Chen Y, Mitra A, Rahimnejad S, Chi S, Kumar V, Tan B, et al. Retrospect of fish meal substitution in Pacific white shrimp (Litopenaeus vannamei) feed: Alternatives, limitations and future prospects. Rev Aquac. 2024;16:382–409. https://doi.org/10.1111/raq.12843.

Article  Google Scholar 

Sivaraman GK, Rajan V, Vijayan A, Elangovan R, Prendiville A, Bachmann TT, et al. Antibiotic resistance profiles and molecular characteristics of extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli and Klebsiella pneumoniae isolated from shrimp aquaculture farms in Kerala. India Front Microbiol. 2021;12: 622891. https://doi.org/10.3389/fmicb.2021.622891.

Article  PubMed  Google Scholar 

Soto-Rodriguez SA, Gomez-Gil B, Lozano-Olvera R, Betancourt-Lozano M, Morales-Covarrubias MS. Field and experimental evidence of Vibrio parahaemolyticus as the causative agent of acute hepatopancreatic necrosis disease of cultured shrimp (Litopenaeus vannamei) in Northwestern Mexico. Appl Environ Microbiol. 2015;81:1689–99. https://doi.org/10.1128/AEM.03610-14.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yang H, Gao X, Li X, Zhang H, Chen N, Zhang Y, et al. Comparative transcriptome analysis of red swamp crayfish (Procambarus clarkia) hepatopancreas in response to WSSV and Aeromonas hydrophila infection. Fish Shellfish Immunol. 2018;83:397–405. https://doi.org/10.1016/j.fsi.2018.09.051.

Article  CAS  PubMed  Google Scholar 

Li T, Raza SHA, Yang B, Sun Y, Wang G, Sun W, et al. Aeromonas veronii infection in commercial freshwater fish: A potential threat to public health. Animals (Basel). 2020;10:608. https://doi.org/10.3390/ani10040608.

Article  CAS  PubMed  Google Scholar 

Yano Y, Hamano K, Tsutsui I, Aue-umneoy D, Ban M, Satomi M, et al. Occurrence, molecular characterization, and antimicrobial susceptibility of Aeromonas spp. in marine species of shrimps cultured at inland low salinity ponds. Food Microbiol. 2015;47:21–7. https://doi.org/10.1016/j.fm.2014.11.003

Jastaniah SD, Alaidaroos BA, Shafi ME, Aljarari RM, Abd El-Aziz YM, Munir MB, et al. Dietary Pediococcus acidilactici improved the growth performance, feed utilization, gut microbiota, and disease resistance against Fusarium solani in Pacific white shrimp. Litopenaeus vannamei Aquac Int. 2023;32:3195–215. https://doi.org/10.1007/s10499-023-01318-x.

Article  CAS  Google Scholar 

Khimmakthong U, Sukkarun P. The spread of Vibrio parahaemolyticus in tissues of the Pacific white shrimp Litopenaeus vannamei analyzed by PCR and histopathology. Microb Pathog. 2017;113:107–12. https://doi.org/10.1016/j.micpath.2017.10.028.

Article  PubMed  Google Scholar 

He Y, Xu W, Qu M, Zhang C, Wang W, Fang C. Recent advances in the application of Raman spectroscopy for fish quality and safety analysis. Compr Rev Food Sci Food Saf. 2022;21:3647–72. https://doi.org/10.1111/1541-4337.12968.

Article  PubMed  Google Scholar 

Zhi W, Appling AP, Golden HE, Podgorski J, Li L. Deep learning for water quality. Nature Water. 2024;2:228–41. https://doi.org/10.1038/s44221-024-00202-z.

Article  PubMed  PubMed Central  Google Scholar 

Islam SI, Ahammad F, Mohammed H. Cutting-edge technologies for detecting and controlling fish diseases: Current status, outlook, and challenges. J World Aquac Soc. 2024;55: e13051. https://doi.org/10.1111/jwas.13051.

Article  CAS  Google Scholar 

Pezzotti G. Raman spectroscopy in cell biology and microbiology. J Raman Spectrosc. 2021;52:2348–443. https://doi.org/10.1002/jrs.6204.

Article  CAS  Google Scholar 

Rodriguez L, Zhang Z, Wang D. Recent advances of Raman spectroscopy for the analysis of bacteria. Anal Sci Adv. 2023;4:81–95. https://doi.org/10.1002/ansa.202200066.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Rahman MH-U, Sikder R, Tripathi M, Zahan M, Ye T, Gnimpieba ZE, et al. Machine learning-assisted Raman spectroscopy and SERS for bacterial pathogen detection: clinical, food safety, and environmental applications. Chemosensors. 2024;12(7):140. https://doi.org/10.3390/chemosensors12070140

Wang L, Liu W, Tang JW, Wang JJ, Liu QH, Wen PB, et al. Applications of Raman spectroscopy in bacterial infections: Principles, advantages, and shortcomings. Front Microbiol. 2021;12: 683580. https://doi.org/10.3389/fmicb.2021.683580.

Article  PubMed  PubMed Central  Google Scholar 

Qi Y, Hu D, Jiang Y, Wu Z, Zheng M, Chen EX, et al. Recent progresses in machine learning assisted Raman spectroscopy. Adv Opt Mater. 2023;11:2203104. https://doi.org/10.1002/adom.202203104.

