Infrastructure Engineering - Research Publications

Permanent URI for this collection

Search Results

Now showing 1 - 10 of 26
  • Item
    Thumbnail Image
    Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance
    Kang, BE ; Park, A ; Yang, H ; Jo, Y ; Oh, TG ; Jeong, SM ; Ji, Y ; Kim, H-L ; Kim, H-N ; Auwerx, J ; Nam, S ; Park, C-Y ; Ryu, D (NATURE PORTFOLIO, 2022-12-17)
    A simple predictive biomarker for fatty liver disease is required for individuals with insulin resistance. Here, we developed a supervised machine learning-based classifier for fatty liver disease using fecal 16S rDNA sequencing data. Based on the Kangbuk Samsung Hospital cohort (n = 777), we generated a random forest classifier to predict fatty liver diseases in individuals with or without insulin resistance (n = 166 and n = 611, respectively). The model performance was evaluated based on metrics, including accuracy, area under receiver operating curve (AUROC), kappa, and F1-score. The developed classifier for fatty liver diseases performed better in individuals with insulin resistance (AUROC = 0.77). We further optimized the classifiers using genetic algorithm. The improved classifier for insulin resistance, consisting of ten microbial genera, presented an advanced classification (AUROC = 0.93), whereas the improved classifier for insulin-sensitive individuals failed to distinguish participants with fatty liver diseases from the healthy. The classifier for individuals with insulin resistance was comparable or superior to previous methods predicting fatty liver diseases (accuracy = 0.83, kappa = 0.50, F1-score = 0.89), such as the fatty liver index. We identified the ten genera as a core set from the human gut microbiome, which could be a diagnostic biomarker of fatty liver diseases for insulin resistant individuals. Collectively, these findings indicate that the machine learning classifier for fatty liver diseases in the presence of insulin resistance is comparable or superior to commonly used methods.
  • Item
    Thumbnail Image
    Pyruvate dehydrogenase kinase 4 promotes ubiquitin-proteasome system-dependent muscle atrophy
    Sinam, IS ; Chanda, D ; Thoudam, T ; Kim, M-J ; Kim, B-G ; Kang, H-J ; Lee, JY ; Baek, S-H ; Kim, S-Y ; Shim, BJ ; Ryu, D ; Jeon, J-H ; Lee, I-K (WILEY, 2022-12)
    BACKGROUND: Muscle atrophy, leading to muscular dysfunction and weakness, is an adverse outcome of sustained period of glucocorticoids usage. However, the molecular mechanism underlying this detrimental condition is currently unclear. Pyruvate dehydrogenase kinase 4 (PDK4), a central regulator of cellular energy metabolism, is highly expressed in skeletal muscle and has been implicated in the pathogenesis of several diseases. The current study was designed to investigated and delineate the role of PDK4 in the context of muscle atrophy, which could be identified as a potential therapeutic avenue to protect against dexamethasone-induced muscle wasting. METHODS: The dexamethasone-induced muscle atrophy in C2C12 myotubes was evaluated at the molecular level by expression of key genes and proteins involved in myogenesis, using immunoblotting and qPCR analyses. Muscle dysfunction was studied in vivo in wild-type and PDK4 knockout mice treated with dexamethasone (25 mg/kg body weight, i.p., 10 days). Body weight, grip strength, muscle weight and muscle histology were assessed. The expression of myogenesis markers were analysed using qPCR, immunoblotting and immunoprecipitation. The study was extended to in vitro human skeletal muscle atrophy analysis. RESULTS: Knockdown of PDK4 was found to prevent glucocorticoid-induced muscle atrophy and dysfunction in C2C12 myotubes, which was indicated by induction of myogenin (0.3271 ± 0.102 vs 2.163 ± 0.192, ****P < 0.0001) and myosin heavy chain (0.3901 ± 0.047 vs. 0.7222 ± 0.082, **P < 0.01) protein levels and reduction of muscle atrophy F-box (10.77 ± 2.674 vs. 1.518 ± 0.172, **P < 0.01) expression. In dexamethasone-induced muscle atrophy model, mice with genetic ablation of PDK4 revealed increased muscle strength (162.1 ± 22.75 vs. 200.1 ± 37.09 g, ***P < 0.001) and muscle fibres (54.20 ± 11.85% vs. 84.07 ± 28.41%, ****P < 0.0001). To explore the mechanism, we performed coimmunoprecipitation and liquid chromatography-mass spectrometry analysis and found that myogenin is novel substrate of PDK4. PDK4 phosphorylates myogenin at S43/T57 amino acid residues, which facilitates the recruitment of muscle atrophy F-box to myogenin and leads to its subsequent ubiquitination and degradation. Finally, overexpression of non-phosphorylatable myogenin mutant using intramuscular injection prevented dexamethasone-induced muscle atrophy and preserved muscle fibres. CONCLUSIONS: We have demonstrated that PDK4 mediates dexamethasone-induced skeletal muscle atrophy. Mechanistically, PDK4 phosphorylates and degrades myogenin via recruitment of E3 ubiquitin ligase, muscle atrophy F-box. Rescue of muscle regeneration by genetic ablation of PDK4 or overexpression of non-phosphorylatable myogenin mutant indicates PDK4 as an amenable therapeutic target in muscle atrophy.
