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The Innovation Life > 2025 Vol. 3 > No. 3 > 100155
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A putative research model for sustainable fisheries driven by noninvasive diagnostic imaging

  • 1.

    Graduate School of Horticulture, Chiba University, Chiba 271-8501, Japan

  • 2.

    RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan

  • 3.

    Japan Eco-science (Nikkan Kagaku) Co. Ltd., Chiba 263-8522, Japan

  • 4.

    Sermas Co., Ltd., Chiba 272-0033, Japan

  • 5.

    Graduate School of Medical Life Science, Yokohama City University, Kanagawa 230-0045, Japan

  • 6.

    Chuubushiryo Co. Ltd., Aichi 474-0011, Japan

  • 7.

    RIKEN BioResource Research Center, Ibaraki 305-0074, Japan

  • 8.

    Institute for Multidisciplinary Sciences, Yokohama National University, Kanagawa 240-8501, Japan

  • 9.

    Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan

  • 10.

    Faculty of Engineering, Chiba University, Chiba 263-8522, Japan

  • 11.

    Division of Hematology, Kitasato University School of Allied Health Sciences, Kanagawa 252-0373, Japan

  • 12.

    Keiyo Gas Energy Solution Co. Ltd., Chiba 272-0033, Japan

  • 13.

    Miroku Co. Ltd., Oita 873-0021, Japan

  • 14.

    RIKEN Center for Sustainable Resource Science, Kanagawa 230-0045, Japan

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The Innovation Life  3(3),  https://doi.org/10.59717/j.xinn-life.2025.100155  |  Cite this article
    GRAPHICAL ABSTRACT
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    1. Anti-pathogenic property of thermophile-fermented compost as a feed additive for fish.

      Noninvasive diagnosis of the compost functional properties using field photographs.

      A novel research model for efficient and rapid sustainable fisheries dissemination.

    • ABSTRACT
  • Diagnostic imaging technology has been developing rapidly in recent years, and the demand for its application is mainly in the medical field. Here, we examined the scope of application that enables rapid functional evaluation using disease-resistant fermented feed for fish as a model for evaluation in the primary industry. A survival test against the fish pathogen Edwardsiella tarda revealed the antipathogenic properties of a compost-type feed additive fermented by thermophilic Bacillaceae using nonedible marine resources. After prior administration of the compost extract to seabream as a fish model for 70 days, the mortality rate after 28 days of exposure to the fish pathogen Edwardsiella peaked at 20%, although the rate was 60% without prior administration. Under such conditions, the serum complement activity of seabream tend to increase. Furthermore, the differences in the degrees of body length, smoothness and glossiness of the fish body surface depending on the administration conditions were computationally analyzed using the field photographs. The texture analysis for the grey level co-occurrence matrix (GLCM) and local binary pattern (LBP), and color extraction analysis for colour tone (hue), colourfulness (saturation), and brightness (value) revealed quantifiable change in color balance of fish body surface after the compost administration. These results suggest that the antipathogenic property of thermophile-fermented compost as a functional feed additive can be efficiently estimated by an external diagnostic technique. This study provides a new perspective for the utilization of noninvasive diagnosis to estimate quantitatively the quality of its production activities in the food and environmental industry.
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  • [1] RockstrÖm J., Steffen W., Noone K., et al. (2009). A safe operating space for humanity. Nature 461:472−475. DOI:10.1038/461472a

    View in Article CrossRef Google Scholar

    [2] RockstrÖm J., Gupta J., Qin D., et al. (2023). Safe and just Earth system boundaries. Nature 619:102−111. DOI:10.1038/s41586-023-06083-8

    View in Article CrossRef Google Scholar

    [3] Xue Y. and Bakshi B. R. (2022). Metrics for a nature-positive world: A multiscale approach for absolute environmental sustainability assessment. Sci. Total Environ. 846:157373. DOI:10.1016/j.scitotenv.2022.157373

    View in Article CrossRef Google Scholar

    [4] Wongkiew S., Polprasert C., Noophan P. L., et al. (2023). Effects of vermicompost leachate on nitrogen, phosphorus, and microbiome in a food waste bioponic system. J. Environ. Manage 339:117860. DOI:10.1016/j.jenvman.2023.117860

