Nd M.A.M.-P.; data curation, O.L.-G. and G.I.P.-L.; writing– original draft Goralatide Description preparation, G.I.P.-L.; writing–review and editing, O.L.-G. and M.A.M.-P.; visualization, G.I.P.-L.; supervision, O.L.-G. and M.A.M.-P.; project administration, G.I.P.-L. and O.L.G.; funding acquisition, G.I.P.-L. All authors have read and agreed towards the published version with the manuscript. Funding: This work was partly supported by the National Council of Science and Technology of Mexico under the scholarship grant 1048425. Institutional Critique Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: We would like to in particular thank Galo Ben Yair Delgado and Laura Moreno as specialists in International Relations, Mar JosSanabria and N tor JosM dez as authorities in Psychology, and Norma Soto as an expert in Sociology, who helped us to label our collected Xenophobia database. Conflicts of Interest: The authors declare no conflict of interest.AbbreviationsThe following abbreviations are used in this manuscript: NLP XAI ML AUC SVM NB GB LR EV RNN LSTM CNN sCNN GRU RNN DT RF KNN RUS UND PBC4cip WFV CVF TFIDF BOW W2V INTER DOB SCV PXD EXD STD AVG All-natural Language Processing Explainable Artificial Intelligence Machine Learning Region Under the Receiver Operating Characteristic Curve Assistance Vector Machine Na e ayes Gradient Boosting Machine Logistic Regression Ensemble Voting Recurrent Neural Networks Long-Short-Term-Memory Convolutional Neural Network Skipped Convolutional Neural Network Gated Recurrent Unit Recurrent Neural Networks Choice Tree Random Forest k-Nearest Neighbor Rusboost Beneath Bagging Pattern-Based Classifier for Class imbalance difficulties Word Frequency Vectorization Count Vector Options Term Frequency-Inverse Document Frequency Bag Of Words Word To Vec Interpretable Function Representation Distribution Optimally Balanced Stratified Cross-Validation Pitropakis Xenophobia Database Professionals Xenophobia Database Typical Deviation AverageAppl. Sci. 2021, 11,24 of
ArticleProcess Development for Newcastle Disease Virus-Vectored Vaccines in Serum-Free Vero Cell Suspension CulturesJulia Puppin Chaves Fulber 1 , Omar Farn 1 , Sascha Kiesslich 1 , Zeyu Yang 1 , Shantoshini Dash 1 , Leonardo Susta two , Sarah K. Wootton two and Amine A. Kamen 1, Viral Vectors and Vaccines Bioprocessing Group, Division of Bioengineering, McGill University, Montreal, QC H3A 0G4, Canada; [email protected] (J.P.C.F.); [email protected] (O.F.); [email protected] (S.K.); [email protected] (Z.Y.); [email protected] (S.D.) Department of BI-0115 MedChemExpress Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, ON N1G 2W1, Canada; [email protected] (L.S.); [email protected] (S.K.W.) Correspondence: [email protected]: Fulber, J.P.C.; Farn , O.; Kiesslich, S.; Yang, Z.; Dash, S.; Susta, L.; Wootton, S.K.; Kamen, A.A. Process Improvement for Newcastle Illness Virus-Vectored Vaccines in Serum-Free Vero Cell Suspension Cultures. Vaccines 2021, 9, 1335. https://doi.org/10.3390/ vaccines9111335 Academic Editor: Antonella Caputo Received: 19 October 2021 Accepted: 12 November 2021 Published: 16 NovemberAbstract: The ongoing COVID-19 pandemic drew international consideration to infectious ailments, attracting numerous sources for development of pandemic preparedness plans and vaccine platforms– technologies with robust manufacturing processes that could speedily be.