  • Item
    No Preview Available
    Improved Trend-Aware Postprocessing of GCM Seasonal Precipitation Forecasts
    Shao, Y ; Wang, QJ ; Schepen, A ; Ryu, D ; Pappenberger, F (AMER METEOROLOGICAL SOC, 2022-01)
    Abstract Climate trends have been observed over the recent decades in many parts of the world, but current global climate models (GCMs) for seasonal climate forecasting often fail to capture these trends. As a result, model forecasts may be biased above or below the trendline. In our previous research, we developed a trend-aware forecast postprocessing method to overcome this problem. The method was demonstrated to be effective for embedding observed trends into seasonal temperature forecasts. In this study, we further develop the method for postprocessing GCM seasonal precipitation forecasts. We introduce new formulation and evaluation features to cater for special characteristics of precipitation amounts, such as having a zero lower bound and highly positive skewness. We apply the improved method to calibrate ECMWF SEAS5 forecasts of seasonal precipitation for Australia. Our evaluation shows that the calibrated forecasts reproduce observed trends over the hindcast period of 36 years. In some regions where observed trends are statistically significant, forecast skill is greatly improved by embedding trends into the forecasts. In most regions, the calibrated forecasts outperform the raw forecasts in terms of bias, skill, and reliability. Wider applications of the new trend-aware postprocessing method are expected to boost user confidence in seasonal precipitation forecasts.
  • Item
    Thumbnail Image
    Explaining changes in rainfall-runoff relationships during and after Australia's Millennium Drought: a community perspective
    Fowler, K ; Peel, M ; Saft, M ; Peterson, TJ ; Western, A ; Band, L ; Petheram, C ; Dharmadi, S ; Tan, KS ; Zhang, L ; Lane, P ; Kiem, A ; Marshall, L ; Griebel, A ; Medlyn, BE ; Ryu, D ; Bonotto, G ; Wasko, C ; Ukkola, A ; Stephens, C ; Frost, A ; Weligamage, HG ; Saco, P ; Zheng, H ; Chiew, F ; Daly, E ; Walker, G ; Vervoort, RW ; Hughes, J ; Trotter, L ; Neal, B ; Cartwright, I ; Nathan, R (COPERNICUS GESELLSCHAFT MBH, 2022-12-06)
    Abstract. The Millennium Drought lasted more than a decade and is notable for causing persistent shifts in the relationship between rainfall and runoff in many southeastern Australian catchments. Research to date has successfully characterised where and when shifts occurred and explored relationships with potential drivers, but a convincing physical explanation for observed changes in catchment behaviour is still lacking. Originating from a large multi-disciplinary workshop, this paper presents and evaluates a range of hypothesised process explanations of flow response to the Millennium Drought. The hypotheses consider climatic forcing, vegetation, soil moisture dynamics, groundwater, and anthropogenic influence. The hypotheses are assessed against evidence both temporally (e.g. why was the Millennium Drought different to previous droughts?) and spatially (e.g. why did rainfall–runoff relationships shift in some catchments but not in others?). Thus, the strength of this work is a large-scale assessment of hydrologic changes and potential drivers. Of 24 hypotheses, 3 are considered plausible, 10 are considered inconsistent with evidence, and 11 are in a category in between, whereby they are plausible yet with reservations (e.g. applicable in some catchments but not others). The results point to the unprecedented length of the drought as the primary climatic driver, paired with interrelated groundwater processes, including declines in groundwater storage, altered recharge associated with vadose zone expansion, and reduced connection between subsurface and surface water processes. Other causes include increased evaporative demand and harvesting of runoff by small private dams. Finally, we discuss the need for long-term field monitoring, particularly targeting internal catchment processes and subsurface dynamics. We recommend continued investment in the understanding of hydrological shifts, particularly given their relevance to water planning under climate variability and change.
