scholarly journals Metabolomic Alteration in the Plasma of Wild Rodents Environmentally Exposed to Lead: A Preliminary Study

2022 ◽  
Vol 19 (1) ◽  
pp. 541
Author(s):  
Hokuto Nakata ◽  
Akifumi Eguchi ◽  
Shouta M. M. Nakayama ◽  
John Yabe ◽  
Kaampwe Muzandu ◽  
...  
Keyword(s):  
Random Forest ◽  
Biological Response ◽  
Control Area ◽  
Small Sample ◽  
Random Forest Model ◽  
Laboratory Animals ◽  
Hydroxybutyric Acid ◽  
Wild Rodents ◽  
Forest Model ◽  
Contaminated Area

Lead poisoning is often considered a traditional disease; however, the specific mechanism of toxicity remains unclear. The study of Pb-induced alterations in cellular metabolic pathways is important to understand the biological response and disorders associated with environmental exposure to lead. Metabolomics studies have recently been paid considerable attention to understand in detail the biological response to lead exposure and the associated toxicity mechanisms. In the present study, wild rodents collected from an area contaminated with lead (N = 18) and a control area (N = 10) were investigated. This was the first ever experimental metabolomic study of wildlife exposed to lead in the field. While the levels of plasma phenylalanine and isoleucine were significantly higher in a lead-contaminated area versus the control area, hydroxybutyric acid was marginally significantly higher in the contaminated area, suggesting the possibility of enhancement of lipid metabolism. In the interregional least-absolute shrinkage and selection operator (lasso) regression model analysis, phenylalanine and isoleucine were identified as possible biomarkers, which is in agreement with the random forest model. In addition, in the random forest model, glutaric acid, glutamine, and hydroxybutyric acid were selected. In agreement with previous studies, enrichment analysis showed alterations in the urea cycle and ATP-binding cassette transporter pathways. Although regional rodent species bias was observed in this study, and the relatively small sample size should be taken into account, the present results are to some extent consistent with those of previous studies on humans and laboratory animals.

scholarly journals Performance of Five Models to Predict the Naturalization of Non-Native Woody Plants in Iowa

2012 ◽  
Vol 30 (1) ◽  
pp. 35-41
Author(s):  
Emily J. Kapler ◽  
Mark P. Widrlechner ◽  
Philip M. Dixon ◽  
Janette R. Thompson
Keyword(s):  
Risk Assessment ◽  
Random Forest ◽  
Woody Plants ◽  
Small Sample Size ◽  
Small Sample ◽  
Random Forest Model ◽  
Classification Rate ◽  
Forest Model ◽  
Risk Assessment Models ◽  
Limiting Errors

Use of risk-assessment models that can predict the naturalization and invasion of non-native woody plants is a potentially beneficial approach for protecting human and natural environments. This study validates the power and accuracy of four risk-assessment models previously tested in Iowa, and examines the performance of a new random forest modeling approach. The random forest model was fitted with the same data used to develop the four earlier risk-assessment models. The validation of all five models was based on a new set of 11 naturalizing and 18 non-naturalizing species in Iowa. The fitted random forest model had a high classification rate (92.0%), no biologically significant errors (accepting a plant that has a high risk of naturalizing), and few horticulturally limiting errors (rejecting a plant that has a low risk of naturalizing) (8.7%). Classification rates for validation of all five models ranged from 62.1 to 93.1%. Horticulturally limiting errors for the four models previously developed for Iowa ranged from 11.1 to 38.5%, and biologically significant errors from 4.2 to 18.5%. Because of the small sample size, few classification and error rate results were significantly different from the original tests of the models. Overall, the random forest model shows promise for powerful and accurate risk-assessment, but mixed results for the other models suggest a need for further refinement.

scholarly journals Clinical trial registries as Scientometric data: A novel solution for linking and deduplicating clinical trials from multiple registries

2021 ◽  
Author(s):  
Christian Thiele ◽  
Gerrit Hirschfeld ◽  
Ruth von Brachel
Keyword(s):  
Clinical Trials ◽  
Random Forest ◽  
Random Forest Model ◽  
Scientometric Analysis ◽  
Data Set ◽  
The Public ◽  
Forest Model ◽  
Clinical Trial Registries ◽  
Multiple Primary ◽  
Clinical Trials Registry

