@article{murray_global_2022,
	title = {Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis},
	volume = {399},
	issn = {01406736},
	shorttitle = {Global burden of bacterial antimicrobial resistance in 2019},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0140673621027240},
	doi = {10.1016/S0140-6736(21)02724-0},
	language = {en},
	number = {10325},
	urldate = {2024-05-23},
	journal = {The Lancet},
	author = {Murray, Christopher J L and Ikuta, Kevin Shunji and Sharara, Fablina and Swetschinski, Lucien and Robles Aguilar, Gisela and Gray, Authia and Han, Chieh and Bisignano, Catherine and Rao, Puja and Wool, Eve and Johnson, Sarah C and Browne, Annie J and Chipeta, Michael Give and Fell, Frederick and Hackett, Sean and Haines-Woodhouse, Georgina and Kashef Hamadani, Bahar H and Kumaran, Emmanuelle A P and McManigal, Barney and Achalapong, Sureeruk and Agarwal, Ramesh and Akech, Samuel and Albertson, Samuel and Amuasi, John and Andrews, Jason and Aravkin, Aleskandr and Ashley, Elizabeth and Babin, François-Xavier and Bailey, Freddie and Baker, Stephen and Basnyat, Buddha and Bekker, Adrie and Bender, Rose and Berkley, James A and Bethou, Adhisivam and Bielicki, Julia and Boonkasidecha, Suppawat and Bukosia, James and Carvalheiro, Cristina and Castañeda-Orjuela, Carlos and Chansamouth, Vilada and Chaurasia, Suman and Chiurchiù, Sara and Chowdhury, Fazle and Clotaire Donatien, Rafai and Cook, Aislinn J and Cooper, Ben and Cressey, Tim R and Criollo-Mora, Elia and Cunningham, Matthew and Darboe, Saffiatou and Day, Nicholas P J and De Luca, Maia and Dokova, Klara and Dramowski, Angela and Dunachie, Susanna J and Duong Bich, Thuy and Eckmanns, Tim and Eibach, Daniel and Emami, Amir and Feasey, Nicholas and Fisher-Pearson, Natasha and Forrest, Karen and Garcia, Coralith and Garrett, Denise and Gastmeier, Petra and Giref, Ababi Zergaw and Greer, Rachel Claire and Gupta, Vikas and Haller, Sebastian and Haselbeck, Andrea and Hay, Simon I and Holm, Marianne and Hopkins, Susan and Hsia, Yingfen and Iregbu, Kenneth C and Jacobs, Jan and Jarovsky, Daniel and Javanmardi, Fatemeh and Jenney, Adam W J and Khorana, Meera and Khusuwan, Suwimon and Kissoon, Niranjan and Kobeissi, Elsa and Kostyanev, Tomislav and Krapp, Fiorella and Krumkamp, Ralf and Kumar, Ajay and Kyu, Hmwe Hmwe and Lim, Cherry and Lim, Kruy and Limmathurotsakul, Direk and Loftus, Michael James and Lunn, Miles and Ma, Jianing and Manoharan, Anand and Marks, Florian and May, Jürgen and Mayxay, Mayfong and Mturi, Neema and Munera-Huertas, Tatiana and Musicha, Patrick and Musila, Lilian A and Mussi-Pinhata, Marisa Marcia and Naidu, Ravi Narayan and Nakamura, Tomoka and Nanavati, Ruchi and Nangia, Sushma and Newton, Paul and Ngoun, Chanpheaktra and Novotney, Amanda and Nwakanma, Davis and Obiero, Christina W and Ochoa, Theresa J and Olivas-Martinez, Antonio and Olliaro, Piero and Ooko, Ednah and Ortiz-Brizuela, Edgar and Ounchanum, Pradthana and Pak, Gideok D and Paredes, Jose Luis and Peleg, Anton Yariv and Perrone, Carlo and Phe, Thong and Phommasone, Koukeo and Plakkal, Nishad and Ponce-de-Leon, Alfredo and Raad, Mathieu and Ramdin, Tanusha and Rattanavong, Sayaphet and Riddell, Amy and Roberts, Tamalee and Robotham, Julie Victoria and Roca, Anna and Rosenthal, Victor Daniel and Rudd, Kristina E and Russell, Neal and Sader, Helio S and Saengchan, Weerawut and Schnall, Jesse and Scott, John Anthony Gerard and Seekaew, Samroeng and Sharland, Mike and Shivamallappa, Madhusudhan and Sifuentes-Osornio, Jose and Simpson, Andrew J and Steenkeste, Nicolas and Stewardson, Andrew James and Stoeva, Temenuga and Tasak, Nidanuch and Thaiprakong, Areerat and Thwaites, Guy and Tigoi, Caroline and Turner, Claudia and Turner, Paul and Van Doorn, H Rogier and Velaphi, Sithembiso and Vongpradith, Avina and Vongsouvath, Manivanh and Vu, Huong and Walsh, Timothy and Walson, Judd L and Waner, Seymour and Wangrangsimakul, Tri and Wannapinij, Prapass and Wozniak, Teresa and Young Sharma, Tracey E M W and Yu, Kalvin C and Zheng, Peng and Sartorius, Benn and Lopez, Alan D and Stergachis, Andy and Moore, Catrin and Dolecek, Christiane and Naghavi, Mohsen},
	month = feb,
	year = {2022},
	pages = {629--655},
}

