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    Plant-wide modelling in wastewater treatment: showcasing experiences using the Biological Nutrient Removal Model

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    [EN] Plant-wide modelling can be considered an appropriate approach to represent the current complexity in water resource recovery facilities, reproducing all known phenomena in the different process units. Nonetheless, novel processes and new treatment schemes are still being developed and need to be fully incorporated in these models. This work presents a short chronological overview of some of the most relevant plant-wide models for wastewater treatment, as well as the authors' experience in plant-wide modelling using the general model BNRM (Biological Nutrient Removal Model), illustrating the key role of general models (also known as supermodels) in the field of wastewater treatment, both for engineering and research.Seco, A.; Ruano, MV.; Ruiz-Martínez, A.; Robles Martínez, Á.; Barat, R.; Serralta Sevilla, J.; Ferrer, J. (2020). Plant-wide modelling in wastewater treatment: showcasing experiences using the Biological Nutrient Removal Model. Water Science & Technology. 81(8):1700-1714. https://doi.org/10.2166/wst.2020.056S17001714818Barat, R., Montoya, T., Seco, A., & Ferrer, J. (2011). Modelling biological and chemically induced precipitation of calcium phosphate in enhanced biological phosphorus removal systems. Water Research, 45(12), 3744-3752. doi:10.1016/j.watres.2011.04.028Barat, R., Serralta, J., Ruano, M. V., Jiménez, E., Ribes, J., Seco, A., & Ferrer, J. (2013). Biological Nutrient Removal Model No. 2 (BNRM2): a general model for wastewater treatment plants. Water Science and Technology, 67(7), 1481-1489. doi:10.2166/wst.2013.004Batstone, D. J., Hülsen, T., Mehta, C. M., & Keller, J. (2015). Platforms for energy and nutrient recovery from domestic wastewater: A review. Chemosphere, 140, 2-11. doi:10.1016/j.chemosphere.2014.10.021Borrás F. L. 2008 Técnicas microbiológicas aplicadas a la identificación y cuantificación de organismos presentes en sistemas EBPR (Microbiological Techniques Applied to Identification and Quantification of Organisms Present in EBPR Systems). PhD Thesis, Universitat Politècnica de València, Valencia, Spain.Claros, J., Jiménez, E., Aguado, D., Ferrer, J., Seco, A., & Serralta, J. (2013). Effect of pH and HNO2 concentration on the activity of ammonia-oxidizing bacteria in a partial nitritation reactor. Water Science and Technology, 67(11), 2587-2594. doi:10.2166/wst.2013.132Copp, J. B., Jeppsson, U., & Rosen, C. (2003). TOWARDS AN ASM1 – ADM1 STATE VARIABLE INTERFACE FOR PLANT-WIDE WASTEWATER TREATMENT MODELING. Proceedings of the Water Environment Federation, 2003(7), 498-510. doi:10.2175/193864703784641207Dorofeev, A. G., Nikolaev, Y. A., Kozlov, M. N., Kevbrina, M. V., Agarev, A. M., Kallistova, A. Y., & Pimenov, N. V. (2017). Modeling of anammox process with the biowin software suite. Applied Biochemistry and Microbiology, 53(1), 78-84. doi:10.1134/s0003683817010100Drewnowski, J., Zaborowska, E., & Hernandez De Vega, C. (2018). Computer Simulation in Predicting Biochemical Processes and Energy Balance at WWTPs. E3S Web of Conferences, 30, 03007. doi:10.1051/e3sconf/20183003007Durán F. 2013 Modelación matemática del tratamiento anaerobio de aguas residuales urbanas incluyendo las bacterias sulfatorreductoras. Aplicación a un biorreactor anaerobio de membranas (Mathematical Model of Urban Wastewater Anaerobic Treatment Including Sulphate Reducing Bacteria. Application to an Anaerobic Membrane Bioreactor). PhD Thesis, Universitat Politècnica de València, Valencia, Spain.Ekama, G. A. (2009). Using bioprocess stoichiometry to build a plant-wide mass balance based steady-state WWTP model. Water Research, 43(8), 2101-2120. doi:10.1016/j.watres.2009.01.036EPA 2006 User's manual version 4.03 2006. Available from: https://www.epa.gov/ceam/minteqa2-equilibrium-speciation-model (accessed July 2019).Fernández-Arévalo, T., Lizarralde, I., Fdz-Polanco, F., Pérez-Elvira, S. I., Garrido, J. M., Puig, S., … Ayesa, E. (2017). Quantitative assessment of energy and resource recovery in wastewater treatment plants based on plant-wide simulations. Water Research, 118, 272-288. doi:10.1016/j.watres.2017.04.001Ferrer, J., Seco, A., Serralta, J., Ribes, J., Manga, J., Asensi, E., … Llavador, F. (2008). DESASS: A software tool for designing, simulating and optimising