Anti-inflammatory activity of hydrosols from Tetragonia tetragonoides in LPS-induced RAW 264.7 cells

The present study was performed to investigate the anti-inflammatory activity of Tetragonia tetragonoides hydro- sols (TTH) and its underlying mechanism in lipopolysaccharide (LPS)-induced RAW 264.7 cells. Gas chromatog- raphy (GC) coupled with mass spectrometry and retention index calculations showed that TTH were mainly com- posed of tetratetracontane (29.5 %), nonacosane (27.6 %), and oleamide (17.1 %). TTH significantly decreased the production of nitric oxide (NO), prostaglandin E2 (PGE2), interleukin (IL)-6, and IL-1β in LPS-stimulated RAW 264.7 cells. Consistent with these observations, TTH treatment decreased the protein expression levels of inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2). The molecular mechanism of its anti-inflamma- tory activity was found to be associated with inhibition of nuclear factor-kappa B (NF-κB) phosphorylation and nuclear translocation of NF-κB 65. Furthermore, TTH markedly suppressed the LPS-induced phosphorylation of mitogen-activated protein kinases (MAPKs). Taken together, these data indicate that TTH exerts an anti-inflam- matory activity by inhibiting the NF-κB and MAPK signaling pathways in LPS-stimulated RAW 264.7 cells

Optimizing Semi-Analytical Algorithms for Estimating Chlorophyll-a and Phycocyanin Concentrations in Inland Waters in Korea

Several semi-analytical algorithms have been developed to estimate the chlorophyll-a (Chl-a) and phycocyanin (PC) concentrations in inland waters. This study aimed at identifying the influence of algorithm parameters on the output variables and searching optimal parameter values. The optimal parameters of seven semi-analytical algorithms were applied to estimate the Chl-a and PC concentrations. The absorption coefficient measurements were coupled with pigment measurements to calibrate the algorithm parameters. For sensitivity analysis, the elementary effect test was conducted to analyze the influence of the algorithm parameters. The sensitivity analysis results showed that the parameters in the Y function and specific absorption coefficient were the most sensitive parameters. Then, the parameters were optimized via a single-objective optimization that involved one objective function being minimized and a multi-objective optimization that contained more than one objective function. The single-objective optimization led to substantial errors in absorption coefficients. In contrast, the multi-objective optimization improved the algorithm performance with respect to both the absorption coefficient estimates and pigment concentration estimates. The optimized parameters of the absorption coefficient reflected the high-particulate content in waters of the Baekje reservoir using an infrared backscattering wavelength and relatively high value of Y. Moreover, the results indicate the value of measuring the site-specific absorption if site-specific optimization of semi-analyical algorithm parameters was envisioned

Quantification of Phycocyanin in Inland Waters through Remote Measurement of Ratios and Shifts in Reflection Spectral Peaks

This study introduces a semi-empirical algorithm to estimate the extent of the phycocyanin (PC) concentration in eutrophic freshwater bodies; this is achieved by studying the reflectance characteristics of the red and near-red spectral regions, especially the shifting of the peak near 700 nm towards longer wavelengths. Spectral measurements in a darkroom environment over the pure-cultured cyanobacteria Microcystis showed that the shift is proportional to the algal biomass. A similar proportional trend was found from extensive field measurement data. The data also showed that the correlation of the magnitude of the shift with the PC concentration was greater than that with chlorophyll-a. This indicates that the characteristic can be a useful index to quantify cyanobacterial biomass. Based on these observations, a new PC algorithm was proposed that uses the remote sensing reflectance of the peak band around 700 nm and the trough band around 620 nm, and the magnitude of the peak shift near 700 nm. The efficacy of the algorithm was tested with 300 sets of field data, and the results were compared to select algorithms for the PC concentration prediction. The new algorithm performed better than the other algorithms with respect to most error indices, especially the mean relative error, indicating that the algorithm can reduce errors when PC concentrations are low. The algorithm was also applied to a hyperspectral dataset obtained through aerial imaging, in order to predict the spatial distribution of the PC concentration in an approximately 86 km long reach of the Nakdong River