  • Item
    Thumbnail Image
    Comparison of KOMPSAT-5 and Sentinel-1 Radar Data for Soil Moisture Estimations Using a New Semi-Empirical Model
    Tao, L ; Ryu, D ; Western, A ; Lee, S-G (MDPI, 2022-08)
    X-band KOMPSAT-5 provides a good perspective for soil moisture retrieval at high-spatial resolution over arid and semi-arid areas. In this paper, an intercomparison of KOMPSAT-5 and C-band Sentinel-1 radar data in soil moisture retrieval was conducted over agricultural fields in Wimmera, Victoria, Australia. Optical images from Sentinel-2 were also used to calculate the scattering contribution of vegetation. This study employed a new semi-empirical vegetation scattering model with a linear association of soil moisture with observed backscatter coefficient and vegetation indices. The Combined Vegetation Index (CVI) was proposed and first used to parameterize vegetation water content. As a result, the vegetation scattering model was developed to monitor soil moisture based on remotely sensed data and ground measurements. Application of the algorithm over dryland wheat field sites demonstrated that the estimated satellite-based soil moisture contents have good linear relationships with the ground measurements. The correlation coefficients (R) are 0.862 and 0.616, and the root mean square errors (RMSEs) have the values of 0.020 cm3/cm3 and 0.032 cm3/cm3 at X- and C-bands, respectively. Furthermore, the validation results also indicated that X-band provided higher consistent accuracy for soil moisture inversion than C-band. These results showed significant promise in retrieving soil moisture using KOMPSAT-5 and Sentinel-1 remotely sensed data at high-spatial resolution over agricultural fields, with subsequent uses for crop growth and yield estimation.
  • Item
    Thumbnail Image
    A multi-model approach to assessing the impacts of catchment characteristics on spatial water quality in the Great Barrier Reef catchments
    Liu, S ; Ryu, D ; Webb, JA ; Lintern, A ; Guo, D ; Waters, D ; Western, AW (ELSEVIER SCI LTD, 2021-11-01)
    Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study uses a multi-model approach: a) to identify the influential catchment characteristics affecting spatial variability in water quality; and b) to predict spatial variability in water quality more reliably and robustly. Tropical catchments in the Great Barrier Reef (GBR) area, Australia, were used as a case study. We developed statistical models using 58 catchment characteristics to predict the spatial variability in water quality in 32 GBR catchments. An exhaustive search method coupled with multi-model inference approaches were used to identify important catchment characteristics and predict the spatial variation in water quality across catchments. Bootstrapping and cross-validation approaches were used to assess the uncertainty in identified important factors and robustness of multi-model structure, respectively. The results indicate that water quality variables were generally most influenced by the natural characteristics of catchments (e.g., soil type and annual rainfall), while anthropogenic characteristics (i.e., land use) also showed significant influence on dissolved nutrient species (e.g., NOX, NH4 and FRP). The multi-model structures developed in this work were able to predict average event-mean concentration well, with Nash-Sutcliffe coefficient ranging from 0.68 to 0.96. This work provides data-driven evidence for catchment managers, which can help them develop effective water quality management strategies.
