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Title: Cascade/Parallel Biocatalysis via Multi-enzyme Encapsulation on Metal–Organic Materials for Rapid and Sustainable Biomass Degradation
Multiple-enzyme cooperation simultaneously is an effective approach to biomass conversion and biodegradation. The challenge, however, lies in the interference of the involved enzymes with each other, especially when a protease is needed, and thus, the difficulty in reusing the enzymes; while extracting/synthesizing new enzymes costs energy and negative impact on the environment. Here, we present a unique approach to immobilize multiple enzymes, including a protease, on a metal–organic material (MOM) via co-precipitation in order to enhance the reusability and sustainability. We prove our strategy on the degradation of starch-containing polysaccharides (require two enzymes to degrade) and food proteins (require a protease to digest) before the quantification of total dietary fiber. As compared to the widely adopted “official” method, which requires the sequential addition of three enzymes under different conditions (pH/temperature), the three enzymes can be simultaneously immobilized on the surface of our MOM crystals to allow for contact with the large substrates (starch), while MOMs offer sufficient protection to the enzymes so that the reusability and long-term storage are improved. Furthermore, the same biodegradation can be carried out without adjusting the reaction condition, further reducing the reaction time. Remarkably, the simultaneous presence of all enzymes enhances the reaction efficiency by more » a factor of ∼3 as compared to the official method. To our best knowledge, this is the first experimental demonstration of using aqueous-phase co-precipitation to immobilize multiple enzymes for large-substrate biocatalysis. The significantly enhanced efficiency can potentially impact the food industry by reducing the labor requirement and enhancing enzyme cost efficiency, leading to reduced food cost. The reduced energy cost of extracting enzymes and adjusting reaction conditions minimize the negative impact on the environment. The strategy to prevent protease damage in a multi-enzyme system can be adapted to other biocatalytic reactions involving proteases. « less
Authors:
; ; ; ; ; ; ; ; ;
Award ID(s):
1942596
Publication Date:
NSF-PAR ID:
10300683
Journal Name:
ACS applied materials interfaces
Volume:
13
Page Range or eLocation-ID:
43085–43093
ISSN:
1944-8244
Sponsoring Org:
National Science Foundation
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Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. 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New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9.« less
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