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Accurate assessment of chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing is challenging due to the optical complexity of case 2 waters. and the inherent optical properties (IOPs) of natural waters are the most significant factors affecting light propagation within water columns, and thus play indispensable roles on estimation of chl-a concentrations. Despite its importance, no IOPs retrieval model was specifically developed for inland water bodies, although significant efforts were made on oceanic inversion models. So we have applied and validated a recently developed Red-NIR three-band model and an IOPs Inversion Model for estimating Chl-a concentration and deriving inland water IOPs in Lake Uiam. Three band and IOPs based Chl-a estimation model accuracy was assessed with samples collected in different seasons. The results indicate that this models can be used to accurately retrieve Chl-a concentration and absorption coefficients. For all datasets the determination coefficients of the 3-band models versus Chl-a concentration ranged 0.65 and 0.88 and IOPs based model versus Chl-a concentration varied from 0.73 to 0.83 respectively. and Comparison between 3-band and IOPs based models showed significant performance with decrease of root mean square error from 18% to 33.6%. The results of this study provides the potential of effective methods for remote monitoring and water quality management in turbid inland water bodies using hyper-spectral remote sensing.clos

Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage

Massive cyanobacterial blooms in river water causes adverse impacts on aquatic ecosystems and water quality. Complex and diverse data sources are available to investigate the cyanobacteria phenomena, including in situ data, synthetic measurements, and remote sensing images. Deep learning attention models can process these intricate sources to forecast cyanobacteria by identifying important variables in the data sources. However, deep learning attention models for predicting cyanobacteria have rarely been studied using an assemblage of various datasets. Thus, in this study, a convolutional neural network (CNN) model with a convolutional block attention module (CNNan) was developed to predict cyanobacterial cell concentrations by using the observed cell data from field monitoring, chlorophyll-a distribution map from hyperspectral image sensing, and simulated water quality outputs from a hydrodynamic model. Then, the prediction performance of the CNNan model was compared to an environmental fluid dynamics code (EFDC) simulation and a CNN model without an attention network. The seasonal variations of the predicted cyanobacteria that was obtained from CNNan showed the best agreement with the observed variations with Nash-Sutcliffe efficiency values higher than 0.76 when compared to the EFDC and CNN predictions. The daily hydrodynamic outputs allowed the prediction of cyanobacteria cells, while the rich information of the chlorophyll-a map contributed to the improvement of the prediction performance at certain periods. Moreover, the attention network visualized the importance of the additional chlorophyll-a map and improved the CNNan model prediction performance by refining the input features. Therefore, this study demonstrated that a deep learning model with data assemblage is practically feasible for predicting the presence of harmful algae in inland water

Exploring the Anti-Diabetic Potential of Quercetagitrin through Dual Inhibition of PTPN6 and PTPN9

Protein tyrosine phosphatases (PTPs) are pivotal contributors to the development of type 2 diabetes (T2DM). Hence, directing interventions towards PTPs emerges as a valuable therapeutic approach for managing type 2 diabetes. In particular, PTPN6 and PTPN9 are targets for anti-diabetic effects. Through high-throughput drug screening, quercetagitrin (QG) was recognized as a dual-target inhibitor of PTPN6 and PTPN9. We observed that QG suppressed the catalytic activity of PTPN6 (IC50 = 1 μM) and PTPN9 (IC50 = 1.7 μM) in vitro and enhanced glucose uptake by mature C2C12 myoblasts. Additionally, QG increased the phosphorylation of adenosine monophosphate-activated protein kinase (AMPK) and insulin-dependent phosphorylation of Akt in mature C2C12 myoblasts. It further promoted the phosphorylation of Akt in the presence of palmitic acid, suggesting the attenuation of insulin resistance. In summary, our results indicate QG’s role as a potent inhibitor targeting both PTPN6 and PTPN9, showcasing its potential as a promising treatment avenue for T2DM

Structure–Activity Relationship of Synthetic Ginkgolic Acid Analogs for Treating Type 2 Diabetes by PTPN9 Inhibition

Ginkgolic acid (C13:0) (GA), isolated from Ginkgo biloba, is a potential therapeutic agent for type 2 diabetes. A series of GA analogs were designed and synthesized for the evaluation of their structure–activity relationship with respect to their antidiabetic effects. Unlike GA, the synthetic analog 1e exhibited improved inhibitory activity against PTPN9 and significantly stimulated glucose uptake via AMPK phosphorylation in differentiated 3T3-L1 adipocytes and C2C12 myotubes; it also induced insulin-dependent AKT activation in C2C12 myotubes in a concentration-dependent manner. Docking simulation results showed that 1e had a better binding affinity through a unique hydrophobic interaction with a PTPN9 hydrophobic groove. Moreover, 1e ameliorated palmitate-induced insulin resistance in C2C12 cells. This study showed that 1e increases glucose uptake and suppresses palmitate-induced insulin resistance in C2C12 myotubes via PTPN9 inhibition; thus, it is a promising therapeutic candidate for treating type 2 diabetes