AbstractRegistries of clinical trials are a potential source for scientometric analysis of medical research and serve important functions for the research community and the public at large. Clinical trials that recruit patients in Germany are usually registered in the German Clinical Trials Register (DRKS) or in international registries such as ClinicalTrials.gov. Furthermore, the International Clinical Trials Registry Platform (ICTRP) aggregates trials from multiple primary registries. We queried the DRKS, ClinicalTrials.gov, and the ICTRP for trials with a recruiting location in Germany. Trials that were registered in multiple registries were linked using the primary and secondary identifiers and a Random Forest model based on various similarity metrics. We identified 35,912 trials that were conducted in Germany. The majority of the trials was registered in multiple databases. 32,106 trials were linked using primary IDs, 26 were linked using a Random Forest model, and 10,537 internal duplicates on ICTRP were identified using the Random Forest model after finding pairs with matching primary or secondary IDs. In cross-validation, the Random Forest increased the F1-score from 96.4% to 97.1% compared to a linkage based solely on secondary IDs on a manually labelled data set. 28% of all trials were registered in the German DRKS. 54% of the trials on ClinicalTrials.gov, 43% of the trials on the DRKS and 56% of the trials on the ICTRP were pre-registered. The ratio of pre-registered studies and the ratio of studies that are registered in the DRKS increased over time.

scholarly journals Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model

2021 ◽  
Vol 10 (8) ◽  
pp. 503
Author(s):  
Hang Liu ◽  
Riken Homma ◽  
Qiang Liu ◽  
Congying Fang
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Cellular Automata ◽  
Land Use Change ◽  
Urban Land ◽  
Urban Land Use ◽  
Random Forest Model ◽  
Growth Trend ◽  
Related Factors ◽  
Forest Model

The simulation of future land use can provide decision support for urban planners and decision makers, which is important for sustainable urban development. Using a cellular automata-random forest model, we considered two scenarios to predict intra-land use changes in Kumamoto City from 2018 to 2030: an unconstrained development scenario, and a planning-constrained development scenario that considers disaster-related factors. The random forest was used to calculate the transition probabilities and the importance of driving factors, and cellular automata were used for future land use prediction. The results show that disaster-related factors greatly influence land vacancy, while urban planning factors are more important for medium high-rise residential, commercial, and public facilities. Under the unconstrained development scenario, urban land use tends towards spatially disordered growth in the total amount of steady growth, with the largest increase in low-rise residential areas. Under the planning-constrained development scenario that considers disaster-related factors, the urban land area will continue to grow, albeit slowly and with a compact growth trend. This study provides planners with information on the relevant trends in different scenarios of land use change in Kumamoto City. Furthermore, it provides a reference for Kumamoto City’s future post-disaster recovery and reconstruction planning.

scholarly journals Estimates of daily ground-level NO2 concentrations in China based on Random Forest model integrated K-means

2021 ◽  
pp. 100017
Author(s):  
Xinyu Dou ◽  
Cuijuan Liao ◽  
Hengqi Wang ◽  
Ying Huang ◽  
Ying Tu ◽  
...  
Keyword(s):  
Random Forest ◽  
Ground Level ◽  
Random Forest Model ◽  
Forest Model

scholarly journals Identification of candidate biomarkers of liver hydatid disease via microarray profiling, bioinformatics analysis, and machine learning

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199398
Author(s):  
Jinwu Peng ◽  
Zhili Duan ◽  
Yamin Guo ◽  
Xiaona Li ◽  
Xiaoqin Luo ◽  
...  
Keyword(s):  
Random Forest ◽  
Hydatid Disease ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Random Forest Model ◽  
Hepatic Hydatid Disease ◽  
Forest Model ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Microarray Profiling

Objectives Liver echinococcosis is a severe zoonotic disease caused by Echinococcus (tapeworm) infection, which is epidemic in the Qinghai region of China. Here, we aimed to explore biomarkers and establish a predictive model for the diagnosis of liver echinococcosis. Methods Microarray profiling followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed in liver tissue from patients with liver hydatid disease and from healthy controls from the Qinghai region of China. A protein–protein interaction (PPI) network and random forest model were established to identify potential biomarkers and predict the occurrence of liver echinococcosis, respectively. Results Microarray profiling identified 1152 differentially expressed genes (DEGs), including 936 upregulated genes and 216 downregulated genes. Several previously unreported biological processes and signaling pathways were identified. The FCGR2B and CTLA4 proteins were identified by the PPI networks and random forest model. The random forest model based on FCGR2B and CTLA4 reliably predicted the occurrence of liver hydatid disease, with an area under the receiver operator characteristic curve of 0.921. Conclusion Our findings give new insight into gene expression in patients with liver echinococcosis from the Qinghai region of China, improving our understanding of hepatic hydatid disease.

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