Electromagnetic Shielding Characteristics of Eco-Friendly Foamed Concrete Wall

The electromagnetic shielding characteristics according to the material composition of foamed concrete, which was manufactured to reduce environmental pollution and to economically apply it in actual building walls, were researched herein. Industrial by-products such as ladle furnace slag (LFS), gypsum, and blast furnace slag (BFS) were added to manufacture foamed concrete with enhanced functionalities such as lightweight, heat insulation, and sound insulation. The electrical characteristics such as permittivity and loss tangent according to the foam and BFS content were calculated and measured. Free space measurement was used to measure the electromagnetic shielding characteristics of the actually manufactured foamed concrete. It was confirmed that electromagnetic signals were better blocked when the foam content was low and the BFS content was high in the measured frequency bands (1–8 GHz) and that approximately 90% of the electromagnetic signals were blocked over 4 GHz

The Effect of Environmental Enrichment on Glutathione-Mediated Xenobiotic Metabolism and Antioxidation in Normal Adult Mice

Olfactory bulb (OB) plays an important role in protecting against harmful substances via the secretion of antioxidant and detoxifying enzymes. Environmental enrichment (EE) is a common rehabilitation method and known to have beneficial effects in the central nervous system. However, the effects of EE in the OB still remain unclear. At 6 weeks of age, CD-1® (ICR) mice were assigned to standard cages or EE cages. After 2 months, we performed proteomic analysis. Forty-four up-regulated proteins were identified in EE mice compared to the control mice. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes Pathway demonstrated that the upregulated proteins were mainly involved in metabolic pathways against xenobiotics. Among those upregulated proteins, 9 proteins, which participate in phase I or II of the xenobiotic metabolizing process and are known to be responsible for ROS detoxification, were validated by qRT-PCR. To explore the effect of ROS detoxification mediated by EE, glutathione activity was measured by an ELISA assay. The ratio of reduced glutathione to oxidized glutathione was significantly increased in EE mice. Based on a linear regression analysis, GSTM2 and UGT2A1 were found to be the most influential genes in ROS detoxification. For further analysis of neuroprotection, the level of iNOS and the ratio of Bax to Bcl-2 were significantly decreased in EE mice. While TUNEL+ cells were significantly decreased, Ki67+ cells were significantly increased in EE mice, implicating that EE creates an optimal state for xenobiotic metabolism and antioxidant activity. Taken together, our results suggested that EE protects olfactory layers via the upregulation of glutathione-related antioxidant and xenobiotic metabolizing enzymes, eventually lowering ROS-mediated inflammation and apoptosis and increasing neurogenesis. This study may provide an opportunity for a better understanding of the beneficial effects of EE in the OB

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash???Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application

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

High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery

Hyperspectral imagery (HSI) provides substantial information on optical features of water bodies that is usually applicable to water quality monitoring. However, it generates considerable uncertainties in assessments of spatial and temporal variation in water quality. Thus, this study explored the influence of different optical methods on the spatial distribution and concentration of phycocyanin (PC), chlorophyll-a (Chl-a), and total suspended solids (TSSs) and evaluated the dependence of algal distribution on flow velocity. Four ground-based and airborne monitoring campaigns were conducted to measure water surface reflectance. The actual concentrations of PC, Chl-a, and TSSs were also determined, while four bio-optical algorithms were calibrated to estimate the PC and Chl-a concentrations. Artificial neural network atmospheric correction achieved Nash-Sutcliffe Efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively. Moderate resolution atmospheric transmission 6 (MODTRAN 6) showed an NSE value >0.8; whereas, atmospheric and topographic correction 4 (ATCOR 4) yielded a negative NSE value. The MODTRAN 6 correction led to the highest R-2 values and lowest root mean square error values for all algorithms in terms of PC and Chl-a. The PC:Chl-a distribution generated using HSI proved to be negatively dependent on flow velocity (p-value = 0.003) and successfully indicated cyanobacteria risk regions in the study area

An Electrophilic Deguelin Analogue Inhibits STAT3 Signaling in H-Ras-Transformed Human Mammary Epithelial Cells: The Cysteine 259 Residue as a Potential Target

Signal transducer and activator of transcription 3 (STAT3) is a point of convergence for numerous oncogenic signals that are often constitutively activated in many cancerous or transformed cells and some stromal cells in the tumor microenvironment. Persistent STAT3 activation in malignant cells stimulates proliferation, survival, angiogenesis, invasion, and tumor-promoting inflammation. STAT3 undergoes activation through phosphorylation on tyrosine 705, which facilitates its dimerization. Dimeric STAT3 translocates to the nucleus, where it regulates the transcription of genes involved in cell proliferation, survival, etc. In the present study, a synthetic deguelin analogue SH48, discovered by virtual screening, inhibited the phosphorylation, nuclear translocation, and transcriptional activity of STAT3 in H-ras transformed human mammary epithelial MCF-10A cells (MCF10A-ras). We speculated that SH48 bearing an alpha,beta-unsaturated carbonyl group could interact with a thiol residue of STAT3, thereby inactivating this transcription factor. Non-electrophilic analogues of SH48 failed to inhibit STAT3 activation, lending support to the above supposition. By utilizing a biotinylated SH48, we were able to demonstrate the complex formation between SH48 and STAT3. SH48 treatment to MCF10A-ras cells induced autophagy, which was verified by staining with a fluorescent acidotropic probe, LysoTracker Red, as well as upregulating the expression of LC3II and p62. In conclusion, the electrophilic analogue of deguelin interacts with STAT3 and inhibits its activation in MCF10A-ras cells, which may account for its induction of autophagic death.

