Viscosity assessed using a tuning-fork viscometer. حسب Bryan S. Margulies (21492667)

Relative abundance (%) of the main coccolithophore species between 1854.6–1820.4 cm (74.353–73.505 ka) of core SO130-289KL. حسب Jose Dominick Guballa (19739909)

Spectral analysis of the relative abundance record of the main coccolithophore species. حسب Jose Dominick Guballa (19739909)

Relative abundance (%) of the small placolith components in the studied interval of core SO130-289KL. حسب Jose Dominick Guballa (19739909)

Ensemble empirical mode decomposition (EEMD) analysis of the major coccolithophore species in core SO130-289KL. حسب Jose Dominick Guballa (19739909)

Multitaper spectral analysis of the main coccolithophore species in core SO130-289KL. حسب Jose Dominick Guballa (19739909)

Comparison of the coccolithophore assemblage to paleo-proxy records between 74.40 to 73.40 ka. حسب Jose Dominick Guballa (19739909)

Geographical distribution of echocardiographic examinations performed with 133 veterinarians on 921 Newfoundland dogs. حسب Valérie Chetboul (579758)

Dynamics of PIF4 protein accumulation and autoinhibition in response to temperature. حسب Sreya Das (20531295)

PIF4 protein level regulates <i>PIF4</i> promoter activity in a temperature-dependent manner. حسب Sreya Das (20531295)

qPCR LL-37 gene expression. حسب Parisa Afzal Haghjoo (22453647)

Raw data for Fig 3a. حسب Parisa Afzal Haghjoo (22453647)

Raw qPCR data for Fig. 1. حسب Parisa Afzal Haghjoo (22453647)

Raw data for Fig 3c. حسب Parisa Afzal Haghjoo (22453647)

Raw data for Fig 3b. حسب Parisa Afzal Haghjoo (22453647)

Evaluation of chronic <i>in vivo</i> efficacy in 21 days rat MCT model in prevention mode. حسب Tobias G. Schips (21799838)

The expression profile of lipogenic genes, oxidative stress mediators as well as UPR^ER and mitochondrial stress markers. حسب Summan Thahiem (20988319)

Quantifying bactericidal effects of cold Ar-CAPP against diverse <i>E. coli</i> strains. حسب Elanie F. Briggs (22103678)

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML حسب Ayush Garg (21090944)

"…The important findings of our studies are as follows: (i) there is no effect of threshold optimization on ranking metrics such as AUC and AUPR, but AUC and AUPR get affected by class-weighting and SMOTTomek; (ii) for ML methods RF and SVM, significant percentage improvement up to 375, 33.33, and 450 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy, which are suitable for performance evaluation of imbalanced data sets; (iii) for AutoML libraries AutoGluon-Tabular and H2O AutoML, significant percentage improvement up to 383.33, 37.25, and 533.33 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy; (iv) the general pattern of percentage improvement in balanced accuracy is that the percentage improvement increases when the class ratio is systematically decreased from 0.5 to 0.1; in the case of F1 score and MCC, maximum improvement is achieved at the class ratio of 0.3; (v) for both ML and AutoML with balancing, it is observed that any individual class-balancing technique does not outperform all other methods on a significantly higher number of data sets based on F1 score; (vi) the three external balancing techniques combined outperformed the internal balancing methods of the ML and AutoML; (vii) AutoML tools perform as good as the ML models and in some cases perform even better for handling imbalanced classification when applied with imbalance handling techniques. …"

Determination of bladder blood flow in LETO and OLETF rats. حسب Takafumi Kabuto (14797727)