WWTPs. Environmental Modelling & Software, 23(1), 19-26. doi:10.1016/j.envsoft.2007.04.005Ferrer J., Seco A., Ruano M. V., Ribes J., Serralta J., Gómez T., Robles A. 2011 LoDif BioControl® Control Software, Intellectual Property. Main Institution: Universitat de València; Universitat Politècnica de València.Flores-Alsina, X., Corominas, L., Snip, L., & Vanrolleghem, P. A. (2011). Including greenhouse gas emissions during benchmarking of wastewater treatment plant control strategies. Water Research, 45(16), 4700-4710. doi:10.1016/j.watres.2011.04.040Flores-Alsina, X., Arnell, M., Amerlinck, Y., Corominas, L., Gernaey, K. V., Guo, L., … Jeppsson, U. (2014). Balancing effluent quality, economic cost and greenhouse gas emissions during the evaluation of (plant-wide) control/operational strategies in WWTPs. Science of The Total Environment, 466-467, 616-624. doi:10.1016/j.scitotenv.2013.07.046Flores-Alsina, X., Kazadi Mbamba, C., Solon, K., Vrecko, D., Tait, S., Batstone, D. J., … Gernaey, K. V. (2015). A plant-wide aqueous phase chemistry module describing pH variations and ion speciation/pairing in wastewater treatment process models. Water Research, 85, 255-265. doi:10.1016/j.watres.2015.07.014Ge, Z. (2017). Review on data-driven modeling and monitoring for plant-wide industrial processes. Chemometrics and Intelligent Laboratory Systems, 171, 16-25. doi:10.1016/j.chemolab.2017.09.021Grau, P., de Gracia, M., Vanrolleghem, P. A., & Ayesa, E. (2007). A new plant-wide modelling methodology for WWTPs. Water Research, 41(19), 4357-4372. doi:10.1016/j.watres.2007.06.019Grau, P., Copp, J., Vanrolleghem, P. A., Takács, I., & Ayesa, E. (2009). A comparative analysis of different approaches for integrated WWTP modelling. Water Science and Technology, 59(1), 141-147. doi:10.2166/wst.2009.589Henze M., Gujer W., Mino T., van Loosdrecht M. C. M. 2000 Activated Sludge Models ASM1, ASM2, ASM2d and ASM3. IWA Scientific and Technical Report No.9. IWA Publishing, London, UK.Jeppsson, U., & Pons, M.-N. (2004). The COST benchmark simulation model—current state and future perspective. Control Engineering Practice, 12(3), 299-304. doi:10.1016/j.conengprac.2003.07.001Jeppsson, U., Rosen, C., Alex, J., Copp, J., Gernaey, K. V., Pons, M.-N., & Vanrolleghem, P. A. (2006). Towards a benchmark simulation model for plant-wide control strategy performance evaluation of WWTPs. Water Science and Technology, 53(1), 287-295. doi:10.2166/wst.2006.031Ji, X., Liu, Y., Zhang, J., Huang, D., Zhou, P., & Zheng, Z. (2018). Development of model simulation based on BioWin and dynamic analyses on advanced nitrate nitrogen removal in deep bed denitrification filter. Bioprocess and Biosystems Engineering, 42(2), 199-212. doi:10.1007/s00449-018-2025-xJiménez, E., Giménez, J. B., Ruano, M. V., Ferrer, J., & Serralta, J. (2011). Effect of pH and nitrite concentration on nitrite oxidation rate. Bioresource Technology, 102(19), 8741-8747. doi:10.1016/j.biortech.2011.07.092Jiménez, E., Giménez, J. B., Seco, A., Ferrer, J., & Serralta, J. (2012). Effect of pH, substrate and free nitrous acid concentrations on ammonium oxidation rate. Bioresource Technology, 124, 478-484. doi:10.1016/j.biortech.2012.07.079Kazadi Mbamba, C., Flores-Alsina, X., John Batstone, D., & Tait, S. (2016). Validation of a plant-wide phosphorus modelling approach with minerals precipitation in a full-scale WWTP. Water Research, 100, 169-183. doi:10.1016/j.watres.2016.05.003Kazadi Mbamba, C., Lindblom, E., Flores-Alsina, X., Tait, S., Anderson, S., Saagi, R., … Jeppsson, U. (2019). Plant-wide model-based analysis of iron dosage strategies for chemical phosphorus removal in wastewater treatment systems. Water Research, 155, 12-25. doi:10.1016/j.watres.2019.01.048Liu, Y., Peng, L., Ngo, H. H., Guo, W., Wang, D., Pan, Y., … Ni, B.