted in 2015. Data were used to estimate HCV genotype distribution and better understand potential sex-specific risk factors that contribute to HCV transmission. HCV genotype distribution by sex and reported risk factors were calculated. We used explanatory logistic regression models stratified by sex to identify behavioral and healthcare-related risk factors for HCV seropositivity, and predictive logistic regression models to identify additional variables that could help predict the presence of infection. Factors associated with HCV seropositivity in explanatory models included, among males, history of injection drug use (IDU) (aOR = 22.4, 95% CI = 12.7, 39.8) and receiving a blood transfusion (aOR = 3.6, 95% CI = 1.4, 8.8), and among females, history of receiving a blood transfusion (aOR = 4.0, 95% CI 2.1, 7.7), kidney dialysis (aOR = 7.3 95% CI 1.5, 35.3) and surgery (aOR = 1.9, 95% CI 1.1, 3.2). The male-specific predictive model additionally identified age, urban residence, and history of incarceration as factors predictive of seropositivity and were used to create a male-specific exposure index (Area under the curve [AUC] = 0.84). The female-specific predictive model had insufficient discriminatory performance to support creating an exposure index (AUC = 0.61). The most prevalent HCV genotype (GT) nationally was GT1b (40.5%), followed by GT3 (34.7%) and GT2 (23.6%). Risk factors for HCV seropositivity and distribution of HCV genotypes in Georgia vary substantially by sex. The HCV exposure index developed for males could be used to inform targeted testing programs.
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Niewiadomski, A. P., H. Badura, and G. Pach. "Recommendations for methane prognostics and adjustment of short-term prevention measures based on methane hazard levels in coal mine longwalls." E3S Web of Conferences 266 (2021): 08001. http://dx.doi.org/10.1051/e3sconf/202126608001.
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The article describes the recommended procedure for conducting methane forecasts and selection of the methane prevention measures that adequately reflect the level of risk of methane combustion and explosion. The appropriate selection of measures to prevent methane exposure can be effective at mitigating the exposure risk of the miners and other mine employees. Implementation of these measures can have the additional benefit of increasing mine output and efficiency. For example, prediction of methane concentrations can reduce the instances of unplanned equipment downtime to maintain mine safety and integrity. The presented procedure is the culmination of extensive research on three predictive models of short-term average methane concentrations. Identifying the advantages and disadvantages of the models became possible by verifying the models against a nearly 500-day dataset obtained from 7 longwalls with identified significant methane content. Furthermore, selected studies were presented based on one of the datasets obtained from the U-ventilated longwall.
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Smith, Lauren A., Meng Qian, Elise Ng, Yongzhao Shao, Marianne Berwick, DeAnn Lazovich, and David Polsky. "Development of a melanoma risk prediction model incorporating MC1R genotype and indoor tanning exposure." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 8574. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.8574.
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8574 Background: Invasive melanoma is the second most common cancer in young adults, yet they exhibit poor skin-protective behavior. We previously demonstrated a significant association between melanoma risk, melanocortin receptor (MC1R) polymorphisms, and indoor ultraviolet light (UV) exposure. As existing melanoma risk models do not take these factors into account, we investigated whether these variables would improve the predictive ability of a clinical risk model, especially in a younger population. Methods: We determined MC1R genotype and collected self-reported phenotypic and UV (indoor and outdoor) exposure variables from 923 melanoma cases and 813 healthy controls between ages 25 – 59 from the Minnesota Skin Health Study. These data were initially used to develop a clinical melanoma risk model (Model A) with conventional risk factors (i.e. age; gender; hair, skin, and eye color; mole count, freckling, and family melanoma history). We then developed a second model (Model B) combining outdoor UV, indoor UV, and MC1R genotype variables with those in Model A to determine if the model’s predictive ability improved. Finally, we assessed the predictive ability of both models when confined to younger subjects (ages 25-35). Results: The clinical model, combining conventional melanoma risk factors (Model A), yielded an area under the receiver operating characteristic curve (AUC) of 0.72 (95% CI 0.70 – 0.75). Incorporating outdoor UV, indoor UV, and MC1R genotype variables (Model B) increased the AUC to 0.75 (p=0.001, 0.72 – 0.77). Confining the analyses to younger subjects substantially increased the AUC of Model A to 0.78 (0.72 – 0.84) and Model B to 0.83 (p=0.007, 0.78-0.88). Conclusions: This preliminary risk model is the first in melanoma to demonstrate that the addition of genotypic data and indoor UV exposure results in a measurable increase in predictive ability when compared to models comprised only of clinical and outdoor UV exposure variables. The enhanced predictive ability of the model (AUC>0.80) when limited to younger individuals suggests the potential for developing tools to facilitate targeted screening and prevention strategies in melanoma.