    View in Article CrossRef Google Scholar

    [5] De Corato U. (2020). Agricultural waste recycling in horticultural intensive farming systems by on-farm composting and compost-based tea application improves soil quality and plant health: A review under the perspective of a circular economy. Sci. Total Environ. 738:139840. DOI:10.1016/j.scitotenv.2020.139840

    View in Article CrossRef Google Scholar

    [6] Zhao S., Schmidt S., Gao H., et al. (2022). A precision compost strategy aligning composts and application methods with target crops and growth environments can increase global food production. Nature Food 3:741−752. DOI:10.1038/s43016-022-00584-x

    View in Article CrossRef Google Scholar

    [7] Qiu X., Zhou G., Chen L., et al. (2021). Additive quality influences the reservoir of antibiotic resistance genes during chicken manure composting. Ecotoxicol Environ. Saf. 220:112413. DOI:10.1016/j.ecoenv.2021.112413

    View in Article CrossRef Google Scholar

    [8] Siddiqui Z., Hagare D., Jayasena V., et al. (2021). Recycling of food waste to produce chicken feed and liquid fertiliser. Waste Manag. 131:386−393. DOI:10.1016/j.wasman.2021.06.016

    View in Article CrossRef Google Scholar

    [9] Dao H. T., Sharma N. K., Swick R. A., et al. (2023). Feeding recycled food waste improved feed efficiency in laying hens from 24 to 43 weeks of age. Sci. Rep. 13:8261. DOI:10.1038/s41598-023-34878-2

    View in Article CrossRef Google Scholar

    [10] Plumed-Ferrer C. and von Wright A. (2009). Fermented pig liquid feed: Nutritional, safety and regulatory aspects. J. Appl. Microbiol. 106:351−368. DOI:10.1111/j.1365-2672.2008.03938.x

    View in Article CrossRef Google Scholar

    [11] Miyamoto H. and Kikuchi J. (2023). An evaluation of homeostatic plasticity for ecosystems using an analytical data science approach. Comput. Struct. Biotechnol. J. 21:869−878. DOI:10.1016/j.csbj.2023.01.001

    View in Article CrossRef Google Scholar

    [12] Niisawa C., Oka S., Kodama H., et al. (2008). Microbial analysis of a composted product of marine animal resources and isolation of bacteria antagonistic to a plant pathogen from the compost. J. Gen. Appl. Microbiol. 54:149−158. DOI:10.2323/jgam.54.149

    View in Article CrossRef Google Scholar

    [13] Ishikawa K., Ohmori T., Miyamoto H., et al. (2013). Denitrification in soil amended with thermophile-fermented compost suppresses nitrate accumulation in plants. Appl. Microbiol. Biotechnol. 97:1349−1359. DOI:10.1007/s00253-012-4004-5

    View in Article CrossRef Google Scholar

    [14] Miyamoto H., Shigeta K., Suda W., et al. (2023). An agroecological structure model of compost-soil-plant interactions for sustainable organic farming. ISME Commun. 3:28. DOI:10.1038/s43705-023-00233-9

    View in Article CrossRef Google Scholar

    [15] Tanaka R., Miyamoto H., Kodama H., et al. (2010). Feed additives with thermophile-fermented compost enhance concentrations of free amino acids in the muscle of the flatfish Paralichthys olivaceus. J. Gen. Appl. Microbiol. 56:61−65. DOI:10.2323/jgam.56.61

    View in Article CrossRef Google Scholar

    [16] Tanaka R., Miyamoto H., Inoue S., et al. (2016). Thermophile-fermented compost as a fish feed additive modulates lipid peroxidation and free amino acid contents in the muscle of the carp, Cyprinus carpio. J. Biosci. Bioeng. 121:530−535. DOI:10.1016/j.jbiosc.2015.10.006

    View in Article CrossRef Google Scholar

    [17] Ito T., Miyamoto H., Kumagai Y., et al. (2016). Thermophile-fermented compost extract as a possible feed additive to enhance fecundity in the laying hen and pig: Modulation of gut metabolism. J. Biosci. Bioeng. 121:659−664. DOI:10.1016/j.jbiosc.2015.10.014