  • Item
    Thumbnail Image
    Introducing long-term trends into subseasonal temperature forecasts through trend-aware postprocessing
    Shao, Y ; Wang, QJ ; Schepen, A ; Ryu, D (WILEY, 2022-07)
    Abstract Skilful subseasonal forecasts are crucial for issuing early warnings of extreme weather events, such as heatwaves and floods. Operational subseasonal climate forecasts are often produced by global climate models not dissimilar to seasonal forecast models, which typically fail to reproduce observed temperature trends. In this study, we identify that the same issue exists in the subseasonal forecasting system. Subsequently, we adapt a trend‐aware forecast postprocessing method, previously developed for seasonal forecasts, to calibrate and correct the trend in subseasonal forecasts. We modify the method to embed 30‐year climate trends into the calibrated forecasts even when the available hindcast period is shorter. The use of 30‐year trends is to robustly represent long‐term climate changes and overcome the problem that trends inferred from a shorter period may be subject to large sampling variability. Calibration is applied to 20‐year ECMWF subseasonal forecasts and AWAP observations of Australian minimum and maximum temperatures with forecast horizons of up to 4 weeks. Relative to day‐of‐year climatology, raw week‐1 forecasts reproduce temperature trends of the 20‐year observations in many regions while raw week‐4 forecasts do not exhibit the 20‐year observed trends. After trend‐aware postprocessing, the behaviour of forecast trends is related to raw forecast skill regarding accuracy. Calibrated week‐1 forecasts show apparent trends consistent with the 20‐year observations, as the calibration transfers forecast skill and embeds the 20‐year observed trends into the forecasts when raw forecasts are inherently skilful. In contrast, calibrated week‐4 forecasts exhibit the 30‐year observed trends, as the calibration reverts the forecasts to the 30‐year observed climatology with trends when raw forecasts have little skill. For both weeks, the trend‐aware calibrated forecasts are more reliable, and as skilful as or more skilful than raw forecasts. The extended trend‐aware method can be applied to deliver high‐quality subseasonal forecasts and support decision‐making in a changing climate.
  • Item
    Thumbnail Image
    Enhancing the Accuracy and Temporal Transferability of Irrigated Cropping Field Classification Using Optical Remote Sensing Imagery
    Gao, Z ; Guo, D ; Ryu, D ; Western, AW (MDPI, 2022-02)
    Mapping irrigated areas using remotely sensed imagery has been widely applied to support agricultural water management; however, accuracy is often compromised by the in-field heterogeneity of and interannual variability in crop conditions. This paper addresses these key issues. Two classification methods were employed to map irrigated fields using normalized difference vegetation index (NDVI) values derived from Landsat 7 and Landsat 8: a dynamic thresholding method (method one) and a random forest method (method two). To improve the representativeness of field-level NDVI aggregates, which are the key inputs in our methods, a Gaussian mixture model (GMM)-based filtering approach was adopted to remove noncrop pixels (e.g., trees and bare soils) and mixed pixels along the field boundary. To improve the temporal transferability of method one we dynamically determined the threshold value to account for the impact of interannual weather variability based on the dynamic range of NDVI values. In method two an innovative training sample pool was designed for the random forest modeling to enable automatic calibration for each season, which contributes to consistent performance across years. The irrigated field mapping was applied to a major irrigation district in Australia from 2011 to 2018, for summer and winter cropping seasons separately. The results showed that using GMM-based filtering can markedly improve field-level data quality and avoid up to 1/3 of omission errors for irrigated fields. Method two showed superior performance, exhibiting consistent and good accuracy (kappa > 0.9) for both seasons. The classified maps in wet winter seasons should be used with caution, because rainfall alone can largely meet plant water requirements, leaving the contribution of irrigation to the surface spectral signature weak. The approaches introduced are transferable to other areas, can support multiyear irrigated area mapping with high accuracy, and significantly reduced model development effort.
  • Item
    Thumbnail Image
    Bio-printing of aligned GelMa-based cell-laden structure for muscle tissue regeneration.
