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  1. 37381
    “…Due to the lack of availability of fodder inside the forest, the elephants move out of their habitat areas and also find agricultural crops attractive, which further results in man–animal conflict. …”
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    Exploring the Nutritional Potential of Wild Grass Fodder for Mega Herbivore (Elephas maximus) in the Foothills of Western Ghats
  2. 37382
    “…Climbing ability, however, did appear to originate within the urban-native range and was maintained within the invasive populations, thereby suggesting it may have been a prior adaptation that provided this species with an advantage during its establishment in urban areas and spread into natural forests. We discuss how this shift in climbing performance may be ecologically related to the success of urban and invasive guttural toad populations, as well as how it may have impacted other island-derived morphological and performance phenotypes.…”
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    Island Hopping through Urban Filters: Anthropogenic Habitats and Colonized Landscapes Alter Morphological and Performance Traits of an Invasive Amphibian
  3. 37383
    por Jiang, Xia, Xu, Chuhan
    Publicado 2022
    “…We developed prediction models using the deep feedforward neural network (DFNN) methods, as well as models using nine other machine learning methods, including naïve Bayes (NB), logistic regression (LR), support vector machine (SVM), LASSO, decision tree (DT), k-nearest neighbor (KNN), random forest (RF), AdaBoost (ADB), and XGBoost (XGB). We used grid search to tune hyperparameters for all methods. …”
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    Deep Learning and Machine Learning with Grid Search to Predict Later Occurrence of Breast Cancer Metastasis Using Clinical Data
  4. 37384
    “…This invasion rate increased in frequency with higher Knosp Grade. The forest plot of persistent disease vs. remission in this surgery approach showed a p < 0.00001 and heterogeneity (I^2 = 0%). …”
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    Endoscopic Endonasal Resection of the Medial Wall of the Cavernous Sinus and Its Impact on Outcomes of Pituitary Surgery: A Systematic Review and Meta-Analysis
  5. 37385
    “…The odds ratio (OR) and 95% confidence interval (CI) were obtained and forest plots were created using random effects models.  …”
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    Long-Term Neurological Sequelae Among Severe COVID-19 Patients: A Systematic Review and Meta-Analysis
  6. 37386
    “…Relatively higher Glossina pallidipes and biting flies, respectively, were caught in a wood-grass land (15.87 F/T/D and 3.69 F/T/D) and riverine forest (15.13 F/T/D and 3.42 F/T/D) than bush land vegetation types (13.87 F/T/D and 1.76 F/T/D). …”
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    Glossina pallidipes Density and Trypanosome Infection Rate in Arba Minch Zuria District of Gamo Zone, Southern Ethiopia
  7. 37387
    “…In machine learning, data were trained and validated by random forest (RF) following Pycharm software and five-fold cross-validation analysis. …”
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    Prediction of axillary lymph node pathological complete response to neoadjuvant therapy using nomogram and machine learning methods
  8. 37388
    “…Equally, nests were searched in natural habitats counting riparian trees, forests, and ornamental trees, and in orchards based on the Common Birds Census (CBC) methodology, in which the singing doves, mating pairs, nesting, and/or feeding behavior were the most monitored signs to discover nests. …”
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    High density and successful breeding of Turtle doves Streptopelia turtur in Moroccan olive groves
  9. 37389
    “…Full (STRATIFY + POCecho variable) and reduced (STRATIFY alone) logistic regression models were fit to calculate adjusted odds ratios (aOR), category‐free net reclassification index (NRI(cont)), ΔSensitivity (NRI(events)), and ΔSpecificity (NRI(nonevents)). Random forest assessed variable importance. To benchmark risk prediction to standard of care, ΔSensitivity and ΔSpecificity were evaluated at risk thresholds more conservative/lower than the actual outcome rate in discharged patients. …”
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    Point‐of‐care echocardiography of the right heart improves acute heart failure risk stratification for low‐risk patients: The REED‐AHF prospective study
  10. 37390
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    Cardiovascular Complications Are the Primary Drivers of Mortality in Hospitalized Patients With SARS-CoV-2 Community-Acquired Pneumonia
  11. 37391
    “…In addition to baseline models (Kaplan Meier (KM), (regularised) Cox), Random Survival Forest (RSF), and gradient boosted trees (GBT) were fit to the data. …”
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    EMT-Related Genes Have No Prognostic Relevance in Metastatic Colorectal Cancer as Opposed to Stage II/III: Analysis of the Randomised, Phase III Trial FIRE-3 (AIO KRK 0306; FIRE-3)
  12. 37392
    “…Next, we focused on 90 TFs contained in these two NUEg modules and predicted their genome-wide targets using the N-and/or-W response datasets using a random forest network inference approach (GENIE3). Next, to validate the GENIE3 TF→target gene predictions, we performed Precision/Recall Analysis (AUPR) using nine datasets for three TFs validated in planta. …”
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    Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice
  13. 37393
    “…Seven multivariate models with different combinations of CP and radiomics were trained on a subset of patients (75%) using least absolute shrinkage, selection operator (LASSO) and random forest classification with 10-fold cross-validation to predict outcome. …”
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    Development of Radiomic-Based Model to Predict Clinical Outcomes in Non-Small Cell Lung Cancer Patients Treated with Immunotherapy
  14. 37394
    “…Review Manager version 5.4 was used to generate odds ratios and forest plots with subgroup analysis from allele and phenotype frequency data. …”
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    Major Histocompatibility Complex Class I and Dengue Hemorrhagic Fever: A Meta-Analysis of Human Leukocyte Antigens A*24 and B*44
  15. 37395
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    Evolution of burnout and psychological distress in healthcare workers during the COVID-19 pandemic: a 1-year observational study
  16. 37396
    por Dutta, Debashree, Pal, Sankar K.
    Publicado 2022
    “…We identify the most important air pollutants influencing the air quality of Kolkata during three different periods using Random Forest, a tree-based machine learning (ML) algorithm. …”
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    Prediction and assessment of the impact of COVID-19 lockdown on air quality over Kolkata: a deep transfer learning approach
  17. 37397
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    Prognostic Indicators of EIF1AX-Mutated Thyroid Tumor Malignancy and Cancer Aggressiveness
  18. 37398
    “…FINDINGS: A fully automated computational approach included classical statistical methods, support vector machine procedures, and machine learning techniques (random forest and sequential feature selection procedures). …”
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    Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: a cross sectional observational study
  19. 37399
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    Impact of dementia care training on nurse care managers’ interactions with family caregivers
  20. 37400
    “…A protein-protein interaction (PPI) network was also constructed for the DE-mRNAs to identify candidate genes, and the receiver operating characteristic curves of the 21 candidate genes were plotted to evaluate the diagnostic value of the candidate genes for HIRI. A random forest (RF) model, support vector machine model and generalized linear model were constructed based on the candidate genes. …”
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    Analysis and identification of potential key genes in hepatic ischemia-reperfusion injury
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