    View in Article CrossRef Google Scholar

    [18] Miyamoto H., Kodama H., Udagawa M., et al. (2012). The oral administration of thermophile-fermented compost extract and its influence on stillbirths and growth rate of pre-weaning piglets. Res. Vet. Sci. 93:137−142. DOI:10.1016/j.rvsc.2011.06.018

    View in Article CrossRef Google Scholar

    [19] Miyamoto H., Shimada E., Satoh T., et al. (2013). Thermophile-fermented compost as a possible scavenging feed additive to prevent peroxidation. J. Biosci. Bioeng. 116:203−208. DOI:10.1016/j.jbiosc.2013.01.024

    View in Article CrossRef Google Scholar

    [20] Yoshikawa S., Itaya K., Hoshina R., et al. (2024). Thermophile-fermented feed modulates the gut microbiota related to lactate metabolism in pigs. J. Appl. Microbiol. 135. DOI:10.1093/jambio/lxae254.

    View in Article Google Scholar

    [21] Miyamoto H., Asano F., Ishizawa K., et al. (2022). A potential network structure of symbiotic bacteria involved in carbon and nitrogen metabolism of wood-utilizing insect larvae. Sci. Total Environ. 836:155520. DOI:10.1016/j.scitotenv.2022.155520

    View in Article CrossRef Google Scholar

    [22] Asano F., Tsuboi A., Moriya S., et al. (2022). Amendment of a thermophile-fermented compost to humus improves the growth of female larvae of the Hercules beetle Dynastes hercules (Coleoptera: scarabaeidae). J. Appl. Microbiol. 134. DOI:10.1093/jambio/lxac006.

    View in Article Google Scholar

    [23] Satoh T., Nishiuchi T., Naito T., et al. (2012). Impact of oral administration of compost extract on gene expression in the rat gastrointestinal tract. J. Biosci. Bioeng. 114:500−505. DOI:10.1016/j.jbiosc.2012.05.026

    View in Article CrossRef Google Scholar

    [24] Miyamoto H., Seta M., Horiuchi S., et al. (2013). Potential probiotic thermophiles isolated from mice after compost ingestion. J. Appl. Microbiol. 114:1147−1157. DOI:10.1111/jam.12131

    View in Article CrossRef Google Scholar

    [25] Miyamoto H., Kawachi N., Kurotani A., et al. (2022). Computational estimation of sediment symbiotic bacterial structures of seagrasses overgrowing downstream of onshore aquaculture. Environ. Res. 219:115130. DOI:10.1016/j.envres.2022.115130

    View in Article CrossRef Google Scholar

    [26] Fujii Y., Yamada J. and Onishi T. (1971). Studies on silvering of fish skin-I purines in the skin of cultured salmon and trout. Bulletin of the Japanese Society of Scientific Fisheries 37:55−62. DOI:10.2331/suisan.37.55

    View in Article CrossRef Google Scholar

    [27] Sugimoto M. (2002). Morphological color changes in fish: regulation of pigment cell density and morphology. Microsc. Res. Tech. 58:496−503. DOI:10.1002/jemt.10168

    View in Article CrossRef Google Scholar

    [28] Nilsson Skold H., Aspengren S. and Wallin M. (2013). Rapid color change in fish and amphibians - Function, regulation, and emerging applications. Pigment Cell Melanoma Res 26:29−38. DOI:10.1111/pcmr.12040

    View in Article CrossRef Google Scholar

    [29] Vissio P. G., Darias M. J., Di Yorio M. P., et al. (2021). Fish skin pigmentation in aquaculture: The influence of rearing conditions and its neuroendocrine regulation. Gen. Comp. Endocrinol. 301:113662. DOI:10.1016/j.ygcen.2020.113662

    View in Article CrossRef Google Scholar

    [30] Noriko O. (2016). Fish skin color and its changes: mechanisms and ethological significance. Japan Soc. Colour Mater. 89:178−183. DOI:10.4011/shikizai.89.178

    View in Article CrossRef Google Scholar

    [31] Tørud B. and Håstein T. (2008). Skin lesions in fish: causes and solutions. Acta. Veterinaria Scandinavica 50. DOI:10.1186/1751-0147-50-s1-s7