-J. (2016). Evaluation of Nitrous Oxide Emission from Sulfide- and Sulfur-Based Autotrophic Denitrification Processes. Environmental Science & Technology, 50(17), 9407-9415. doi:10.1021/acs.est.6b02202Lizarralde, I., Fernández-Arévalo, T., Brouckaert, C., Vanrolleghem, P., Ikumi, D. S., Ekama, G. A., … Grau, P. (2015). A new general methodology for incorporating physico-chemical transformations into multi-phase wastewater treatment process models. Water Research, 74, 239-256. doi:10.1016/j.watres.2015.01.031Lizarralde, I., Fernández-Arévalo, T., Manas, A., Ayesa, E., & Grau, P. (2019). Model-based opti mization of phosphorus management strategies in Sur WWTP, Madrid. Water Research, 153, 39-52. doi:10.1016/j.watres.2018.12.056Maere, T., Verrecht, B., Moerenhout, S., Judd, S., & Nopens, I. (2011). BSM-MBR: A benchmark simulation model to compare control and operational strategies for membrane bioreactors. Water Research, 45(6), 2181-2190. doi:10.1016/j.watres.2011.01.006Mannina, G., Ekama, G., Caniani, D., Cosenza, A., Esposito, G., Gori, R., … Olsson, G. (2016). Greenhouse gases from wastewater treatment — A review of modelling tools. Science of The Total Environment, 551-552, 254-270. doi:10.1016/j.scitotenv.2016.01.163Martí, N., Barat, R., Seco, A., Pastor, L., & Bouzas, A. (2017). Sludge management modeling to enhance P-recovery as struvite in wastewater treatment plants. Journal of Environmental Management, 196, 340-346. doi:10.1016/j.jenvman.2016.12.074Moretti, P., Choubert, J.-M., Canler, J.-P., Buffière, P., Pétrimaux, O., & Lessard, P. (2017). Dynamic modeling of nitrogen removal for a three-stage integrated fixed-film activated sludge process treating municipal wastewater. Bioprocess and Biosystems Engineering, 41(2), 237-247. doi:10.1007/s00449-017-1862-3Nagy, J., Kaljunen, J., & Toth, A. J. (2019). Nitrogen recovery from wastewater and human urine with hydrophobic gas separation membrane: experiments and modelling. Chemical Papers, 73(8), 1903-1915. doi:10.1007/s11696-019-00740-xNewhart, K. B., Holloway, R. W., Hering, A. S., & Cath, T. Y. (2019). Data-driven performance analyses of wastewater treatment plants: A review. Water Research, 157, 498-513. doi:10.1016/j.watres.2019.03.030Nopens, I., Batstone, D. J., Copp, J. B., Jeppsson, U., Volcke, E., Alex, J., & Vanrolleghem, P. A. (2009). An ASM/ADM model interface for dynamic plant-wide simulation. Water Research, 43(7), 1913-1923. doi:10.1016/j.watres.2009.01.012Nopens, I., Benedetti, L., Jeppsson, U., Pons, M.-N., Alex, J., Copp, J. B., … Vanrolleghem, P. A. (2010). Benchmark Simulation Model No 2: finalisation of plant layout and default control strategy. Water Science and Technology, 62(9), 1967-1974. doi:10.2166/wst.2010.044Ontiveros, G. A., & Campanella, E. A. (2013). Environmental performance of biological nutrient removal processes from a life cycle perspective. Bioresource Technology, 150, 506-512. doi:10.1016/j.biortech.2013.08.059Penya-Roja, J. M., Seco, A., Ferrer, J., & Serralta, J. (2002). Calibration and Validation of Activated Sludge Model No.2d for Spanish Municipal Wastewater. Environmental Technology, 23(8), 849-862. doi:10.1080/09593332308618360Pretel, R., Robles, A., Ruano, M. V., Seco, A., & Ferrer, J. (2016). A plant-wide energy model for wastewater treatment plants: application to anaerobic membrane bioreactor technology. Environmental Technology, 37(18), 2298-2315. doi:10.1080/09593330.2016.1148903Pretel, R., Robles, A., Ruano, M. V., Seco, A., & Ferrer, J. (2016). Economic and environmental sustainability of submerged anaerobic MBR-based (AnMBR-based) technology as compared to aerobic-based technologies for moderate-/high-loaded urban wastewater treatment. Journal of Environmental Management, 166, 45-54. doi:10.1016/j.jenvman.2015.10.004Rehman, U., Audenaert, W., Amerlinck, Y., Maere, T., Arnaldos, M., & Nopens, I. (2017). How well-mixed is well mixed? Hydrodynamic-biokinetic model integration in an aerated tank of a full-scale water resource recovery facility. Water Science and Technology, 76(8), 1950-1965. doi:10.2166/wst.2017.330Rieger L., Gillot S., Langergraber G., Ohtsuki T., Shaw A., Takacs I., Winkler S. 2012 Guidelines for Using Activated Sludge Models Scientific and Technical report No. 21. EWA Task Group on Good Modelling Practice. IWA Publishing Volume 11.Robles, A., Ruano, M. V., Ribes, J., Seco, A., & Ferrer, J. (2014). Model-based automatic tuning of a filtration control system for submerged anaerobic membrane bioreactors (AnMBR). Journal of Membrane Science, 465, 14-26. doi:10.1016/j.memsci.2014.04.012Robles, A., Capson-Tojo, G., Ruano, M. V., Seco, A., & Ferrer, J. (2018). Real-time optimization of the key filtration parameters in an AnMBR: Urban wastewater mono-digestion vs. co-digestion with domestic food waste. Waste Management, 80, 299-309. doi:10.1016/j.wasman.2018.09.031Ruano, M. V., Serralta, J., Ribes, J., Garcia-Usach, F., Bouzas, A., Barat, R., … Ferrer, J. (2012). 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    In situ assessment and minimization of nonlinear propagation effects for femtosecond-laser waveguide writing in dielectrics

