Protective activity from hydrophilic and lipophilic free radical generators of Wen-Pi-Tang and its crude drug extracts in LLC-PK_1 cells

We investigated Wen-Pi-Tang and its crude drug extracts to determine their protective effect from oxidative stress caused by the hydrophilic and lipophilic free radical generators, 2,2\u27-azobis (2-amidino-propane) dihydrochloride (AAPH) and 2,2\u27-azobis(2,4-dimethylvaleronitrile) (AMVN) in LLC-PK_1 renal tubular epithelial cells. In response to AAPH and AMVN treatment, cell viability decreased significantly and significantly enhanced thiobarbituric acid-reactive substances (TBARS) formation was observed. However, Wen-Pi-Tang and its crude drug extracts showed scavenging of peroxyl radicals, which were generated by AAPH and AMVN, resulting in greater cell viability and lower TBARS formation than controls treated only with free radical generators. In particular, Wen-Pi-Tang, Rhei Rhizoma and Ginseng Radix demonstrated high protective activity, whereas Aconiti Tuber, Zingiberis Rhizoma and Glycyrrhizae Radix showed relatively low activity. This result suggests that the antioxidant activity of Wen-Pi-Tang was attributable to the crude extracts, and that both act as hydrophilic and lipophilic antioxidants. 水溶性アゾ化合物のAAPH(2,2\u27-azobis(2-amidino-propane)dihydrochloride)と脂溶性アゾ化合物のAMVN(2,2\u27-azobis(2,4dimethylvaleronitrile))で腎上皮細胞のLLC-PK1に酸化的ストレスを惹起させ,温脾湯と5種類の構成和漢薬の効果を検討した。AAPHとAMVNで処埋した場合,細胞生有率が著しく抵ドし,チオバルビツール酸反応物質の生成が著しく上昇したが,温脾湯と各構成和漢薬エキスをそれぞれ添加した場合,温脾湯と大黄,薬用人参では高い抗酸化活性を示した。しかし附子,乾姜,甘草では相対的に低いi活牲であった。このことから,温脾湯の抗酸化活性は構成和漢薬に起因し,また温脾湯は水溶性抗酸化物と脂溶性抗酸化物の両方の特徴を有していることが示唆された

Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models

Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms

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

Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

Faulty field sensors cause unreliability in the observed data that needed to calibrate and assess hydrology models. However, it is illogical to ignore abnormal or missing values if there are limited data available. This study addressed this problem by applying data imputation to replace incorrect values and recover missing streamflow information in the dataset of the Samho gauging station at Taehwa River (TR), Korea from 2004 to 2006. Soil and Water Assessment Tool (SWAT) and two machine learning techniques, Artificial Neural Network (ANN) and Self Organizing Map (SOM), were employed to estimate streamflow using reasonable flow datasets of Samho station from 2004 to 2009. The machine learning models were generally better at capturing high flows, while SWAT was better at simulating low flows.open

Optimal Band Selection for Airborne Hyperspectral Imagery to Retrieve a Wide Range of Cyanobacterial Pigment Concentration Using a Data-Driven Approach

Understanding the concentration and distribution of cyanobacteria blooms is an important aspect of managing water quality problems and protecting aquatic ecosystems. Airborne hyperspectral imagery (HSI)-which has high temporal, spatial, and spectral resolutions-is widely used to remotely sense cyanobacteria bloom, and it provides the distribution of the bloom over a wide area. In this study, we determined the input spectral bands that were relevant in effectively estimating the main two pigments (PC, Phycocyanin; Chl-a, Chlorophyll-a) of cyanobacteria by applying data-driven algorithms to HSI and then evaluating the change in the spatio-temporal distribution of cyanobacteria. The input variables for the algorithms consisted of reflectance band ratios associated with the optical properties of PC and Chl-a, which were calculated by the selected hyperspectral bands using a feature selection method. The selected input variable was composed of six reflectance bands (465.7-589.6, 603.6-631.8, 641.2-655.35, 664.8-679.0, 698.0-712.3, and 731.4-784.1 nm). The artificial neural network showed the best results for the estimation of the two pigments with average coefficients of determination 0.80 and 0.74. This study proposes relevant input spectral information and an algorithm that can effectively detect the occurrence of cyanobacteria in the weir pool along the Geum river, South Korea. The algorithm is expected to help establish a preemptive response to the formation of cyanobacterial blooms, and to contribute to the preparation of suitable water quality management plans for freshwater environments

Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir

Colored dissolved organic matter (CDOM) in inland waters is used as a proxy to estimate dissolved organic carbon (DOC) and may be a key indicator of water quality and nutrient enrichment. CDOM is optically active fraction of DOC so that remote sensing techniques can remotely monitor CDOM with wide spatial coverage. However, to effectively retrieve CDOM using optical algorithms, it may be critical to select the absorption co-efficient at an appropriate wavelength as an output variable and to optimize input reflectance wavelengths. In this study, we constructed a CDOM retrieval model using airborne hyperspectral reflectance data and a machine learning model such as random forest. We evaluated the best combination of input wavelength bands and the CDOM absorption coefficient at various wavelengths. Seven sampling events for airborne hyperspectral imagery and CDOM absorption coefficient data from 350 nm to 440 nm over two years (2016-2017) were used, and the collected data helped train and validate the random forest model in a freshwater reservoir. An absorption co-efficient of 355 nm was selected to best represent the CDOM concentration. The random forest exhibited the best performance for CDOM estimation with an R2 of 0.85, Nash-Sutcliffe efficiency of 0.77, and percent bias of 3.88, by using a combination of three reflectance bands: 475, 497, and 660 nm. The results show that our model can be utilized to construct a CDOM retrieving algorithm and evaluate its spatiotemporal variation across a reservoir

Npas4 regulates IQSEC3 expression in hippocampal somatostatin interneurons to mediate anxiety-like behavior

Activity-dependent GABAergic synapse plasticity is important for normal brain functions, but the underlying molecular mechanisms remain incompletely understood. Here, we show that Npas4 (neuronal PAS-domain protein 4) transcriptionally regulates the expression of IQSEC3, a GABAergic synapse-specific guanine nucleotide-exchange factor for ADP-ribosylation factor (ARF-GEF) that directly interacts with gephyrin. Neuronal activation by an enriched environment induces Npas4-mediated upregulation of IQSEC3 protein specifically in CA1 stratum oriens layer somatostatin (SST)-expressing GABAergic interneurons. SST+ interneuron-specific knockout (KO) of Npas4 compromises synaptic transmission in these GABAergic interneurons, increases neuronal activity in CA1 pyramidal neurons, and reduces anxiety behavior, all of which are normalized by the expression of wild-type IQSEC3, but not a dominant-negative ARF-GEF-inactive mutant, in SST+ interneurons of Npas4-KO mice. Our results suggest that IQSEC3 is a key GABAergic synapse component that is directed by Npas4 and ARF activity, specifically in SST+ interneurons, to orchestrate excitation-to-inhibition balance and control anxiety-like behavior.1

ESI/X-ray Chamber Instrumentation Compatible for XFEL Beamline for studying Droplet Size-Dependent Molecular Behavior in the Chatged Nanodroplets

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Micro-fabricated flexible PZT cantilever using d33 mode for energy harvesting

Abstract This paper presents a micro-fabricated flexible and curled PZT [Pb(Zr0.52Ti0.48)O3] cantilever using d33 piezoelectric mode for vibration based energy harvesting applications. The proposed cantilever based energy harvester consists of polyimide, PZT thin film, and inter-digitated IrOx electrodes. The flexible cantilever was formed using bulk-micromachining on a silicon wafer to integrate it with ICs. The d33 piezoelectric mode was applied to achieve a large output voltage by using inter-digitated electrodes, and the PZT thin film on polyimide layer has a remnant polarization and coercive filed of approximately 2P r  = 47.9 μC/cm2 and 2E c  = 78.8 kV/cm, respectively. The relative dielectric constant was 900. The fabricated micro-electromechanical systems energy harvester generated output voltages of 1.2 V and output power of 117 nW at its optimal resistive load of 6.6 MΩ from its resonant frequency of 97.8 Hz with an acceleration of 5 m/s2

Studying Unusual Molecular Aggregation of Quinocyanine in the Charged Droplets

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