@article{zhu_clinical_2022,
	title = {Clinical {Perspective} of {Antimicrobial} {Resistance} in {Bacteria}},
	volume = {15},
	issn = {1178-6973},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8899096/},
	doi = {10.2147/IDR.S345574},
	abstract = {Antimicrobial resistance (AMR) has become a global clinical problem in recent years. With the discovery of antibiotics, infections were not a deadly problem for clinicians as they used to be. However, worldwide AMR comes with the overuse/misuse of antibiotics and the spread of resistance is deteriorated by a multitude of mobile genetic elements and relevant resistant genes. This review provides an overview of the current situation, mechanism, epidemiology, detection methods and clinical treatment for antimicrobial resistant genes in clinical important bacteria including methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), penicillin-resistant Streptococcus pneumoniae (PRSP), extended-spectrum β-lactamase-producing Enterobacteriaceae, acquired AmpC β-lactamase-producing Enterobacteriaceae, carbapenemase-producing Enterobacteriaceae (CPE), multidrug-resistant (MDR) Acinetobacter baumannii and Pseudomonas aeruginosa.},
	urldate = {2024-05-23},
	journal = {Infection and Drug Resistance},
	author = {Zhu, Ying and Huang, Wei E and Yang, Qiwen},
	month = mar,
	year = {2022},
	pmid = {35264857},
	pmcid = {PMC8899096},
	pages = {735--746},
}

@book{wickham_welcome_nodate,
	title = {Welcome {\textbar} {Mastering} {Shiny}},
	url = {https://mastering-shiny.org/index.html},
	abstract = {A book created with bookdown.},
	language = {en},
	urldate = {2024-05-26},
	author = {Wickham, Hadley},
}

@misc{hejja-brichard_using_2024,
	title = {Using {Neural} {Style} {Transfer} to study the evolution of animal signal design: {A} case study in an ornamented fish},
	copyright = {© 2024, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
	shorttitle = {Using {Neural} {Style} {Transfer} to study the evolution of animal signal design},
	url = {https://www.biorxiv.org/content/10.1101/2023.03.13.532060v2},
	doi = {10.1101/2023.03.13.532060},
	abstract = {The sensory drive hypothesis of animal signal evolution describes how animal communication signals and preferences evolve as adaptations to local environments. While classical approaches to testing this hypothesis often focus on preference for one aspect of a signal, deep learning techniques like generative models can create and manipulate stimuli without targeting a specific feature. Here, we used an artificial intelligence technique called neural style transfer to experimentally test preferences for color patterns in a fish. Findings in empirical aesthetics show that humans tend to prefer images with the visual statistics of the environment because the visual system is adapted to process them efficiently, making those images easier to process. Whether this is the case in other species remains to be tested. We therefore manipulated how similar or dissimilar male body patterns were to their habitats using the Neural Style Transfer (NST) algorithm. We predicted that males whose body patterns are more similar to their native habitats will be preferred by conspecifics. Our findings suggest that both males and females are sensitive to habitat congruence in their preferences, but to different extents, requiring additional investigation. Nonetheless, this study demonstrates the potential of artificial intelligence for testing hypotheses about animal communication signals.
Highlights- Neural style transfer was used to test preferences for patterns in a colorful fish- Results trend in support of the processing bias hypothesis, a component of sensory drive- This study demonstrates the use of generative AI for animal behavioral studies},
	language = {en},
	urldate = {2024-05-26},
	publisher = {bioRxiv},
	author = {Héjja-Brichard, Yseult and Million, Kara and Renoult, Julien P. and Mendelson, Tamra C.},
	month = feb,
	year = {2024},
}