Structure–Activity Relationship of Synthetic Ginkgolic Acid Analogs for Treating Type 2 Diabetes by PTPN9 Inhibition

Ginkgolic acid (C13:0) (GA), isolated from Ginkgo biloba, is a potential therapeutic agent for type 2 diabetes. A series of GA analogs were designed and synthesized for the evaluation of their structure–activity relationship with respect to their antidiabetic effects. Unlike GA, the synthetic analog 1e exhibited improved inhibitory activity against PTPN9 and significantly stimulated glucose uptake via AMPK phosphorylation in differentiated 3T3-L1 adipocytes and C2C12 myotubes; it also induced insulin-dependent AKT activation in C2C12 myotubes in a concentration-dependent manner. Docking simulation results showed that 1e had a better binding affinity through a unique hydrophobic interaction with a PTPN9 hydrophobic groove. Moreover, 1e ameliorated palmitate-induced insulin resistance in C2C12 cells. This study showed that 1e increases glucose uptake and suppresses palmitate-induced insulin resistance in C2C12 myotubes via PTPN9 inhibition; thus, it is a promising therapeutic candidate for treating type 2 diabetes

Deep learning-based retrieval of cyanobacteria pigment in inland water for in-situ and airborne hyperspectral data

Worldwide proliferation of cyanobacteria blooms in inland waters not only affects the intended use of water but potentially threatens human and animal health. In this study, a stacked autoencoder-deep neural network (SAE-DNN) was developed to estimate phycocyanin (PC) concentration by using in situ reflectance spectra in productive inland water. The estimated PC using the SAE-DNN was in close agreement with the measured PC, with an R2 of 0.87, root mean square error (RMSE) of 14.45?????g/L, and relative RMSE of 86.42%. The performance of the SAE-DNN was superior to that of the DNN and band-ratio algorithms. An analysis on the deep spectral features extracted using the SAE yielded the most useful spectral bands, namely 538, 596, and 735???nm, for the retrieval of PC. The estimation accuracy of the SAE-DNNPeaks, using only the aforementioned spectral bands as input variables, was comparable to that of the SAE-DNN, demonstrating that the high-level of abstraction using the SAE facilitated the improvement in feature learning. The application of the SAE-DNNPeaks to airborne hyperspectral image data resulted in an acceptable estimation accuracy, despite a bias toward underestimation, potentially arising from uncertainty associated with atmospheric correction, at high PC concentrations. Our results suggest that simple, empirical-based approaches, such as the SAE-DNNPeaks, have the potential to serve as a rapid assessment tool for the abundance and spatial distribution of cyanobacteria

Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery

Although remote sensing techniques have been used to monitor toxic cyanobacteria with hyperspectral data in inland water, it is difficult to optimize conventional bio-optical algorithms for individual water bodies because of the complex optical properties of various water components. Therefore, this study adopted a spatial attention convolutional neural network (spatial attention CNN) to estimate the chlorophyll-a (Chl-a) and phycocyanin (PC) concentrations in the Geum, Nakdong, and Yeongsan rivers in South Korea in order to evaluate cyanobacteria using remote sensing reflectance data. The CNN model utilized a spatial attention module to analyze the importance of the bands in the reflectance data. Then, the spatial attention CNN model was compared with different bio-optical algorithms for each study area. The spatial attention CNN model was generalized to estimate the pigment concentrations in the target rivers, and the model performance was evaluated by correlation coefficient (R) and root mean squared error (RMSE) between the observed and estimated concentrations of the algal pigments. The spatial attention CNN model, which was generalized to estimate the pigment concentrations in the target rivers, had R values above 0.87 and 0.88 for Chl-a and PC, respectively. However, the optimized band ratio algorithms for Chl-a and PC had R values above 0.83 and 0.70, respectively. Hence, it showed better performance than the conventional bio-optical algorithms. The spatial attention module provided attention weights for visualizing important features in the reflectance data. Specifically, the 600 nm, 650 nm, and near-infrared regions had high attention weights for estimating the concentrations of Chl-a and PC. Based on these findings, this study demonstrated that the spatial attention CNN model has a high potential for good application performance in various water bodies