Investigating Influence of Hydrological Regime on Organic Matters Characteristic in a Korean Watershed

Source tracking of dissolved organic matter (DOM) is important to manage water quality in rivers. However, it is difficult to find the source of this DOM because various DOMs can be added from the river watershed. Moreover, the DOM composition can be changed due to environmental conditions. This study investigated the change of organic matter characteristics in the Taewha River of Ulsan City, Korea, before and after rainfall. A Soil and Water Assessment Tool (SWAT) was used to simulate water flow from various sources, and dissolved organic matter characterization was conducted in terms of molecular size distribution, hydrophobicity, fluorescence excitation and emission, and molecular composition. From the results, it was found that lateral flow transported hydrophobic and large-molecule organic matter after rainfall. According to the orbitrap mass spectrometer analysis, the major molecular compound of the DOM was lignin. Coupling the SWAT model with organic matter characterization was an effective approach to find sources of DOM in river

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

Growth differentiation factor 11 locally controls anterior-posterior patterning of the axial skeleton.

Growth and differentiation factor 11 (GDF11) is a transforming growth factor β family member that has been identified as the central player of anterior-posterior (A-P) axial skeletal patterning. Mice homozygous for Gdf11 deletion exhibit severe anterior homeotic transformations of the vertebrae and craniofacial defects. During early embryogenesis, Gdf11 is expressed predominantly in the primitive streak and tail bud regions, where new mesodermal cells arise. On the basis of this expression pattern of Gdf11 and the phenotype of Gdf11 mutant mice, it has been suggested that GDF11 acts to specify positional identity along the A-P axis either by local changes in levels of signaling as development proceeds or by acting as a morphogen. To further investigate the mechanism of action of GDF11 in the vertebral specification, we used a Cdx2-Cre transgene to generate mosaic mice in which Gdf11 expression is removed in posterior regions including the tail bud, but not in anterior regions. The skeletal analysis revealed that these mosaic mice display patterning defects limited to posterior regions where Gdf11 expression is deficient, whereas displaying normal skeletal phenotype in anterior regions where Gdf11 is normally expressed. Specifically, the mosaic mice exhibited seven true ribs, a pattern observed in wild-type (wt) mice (vs. 10 true ribs in Gdf11-/- mice), in the anterior axis and nine lumbar vertebrae, a pattern observed in Gdf11 null mice (vs. six lumbar vertebrae in wt mice), in the posterior axis. Our findings suggest that GDF11, rather than globally acting as a morphogen secreted from the tail bud, locally regulates axial vertebral patterning

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 Extracellular Calcium Increased Expression of Ank, PC-1 andOsteopontin in Mouse Calvarial Cells

In the process of bone remodeling, mineral phase of bone is dissolved by osteoclasts, resulting in elevation of calcium concentration in micro-environment. This study was performed to explore the effect of high extracellular calcium (Ca 2+ e) on mineralized nodule formation and on the expression of progressive ankylosis (Ank), plasma cell membrane glycoprotein-1 (PC-1) and osteopontin by primary cultured mouse calvarial cells. Osteoblastic differentiation and mineralized nodule formation was induced by culture of mouse calvarial cells in osteoblast differentiation medium containing ascorbic acid and β-glycerophosphate. Although Ank, PC-1 and osteopontin are well known inhibitors of mineralization, expression of these genes were induced at the later stage of osteoblast differentiation during when expression of osteocalcin, a late marker gene of osteoblast differentiation, was induced and mineralization was actively progressing. High Ca 2+ e (10 mM) treatment highly enhanced mRNA expression of Ank, PC-1 and osteopontin in the late stage of osteoblast differentiation but not in the early stage. Inhibition of p44/42 MAPK activation but not that of protein kinase C suppressed high Ca 2+ e-induced expression of Ank, PC-1 and osteopontin. When high Ca 2+ e (5 mM or 10 mM) was present in culture medium during when mineral deposition was actively progressing, matrix calcifiation was significantly increased by high Ca 2+ e. This stimulatory effect was abolished by pyrophosphate (5 mM) or levamisole (0.1-0.5 mM), an alkaline phosphatase inhibitor. In addition, probenecid (2mM), an inhibitor of Ank, suppressed matrix calcification in both control and high Ca 2+ e-treated group, suggesting the possible role of Ank in matrix calcification by osteoblasts. Taken together, these results showed that high Ca 2+ e stimulates expression of Ank, PC-1 and osteopontin as well as matrix calcification in late differentiation stage of osteoblasts and that p44/42 MAPK activation is involved in high Ca 2+ e- induced expression of Ank, PC-1 and osteopontin