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    The effect of nonlinear propagation on the shape of the focal volume has been assessed by in situ plasma emission imaging during the subsurface processing of a commercial phosphate glass. The sample was processed with an elliptically shaped femtosecond-laser beam at 1 kHz repetition rate and scanned transversely with respect to the writing beam axis. As a consequence, optimal conditions for minimizing undesirable nonlinear propagation effects during the production of optical waveguides by direct laser writing have been determined. Under these conditions, it is possible to induce structural transformations and still preserve the focal volume shape associated with the linear propagation regime. While at low pulse energy a single scan laser-written structure does not support a guided mode, the use of multiple scans with minimized nonlinear propagation effects enables the production of optical waveguides. The latter show a significantly improved performance in terms of the refractive index change and propagation losses when compared to single scan waveguides. © 2010 Optical Society of America.This work was partially supported by the University of Zaragoza under Project 223/88 and by the Spanish Ministry of Science and Innovation under TEC2008-01183 project. A. Ruiz de la Cruz and W. Gawelda acknowledge their I3P-CSIC postdoctoral contracts (co-funded by the European Social Fund). D. Puerto and A. Ferrer acknowledge their grants under Projects TEC 2005-00074 and TEC 2006-04538.Peer Reviewe

    A Complete Set of Firmware for the TileCal Read-Out Driver

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    TileCal is the hadronic tile calorimeter of the ATLAS experiment at LHC/CERN. The Read-Out Driver (ROD) is the main component of the TileCal back-end electronics. The ROD is a VME 64x 9u board with multiple programmable devices which requires a complete set of firmware. This paper describes the firmware and functionalities of all these programmable devices, especially the DSP Processing Units daughterboards where the data processing takes place