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Narimane, Kebieche, Ali Farzana Liakath, Yim Seungae, Ali Mohamed, Lambert Claude, and Soulimani Rachid. "Exploring Environmental Neurotoxicity Assessment Using Human Stem Cell-Derived Models." Journal of Stem Cell Therapy and Transplantation 8, no. 1 (2024): 054–68. http://dx.doi.org/10.29328/journal.jsctt.1001044.
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Neurotoxicity is increasingly recognized as a critical factor impacting long-term health, with growing evidence linking it to both neurodevelopmental and neurodegenerative diseases. Pesticides, widely used in agriculture and industry, have emerged as significant contributors to neurotoxic risk, given their capacity to disrupt key neurodevelopmental processes at low exposure levels. As conventional animal models present limitations in interspecies translation, human-derived neuron-based in vitro screening strategies are urgently needed to assess potential toxicants accurately. Human-induced pluripotent stem cells (hiPSCs) offer an innovative and scalable source for human-specific neuronal models that complement traditional animal-based approaches and support the development of predictive assays for neurotoxicity. Recent various stem cell models, including 2D cultures, 3D organoids, and microfluidic systems, are now available, advancing predictive neurotoxicology by simulating key aspects of human neural development and function. With the integration of High-Throughput (HT) and High-Content (HC) screening methodologies, these hiPSC-based systems enable efficient, large-scale evaluation of chemical effects on neural cells, enhancing our ability to detect early biomarkers of neurotoxic effects. Identifying early biomarkers of neurotoxic is essential to developing therapeutic interventions before irreversible damage occurs. This is particularly crucial in the context of developmental neurotoxicity, where early exposure to toxicants can have lifelong consequences. This review specifically presents an in-depth overview of the current progress in hiPSC-derived neural models and their applications in neurotoxicity testing, with a specific focus on their utility in assessing pesticide-induced neurotoxicity. Emphasizing future research priorities, we highlight the potential of these models to transform predictive toxicology, offering more human-relevant assessments and advancing the field toward a more precise evaluation of environmental neurotoxicants.
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Bliznyuk, Nikolay, Christopher J. Paciorek, Joel Schwartz, and Brent Coull. "Nonlinear predictive latent process models for integrating spatio-temporal exposure data from multiple sources." Annals of Applied Statistics 8, no. 3 (September 2014): 1538–60. http://dx.doi.org/10.1214/14-aoas737.
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Ejohwomu, Obuks Augustine, Olakekan Shamsideen Oshodi, Majeed Oladokun, Oyegoke Teslim Bukoye, Nwabueze Emekwuru, Adegboyega Sotunbo, and Olumide Adenuga. "Modelling and Forecasting Temporal PM2.5 Concentration Using Ensemble Machine Learning Methods." Buildings 12, no. 1 (January 4, 2022): 46. http://dx.doi.org/10.3390/buildings12010046.
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Exposure of humans to high concentrations of PM2.5 has adverse effects on their health. Researchers estimate that exposure to particulate matter from fossil fuel emissions accounted for 18% of deaths in 2018—a challenge policymakers argue is being exacerbated by the increase in the number of extreme weather events and rapid urbanization as they tinker with strategies for reducing air pollutants. Drawing on a number of ensemble machine learning methods that have emerged as a result of advancements in data science, this study examines the effectiveness of using ensemble models for forecasting the concentrations of air pollutants, using PM2.5 as a representative case. A comprehensive evaluation of the ensemble methods was carried out by comparing their predictive performance with that of other standalone algorithms. The findings suggest that hybrid models provide useful tools for PM2.5 concentration forecasting. The developed models show that machine learning models are efficient in predicting air particulate concentrations, and can be used for air pollution forecasting. This study also provides insights into how climatic factors influence the concentrations of pollutants found in the air.