Developing a deep learning model for the simulation of micro-pollutants in a watershed

In recent years, as agricultural activities and types of crops have become diverse, the occurrence of micro-pollutants has been reported more frequently in rural areas. These pollutants have detrimental effects on human health and ecological systems; thus, it is important to manage and monitor their presence in the environment. The modeling approach could be an effective way to understand and manage these pollutants. This study predicts the concentrations of micro-pollutants (MPs) using deep learning (DL) models, and the results are then compared with simulation results obtained from the soil water assessment tool (SWAT) model. The SWAT model showed an unacceptable performance owing to the resulting negative NasheSutcliffe efficiency (NSE) values for the simulations. This may be caused by the limitations of SWAT, which pertains to adopting simplified equations to simulate micro-pollutants. In addition, the ambiguous plan of pesticide application increased the model uncertainty, thereby deteriorating the model result. Here, we developed two different DL models: long short-term memory (LSTM) and convolutional neural network (CNN). LSTM exhibited the highest model performance, with NSE values of 0.99 and 0.75 for the training and validation steps, respectively. In the multi-target MP model, the error decreased as the number of simulated pollutants increased. The simulation of the four pollutants had the highest error, while the six-target simulation had the lowest error. In conclusion, this study demonstrated that the LSTM model has the potential to improve the prediction of MPs in aquatic systems. (c) 2021 Elsevier Ltd. All rights reserved

The Expression of Matrix Metalloprotease 20 is Stimulated by Wild Type but not by 4 bp- or 2 bp- Deletion Mutant DLX3

Mutations in DLX3 are associated with both autosomal dominant hypoplastic hypomaturation amelogenesis imperfecta (ADHHAI) and tricho-dento-osseous (TDO) syndrome. ADHHAI is caused by a c.561_562delCT (2bpdel DLX3) mutation whereas TDO syndrome is associated with a c.571_574delGGGG (4bp-del DLX3) mutation. However, although the causal relationships between DLX3 and an enamel phenotype have been established, the pathophysiological role of DLX3 mutations in enamel development has not yet been clarified. In our current study, we prepared expression vectors for wild type and deletion mutant DLX3 products (4bp-del DLX3, 2bp-del DLX3) and examined the effects of their overexpression on the expression of the enamel matrix proteins and proteases. Wild type DLX3 enhanced the expression of matrix metalloprotease 20 (MMP20) mRNA and protein in murine ameloblast-like cells. However, neither a 4bp-del nor 2bpdel DLX3 increased MMP20 expression. Wild type DLX3, but not the above DLX3 mutants, also increased the activity of reporters containing 1.5 kb or 0.5 kb of the MMP20 promoter. An examination of protein stability showed that the half-life of wild type DLX3 protein was less than 12 h whilst that of both deletion mutants was longer than 24 h. Endogenous Dlx3 was also found to be continuously expressed during ameloblast differentiation. Since inactivating mutations in the gene encoding MMP20 are associated with amelogenesis imperfecta, the inability of 4bp-del or 2bp-del DLX3 to induce MMP20 expression suggests a possible involvement of such mutations in the enamel phenotype associated with TDO syndrome or ADHHAI

Tricho-dento-osseous Syndrome Mutant Dlx3 Shows Lower Transactivation Potential but Has Longer Half-life than Wild-type Dlx3