    Tauroursodeoxycholic bile acid arrests axonal degeneration by inhibiting the unfolded protein response in X-linked adrenoleukodystrophy

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    Altres ajuts: We are indebted to the NIH NeuroBioBank for supplying the case material used for the human studies. This study was supported by grants from the European Leukodystrophy Association [ELA2012-033C1], the Center for Biomedical Research on Rare Diseases (CIBERER) to N.L. and M.R. Locomotor experiments were performed by the SEFALer unit F5 led by A.P. which belongs to the CIBERER structure.The online version of this article (doi:10.1007/s00401-016-1655-9) contains supplementary material, which is available to authorized users

    Status of the Optical Multiplexer Board 9U Prototype

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    This paper presents the architecture and the status of the Optical Multiplexer Board (OMB) 9U for the ATLAS/LHC Tile hadronic calorimeter (TileCal). This board will analyze the front-end data CRC to prevent bit and burst errors produced by radiation. Besides, due to its position within the data acquisition chain it will be used to emulate front-end data for tests. The first two prototypes of the final OMB 9U version have been produced at CERN. Detailed design issues and manufacture features of these prototypes are described. Functional descriptions of the board on its two main operation modes as CRC checking and data ROD injector are explained as well as other functionalities. Finally, the schedule for next year when the production of the OMB will be take place is also presented

    Optical Buffer 1:16

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    This document is a manual describing the functionality and the operation of the Optical Buffer 1:16 (OB). The OB was specially designed to repeat optical signals during the TileCal Read-Out drivers (ROD) production. The data generated in one Optical Multiplexer Board (OMB) 6U prototypes were repeated with two OB in order to inject data simultaneously to four RODs

    Monte Carlo Performance of the TileCal Low pT Muon Identification Algorithm

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    This note describes the TileCal standalone low pT muon identification algorithm (TileMuId) developed to contribute to the Level-2 trigger. This algorithm is based on the characteristic muon energy deposition inside the calorimeter. The implementation of this algorithm in the core of the Digital Signal Processors (DSPs) in the TileCal Read-Out Drivers (RODs) is also discussed in this paper. The TileMuId performance with Monte Carlo data from single muons and bb events is shown in terms of efficiencies and fraction of fakes for both a fully Level-2 version and a ROD-based version of the algorithm

    Setup, tests and results for the ATLAS TileCal Read Out Driver production

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    In this paper we describe the performance and test results of the production of the 38 ATLAS TileCal Read Out Drivers (RODs). We first describe the basic hardware specifications and firmware functionality of the modules, the test-bench setup used for production and the test procedure to qualify the boards. We then finally show and discuss the performance results

    Cardiopulmonary Exercise Test in Patients with Hypertrophic Cardiomyopathy: A Systematic Review and Meta-Analysis.