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Howard-Azzeh, Mohammad, David L. Pearl, Terri L. O’Sullivan, and Olaf Berke. "Comparing the diagnostic performance of ordinary, mixed, and lasso logistic regression models at identifying opioid and cannabinoid poisoning in U.S. dogs using pet demographic and clinical data reported to an animal poison control center (2005–2014)." PLOS ONE 18, no. 7 (July 10, 2023): e0288339. http://dx.doi.org/10.1371/journal.pone.0288339.
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Researchers have begun studying the impact of human opioid and cannabinoid use on dog populations. These studies have used data from an animal poison control center (APCC) and there are concerns that due to the illicit nature and social stigma concerning the use of these drugs, owners may not always be forthcoming with veterinarians or APCC staff regarding pet exposures to these toxicants. As a result, models derived from APCC data that examine the predictability of opioid and cannabinoid dog poisonings using pet demographic and health disorder information may help veterinarians or APCC staff more reliably identify these toxicants when examining or responding to a call concerning a dog poisoned by an unknown toxicant. The fitting of epidemiologically informed statistical models has been useful for identifying factors associated with various health conditions and as predictive tools. However, machine learning, including lasso regression, has many useful features as predictive tools, including the ability to incorporate large numbers of independent variables. Consequently, the objectives of our study were: 1) identify pet demographic and health disorders associated with opioid and cannabinoid dog poisonings using ordinary and mixed logistic regression models; and 2) compare the predictive performance of these models to analogous lasso logistic regression models. Data were obtained from reports of dog poisoning events collected by the American Society for the Prevention of Cruelty to Animals’ (ASPCA) Animal Poisoning Control Center, from 2005–2014. We used ordinary and mixed logistic regression models as well as lasso logistic regression models with and without controlling for autocorrelation at the state level to train our models on half the dataset and test their predictive performance on the remainder. Although epidemiologically informed logistic regression models may require substantial knowledge of the disease systems being investigated, they had the same predictive abilities as lasso logistic regression models. All models had relatively high predictive parameters except for positive predictive values, due to the rare nature of calls concerning opioid and cannabinoid poisonings. Ordinary and mixed logistic regression models were also substantially more parsimonious than their lasso equivalents while still allowing for the epidemiological interpretation of model coefficients. Controlling for autocorrelation had little effect on the predictive performance of all models, but it did reduce the number of variables included in lasso models. Several disorder variables were associated with opioid and cannabinoid calls that were consistent with the acute effects of these toxicants. These models may help build diagnostic evidence concerning dog exposure to opioids and cannabinoids, saving time and resources when investigating these cases.
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Fagerholm, Urban, Sven Hellberg, and Ola Spjuth. "Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology." Molecules 26, no. 9 (April 28, 2021): 2572. http://dx.doi.org/10.3390/molecules26092572.
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Oral bioavailability (F) is an essential determinant for the systemic exposure and dosing regimens of drug candidates. F is determined by numerous processes, and computational predictions of human estimates have so far shown limited results. We describe a new methodology where F in humans is predicted directly from chemical structure using an integrated strategy combining 9 machine learning models, 3 sets of structural alerts, and 2 physiologically-based pharmacokinetic models. We evaluate the model on a benchmark dataset consisting of 184 compounds, obtaining a predictive accuracy (Q2) of 0.50, which is successful according to a pharmaceutical industry proposal. Twenty-seven compounds were found (beforehand) to be outside the main applicability domain for the model. We compare our results with interspecies correlations (rat, mouse and dog vs. human) using the same dataset, where animal vs. human-correlations (R2) were found to be 0.21 to 0.40 and maximum prediction errors were smaller than maximum interspecies differences. We conclude that our method has sufficient predictive accuracy to be practically useful with applications in human exposure and dose predictions, compound optimization and decision making, with potential to rationalize drug discovery and development and decrease failures and overexposures in early clinical trials with candidate drugs.