Dlx3 is a homeodomain protein and is known to play a role in development and differentiation of many tissues. Deletion of four base pairs in DLX3 (NT3198) is causally related to tricho-dento-osseous (TDO) syndrome (OMIM #190320), a genetic disorder manifested by taurodontism, hair abnormalities, and increased bone density in the cranium. The molecular mechanisms that explain the phenotypic characteristics of TDO syndrome have not been clearly determined. In this study, we examined phenotypic characteristics of wild type DLX3 (wtDlx3) and 4-BP DEL DLX3 (TDO mtDlx3) in C2C12 cells. To investigate how wtDlx3 and TDO mtDlx3 differentially regulate osteoblastic differentiation, reporter assays were performed by using luciferase reporters containing the promoters of alkaline phosphatase, bone sialoprotein or osteocalcin. Both wtDlx3 and TDO mtDlx3 enhanced significantly all the reporter activities but the effect of mtDlx3 was much weaker than that of wtDlx3. In spite of these differences in reporter activity, electrophoretic mobility shift assay showed that both wtDlx3 and TDO mtDlx3 formed similar amounts of DNA binding complexes with Dlx3 binding consensus sequence or with ALP promoter oligonucleotide bearing the Dlx3 binding core sequence. TDO mtDlx3 exhibits a longer half-life than wtDlx3 and it corresponds to PESTfind analysis result showing that potential PEST sequence was missed in carboxy terminal of TDO mtDlx3. In addition, co-immunoprecipitation demonstrated that TDO mtDlx3 binds to Msx2 more strongly than wtDlx3. Taken together, though TDO mtDlx3 acted as a weaker transcriptional activator than wtDlx3 in osteoblastic cells, there is possibility that during in vivo osteoblast differentiation TDO mtDlx3 may antagonize transcriptional repressor activity of Msx2 more effectively and for longer period than wtDlx3, resulting in enhancement of osteoblast differentiation

Modeling the Fate and Transport of Malathion in the Pagsanjan-Lumban Basin, Philippines

Exposure to highly toxic pesticides could potentially cause cancer and disrupt the development of vital systems. Monitoring activities were performed to assess the level of contamination; however, these were costly, laborious, and short-term leading to insufficient monitoring data. However, the performance of the existing Soil and Water Assessment Tool (SWAT model) can be restricted by its two-phase partitioning approach, which is inadequate when it comes to simulating pesticides with limited dataset. This study developed a modified SWAT pesticide model to address these challenges. The modified model considered the three-phase partitioning model that classifies the pesticide into three forms: dissolved, particle-bound, and dissolved organic carbon (DOC)-associated pesticide. The addition of DOC-associated pesticide particles increases the scope of the pesticide model by also considering the adherence of pesticides to the organic carbon in the soil. The modified SWAT and original SWAT pesticide model was applied to the Pagsanjan-Lumban (PL) basin, a highly agricultural region. Malathion was chosen as the target pesticide since it is commonly used in the basin. The pesticide models simulated the fate and transport of malathion in the PL basin and showed the temporal pattern of selected subbasins. The sensitivity analyses revealed that application efficiency and settling velocity were the most sensitive parameters for the original and modified SWAT model, respectively. Degradation of particulate-phase malathion were also significant to both models. The rate of determination (R2) and Nash-Sutcliffe efficiency (NSE) values showed that the modified model (R2 = 0.52; NSE = 0.36) gave a slightly better performance compared to the original (R2 = 0.39; NSE = 0.18). Results from this study will be able to aid the government and private agriculture sectors to have an in-depth understanding in managing pesticide usage in agricultural watersheds

Potential Cause of Decrease in Bloom Events of the Harmful DinoflagellateCochlodinium polykrikoidesin Southern Korean Coastal Waters in 2016

Blooms of the ichthyotoxic dinoflagellateCochlodinium polykrikoidesare responsible for massive fish mortality events in Korean coastal waters (KCW). They have been consistently present in southern KCW over the last two decades, but they were not observed in 2016, unlike in the previous years. Despite extensive studies, the cause of this absence of this dinoflagellate bloom remains largely unknown. Thus, we compared physico-chemical and biological data from along the Tongyeong coast between 2016 and the previous four years (2012-2015). The averages of water temperature and salinity in August, 2016 were significantly (p< 0.001) different from those in the previous years. The amount of Changjiang River discharge, which can affect the environmental conditions in the southern Korean coastal area via ocean currents, was larger than in the previous years, resulting in a reduction in the salinity level in August when blooms ofC. polykrikoidesusually occurred. Moreover, compared to previous years, in 2016, there was a weak expansion ofC. polykrikoidesblooms in the Goheung-Oenarodo area whereC. polykrikoidesblooms were annually initiated in KCW. Lastly, the strong winds from the typhoon Lionrock may also have contributed to the early termination of this dinoflagellate bloom. Together with these findings, the combination of these environmental conditions in 2016, unlike in previous years, may have inhibited the formation ofC. polykrikoidesblooms along the Tongyeong coast