Environmental Enrichment Upregulates Striatal Synaptic Vesicle-Associated Proteins and Improves Motor Function

Environmental enrichment (EE) is a therapeutic paradigm that consists of complex combinations of physical, cognitive, and social stimuli. The mechanisms underlying EE-mediated synaptic plasticity have yet to be fully elucidated. In this study, we investigated the effects of EE on synaptic vesicle-associated proteins and whether the expression of these proteins is related to behavioral outcomes. A total of 44 CD-1® (ICR) mice aged 6 weeks were randomly assigned to either standard cages or EE (N = 22 each). Rotarod and ladder walking tests were then performed to evaluate motor function. To identify the molecular mechanisms underlying the effects of EE, we assessed differentially expressed proteins (DEPs) in the striatum by proteomic analysis. Quantitative real-time polymerase chain reaction (qRT-PCR), western blot, and immunohistochemistry were conducted to validate the expressions of these proteins. In the behavioral assessment, EE significantly enhanced performance on the rotarod and ladder walking tests. A total of 116 DEPs (54 upregulated and 62 downregulated proteins) were identified in mice exposed to EE. Gene ontology (GO) analysis demonstrated that the upregulated proteins in EE mice were primarily related to biological processes of synaptic vesicle transport and exocytosis. The GO terms for these biological processes commonly included Synaptic vesicle glycoprotein 2B (SV2B), Rabphilin-3A, and Piccolo. The qRT-PCR and western blot analyses revealed that EE increased the expression of SV2B, Rabphilin-3A and Piccolo in the striatum compared to the control group. Immunohistochemistry showed that the density of Piccolo in the vicinity of the subventricular zone was significantly increased in the EE mice compared with control mice. In conclusion, EE upregulates proteins associated with synaptic vesicle transport and exocytosis such as SV2B, Rabphilin-3A and Piccolo in the striatum. These upregulated proteins may be responsible for locomotor performance improvement, as shown in rotarod and ladder walking tests. Elucidation of these changes in synaptic protein expression provides new insights into the mechanism and potential role of EE

A Trainable Hearing Aid Algorithm Reflecting Individual Preferences for Degree of Noise-Suppression, Input Sound Level, and Listening Situation

Objectives In an effort to improve hearing aid users’ satisfaction, recent studies on trainable hearing aids have attempted to implement one or two environmental factors into training. However, it would be more beneficial to train the device based on the owner’s personal preferences in a more expanded environmental acoustic conditions. Our study aimed at developing a trainable hearing aid algorithm that can reflect the user’s individual preferences in a more extensive environmental acoustic conditions (ambient sound level, listening situation, and degree of noise suppression) and evaluated the perceptual benefit of the proposed algorithm. Methods Ten normal hearing subjects participated in this study. Each subjects trained the algorithm to their personal preference and the trained data was used to record test sounds in three different settings to be utilized to evaluate the perceptual benefit of the proposed algorithm by performing the Comparison Mean Opinion Score test. Results Statistical analysis revealed that of the 10 subjects, four showed significant differences in amplification constant settings between the noise-only and speech-in-noise situation (P<0.05) and one subject also showed significant difference between the speech-only and speech-in-noise situation (P<0.05). Additionally, every subject preferred different β settings for beamforming in all different input sound levels. Conclusion The positive findings from this study suggested that the proposed algorithm has potential to improve hearing aid users’ personal satisfaction under various ambient situations

Sensitivity Analysis and Optimization of a Radiative Transfer Numerical Model for Turbid Lake Water

Remote sensing can detect and map algal blooms. The HydroLight (Sequoia Scientific Inc., Bellevue, Washington, DC, USA) model generates the reflectance profiles of various water bodies. However, the influence of model parameters has rarely been investigated for inland water. Moreover, the simulation time of the HydroLight model increases as the amount of input data increases, which limits the practicality of the HydroLight model. This study developed a graphical user interface (GUI) software for the sensitivity analysis of the HydroLight model through multiple executions. The GUI software stably performed parameter sensitivity analysis and substantially reduced the simulation time by up to 92%. The GUI software results for lake water show that the backscattering ratio was the most important parameter for estimating vertical reflectance profiles. Based on the sensitivity analysis results, parameter calibration of the HydroLight model was performed. The reflectance profiles obtained using the optimized parameters agreed with observed profiles, with R-2 values of over 0.98. Thus, a strong relationship between the backscattering coefficient and the observed cyanobacteria genera cells was identified