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Kantasiripitak, W., A. Outtier, D. Thomas, A. Kensert, Z. Wang, J. Sabino, S. G. Wicha, S. Vermeire, M. Ferrante, and E. Dreesen. "P333 Precise and unbiased infliximab dosing in patients with inflammatory bowel diseases using a multi-model averaging approach." Journal of Crohn's and Colitis 16, Supplement_1 (January 1, 2022): i350—i351. http://dx.doi.org/10.1093/ecco-jcc/jjab232.460.
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Abstract Background Underexposure to IFX is a common cause of loss of response in patients with inflammatory bowel disease (IBD). To ensure adequate – but not unnecessarily high – exposure, we aimed to identify a precise and unbiased approach for model-based dosing of IFX in patients with IBD. Methods A retrospective study was performed using data from, 54 patients on IFX maintenance therapy. The predictive performance of, 18 published IFX population pharmacokinetic (popPK) models was evaluated using NONMEM (v7.5). A priori prediction (only based on covariates) and Bayesian forecasting (BF; also based on one to three consecutively measured IFX trough concentrations; TC-2, TC-1, and TC0) of the IFX TC+1 was evaluated (Fig, 1). The predictive performance of a single-model approach was compared with two automated multi-model approaches: a model selection algorithm (MSA) and a model averaging algorithm (MAA).1 Relative bias (rBias) and relative root mean square error (rRMSE) were used to determine bias and imprecision of the predicted versus observed TC+1. Clinical acceptability was defined as an rBias between ±20% with a, 95%CI including zero. The predicted and observed TC+1 were classified at, 5.0 mg/L TC target.2 Results Four models were selected based on their predictive performances and implemented in the TDMx software tool to support model-based IFX dosing in the forthcoming prospective MODIFI study (NCT04982172). A priori prediction of TC+1 was clinically unacceptable with both single- and multi-model approaches (rBias +30% to +97%, rRMSE, 107% to, 158%; Fig, 2A). Also, a priori prediction had the lowest classification accuracy (median, 59%, IQR, 59%-63%; Fig, 3A). Providing one IFX TC greatly improved predictive performance (rBias -10% to +15%, rRMSE, 30% to, 49%; Fig, 2B, 2C) and classification accuracy (TC-1: median, 70%, IQR, 63%-72%; TC0: median, 80%, IQR, 76%-84 %; Fig, 3B, 3C). More specifically, BF resulted in a significantly lower chance of a falsely predicted ≥5 mg/L TC+1 than a priori prediction (p<0.01). In comparison with BF with TC-1, the availability of TC0 significantly lowered the chance of falsely predicting TC+1 <5 mg/L (unnecessary dose optimisation) (p=0.0034). Providing more than one previous TC improved predictive performances only marginally (data not shown). In general, MAA performed better than MSA. Conclusion Conclusion: A multi-model averaging approach provided more reliable Bayesian forecasts than the single-model approach. Adding one previous trough concentration in addition to covariate information sufficed to provide accurate and unbiased predictions of future exposure. Concentration data collected with a rapid assay may reduce the chance of performing unnecessary dose optimisation. Reference1. Uster D CPT,2021;2Vande Casteele N Gastroenterology,2017
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Wang, Zongming, Yuyan Wu, Shiping Xi, and Xuerong Sun. "Predictive Study on Extreme Precipitation Trends in Henan and Their Impact on Population Exposure." Atmosphere 14, no. 10 (September 25, 2023): 1484. http://dx.doi.org/10.3390/atmos14101484.