@article{kragness_data_2021,
	title = {Data and {R} code},
	url = {https://osf.io/4pqx9/},
	abstract = {Hosted on the Open Science Framework},
	language = {en},
	urldate = {2024-05-26},
	author = {Kragness, Haley E. and Cirelli, Laura},
	month = dec,
	year = {2021},
}

@misc{noauthor_data_2022,
	title = {Data on the daily number of new reported {COVID}-19 cases and deaths by {EU}/{EEA} country},
	url = {https://www.ecdc.europa.eu/en/publications-data/data-daily-new-cases-covid-19-eueea-country},
	abstract = {Data in various file formats with the number of new COVID-19 cases and deaths reported per day and per country in the EU/EEA.},
	language = {en},
	urldate = {2024-05-26},
	month = oct,
	year = {2022},
}

@misc{noauthor_r_nodate,
	title = {R {Packages} (2e) - 1  {The} {Whole} {Game}},
	url = {https://r-pkgs.org/whole-game.html},
	abstract = {Learn how to create a package, the fundamental unit of shareable, reusable, and reproducible R code.},
	urldate = {2024-05-26},
}

@article{rich_practical_2010,
	title = {A practical guide to understanding {Kaplan}‐{Meier} curves},
	volume = {143},
	issn = {0194-5998, 1097-6817},
	url = {https://aao-hnsfjournals.onlinelibrary.wiley.com/doi/10.1016/j.otohns.2010.05.007},
	doi = {10.1016/j.otohns.2010.05.007},
	abstract = {In 1958, Edward L. Kaplan and Paul Meier collaborated to publish a seminal paper on how to deal with incomplete observations. Subsequently, the Kaplan‐Meier curves and estimates of survival data have become a familiar way of dealing with differing survival times (times‐to‐event), especially when not all the subjects continue in the study. “Survival” times need not relate to actual survival with death being the event; the “event” may be any event of interest. Kaplan‐Meier analyses are also used in nonmedical disciplines.
            The purpose of this article is to explain how Kaplan‐Meier curves are generated and analyzed. Throughout this article, we will discuss Kaplan‐Meier estimates in the context of “survival” before the event of interest. Two small groups of hypothetical data are used as examples in order for the reader to clearly see how the process works. These examples also illustrate the crucially important point that comparative analysis depends upon the whole curve and not upon isolated points.
            © 2010 American Academy of Otolaryngology‐Head and Neck Surgery Foundation. All rights reserved.},
	language = {en},
	number = {3},
	urldate = {2024-05-26},
	journal = {Otolaryngology–Head and Neck Surgery},
	author = {Rich, Jason T. and Neely, J. Gail and Paniello, Randal C. and Voelker, Courtney C. J. and Nussenbaum, Brian and Wang, Eric W.},
	month = sep,
	year = {2010},
	pages = {331--336},
}

@misc{noauthor_survminer_nodate,
	title = {survminer {R} package: {Survival} {Data} {Analysis} and {Visualization} - {Easy} {Guides} - {Wiki} - {STHDA}},
	shorttitle = {survminer {R} package},
	url = {http://www.sthda.com/english/wiki/survminer-r-package-survival-data-analysis-and-visualization},
	abstract = {Statistical tools for data analysis and visualization},
	language = {en},
	urldate = {2024-05-26},
}

@misc{finnstats_log_2021,
	title = {Log {Rank} {Test} in {R}-{Survival} {Curve} {Comparison} {\textbar} {R}-bloggers},
	url = {https://www.r-bloggers.com/2021/08/log-rank-test-in-r-survival-curve-comparison/},
	abstract = {Log Rank Test in R, the most frequent technique to compare survival curves between two groups is to use a log-rank test. Test hypotheses:... The post Log Rank Test in R-Survival Curve Comparison appeared first on finnstats.},
	language = {en-US},
	urldate = {2024-05-26},
	author = {{finnstats}},
	month = aug,
	year = {2021},
}