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    BACKGROUND: Patients with chronic diseases frequently adapt their lifestyles to their functional limitations. Functional capacity in Hypertrophic Cardiomyopathy (HCM) can be assessed by stress testing. We aim to review and analyze the available data from the literature on the value of Cardiopulmonary Exercise Test (CPET) in HCM. Objective measurements from CPET are used for evaluation of patient response to traditional and new developing therapeutic measurements. METHODS: A systematic review of the literature was conducted in PubMed, Web of Science and Cochrane in Mar-20. The original search yielded 2628 results. One hundred and two full texts were read after the first screening, of which, 69 were included for qualitative synthesis. Relevant variables to be included in the review were set and 17 were selected, including comorbidities, body mass index (BMI), cardiac-related symptoms, echocardiographic variables, medications and outcomes. RESULTS: Study sample consisted of 69 research articles, including 11,672 patients (48 ± 14 years old, 65.9%/34.1% men/women). Treadmill was the most common instrument employed (n = 37 studies), followed by upright cycle-ergometer (n = 16 studies). Mean maximal oxygen consumption (VO2max) was 22.3 ± 3.8 mL·kg-1·min-1. The highest average values were observed in supine and upright cycle-ergometer (25.3 ± 6.5 and 24.8 ± 9.1 mL·kg-1·min-1; respectively). Oxygen consumption in the anaerobic threshold (ATVO2) was reported in 18 publications. Left ventricular outflow tract gradient (LVOT) > 30 mmHg was present at baseline in 31.4% of cases. It increased to 49% during exercise. Proportion of abnormal blood pressure response (ABPRE) was higher in severe (>20 mm) vs. mild hypertrophy groups (17.9% vs. 13.6%, p < 0.001). Mean VO2max was not significantly different between severe vs. milder hypertrophy, or for obstructive vs. non-obstructive groups. Occurrence of arrhythmias during functional assessment was higher among younger adults (5.42% vs. 1.69% in older adults, p < 0.001). Twenty-three publications (9145 patients) evaluated the prognostic value of exercise capacity. There were 8.5% total deaths, 6.7% cardiovascular deaths, 3.0% sudden cardiac deaths (SCD), 1.2% heart failure death, 0.6% resuscitated cardiac arrests, 1.1% transplants, 2.6% implantable cardioverter defibrillator (ICD) therapies and 1.2 strokes (mean follow-up: 3.81 ± 2.77 years). VO2max, ATVO2, METs, % of age-gender predicted VO2max, % of age-gender predicted METs, ABPRE and ventricular arrhythmias were significantly associated with major outcomes individually. Mean VO2max was reduced in patients who reached the combined cardiovascular death outcome compared to those who survived (-6.20 mL·kg-1·min-1; CI 95%: -7.95, -4.46; p < 0.01). CONCLUSIONS: CPET is a valuable tool and can safely perform for assessment of physical functional capacity in patients with HCM. VO2max is the most common performance measurement evaluated in functional studies, showing higher values in those based on cycle-ergometer compared to treadmill. Subgroup analysis shows that exercise intolerance seems to be more related to age, medication and comorbidities than HCM phenotype itself. Lower VO2max is consistently seen in HCM patients at major cardiovascular risk

    Tauroursodeoxycholic bile acid arrests axonal degeneration by inhibiting the unfolded protein response in X-linked adrenoleukodystrophy

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    The activation of the highly conserved unfolded protein response (UPR) is prominent in the pathogenesis of the most prevalent neurodegenerative disorders, such as Alzheimer's disease (AD), Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS), which are classically characterized by an accumulation of aggregated or misfolded proteins. This activation is orchestrated by three endoplasmic reticulum (ER) stress sensors: PERK, ATF6 and IRE1. These sensors transduce signals that induce the expression of the UPR gene programme. Here, we first identified an early activator of the UPR and investigated the role of a chronically activated UPR in the pathogenesis of X-linked adrenoleukodystrophy (X-ALD), a neurometabolic disorder that is caused by ABCD1 malfunction; ABCD1 transports very long-chain fatty acids (VLCFA) into peroxisomes. The disease manifests as inflammatory demyelination in the brain or and/or degeneration of corticospinal tracts, thereby resulting in spastic paraplegia, with the accumulation of intracellular VLCFA instead of protein aggregates. Using X-ALD mouse model (Abcd1 - and Abcd1 - /Abcd2 -/- mice) and X-ALD patient's fibroblasts and brain samples, we discovered an early engagement of the UPR. The response was characterized by the activation of the PERK and ATF6 pathways, but not the IRE1 pathway, showing a difference from the models of AD, PD or ALS. Inhibition of PERK leads to the disruption of homeostasis and increased apoptosis during ER stress induced in X-ALD fibroblasts. Redox imbalance appears to be the mechanism that initiates ER stress in X-ALD. Most importantly, we demonstrated that the bile acid tauroursodeoxycholate (TUDCA) abolishes UPR activation, which results in improvement of axonal degeneration and its associated locomotor impairment in Abcd1 - /Abcd2 -/- mice. Altogether, our preclinical data provide evidence for establishing the UPR as a key drug target in the pathogenesis cascade. Our study also highlights the potential role of TUDCA as a treatment for X-ALD and other axonopathies in which similar molecular mediators are implicated
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