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"en": "Abstract Background Chitin, the main form of aminated polysaccharide in nature, is a biocompatible, polycationic, and antimicrobial biopolymer used extensively in industrial processes. Despite the abundance of chitin, applications thereof are hampered by difficulties in feedstock harvesting and limited structural versatility. To address these problems, we proposed a two-step cascade employing carbohydrate oxidoreductases and amine transaminases for plant polysaccharide aminations via one-pot reactions. Using a galactose oxidase from Fusarium graminearum for oxidation, this study compared the performance of CvATA (from Chromobacterium violaceum) and SpATA (from Silicibacter pomeroyi) on a range of oxidized carbohydrates with various structures and sizes. Using a rational enzyme engineering approach, four point mutations were introduced on the SpATA surface, and their effects on enzyme activity were evaluated. Results Herein, a quantitative colorimetric assay was developed to enable simple and accurate time-course measurement of the yield of transamination reactions. With higher operational stability, SpATA produced higher product yields in 36 h reactions despite its lower initial activity. Successful amination of oxidized galactomannan by SpATA was confirmed using a deuterium labeling method; higher aminated carbohydrate yields achieved with SpATA compared to CvATA were verified using HPLC and XPS. By balancing the oxidase and transaminase loadings, improved operating conditions were identified where the side product formation was largely suppressed without negatively impacting the product yield. SpATA mutants with multiple alanine substitutions besides E407A showed improved product yield. The E407A mutation reduced SpATA activity substantially, supporting its predicted role in maintaining the dimeric enzyme structure. Conclusions Using oxidase–amine transaminase cascades, the study demonstrated a fully enzymatic route to polysaccharide amination. Although the activity of SpATA may be further improved via enzyme engineering, the low operational stability of characterized amine transaminases, as a result of low retention of PMP cofactors, was identified as a key factor limiting the yield of the designed cascade. To increase the process feasibility, future efforts to engineer improved SpATA variants should focus on improving the cofactor affinity, and thus the operational stability of the enzyme. Graphical Abstract"
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"en": "Data description: Near-continuous hourly video data was collected using a statically installed thermal infrared and RGB camera system during the ice-free channel seasons of 2023 and 2024 at two study sites in Finland: the Vantaanjoki River (Pitkäkoski site) and the Oulankajoki River. The dataset includes 30-second video clips from both thermal infrared and RGB cameras, featuring six consecutive hourly recordings captured during the spring and autumn periods of both years at each river site. These video samples were used to derive surface flow velocities using Large-Scale Particle Image Velocimetry (LSPIV) (Muste et al., 2008) and Space-Time Image Velocimetry (STIV) (Fujita et al., 2007) techniques and were validated against subsequent Acoustic Doppler Current Profiler (ADCP) measurements. In addition to the video data, the dataset contains a complete series of hourly river discharge estimates derived using the STIV technique. These discharge time series span the full ice-free channel seasons of 2023 and 2024 for both rivers. Daily discharges were computed from the hourly estimates and compared against national hydrological monitoring data (Ympäristö.fi, 2025). In addition, the following data is provided: Processed results of surface flow velocity and discharge computations, 1-dimensional bathymetry profiles obtained from ADCP depth measurements, Photogrammetric camera models and search lines for processing in Hydro-STIV software, Processed daily water level and discharge data from national hydrological monitoring stations, Flow velocity measurements obtained with ADCP. These datasets support the photogrammetric input required for the image velocimetry techniques (LSPIV and STIV) and facilitate comparative analyses with conventional flow measurement methods. Software used in processing: Hydro-STIV (version v124) – Used for flow velocity and discharge computation. Available at: https://www.hydrosoken.co.jp/en/hydrostiv/. A free 30-day trial is available upon request. PIVLab (version v2.58) – Used for surface flow velocity estimation using the LSPIV technique. Openly available at: https://www.pivlab.de/ RIVeR (version v2.6) – Used for the rectification of image-derived flow velocity fields. Available at: https://doi.org/10.1016/j.cageo.2017.07.009 Acknowledgements: The work was funded by The European Union – NextGenerationEU Recovery instrument (RRF) through Research Council of Finland projects Hydro RI Platform (346167) and Green-Digi-Basin (347703). This research has been conducted with Flagship Programme funding granted by the Research Council of Finland for Digital Waters [DIWA] Flagship (decision no. 359248). Also work was funded by Research Council of Finland via DefrostingRivers project (338480). This work was a part of the Ministry of Education and Culture’s Doctoral Education Pilot under Decision No. VN/3137/2024-OKM-6 (Digital Waters [DIWA] Doctoral Education Pilot related to the DIWA Flagship [decision no. 359248] funded by the Research Council of Finland's Flagship Programme). We thank the following personnel for assisting in the field on 12.4.2023, 6.9.2023, 9.4.2024 and 3.9.2024 at Vantaanjoki Pitkäkoski site: Virpi Pajunen, Reeta Vaahtera, Walter Box and Tuure Takala. Also Erik van Rooijen and Joshua Johnson assisted on 20.5.2023, 22.9.2023, 18.5.2024 and 19.9.2024 for gathering the data at Oulankajoki site. We also thank for Oulanka Research station of the University of Oulu for assisting with the installation and maintenance of the Oulankajoki static camera system. Also we thank HSY (Vantaanjoki Pitkäkoski) and Metsähallitus (Oulankajoki) for allowing the building of the systems at their premises. References: Muste, M., Fujita, I., & Hauet, A. (2008). Large‐scale particle image velocimetry for measurements in riverine environments. Water Resources Research., 46(4). https://doi.org/10.1029/2008WR006950 Fujita, I., Watanabe, H., & Tsubaki, R. (2007) Development of a non-intrusive and efficient flow monitoring technique: The Space Time Image Velocimetry (STIV). Int J River Basin Man, 5(2), 105–114. https://doi.org/10.1080/15715124.2007.9635310 Ympäristö.fi. River discharge observations. https://wwwi3.ymparisto.fi/i3/paasivu/ENG/2023/Virtaama/Virtaama.htm. Accessed 14.1.2025."
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