Article  CAS  Google Scholar 

Uysal Ciloglu F, Saridag AM, Kilic IH, Tokmakci M, Kahraman M, Aydin O. Identification of methicillin-resistant Staphylococcus aureus bacteria using surface-enhanced Raman spectroscopy and machine learning techniques. Analyst. 2020;145:7559–70. https://doi.org/10.1039/d0an00476f.

Article  CAS  PubMed  Google Scholar 

Strola SA, Baritaux JC, Schultz E, Simon AC, Allier C, Espagnon I, et al. Single bacteria identification by Raman spectroscopy. J Biomed Opt. 2014;19(11): 111610. https://doi.org/10.1117/1.jbo.19.11.111610.

Article  PubMed  Google Scholar 

Moawad AA, Silge A, Bocklitz T, Fischer K, Rösch P, Roesler U, et al. A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species. Molecules. 2019;24:4516. https://doi.org/10.3390/molecules24244516.

Article  CAS  PubMed  PubMed Central  Google Scholar 

LeCun Y, Bengio Y, Hinton G. Deep Learning. Nature. 2015;521:436–44.

Article  CAS  PubMed  Google Scholar 

Luo R, Popp J, Bocklitz T. Deep Learning for Raman Spectroscopy: A Review. Analytica. 2022;3:287–301. https://doi.org/10.3390/analytica3030020.

Article  Google Scholar 

Ding J, Lin Q, Zhang J, Young GM, Jiang C, Zhong Y, et al. Rapid identification of pathogens by using surface-enhanced Raman spectroscopy and multi-scale convolutional neural network. Anal Bioanal Chem. 2021;413(14):3801–11. https://doi.org/10.1007/s00216-021-03331-4.

Article  CAS  PubMed  Google Scholar 

Singh S, Kumbhar D, Reghu D, Venugopal SJ, Rekha PT, Silpa Mohandas, et al. Culture-Independent Raman Spectroscopic Identification of Bacterial Pathogens from Clinical Samples Using Deep Transfer Learning. Anal Chem. 2022;94:14745–54. https://doi.org/10.1021/acs.analchem.2c03391

Zhou B, Tong YK, Zhang R, Ye A. RamanNet: a lightweight convolutional neural network for bacterial identification based on Raman spectra. RSC Adv. 2022;12:26463–9. https://doi.org/10.1039/d2ra03722j

Zhang S, Fan R, Liu Y, Chen S, Liu Q, Zeng W. Applications of transformer-based language models in bioinformatics: a survey. Bioinformatics Advances. 2023;3:vbad001. https://doi.org/10.1093/bioadv/vbad001

Xiao H, Li L, Liu Q, Zhu X, Zhang Q. Transformers in medical image segmentation: A review. Biomed Signal Process Control. 2023;84: 104791. https://doi.org/10.1016/j.bspc.2023.104791.

Article  Google Scholar 

Luong KD, Singh A. Application of Transformers in Cheminformatics. J Chem Inf Model. 2024;64:4392–409. https://doi.org/10.1021/acs.jcim.3c02070.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention Is All You Need. Advances in neural information processing systems.2017;30.

Kang Z, Liu J, Ma C, Chen C, Lv X, Chen C. Early screening of cervical cancer based on tissue Raman spectroscopy combined with deep learning algorithms. Photodiagnosis Photodyn Ther. 2023;42: 103557. https://doi.org/10.1016/j.pdpdt.2023.103557.

Article  PubMed  Google Scholar 

Wang Z, Zhang J, Zhang X, Chen P, Wang B. Transformer Model for Functional Near-Infrared Spectroscopy Classification. IEEE J Biomed Health Inf. 2022;26:2559–69. https://doi.org/10.1109/jbhi.2022.3140531.

Article  Google Scholar 

Wu M, Wang S, Pan S, Terentis AC, Strasswimmer J, Zhu X. Deep learning data augmentation for Raman spectroscopy cancer tissue classification. Sci Rep. 2021. https://doi.org/10.1038/s41598-021-02687-0.

Article  PubMed  PubMed Central  Google Scholar 

Du Y, Han D, Liu S, Sun X, Ning B, Han T, et al. Raman spectroscopy-based adversarial network combined with SVM for detection of foodborne pathogenic bacteria. Talanta. 2022;237: 122901. https://doi.org/10.1016/j.talanta.2021.122901.

Article  CAS  PubMed  Google Scholar 

Wang K, Gou C, Duan Y, Lin Y, Zheng X, Wang FY. Generative adversarial networks: introduction and outlook. IEEE-CAA J Automtic. 2017;4(4):588–98. https://doi.org/10.1109/JAS.2017.7510583.

Article  Google Scholar 

Mao X, Li Q, Xie H, et al. Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision. 2017; https://doi.org/10.48550/arXiv.1611.04076

Byrne HJ, Knief P, Keating ME, Bonnier F. Spectral pre and post processing for infrared and Raman spectroscopy of biological tissues and cells. Chem Soc Rev. 2016;45(7):1865–78. https://doi.org/10.1039/c5cs00440c.

Article  CAS  PubMed