    View in Article Google Scholar

    [32] Kondo M., Kawai K., Kurohara K., et al. (2002). Adherence of Flavobacterium psychrophilum on the body surface of the ayu Plecoglossus altivelis. Microbes Infect. 4:279−283. DOI:10.1016/S1286-4579(02)01539-3

    View in Article CrossRef Google Scholar

    [33] Neggaz I. and Fizazi H. (2022). An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis. Soft comput. 26:10435−10464. DOI:10.1007/s00500-022-06886-3

    View in Article CrossRef Google Scholar

    [34] Belkhatir Z., Estepar R. S. J. and Tannenbaum A. R. (2022). Wasserstein-based texture analysis in radiomic studies. Comput. Med. Imaging Graph. 102:102129. DOI:10.1016/j.compmedimag.2022.102129

    View in Article CrossRef Google Scholar

    [35] Varma M. and Zisserman A. (2005). A statistical approach to texture classification from single images. Int. J. Comput. Vis. 62:61−81. DOI:10.1023/B:VISI.0000046589.39864.ee

    View in Article CrossRef Google Scholar

    [36] Yang W. L., Zhu F. and Chen W. X. (2022). Texture analysis of contrast-enhanced magnetic resonance imaging predicts microvascular invasion in hepatocellular carcinoma. Eur. J. Radiol. 156:110528. DOI:10.1016/j.ejrad.2022.110528

    View in Article CrossRef Google Scholar

    [37] Mustaqeem A., Baranova V., Kieu N. B., et al. (2025). Application of the gray level co-occurrence matrix (GLCM) technique for analyzing fracture networks in the granitic basement of cuu long basin, Vietnam. J. Jpn. Pet. Inst. 68:20−26. DOI:10.1627/jpi.68.20

    View in Article CrossRef Google Scholar

    [38] Haralick R. M., Shanmugam K. and Dinstein I. H. (1973). Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3:610−621. DOI:10.1109/tsmc.1973.4309314

    View in Article CrossRef Google Scholar

    [39] Pfaehler E., Zwanenburg A., de Jong J. R., et al. (2019). RaCaT: An open source and easy to use radiomics calculator tool. PLoS One 14:e0212223. DOI:10.1371/journal.pone.0212223

    View in Article CrossRef Google Scholar

    [40] Aerts H. J., Velazquez E. R., Leijenaar R. T., et al. (2014). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5:4006. DOI:10.1038/ncomms5006

    View in Article CrossRef Google Scholar

    [41] Kobayashi T. (2022). RadiomicsJ: A library to compute radiomic features. Radiol Phys. Technol. 15:255−263. DOI:10.1007/s12194-022-00664-4

    View in Article CrossRef Google Scholar

    [42] Fedorov A., Clunie D., Ulrich E., et al. (2016). DICOM for quantitative imaging biomarker development: A standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ 4:e2057. DOI:10.7717/peerj.2057

    View in Article CrossRef Google Scholar

    [43] Herz C., Fillion-Robin J. C., Onken M., et al. (2017). dcmqi: An open source library for standardized communication of quantitative image analysis results using DICOM. Cancer Res. 77:e87−e90. DOI:10.1158/0008-5472.CAN-17-0336

    View in Article CrossRef Google Scholar

    [44] Vijithananda S. M., Jayatilake M. L., Goncalves T. C., et al. (2023). Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques. Sci. Rep. 13:15772. DOI:10.1038/s41598-023-41353-5

    View in Article CrossRef Google Scholar

    [45] Kumar V. S., Kumar P. R., Yadalam P. K., et al. (2023). Machine learning in the detection of dental cyst, tumor, and abscess lesions. BMC Oral Health 23:833. DOI:10.1186/s12903-023-03571-1

    View in Article CrossRef Google Scholar

    [46] Daneshvari M. H., Nourmohammadi E., Ameri M., et al. (2023). Efficient LBP-GLCM texture analysis for asphalt pavement raveling detection using eXtreme Gradient Boost. Constr. Build Mater. 401. DOI:10.1016/j.conbuildmat.2023.132731

    View in Article Google Scholar

    [47] Yano T., Matsuyama H., Tanaka K., et al. (1987). Alternative pathway-hemolytic complement titer (ACH50) of porgy pagrus major serum. Nippon Suisan Gakk. 53:1887−1891. DOI:10.2331/suisan.53.1887

    View in Article CrossRef Google Scholar

    [48] Hatai K., Ogawa S. and Yasunaga N. (1982). Pathogenicity of Edwardsiella tarda isolated from cultured red sea bream, Pagrus major. Bulletin of Nagasaki Prefectural Institute of Fisheries:67-73.