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This study employs precipitation data sets from historical trials on 20 CMIP6 global climate models and four shared socioeconomic pathway scenario trials (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) to predict trends in extreme precipitation changes in Henan Province quantitatively, while ascertaining the risk of population exposure to extreme precipitation in this area. The capacity of the CMIP6 models to simulate extreme precipitation indices from 1985 to 2014 is assessed using CN05.1 daily precipitation observational data. The correlation coefficients of the multi-model ensemble median’s simulation of the extreme precipitation indices are approximately 0.8, with a standard deviation ratio closer to 1 compared with the single models, demonstrating superior modeling ability. Analyses using the multi-model ensemble median demonstrate an overall increase in the total amount, frequency, and intensity of extreme precipitation in Henan throughout this century, particularly in its southern regions; in the mid-century high-emission scenario (SSP5-8.5), the maximum increase in annual total precipitation exceeds 150 mm, and it can be over 250 mm in the late-century period. For the entire province, the maximum five-day precipitation increase relative to the historical period is nearly 25 mm in the late-century SSP5-8.5 scenario. The spatiotemporal concentration of precipitation will significantly increase, heightening the risk of flood disasters. Comparative analysis reveals that, under the same population prediction, the total population exposure will be higher in high radiative forcing scenarios than in low radiative forcing scenarios, especially in Kaifeng City, where the total population exposure in SSP1 and SSP5-8.5 exceeds that in SSP1-2.6 by 2 million person-days. However, in the same radiative forcing scenario, the total population exposure in the development pathway dominated by traditional fossil fuels (SSP5) will not be significantly higher than that in the sustainable development pathway (SSP1), indicating that population activity in this century will not be the main contributor to changes in total exposure. Overall, for Henan, in the same population forecast scenario, population exposure to extreme precipitation will gradually rise with global warming.
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Yang, Guang, HwaMin Lee, and Giyeol Lee. "A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea." Atmosphere 11, no. 4 (March 31, 2020): 348. http://dx.doi.org/10.3390/atmos11040348.
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Both long- and short-term exposure to high concentrations of airborne particulate matter (PM) severely affect human health. Many countries now regulate PM concentrations. Early-warning systems based on PM concentration levels are urgently required to allow countermeasures to reduce harm and loss. Previous studies sought to establish accurate, efficient predictive models. Many machine-learning methods are used for air pollution forecasting. The long short-term memory and gated recurrent unit methods, typical deep-learning methods, reliably predict PM levels with some limitations. In this paper, the authors proposed novel hybrid models to combine the strength of two types of deep learning methods. Moreover, the authors compare hybrid deep-learning methods (convolutional neural network (CNN)—long short-term memory (LSTM) and CNN—gated recurrent unit (GRU)) with several stand-alone methods (LSTM, GRU) in terms of predicting PM concentrations in 39 stations in Seoul. Hourly air pollution data and meteorological data from January 2015 to December 2018 was used for these training models. The results of the experiment confirmed that the proposed prediction model could predict the PM concentrations for the next 7 days. Hybrid models outperformed single models in five areas selected randomly with the lowest root mean square error (RMSE) and mean absolute error (MAE) values for both PM10 and PM2.5. The error rate for PM10 prediction in Gangnam with RMSE is 1.688, and MAE is 1.161. For hybrid models, the CNN–GRU better-predicted PM10 for all stations selected, while the CNN–LSTM model performed better on predicting PM2.5.
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Nji, Queenta Ngum, Olubukola Oluranti Babalola, and Mulunda Mwanza. "Aflatoxins in Maize: Can Their Occurrence Be Effectively Managed in Africa in the Face of Climate Change and Food Insecurity?" Toxins 14, no. 8 (August 22, 2022): 574. http://dx.doi.org/10.3390/toxins14080574.
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The dangers of population-level mycotoxin exposure have been well documented. Climate-sensitive aflatoxins (AFs) are important food hazards. The continual effects of climate change are projected to impact primary agricultural systems, and consequently food security. This will be due to a reduction in yield with a negative influence on food safety. The African climate and subsistence farming techniques favour the growth of AF-producing fungal genera particularly in maize, which is a food staple commonly associated with mycotoxin contamination. Predictive models are useful tools in the management of mycotoxin risk. Mycotoxin climate risk predictive models have been successfully developed in Australia, the USA, and Europe, but are still in their infancy in Africa. This review aims to investigate whether AFs’ occurrence in African maize can be effectively mitigated in the face of increasing climate change and food insecurity using climate risk predictive studies. A systematic search is conducted using Google Scholar. The complexities associated with the development of these prediction models vary from statistical tools such as simple regression equations to complex systems such as artificial intelligence models. Africa’s inability to simulate a climate mycotoxin risk model in the past has been attributed to insufficient climate or AF contamination data. Recently, however, advancement in technologies including artificial intelligence modelling has bridged this gap, as climate risk scenarios can now be correctly predicted from missing and unbalanced data.