    Hwangbo, H ; Lee, H ; Jin, E-J ; Lee, J ; Jo, Y ; Ryu, D ; Kim, G (Elsevier BV, 2022-02)
    Volumetric muscle loss (VML) is associated with a severe loss of muscle tissue that overwhelms the regenerative potential of skeletal muscles. Tissue engineering has shown promise for the treatment of VML injuries, as evidenced by various preclinical trials. The present study describes the fabrication of a cell-laden GelMa muscle construct using an in situ crosslinking (ISC) strategy to improve muscle functionality. To obtain optimal biophysical properties of the muscle construct, two UV exposure sources, UV exposure dose, and wall shear stress were evaluated using C2C12 myoblasts. Additionally, the ISC system showed a significantly higher degree of uniaxial alignment and myogenesis compared to the conventional crosslinking strategy (post-crosslinking). To evaluate the in vivo regenerative potential, muscle constructs laden with human adipose stem cells were used. The VML defect group implanted with the bio-printed muscle construct showed significant restoration of functionality and muscular volume. The data presented in this study suggest that stem cell-based therapies combined with the modified bioprinting process could potentially be effective against VML injuries.
  • Item
    Thumbnail Image
    Satellite cell-specific ablation of Cdon impairs integrin activation, FGF signalling, and muscle regeneration.
    Bae, J-H ; Hong, M ; Jeong, H-J ; Kim, H ; Lee, S-J ; Ryu, D ; Bae, G-U ; Cho, SC ; Lee, Y-S ; Krauss, RS ; Kang, J-S (Wiley, 2020-08)
    BACKGROUND: Perturbation in cell adhesion and growth factor signalling in satellite cells results in decreased muscle regenerative capacity. Cdon (also called Cdo) is a component of cell adhesion complexes implicated in myogenic differentiation, but its role in muscle regeneration remains to be determined. METHODS: We generated inducible satellite cell-specific Cdon ablation in mice by utilizing a conditional Cdon allele and Pax7 CreERT2 . To induce Cdon ablation, mice were intraperitoneally injected with tamoxifen (tmx). Using cardiotoxin-induced muscle injury, the effect of Cdon depletion on satellite cell function was examined by histochemistry, immunostaining, and 5-ethynyl-2'-deoxyuridine (EdU) incorporation assay. Isolated myofibers or myoblasts were utilized to determine stem cell function and senescence. To determine pathways related to Cdon deletion, injured muscles were subjected to RNA sequencing analysis. RESULTS: Satellite cell-specific Cdon ablation causes impaired muscle regeneration with fibrosis, likely attributable to decreased proliferation, and senescence, of satellite cells. Cultured Cdon-depleted myofibers exhibited 32 ± 9.6% of EdU-positive satellite cells compared with 58 ± 4.4% satellite cells in control myofibers (P < 0.05). About 32.5 ± 3.7% Cdon-ablated myoblasts were positive for senescence-associated β-galactosidase (SA-β-gal) while only 3.6 ± 0.5% of control satellite cells were positive (P < 0.001). Transcriptome analysis of muscles at post-injury Day 4 revealed alterations in genes related to mitogen-activated protein kinase signalling (P < 8.29 e-5 ) and extracellular matrix (P < 2.65 e-24 ). Consistent with this, Cdon-depleted tibialis anterior muscles had reduced phosphorylated extracellular signal-regulated kinase (p-ERK) protein levels and expression of ERK targets, such as Fos (0.23-fold) and Egr1 (0.31-fold), relative to mock-treated control muscles (P < 0.001). Cdon-depleted myoblasts exhibited impaired ERK activation in response to basic fibroblast growth factor. Cdon ablation resulted in decreased and/or mislocalized integrin β1 activation in satellite cells (weak or mislocalized integrin1 in tmx = 38.7 ± 1.9%, mock = 21.5 ± 6%, P < 0.05), previously linked with reduced fibroblast growth factor (FGF) responsiveness in aged satellite cells. In mechanistic studies, Cdon interacted with and regulated cell surface localization of FGFR1 and FGFR4, likely contributing to FGF responsiveness of satellite cells. Satellite cells from a progeria model, Zmpste24-/- myofibers, showed decreased Cdon levels (Cdon-positive cells in Zmpste24-/- = 63.3 ± 11%, wild type = 90 ± 7.7%, P < 0.05) and integrin β1 activation (weak or mislocalized integrin β1 in Zmpste24-/- = 64 ± 6.9%, wild type = 17.4 ± 5.9%, P < 0.01). CONCLUSIONS: Cdon deficiency in satellite cells causes impaired proliferation of satellite cells and muscle regeneration via aberrant integrin and FGFR signalling.