    View in Article Google Scholar

    [49] Breiman L. (2001). Random Forests. Machine Learning 45:5−32. DOI:10.1023/A:1010933404324

    View in Article CrossRef Google Scholar

    [50] Chen T. and Guestrin C. (2016). XGBoost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 785-794. DOI: 10.1145/2939672.2939785

    View in Article Google Scholar

    [51] Ke G., Meng Q., Finley T., et al. (2017). LightGBM: A highly efficient gradient boosting decision tree. Ke, Guolin et al. “LightGBM: A Highly Efficient Gradient Boosting Decision Tree.” Neural Information Processing Systems, pp: 3146-3154

    View in Article Google Scholar

    [52] Yu Z., Ma J., Qu Y., et al. (2023). PM(2.5) extended-range forecast based on MJO and S2S using LightGBM. Sci. Total Environ. 880:163358. DOI:10.1016/j.scitotenv.2023.163358.

    View in Article Google Scholar

    [53] Taguchi Y., Kurotani A., Yamano H., et al. (2024). Causal estimation of maternal-offspring gut commensal bacterial associations under livestock grazing management conditions. Comput. Struct. Biotechnol. J. 1. DOI:10.1016/j.csbr.2024.100012

    View in Article Google Scholar

    [54] Becker V., Kavaliauskiene S., Sandblom E., et al. (2024). Echocardiography - a non-invasive alternative for assessing cardiac morphology and function in Atlantic salmon (Salmo salar L.). Aquaculture 592. DOI:10.1016/j.aquaculture.2024.741223

    View in Article Google Scholar

    [55] Setthawong P., Khemthong M., Lertwanakarn T., et al. (2024). Ultrasonography for non-invasive sex identification and reproductive assessment in Nile tilapia (Oreochromis niloticus). Front Vet. Sci. 11:1467158. DOI:10.3389/fvets.2024.1467158

    View in Article CrossRef Google Scholar

    [56] Deng Y., Hu T., Chen J., et al. (2024). Non-invasive methods for heart rate measurement in fish based on photoplethysmography. J. Exp. Biol. 227. DOI:10.1242/jeb.246464

    View in Article Google Scholar

    [57] Shang N., Zhang C., Zhang W., et al. (2025). Assessment of fish health status for waterless transportation based on image features and deep learning models. Aquaculture 595. DOI:10.1016/j.aquaculture.2024.741697

    View in Article Google Scholar

    [58] Mario Perl, Stephanie Denk, Miriam Kalbitz, et al. (2012). Granzyme B: A new crossroad of complement and apoptosis. Adv. Exp. Med. Biol. 946. DOI: 10.1007/978-1-4614-0106-3_8

    View in Article Google Scholar

    [59] Nishida A., Miyamoto H., Horiuchi S., et al. (2015). Bacillus hisashii sp. nov., isolated from the caeca of gnotobiotic mice fed with thermophile-fermented compost. Int. J. Syst. Evol. Microbiol. 65:3944−3949. DOI:10.1099/ijsem.0.000516

    View in Article CrossRef Google Scholar

    [60] Inabu Y., Taguchi Y., Miyamoto H., et al. (2022). Development of a novel feeding method for Japanese black calves with thermophile probiotics at postweaning. J. Appl. Microbiol. 132:3870−3882. DOI:10.1111/jam.15519

    View in Article CrossRef Google Scholar

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  • Cite this article:

    Miyamoto H., Ito S., Suzuki K., et al. (2025). A putative research model for sustainable fisheries driven by noninvasive diagnostic imaging. The Innovation Life 3:100155. https://doi.org/10.59717/j.xinn-life.2025.100155
    Miyamoto H., Ito S., Suzuki K., et al. (2025). A putative research model for sustainable fisheries driven by noninvasive diagnostic imaging. The Innovation Life 3:100155. https://doi.org/10.59717/j.xinn-life.2025.100155

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